CN118336887B - Power supplementing method and system for emergency repair power supply - Google Patents
Power supplementing method and system for emergency repair power supply Download PDFInfo
- Publication number
- CN118336887B CN118336887B CN202410774239.8A CN202410774239A CN118336887B CN 118336887 B CN118336887 B CN 118336887B CN 202410774239 A CN202410774239 A CN 202410774239A CN 118336887 B CN118336887 B CN 118336887B
- Authority
- CN
- China
- Prior art keywords
- power
- module
- power supply
- charging
- emergency repair
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000008439 repair process Effects 0.000 title claims abstract description 126
- 238000000034 method Methods 0.000 title claims abstract description 57
- 230000001502 supplementing effect Effects 0.000 title claims abstract description 35
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 49
- 239000002245 particle Substances 0.000 claims description 59
- 230000035772 mutation Effects 0.000 claims description 45
- 238000012937 correction Methods 0.000 claims description 16
- 238000005457 optimization Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000005516 engineering process Methods 0.000 abstract description 7
- 230000008569 process Effects 0.000 description 27
- 230000005611 electricity Effects 0.000 description 14
- 230000007613 environmental effect Effects 0.000 description 13
- 238000005286 illumination Methods 0.000 description 10
- 230000005855 radiation Effects 0.000 description 8
- 230000004044 response Effects 0.000 description 8
- 230000008859 change Effects 0.000 description 6
- 230000000295 complement effect Effects 0.000 description 6
- 239000013589 supplement Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/0071—Regulation of charging or discharging current or voltage with a programmable schedule
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J9/00—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/0003—Details of control, feedback or regulation circuits
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- General Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Primary Health Care (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Emergency Management (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the technical field of intelligent charging control, and discloses a power supplementing method and a system of an emergency repair power supply, wherein the method comprises the following steps: under the condition that an emergency repair power supply needs to be charged, determining a candidate charging module capable of charging the emergency repair power supply; under the condition that the candidate charging module does not comprise an alternating current charging module but comprises a photovoltaic module, optimizing the current output power of the photovoltaic module through GSLPSO algorithm to obtain the optimized output power of the photovoltaic module; correcting the current power threshold to obtain a corrected power threshold; and outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold. Compared with the related technology, the invention improves the power supplementing efficiency of the emergency repair power supply.
Description
Technical Field
The invention relates to the technical field of intelligent charging control, in particular to a power supplementing method and system of an emergency repair power supply.
Background
In an electric power system, emergency repair power supplies play a key role, and when facing to power grid faults, natural disasters or other emergency situations, the emergency repair power supplies can quickly respond to an emergency repair task and quickly provide electric power support, so that the power grid can be helped to quickly recover operation, and public safety is guaranteed. In the related art, common emergency repair power supply types comprise a traditional diesel generator set and a traditional gas generator set, and the two types have the problems of environmental pollution, high fuel storage cost, inconvenient operation and the like. As an improvement scheme, the photovoltaic module is used as an emergency repair power supply in the related technology, so that the photovoltaic module has smaller influence on the environment, is more environment-friendly and energy-saving, and has lower cost than the traditional diesel generator set.
However, the efficiency of supplementing electricity for the emergency repair power supply by adopting the photovoltaic module needs to be improved.
Disclosure of Invention
The invention provides a power supplementing method and system for an emergency repair power supply, which are used for improving the power supplementing efficiency of the emergency repair power supply in the related technology.
In order to solve the technical problems, an embodiment of the present invention provides a power supplementing method for an emergency repair power supply, including:
Under the condition that an emergency repair power supply needs to be charged, determining a candidate charging module capable of charging the emergency repair power supply;
Under the condition that the candidate charging module does not comprise an alternating current charging module but comprises a photovoltaic module, optimizing the current output power of the photovoltaic module through GSLPSO algorithm to obtain the optimized output power of the photovoltaic module;
Correcting the current power threshold to obtain a corrected power threshold;
Outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold; the charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module.
According to the invention, the charging mode instruction is output by comparing the output power of the photovoltaic module with the power threshold value, and the power threshold value is continuously corrected in the calculation process, so that the accuracy of the charging mode instruction is improved, the power supplementing efficiency of the emergency repair power supply is maintained at a higher level, and the requirement of the emergency repair power supply on emergency conditions is met.
As a preferred solution, the optimizing the current output power of the photovoltaic module by using the GSLPSO algorithm to obtain the optimized output power of the photovoltaic module specifically includes:
Initializing parameters in GSLPSO algorithm, and randomly distributing the positions of particles, wherein the positions of the particles represent the duty ratio of the photovoltaic module;
evaluating the fitness of the particle population, wherein the fitness is the output power of the photovoltaic module, and updating the optimal fitness when the fitness of the particle population is better;
performing variation on optimal individuals of the particle population;
Calculating the mass and the gravitation of each particle according to the gravitation constant, and updating the position and the speed of each particle according to the speed updating coefficient;
updating the speed, updating the coefficient and the gravitation constant, returning to perform iterative calculation again until the iterative times reach the maximum iterative times, and outputting the output power of the photovoltaic module at the moment as the optimized output power.
The maximum output power of the photovoltaic module is tracked through GSLPSO algorithm, and the output power of the photovoltaic module is ensured to be at the highest level through adjusting the working point of the photovoltaic module. Compared with the conventional algorithm, the GSLPSO algorithm shortens the convergence time of maximum output power tracking, improves the response speed of the photovoltaic module to environmental factor changes, can reduce the probability of sinking into local optimum in the searching process, and improves the power output level of the photovoltaic module.
Preferably, the mutation of the optimal individual of the particle population specifically includes:
The optimal individual is mutated, the gravitation of the mutated optimal individual is calculated, and when the gravitation of the mutated optimal individual is larger than that of the current optimal individual, the optimal individual is updated;
and returning to the next type of mutation when the mutation times do not reach the preset mutation times, and ending the mutation otherwise.
Preferably, the mutations include gaussian mutations, cauchy mutations, elite mutations and zoom mutations.
According to the invention, the optimal individuals in the particle population are subjected to multiple variation, so that the search range of the maximum output power is enlarged, and the probability of being trapped into local optimal in the search process is reduced compared with a common algorithm.
As a preferred solution, the correcting the current power threshold value to obtain a corrected power threshold value specifically includes:
and carrying out repeated iterative correction on the current power threshold according to the actual charging power of the emergency repair power supply and the power error between the optimized output power until the power error is smaller than the error threshold, so as to obtain a corrected power threshold.
According to the invention, the power threshold is corrected through iterative learning according to the power error between the actual charging power and the optimized output power, so that the power error is reduced, the accuracy of a charging mode instruction is improved, the emergency repair power supply is always powered up in a charging mode with higher power, and the problem that the response speed required under the emergency condition cannot be met due to low power-up efficiency of the emergency repair power supply in the related art is solved.
Preferably, the power error is determined by:
outputting a corresponding charging mode instruction according to the magnitude relation between the optimized output power and the current power threshold value;
connecting the emergency repair power supply with a candidate charging module corresponding to the charging mode instruction to obtain the actual charging power of the emergency repair power supply;
And obtaining the power error by carrying out difference between the actual charging power and the optimized output power.
According to the invention, the power error is reduced through iterative learning, so that the actual charging power of the emergency repair power supply is close to the maximum output power obtained through GSLPSO algorithm, and the power supplementing efficiency of the emergency repair power supply is improved.
Preferably, the method further comprises:
and when the candidate charging module comprises an alternating current charging module, controlling the emergency repair power supply to be connected with the alternating current charging module.
In the invention, compared with other charging modules, the alternating-current charging module has higher and more stable output power and smaller influence on the environment, so that when the candidate charging module comprises the alternating-current charging module, the module can be directly selected to be connected with an emergency repair power supply for power supply.
Preferably, the candidate charging module further comprises an automobile power module with the electric quantity higher than an electric quantity threshold value; the outputting a corresponding charging mode instruction according to the optimized output power of the photovoltaic module and the corrected power threshold value comprises:
Outputting a first charging mode instruction when the optimized output power is greater than the corrected power threshold; the first charging mode instruction is used for controlling the emergency repair power supply to be connected with the photovoltaic module;
Outputting a second charging mode instruction when the optimized output power is smaller than the corrected power threshold; the second charging mode instruction is used for controlling the emergency repair power supply to be connected with the automobile power supply module.
The candidate charging module further comprises an automobile power supply module with the electric quantity higher than the electric quantity threshold, when the output power of the photovoltaic module cannot meet the power threshold, the automobile power supply module can be used for supplementing electricity for the emergency repair power supply, the problem that the electricity supplementing process of the emergency repair power supply is limited by time and environment is solved, and the electricity supplementing efficiency of the emergency repair power supply is improved when the environment condition does not meet the working condition of the photovoltaic module.
Preferably, when the power threshold is smaller than the output power of the automobile power supply module, the correction is stopped.
When the power threshold is smaller than the output power of the automobile power supply module, if a charging mode instruction is still output according to the power threshold, the fact that the actual charging power of the emergency repair power supply is smaller than the maximum power which can be output by the candidate charging module can occur, and the power supplementing efficiency of the emergency repair power supply is reduced. The actual charging power of the emergency repair power supply is ensured to be at a higher level by stopping the correction.
The embodiment of the invention also provides an electricity supplementing system of the emergency repair power supply, which comprises:
The candidate module determining module is used for determining a candidate charging module capable of charging the emergency repair power supply under the condition that the emergency repair power supply needs to be charged;
The photovoltaic power optimization module is used for optimizing the current output power of the photovoltaic module through GSLPSO algorithm under the condition that the candidate charging module does not comprise the alternating current charging module but comprises the photovoltaic module, so as to obtain the optimized output power of the photovoltaic module;
the power threshold value correction module is used for correcting the current power threshold value to obtain a corrected power threshold value;
The charging instruction output module is used for outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold; the charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module.
Compared with the related art, the embodiment of the invention has the following beneficial effects:
1. According to the invention, the charging mode instruction is output by comparing the output power of the photovoltaic module with the power threshold value, and the power threshold value is continuously corrected in the calculation process, so that the accuracy of the charging mode instruction is improved, the power supplementing efficiency of the emergency repair power supply is maintained at a higher level, and the requirement of the emergency repair power supply on emergency conditions is met;
2. The maximum output power of the photovoltaic module is tracked through GSLPSO algorithm, and the output power of the photovoltaic module is ensured to be at the highest level through adjusting the working point of the photovoltaic module; the GSLPSO algorithm adopted by the invention shortens the convergence time of maximum output power tracking, improves the response speed of the photovoltaic module to environmental factor change, can reduce the probability of sinking into local optimum in the searching process, and improves the power output level of the photovoltaic module;
3. The candidate charging module does not comprise a traditional diesel generator set and a traditional gas generator set in the related technology, and adopts solar energy and electric energy of an automobile power supply as complementary energy sources, so that the carbon emission is reduced, the influence of the complementary process on the environment is reduced, and the cost for generating energy is reduced.
Drawings
Fig. 1: the step schematic diagram of the power supplementing method of the emergency repair power supply provided by the embodiment of the invention;
fig. 2: a schematic flow chart for selecting a charging mode in the embodiment of the invention;
Fig. 3: a schematic flow chart for optimizing output power of a photovoltaic module in the embodiment of the invention;
fig. 4: the graph of the optimized output power in the embodiment of the invention;
fig. 5: the method is a graph of actual charging voltage of the emergency repair power supply in the embodiment of the invention;
Fig. 6: the method is a graph of actual charging current of the emergency repair power supply in the embodiment of the invention;
fig. 7: the module schematic diagram of the power supply system of the emergency repair power supply provided by the embodiment of the invention;
Wherein, the reference numerals of the specification drawings are as follows: 1. the device comprises a candidate module determining module, a photovoltaic power optimizing module, a power threshold correcting module and a charging instruction output module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the related art, as the photovoltaic module has the problem of being greatly influenced by environmental factors, the output power of the photovoltaic module can be reduced at night or in a period of poor weather conditions, the power supplementing efficiency in the power supplementing process of the emergency repair power supply is influenced, and even the power supplementing of the emergency repair power supply cannot be effectively realized, so that the response speed of the emergency repair power supply is influenced.
Example 1
According to the invention, the charging mode instruction is output by comparing the output power of the photovoltaic module with the power threshold value, and the power threshold value is continuously corrected in the calculation process, so that the accuracy of the charging mode instruction is improved, the possibility that the power supplementing efficiency of the emergency repair power supply is too low for a long time is reduced, the power supplementing efficiency is maintained at a higher level, and the requirement of the emergency repair power supply on emergency conditions is met.
The maximum output power of the photovoltaic module is tracked through the GSLPSO algorithm, compared with the conventional algorithm, the GSLPSO algorithm shortens the convergence time of maximum output power tracking, improves the response speed of the photovoltaic module to environmental factor changes, can reduce the probability of sinking into local optimum in the searching process, and improves the power output level of the photovoltaic module.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating steps of a power supply supplementing method for an emergency repair power supply according to an embodiment of the present invention, in the method, for the emergency repair power supply in the embodiment, whether power supply is needed is first determined according to the current electric quantity of the emergency repair power supply, and when the electric quantity of the emergency repair power supply is higher than the requirement of the emergency electric quantity, the emergency repair power supply works normally without power supply; when the electric quantity of the emergency repair power supply is lower than the requirement of the emergency electric quantity, the emergency repair power supply is required to be supplemented, and at the moment, candidate charging modules are determined according to the states of the charging modules.
Step S1: under the condition that the emergency repair power supply needs to be charged, a candidate charging module capable of charging the emergency repair power supply is determined.
The charging modules that may be used as candidate charging modules in this embodiment include an ac charging module, a photovoltaic module, and an automobile power module. The alternating-current charging module comprises an alternating-current socket, and the alternating-current socket can provide reliable and stable electric quantity supply for emergency repair power supply. The photovoltaic module comprises a photovoltaic array formed by photovoltaic panels, a direct current converter and a microcontroller for controlling the photovoltaic module to work, and the working point of the photovoltaic panels can be controlled by PWM waves with proper output duty ratio through the microcontroller, so that the output power of the photovoltaic module is maximized. The automobile power supply module comprises a plurality of electric automobiles, and the emergency repair power supply can charge the automobile through the electric quantity in the electric automobiles by using an automobile charger under the condition of needing power supplement. In other embodiments, other charging modules may be selected according to practical application conditions, and the scope thereof includes, but is not limited to: wind power modules comprising wind power generating sets, hydroelectric modules comprising hydroelectric generating sets, and the like.
The determination mode of the candidate charging module specifically comprises the following steps: for the alternating-current charging module, if the alternating-current charging module exists in a certain range around the emergency repair power supply and can be connected with the alternating-current charging module, the candidate charging module comprises the alternating-current charging module. For the photovoltaic module, if the photovoltaic module exists in a certain range around the emergency repair power supply and can be connected with the photovoltaic module, the candidate charging module comprises the photovoltaic module. For the automobile power supply module, if a plurality of electric automobiles which are being charged exist in a charging station within a certain range around the emergency repair power supply and the electric quantity of the electric automobiles is higher than an electric quantity threshold value, the candidate charging modules comprise the automobile power supply module.
In the step, the types of the candidate charging modules are determined, so that the reliability of the candidate charging modules is improved, the emergency repair power supply can supplement electricity under different time and environmental conditions, and the problem that the emergency repair power supply cannot store enough electric quantity to deal with emergency due to the fact that the electricity supplementing process is limited by environmental factors in the related technology is solved.
Referring to fig. 2, fig. 2 is a schematic flow chart of charging mode selection in the embodiment of the invention, in the process of supplementing electricity, different charging modules are selected according to the specific types of the candidate charging modules determined in step S1 to be connected with the emergency repair power supply, and the uncertainty of a single charging mode is reduced through coexistence of multiple charging modes, so that the reliability of the emergency repair power supply is improved.
When the candidate charging module comprises an alternating current charging module, the emergency repair power supply is controlled to be connected with the alternating current charging module and to supplement electricity. Compared with other candidate charging modules, the alternating-current charging module has higher and more stable output power, and meanwhile, the alternating-current charging module has smaller influence on the environment, so that the candidate charging module can be directly connected with an emergency repair power supply for supplementing electricity when the candidate charging module comprises the alternating-current charging module.
Step S2: and under the condition that the candidate charging module does not comprise the alternating current charging module but comprises the photovoltaic module, optimizing the current output power of the photovoltaic module through GSLPSO algorithm to obtain the optimized output power of the photovoltaic module.
When the candidate charging module does not include the alternating current charging module but includes the photovoltaic module, the method optimizes the current output power of the photovoltaic module through GSLPSO algorithm. The GSLPSO algorithm is a hybrid gravitation search leading particle swarm optimization algorithm, combines a GSA algorithm and an LPSO algorithm, has the global searching capability of the GSA algorithm, has the randomness provided by the LPSO algorithm, and reduces the probability that particles fall into local optimum due to premature convergence.
Referring to fig. 3, fig. 3 is a schematic flow chart of optimizing output power of a photovoltaic module according to an embodiment of the invention, which includes the following steps:
Step S2.1: parameters in the GSLPSO algorithm are initialized and the positions of the particles are randomly allocated.
In the process of optimizing the current output power of the photovoltaic module through GSLPSO algorithm, firstly initializing parameters in GSLPSO algorithm, including the position, quality and speed of particle population, randomly distributing the position of each particle by using a rand function, wherein the positions of the particles in the embodiment represent the duty ratio of the photovoltaic module, different duty ratios represent different working points of the photovoltaic module, and various scenes existing in actual working can be simulated through randomly distributing the positions of the particles, so that global search of the optimal duty ratio is realized.
Step S2.2: and carrying out fitness evaluation on the particle population.
In this embodiment, the fitness function is the output power of the photovoltaic module, which is affected by the duty cycle, and the output powers of the photovoltaic modules working under different duty cycles are different. And regarding the initial particle population, taking the optimal fitness in the particle population as the optimal fitness.
Step S2.3: the optimal individuals of the particle population are mutated.
After the fitness evaluation is finished, the optimal individuals in the current particle population, namely the individuals with the largest mass, are mutated in sequence for a plurality of times in the embodiment, and the gravitation of the mutated optimal individuals is calculated after each mutation is finished. The gravitation of the optimal individual represents the searching degree of the optimal solution in the current iteration times, if the gravitation of the mutated optimal individual is larger than that of the current optimal individual, the current optimal individual is updated to the mutated optimal individual, otherwise, the updating is not performed.
When the mutation times do not reach the preset mutation times, the following types of mutation are returned, wherein the mutation types in the embodiment comprise Gaussian mutation, cauchy mutation, elite mutation and zoom mutation, and the first stage of mutation is Gaussian mutation, and the form is as follows:
Wherein, For the optimal individual after the first stage mutation,For the currently optimal individual to be present,AndThe upper and lower boundaries of the control variable, in this embodiment the position of the particle,For obeying mean value toStandard deviation isRandom values of gaussian distribution of (c). For the optimal individuals after the first stage mutationCalculating the magnitude of the attraction force whenIs greater thanWill be when the attraction force of (2)Updated to。
The second stage of mutation is a cauchy mutation, which is of the form:
Wherein, For the optimal individual after the second stage mutation,To obey the random values of the cauchy distribution,Is a scale factor, which is of the form:
Wherein, For the current number of iterations,Is the maximum number of iterations. For the optimal individuals after the second-stage mutationCalculating the magnitude of the attraction force whenIs greater thanWill be when the attraction force of (2)Updated to。
The third stage of mutation is elite mutation, which is of the form:
Wherein, Is the optimal individual after the third-stage mutation. For the optimal individuals after the third-stage mutationCalculating the magnitude of the attraction force whenIs greater thanWill be when the attraction force of (2)Updated to。
The fourth stage of mutation is a scaling mutation, which is of the form:
Wherein, For the optimal individuals after the fourth stage mutation,In order for the scaling factor to be a factor,AndRespectively represent the firstTwo random particles at each iteration. For the optimal individuals after the fourth-stage mutationCalculating the magnitude of the attraction force whenIs greater thanWill be when the attraction force of (2)Updated to. When the mutation times reach the preset mutation times, the mutation is ended and the optimal individual at the moment is output.
According to the method, the optimal individuals in the particle population are subjected to multi-stage variation, so that the searching range of GSLPSO algorithm is enlarged, the probability of sinking into local optimal in the searching process is reduced compared with the conventional algorithm, and the global optimal solution can be more accurately determined, so that the photovoltaic module can work at the maximum output power.
Step S2.4: and calculating the mass and the attraction of each particle according to the attraction constant, and updating the position and the speed of each particle according to the speed updating coefficient.
After the mutation is finished, calculating the mass and attraction of each particle in the particle population comprising the optimal individual obtained by the mutation, wherein the mass of the particle is represented by the following formula:
Wherein, Is the firstThe first iterationThe mass of the individual particles is such that,The form of (a) is as follows:
Wherein, Is the firstThe first iterationThe degree of suitability of the individual particles,AndRespectively the firstOptimal fitness and worst fitness in the particle population at the next iteration.
After determining the mass of the particles, the attractive force of the particles can be calculated according to the mass, and the attractive force is expressed as follows:
Wherein, Is the firstThe first iterationIndividual particles and the firstThe magnitude of the attractive force between the individual particles,AndRespectively represent the firstThe first iterationIndividual particles and the firstThe mass of the individual particles is such that,Is the firstThe first iterationIndividual particles and the firstThe distance between the individual particles is such that,Is the firstThe gravitational constant at the time of iteration is as follows:
Wherein, For the initial gravitational constant,Is the coefficient of variation of the gravitational constant.
After the gravitational force is obtained, the acceleration of the particles at the moment can be calculated according to the gravitational force, and the position and the speed of the particles are updated according to the acceleration, wherein the position and the speed of the particles are expressed as follows:
Wherein, AndIs the firstSecondary and tertiaryThe first iterationThe position of the individual particles is determined,AndIs the firstSecondary and tertiaryThe first iterationThe velocity of the individual particles is such that,The coefficients are updated for the particles and,Is the firstThe first iterationThe acceleration of the individual particles is determined,AndAre all velocity update coefficients, which are of the form:
Wherein, 、、AndThe constant may be set according to actual conditions.
Step S2.5: updating the speed, updating the coefficient and the gravitation constant, returning to perform iterative calculation again until the iterative times reach the maximum iterative times, and outputting the output power of the photovoltaic module at the moment as the optimized output power.
And if the iteration number does not reach the maximum iteration number, updating the speed update coefficient and the gravitation constant according to the iteration number, and performing next iteration calculation, namely returning to the step S2.2. In the process of evaluating the fitness, if the iterated fitness is due to the current optimal fitness, the iterated fitness is updated to the optimal fitness. And if the iteration number reaches the maximum iteration number, outputting an optimal duty ratio according to the position of the optimal individual in the current particle population, controlling the photovoltaic module according to the optimal duty ratio, and maximizing the output of the photovoltaic module to obtain the optimized output power of the photovoltaic module.
Compared with the conventional algorithm, the GSLPSO algorithm provided by the method effectively shortens the convergence time of maximum output power tracking through the GSA algorithm and the updating of particle speed and position, improves the response speed of the photovoltaic module to environmental factor change, can reduce the probability of sinking into local optimum in the searching process through the LPSO algorithm, and improves the power output level of the photovoltaic module.
Step S3: and correcting the current power threshold value to obtain a corrected power threshold value.
And comparing the actual charging power of the emergency repair power supply with the optimized output power to obtain a power error, and carrying out iterative correction on the power threshold according to the power error until the power error is smaller than the error threshold to obtain a corrected power threshold.
The power error calculation method in the step comprises the following steps: the optimized output power is input into a controller, the controller comprises an iterative learning controller, a charging mode switching system and a memory, wherein the charging mode switching system compares the optimized output power with a current power threshold value and outputs a corresponding charging mode instruction according to the magnitude relation between the optimized output power and the current power threshold value. In this embodiment, the charging mode instruction is used to control the emergency repair power supply to be connected with the corresponding charging module, connect the emergency repair power supply with the candidate charging module corresponding to the output charging mode instruction, and supplement electricity to the emergency repair power supply, so as to obtain the actual charging power of the emergency repair power supply at the moment. And obtaining a power error by differentiating the actual charging power and the optimized output power.
When the power error is smaller than the error threshold, the charging mode instruction output at the moment is accurate and meets the requirement, and the current power threshold does not need to be corrected; when the power error is larger than the error threshold, the output charging mode instruction is wrong, and the current power threshold needs to be corrected.
In the correction process, the power error and the current power threshold value are firstly input into a memory, and are input into an iterative learning controller. And correcting the current power threshold value in the iterative learning controller according to the set proportional gain and the learning rate, wherein the correction form is as follows:
Wherein, AndRespectively the firstSecondary and tertiaryA corrected power threshold at the number of iterations,In order for the rate of learning to be high,In order to achieve a proportional gain,AndRespectively the firstSecondary and tertiaryPower error at the number of iterations.
According to the method, through repeated iterative learning, the error between the actual charging power of the emergency repair power supply and the maximum output power of the candidate charging module is continuously reduced, so that the error rate of charging mode selection is reduced, the control effect is effectively improved, and the problem that the power supply efficiency of the emergency repair power supply is limited due to single charging mode in the related art is solved.
Step S4: outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold; the charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module.
And inputting the corrected power threshold value into a charging mode switching system, comparing the corrected power threshold value with the input optimized output power, and outputting a corresponding charging mode instruction. The charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module, so that power supply is started.
Compared with the related art, the embodiment of the invention has the following beneficial effects:
1. According to the invention, the charging mode instruction is output by comparing the output power of the photovoltaic module with the power threshold value, and the power threshold value is continuously corrected in the calculation process, so that the accuracy of the charging mode instruction is improved, the power supplementing efficiency of the emergency repair power supply is maintained at a higher level, and the requirement of the emergency repair power supply on emergency conditions is met;
2. According to the invention, the maximum output power of the photovoltaic module is tracked through GSLPSO algorithm, the maximum output power of the photovoltaic module is ensured to be at the highest level through adjusting the duty ratio of the photovoltaic module, the convergence time of the maximum output power tracking is shortened, the corresponding speed of the photovoltaic module to the change of environmental factors is improved, meanwhile, the probability of sinking into local optimum in the searching process can be reduced, and the power output level of the photovoltaic module is improved;
3. The candidate charging module does not comprise a traditional diesel generator set and a traditional gas generator set in the related technology, and adopts solar energy and electric energy of an automobile power supply as complementary energy sources, so that the carbon emission is reduced, the influence of the complementary process on the environment is reduced, and the cost for generating energy is reduced.
Example two
The method of this embodiment is the same as described in embodiment one, but the candidate charging modules in this embodiment include a photovoltaic module and an automotive power module, where the electrical power of the automotive power module is above the electrical power threshold.
In step S4, the charging mode switching system compares the optimized output power with the corrected power threshold. When the optimized output power is larger than the corrected power threshold value, a first charging mode instruction is output, and the first charging mode instruction is used for controlling the emergency repair power supply to be connected with the photovoltaic module, and the photovoltaic module is used for supplementing electricity for the emergency repair power supply. And when the optimized output power is smaller than the corrected power threshold value, outputting a second charging mode instruction, wherein the second charging mode instruction is used for controlling the emergency repair power supply to be connected with the automobile power supply module, and supplementing electricity for the emergency repair power supply through the automobile power supply module. According to the embodiment, different charging mode instructions are output, so that the charging mode can be flexibly switched according to the environmental conditions, and the normal power supply efficiency of the emergency repair power supply is ensured.
In the present embodiment, in the correction process of the current power threshold, when the power threshold is smaller than the output power of the power module of the automobile, the correction is stopped. At this time, if the charging mode instruction is still output according to the power threshold, the photovoltaic module with the output power not meeting the requirement after the emergency repair power supply is selected and optimized is subjected to power supply, the actual charging power of the emergency repair power supply is smaller than the maximum power which can be output by the candidate charging module, and the power supply efficiency of the emergency repair power supply is limited. According to the embodiment, the power threshold is maintained above the output power of the automobile power supply module, so that when the output power is still low after optimization of the photovoltaic mode in the power supplementing process and cannot meet the requirements, the emergency repair power supply is connected with the automobile power supply module and supplemented with power, the emergency repair power supply is ensured to be capable of supplementing power normally under various time and environmental conditions, and the reliability of the emergency repair power supply is improved.
Example III
In this embodiment, a simulation experiment is performed according to the method provided in the second embodiment, where the photovoltaic module in this embodiment uses a simulated photovoltaic panel, the ambient temperature of the simulated photovoltaic panel is set to 25 degrees celsius, the illumination radiation in the environment is set to a step signal, the illumination radiation is 1000W/m 2 in 0 to 1 second, and the illumination radiation is 400W/m 2 in 1 to 2 seconds.
Referring to fig. 4, fig. 4 is a graph of the optimized output power in the embodiment of the present invention, it can be seen that, in an initial period of receiving illumination radiation, the optimized output power of the photovoltaic module generates a certain fluctuation, and the duration of the fluctuation is less than 0.1 seconds, which does not affect the actual power supply process. After the initial period, the optimized output power converges to the maximum power when the illumination radiance is 1000W/m 2, and the waveform of the output power after the subsequent optimization is smooth and stable. When reaching 1 second, the output power is rapidly reduced after optimization due to the fact that the illumination radiation degree is reduced to 400W/m 2, and the maximum power when the illumination radiation degree is converged to 400W/m 2 in a short time, and the waveform is smooth and stable in 1 to 2 seconds.
As can be seen from fig. 4, the GSLPSO algorithm provided in this embodiment can rapidly cope with the change of the illumination radiation degree in the environment, and has fast convergence speed and good working performance.
Referring to fig. 5 and fig. 6, fig. 5 is a graph of an actual charging voltage of the emergency repair power supply in the embodiment of the present invention, and fig. 6 is a graph of an actual charging current of the emergency repair power supply in the embodiment of the present invention, as can be seen from fig. 5 and fig. 6, in 0 to 1 second, since the illumination radiation is strong, the output power of the photovoltaic module after optimization is high, and the connected charging module is the photovoltaic module. In 1 to 2 seconds, due to the decrease of the illumination radiance, the optimized output power is reduced according to the optimization as shown in fig. 4, and the comparison of the charging mode switching system is performed to obtain that the optimized output power is lower than the corrected power threshold, and the correction of the power threshold is stopped due to the fact that the corrected power threshold is lower than the output power of the automobile power supply module in the correction process. Therefore, the emergency repair power supply is controlled to be connected with the automobile power supply module and to supplement electricity within 1 to 2 seconds.
As can be seen from fig. 5 and fig. 6, the power supply compensation method for the emergency repair power supply provided in this embodiment adopts an iterative learning method, and realizes the switching of the charging modes by correcting the power threshold value to output an accurate charging mode instruction, and has a fast response speed to the change of environmental factors, so that the problem that the emergency repair power supply cannot normally compensate power due to the environmental limitation of the power supply compensation efficiency in the related art is solved.
Example IV
Referring to fig. 7, fig. 7 is a schematic block diagram of a power supply system for emergency repair according to an embodiment of the present invention, and the invention further provides a power supply system for emergency repair, which includes a candidate module determining module 1, a photovoltaic power optimizing module 2, a power threshold correcting module 3, and a charging command outputting module 4.
The candidate module determining module 1 is used for determining a candidate charging module capable of charging the emergency repair power supply under the condition that the emergency repair power supply needs to be charged. And the photovoltaic power optimization module 2 is used for optimizing the current output power of the photovoltaic module through GSLPSO algorithm under the condition that the candidate charging module does not comprise the alternating current charging module but comprises the photovoltaic module, so as to obtain the optimized output power of the photovoltaic module. The power threshold value correction module 3 is used for correcting the current power threshold value to obtain a corrected power threshold value. The charging instruction output module 4 is configured to output a corresponding charging mode instruction according to the optimized output power and the corrected power threshold; the charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module. The system provided by the embodiment can be used for executing the power supplementing method of the emergency repair power supply provided by any one of the first to third embodiments.
Compared with the related art, the embodiment of the invention has the following beneficial effects:
1. According to the invention, the charging mode instruction is output by comparing the output power of the photovoltaic module with the power threshold value, and the power threshold value is continuously corrected in the calculation process, so that the accuracy of the charging mode instruction is improved, the power supplementing efficiency of the emergency repair power supply is maintained at a higher level, and the requirement of the emergency repair power supply on emergency conditions is met;
2. The maximum output power of the photovoltaic module is tracked through GSLPSO algorithm, and the output power of the photovoltaic module is ensured to be at the highest level through adjusting the working point of the photovoltaic module; the GSLPSO algorithm adopted by the invention shortens the convergence time of maximum output power tracking, improves the response speed of the photovoltaic module to environmental factor change, can reduce the probability of sinking into local optimum in the searching process, and improves the power output level of the photovoltaic module;
3. The candidate charging module does not comprise a traditional diesel generator set and a traditional gas generator set in the related technology, and adopts solar energy and electric energy of an automobile power supply as complementary energy sources, so that the carbon emission is reduced, the influence of the complementary process on the environment is reduced, and the cost for generating energy is reduced.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (8)
1. The power supplementing method of the emergency repair power supply is characterized by comprising the following steps of:
Under the condition that an emergency repair power supply needs to be charged, determining a candidate charging module capable of charging the emergency repair power supply;
Under the condition that the candidate charging module does not comprise an alternating current charging module but comprises a photovoltaic module, optimizing the current output power of the photovoltaic module through GSLPSO algorithm to obtain the optimized output power of the photovoltaic module; the GSLPSO algorithm is a hybrid gravitation search leading particle swarm optimization algorithm obtained by combining the global searching capability of the GSA algorithm and the randomness provided by the LPSO algorithm, the photovoltaic module corresponds to a power threshold value, and the power threshold value is used for comparing with the output power of the photovoltaic module to output a charging mode instruction;
Correcting the current power threshold of the photovoltaic module to obtain a corrected power threshold; the correcting the current power threshold of the photovoltaic module to obtain a corrected power threshold comprises the following steps: performing repeated iterative correction on the current power threshold according to the actual charging power of the emergency repair power supply and the power error between the optimized output power until the power error is smaller than an error threshold, so as to obtain a corrected power threshold; wherein the power error is determined by: outputting a corresponding charging mode instruction according to the magnitude relation between the optimized output power and the current power threshold value; connecting the emergency repair power supply with a candidate charging module corresponding to the charging mode instruction to obtain the actual charging power of the emergency repair power supply; the actual charging power and the optimized output power are subjected to difference to obtain the power error;
Outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold; the charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module; the candidate charging module further comprises an automobile power supply module with the electric quantity higher than an electric quantity threshold value; the outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold value comprises the following steps: outputting a second charging mode instruction when the optimized output power is smaller than the corrected power threshold; the second charging mode instruction is used for controlling the emergency repair power supply to be connected with the automobile power supply module.
2. The method according to claim 1, wherein the optimizing the current output power of the photovoltaic module by GSLPSO algorithm to obtain the optimized output power of the photovoltaic module specifically comprises:
Initializing parameters in GSLPSO algorithm, and randomly distributing the positions of particles, wherein the positions of the particles represent the duty ratio of the photovoltaic module;
evaluating the fitness of the particle population, wherein the fitness is the output power of the photovoltaic module, and updating the optimal fitness when the fitness of the particle population is better;
performing variation on optimal individuals of the particle population;
Calculating the mass and the gravitation of each particle according to the gravitation constant, and updating the position and the speed of each particle according to the speed updating coefficient;
updating the speed, updating the coefficient and the gravitation constant, returning to perform iterative calculation again until the iterative times reach the maximum iterative times, and outputting the output power of the photovoltaic module at the moment as the optimized output power.
3. The method of claim 2, wherein the mutating the optimal individual of the population of particles comprises:
The optimal individual is mutated, the gravitation of the mutated optimal individual is calculated, and when the gravitation of the mutated optimal individual is larger than that of the current optimal individual, the optimal individual is updated;
and returning to the next type of mutation when the mutation times do not reach the preset mutation times, and ending the mutation otherwise.
4. The method of claim 3, wherein the mutations comprise gaussian mutations, cauchy mutations, elite mutations and zoom mutations.
5. The method of claim 1, wherein the method further comprises:
and when the candidate charging module comprises an alternating current charging module, controlling the emergency repair power supply to be connected with the alternating current charging module.
6. The method of claim 1, wherein outputting the corresponding charging mode instruction according to the optimized output power of the photovoltaic module and the modified power threshold value further comprises:
outputting a first charging mode instruction when the optimized output power is greater than the corrected power threshold; the first charging mode instruction is used for controlling the emergency repair power supply to be connected with the photovoltaic module.
7. The method of claim 6, wherein correction is stopped when the power threshold is less than the output power of the automotive power supply module.
8. The utility model provides a power supply system of emergency repair power supply which characterized in that includes:
The candidate module determining module is used for determining a candidate charging module capable of charging the emergency repair power supply under the condition that the emergency repair power supply needs to be charged;
The photovoltaic power optimization module is used for optimizing the current output power of the photovoltaic module through GSLPSO algorithm under the condition that the candidate charging module does not comprise the alternating current charging module but comprises the photovoltaic module, so as to obtain the optimized output power of the photovoltaic module; the GSLPSO algorithm is a hybrid gravitation search leading particle swarm optimization algorithm obtained by combining the global searching capability of the GSA algorithm and the randomness provided by the LPSO algorithm, the photovoltaic module corresponds to a power threshold value, and the power threshold value is used for comparing with the output power of the photovoltaic module to output a charging mode instruction;
The power threshold correction module is used for correcting the current power threshold of the photovoltaic module to obtain a corrected power threshold; the correcting the current power threshold of the photovoltaic module to obtain a corrected power threshold comprises the following steps: performing repeated iterative correction on the current power threshold according to the actual charging power of the emergency repair power supply and the power error between the optimized output power until the power error is smaller than an error threshold, so as to obtain a corrected power threshold; wherein the power error is determined by: outputting a corresponding charging mode instruction according to the magnitude relation between the optimized output power and the current power threshold value; connecting the emergency repair power supply with a candidate charging module corresponding to the charging mode instruction to obtain the actual charging power of the emergency repair power supply; the actual charging power and the optimized output power are subjected to difference to obtain the power error;
The charging instruction output module is used for outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold; the charging mode instruction is used for controlling the emergency repair power supply to be connected with the corresponding charging module; the candidate charging module further comprises an automobile power supply module with the electric quantity higher than an electric quantity threshold value; the outputting a corresponding charging mode instruction according to the optimized output power and the corrected power threshold value comprises the following steps: outputting a second charging mode instruction when the optimized output power is smaller than the corrected power threshold; the second charging mode instruction is used for controlling the emergency repair power supply to be connected with the automobile power supply module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410774239.8A CN118336887B (en) | 2024-06-17 | 2024-06-17 | Power supplementing method and system for emergency repair power supply |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410774239.8A CN118336887B (en) | 2024-06-17 | 2024-06-17 | Power supplementing method and system for emergency repair power supply |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118336887A CN118336887A (en) | 2024-07-12 |
| CN118336887B true CN118336887B (en) | 2024-08-30 |
Family
ID=91773166
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410774239.8A Active CN118336887B (en) | 2024-06-17 | 2024-06-17 | Power supplementing method and system for emergency repair power supply |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118336887B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116024747A (en) * | 2023-01-12 | 2023-04-28 | 广东电网有限责任公司 | Dynamic balance autonomous method, device and equipment of power grid and storage medium |
| CN118157173A (en) * | 2023-12-25 | 2024-06-07 | 武汉理工大学 | Power distribution network rush-repair method and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6480196B2 (en) * | 2015-01-26 | 2019-03-06 | 株式会社日立製作所 | Storage battery system and photovoltaic power generation system having the same |
| KR102338672B1 (en) * | 2020-08-21 | 2021-12-13 | 주식회사 한미이앤씨 | The Energy Management System of Energy Storage System -connected Photovoltaic Power System |
| CN116540832A (en) * | 2023-06-02 | 2023-08-04 | 西安热工研究院有限公司 | Photovoltaic maximum power tracking method and system based on variant group quantum whale optimization |
-
2024
- 2024-06-17 CN CN202410774239.8A patent/CN118336887B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116024747A (en) * | 2023-01-12 | 2023-04-28 | 广东电网有限责任公司 | Dynamic balance autonomous method, device and equipment of power grid and storage medium |
| CN118157173A (en) * | 2023-12-25 | 2024-06-07 | 武汉理工大学 | Power distribution network rush-repair method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118336887A (en) | 2024-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Aziz et al. | Optimal sizing of stand-alone photovoltaic system by minimizing the loss of power supply probability | |
| CN109814651B (en) | Particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system | |
| Atri et al. | Comparison of different MPPT control strategies for solar charge controller | |
| Nagarajan et al. | Implementation of PV-based boost converter using PI controller with PSO algorithm | |
| Ramaprabha et al. | Design and modeling of standalone solar photovoltaic charging system | |
| Safarudin et al. | Maximum power point tracking algorithm for photovoltaic system under partial shaded condition by means updating β firefly technique | |
| Rouhani et al. | A comprehensive method for optimum sizing of hybrid energy systems using intelligence evolutionary algorithms | |
| Ahmed et al. | PSO-SMC controller based GMPPT technique for photovoltaic panel under partial shading effect | |
| CN116667325B (en) | Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm | |
| Issaadi | Control of a photovoltaic system by fuzzy logic, comparative studies with conventional controls: results, improvements and perspectives | |
| CN112531737A (en) | Wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization | |
| Abdelhak et al. | Optimum sizing of hybrid PV/wind/battery using Fuzzy-Adaptive Genetic Algorithm in real and average battery service life | |
| Raj et al. | Numerical simulation and performance assessment of ANN-INC improved maximum power point tracking system for solar photovoltaic system under changing irradiation operation | |
| CN111665900B (en) | Photovoltaic array multimodal maximum power tracking method based on filling function algorithm | |
| Wongsathan et al. | Optimal hybrid neuro-fuzzy based controller using MOGA for photovoltaic (PV) battery charging system | |
| CN118336887B (en) | Power supplementing method and system for emergency repair power supply | |
| Abadi et al. | Design of maximum power point tracking (MPPT) on two axes solar tracker based on particle swarm fuzzy | |
| CN112952914B (en) | Multi-objective operation optimization method for multi-energy complementary power system | |
| CN114357725A (en) | Source-to-load double-end uncertainty modeling method considering carbon capture emission | |
| Kurniawan et al. | Solar panel modeling and simulation with MPPT technique on micro grid | |
| Muthamizhan et al. | A Multi-Port Autonomous Reconfigurable Solar PV System for Renewable Energy Integration in Hybrid AC/DC System | |
| Yang et al. | An improved variable step size MPPT method for photovoltaic array under partial shading conditions | |
| Larbi et al. | Design of a buck converter battery charging controller in PV plant | |
| Keyrouz et al. | Bayesian fusion for maximum power output in hybrid wind-solar systems | |
| Abdelhalim et al. | Enhanced Vdc Bus Stability for PV and Battery Systems through an Optimized FOPID Controller using a Bidirectional DC-DC Converter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |