CN112557047B - Method and system for identifying stable operation condition of marine gas turbine - Google Patents
Method and system for identifying stable operation condition of marine gas turbine Download PDFInfo
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Abstract
The invention relates to a method and a system for identifying stable operation working conditions of a marine gas turbine, belongs to the technical field of gas turbines, and solves the problems of low efficiency and low accuracy of a method for identifying stable operation working conditions of a gas turbine. The method comprises the steps of obtaining power data of a gas turbine in a historical time period to obtain an original power data set; obtaining a power data set in a starting state based on the original power data set and a power threshold value; performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set; obtaining a power-on state stable power dataset based on the power-on state dataset and the stable power dataset; obtaining a final stable power data set based on the startup state stable power data set and the time threshold; based on the final stable power data set, the stable power data sets under all the operation conditions are obtained by combining the standard power under all the operation conditions, and the stable operation conditions of the gas turbine can be identified quickly, effectively and with high precision.
Description
Technical Field
The invention relates to the technical field of gas turbines, in particular to a method and a system for identifying stable operation conditions of a marine gas turbine.
Background
The gas turbine is an important ship power device, and the main function of the gas turbine is to convert chemical energy into mechanical energy, transmit the force to a propeller through a transmission device and finally push a ship to sail. When the gas turbine is matched with complex transmission equipment such as a multistage gear box, a controllable pitch propeller and the like, the operation characteristics of the gas turbine are complex, and the operation state of the marine gas turbine can be changed continuously by considering the influence of various environmental factors such as temperature, wind speed, sea waves and the like.
In order to enable a ship to sail more efficiently, safely and reliably under various possible working conditions, a health state evaluation model of the marine gas turbine is constructed, continuous and stable operation data of the gas turbine must be acquired, and therefore the condition that the continuous and stable operation data of the gas turbine are acquired is the premise for constructing the health evaluation model of the gas turbine. During the sailing process of the ship, under the conditions of adjusting the ship speed, adjusting the pitch to change the thrust of the ship and the like, the switching of working conditions can be involved, and the operation of the gas turbine at the moment is in a working condition transition stage. Such operations are very frequent during the actual use of the marine gas turbine, and the difficulty of extracting stable operation data is increased.
The operation characteristics of the ship power device are complex, the actual navigation conditions are complex and diverse, the accurate and stable operation condition of the ship gas turbine is difficult to describe only by adopting a simple data statistical method, for example, a large-scale judgment error and an improper parameter setting can occur only by a 6 sigma method for controlling an upper boundary line and a lower boundary line, the condition that data screening is too tight or too wide is very easy to occur, the identification error of the stable operation condition is caused, and the subsequent equipment health state evaluation accuracy is further influenced. Therefore, the traditional method for identifying the stable operation condition of the gas turbine has low efficiency and low accuracy.
Disclosure of Invention
In view of the foregoing analysis, embodiments of the present invention provide a method and a system for identifying stable operating conditions of a marine gas turbine, so as to solve the problems of low efficiency and low accuracy of the existing method for identifying stable operating conditions of a gas turbine.
In one aspect, an embodiment of the present invention provides a method for identifying stable operation conditions of a marine gas turbine, including:
acquiring power data of the gas turbine in a historical time period to obtain an original power data set;
obtaining a power data set of a starting state based on the original power data set and a power threshold value;
performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set;
obtaining a power-on state stable power dataset based on the power-on state power dataset and the stable power dataset;
obtaining a final stable power data set based on the starting-up state stable power data set and a time threshold;
and obtaining the final stable power data set under each operation condition by combining the standard power under each operation condition based on the final stable power data set.
Further, the obtaining a power-on state data set based on the raw power data set and a power threshold comprises:
comparing the power of the raw power data set to the power threshold;
setting a set of powers greater than or equal to the power threshold as an on-state power dataset.
Further, the performing a moving average and a difference quotient calculation on the original power data set to obtain a stable power data set includes:
carrying out moving average on the original power data set to obtain a smooth power data set;
carrying out difference quotient calculation on the smooth power data set to obtain a power difference quotient data set;
and obtaining a stable power data set based on the power difference quotient data set and the power difference quotient threshold.
Further, the obtaining a power-on state stable power dataset based on the power-on state power dataset and the stable power dataset comprises:
and performing intersection processing on the power-on state data set and the stable power data set to obtain a power-on state stable power data set.
Further, dividing the power-on state stable power data set into a plurality of power-on state stable power data segments according to time continuity, where each power-on state stable power data segment corresponds to an operating time, and obtaining a final stable power data set based on the power-on state stable power data set and a time threshold includes:
comparing the running time corresponding to each startup state stable power data segment with the time threshold;
and setting the corresponding startup state stable power data segment with the running time larger than the time threshold value as a final stable power data segment, wherein the final stable power data segments form a final stable power data set.
Further, the time threshold is selected from 300s to 900 s.
Further, the obtaining of the final stable power data set under each operating condition based on the final stable power data set in combination with the standard power under each operating condition includes:
respectively calculating the average power of each final stable power data segment;
calculating the power deviation between each average power and the standard power under each operation condition;
and classifying each final stable power data segment into each operation working condition according to the power deviation to obtain a final stable power data set under each operation working condition.
In another aspect, an embodiment of the present invention provides a system for identifying stable operating conditions of a marine gas turbine, including:
the original power data set acquisition module is used for acquiring power data of the gas turbine in a historical period to obtain an original power data set;
a power-on state data set acquisition module for acquiring a power-on state data set based on the original power data set and a power threshold;
the stable power data set acquisition module is used for performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set;
a startup state stable power dataset obtaining module, configured to obtain a startup state stable power dataset based on the startup state power dataset and the stable power dataset;
a final stable power data set obtaining module, configured to obtain a final stable power data set based on the startup state stable power data set and a time threshold;
and the final stable power data set acquisition module under each operating condition is used for acquiring a stable power data set under each operating condition by combining the standard power under each operating condition based on the final stable power data set.
Further, the power-on state power dataset acquisition module comprises:
a comparison module to compare the power of the raw power data set to the power threshold;
and the power-on state power data set acquisition submodule is used for setting the set of powers which are greater than or equal to the power threshold value as a power-on state power data set.
Further, the stable power dataset acquisition module comprises:
the moving average calculation module is used for carrying out moving average on the original power data set to obtain a smooth power data set;
the power difference quotient calculation module is used for carrying out difference quotient calculation on the smooth power data set to obtain a power difference quotient data set;
and the stable power data set acquisition submodule is used for acquiring a stable power data set based on the power difference quotient data set and the power difference quotient threshold.
Compared with the prior art, the invention can realize at least one of the following beneficial effects:
according to the technical scheme, only the power parameter of the gas turbine is utilized, and the original power data set is screened to obtain the starting state stable power data set in consideration of obtaining the starting state power data set and the stable power data set; further screening the starting-up state stable power data set by combining a time threshold to obtain a final stable power data set; and classifying the final stable power data set into each operation condition to obtain the stable power data set under each operation condition. The technical scheme in the application is not limited by complicated control logic of the marine gas turbine, and the final stable power data sets under each operation condition are obtained only through the power parameters in the historical data of the marine gas turbine, so that the efficiency and the accuracy of identifying the stable operation condition of the gas turbine are improved, and the identification of the stable operation condition of the gas turbine can be quickly, effectively and accurately realized.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a schematic flow chart of a method for identifying stable operation conditions of a marine gas turbine according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a system for identifying stable operation conditions of a marine gas turbine according to an embodiment of the present application.
FIG. 3 is a diagram illustrating a raw power data set according to an embodiment of the present application;
FIG. 4 is a diagram illustrating an exemplary power data set for a power-on state, according to an embodiment of the present application;
FIG. 5 is a schematic illustration of a stabilized power data set according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a power-on state stable power data set according to an embodiment of the present application;
FIG. 7 is a diagram illustrating a final stable power data set when a time threshold is 360s according to an embodiment of the present application;
fig. 8 is a diagram illustrating a corresponding final stable power data set when the time threshold is 600s according to an embodiment of the present application.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The invention discloses a method for identifying stable operation conditions of a marine gas turbine, and the flow schematic diagram of the method is shown in figure 1.
The method comprises the following steps:
step S10: acquiring power data of the gas turbine in a historical time period to obtain an original power data set;
step S20: obtaining a power-on state data set based on the original power data set and a power threshold;
step S30: performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set;
step S40: obtaining a power-on state stable power dataset based on the power-on state power dataset and the stable power dataset;
step S50: obtaining a final stable power data set based on the starting-up state stable power data set and a time threshold;
step S60: and obtaining the final stable power data set under each operation condition by combining the standard power under each operation condition based on the final stable power data set.
Compared with the prior art, the method for identifying the stable operation condition of the marine gas turbine provided by the embodiment utilizes the power parameters of the gas turbine, and takes the power data set in the starting state and the stable power data set into consideration, and screens the original power data set to obtain the stable power data set in the starting state; further screening the starting-up state stable power data set by combining a time threshold to obtain a final stable power data set; and classifying the final stable power data set into each operation condition to obtain the final stable power data set under each operation condition. The technical scheme in the application is not limited by complicated control logic of the marine gas turbine, and the stable power data sets under each operation condition are obtained only through the power parameters in the historical data of the marine gas turbine, so that the efficiency and the accuracy of identifying the stable operation condition of the gas turbine are improved, and the identification of the stable operation condition of the gas turbine can be quickly, effectively and accurately realized.
In one embodiment of the present application, step S10 includes: the power data of the gas turbine in the historical period is obtained through an acquisition system through a power sensor arranged on the marine gas turbine, and an original power data set is obtained.
In one embodiment of the present application, step S20 includes:
comparing the power of the raw power data set to the power threshold;
setting a set of powers greater than or equal to the power threshold as an on-state power dataset.
Specifically, when the power is greater than or equal to the power threshold, the state of the ship corresponding to the power is a startup state, the power is classified into a switching state power data set, and a logic sequence value corresponding to the switching state power data set is 1; when the power is smaller than the power threshold, the state of the ship corresponding to the power is a shutdown state, and the logic sequence value corresponding to the power of the shutdown state is 0, where the logic sequence is defined to facilitate subsequent computer algorithm calculation. The power-on state data set representing the power-on state may be filtered from the raw power data set by step S10. Optionally, the power threshold is selected from 5kw to 10kw, and a specific value of the power threshold may be confirmed according to an actual situation, which is not limited in this application.
In one embodiment of the present application, step S30 includes:
step S31: carrying out moving average on the original power data set to obtain a smooth power data set;
specifically, performing a sliding average on the power in the original power data set, setting a length of the sliding average, and sequentially performing calculation of the sliding average on the power in the original power data set to obtain a smooth power data set; optionally, the moving average length is 1/1000 of the total length of the power data in the original power data set, and the moving average length may be set according to practical situations, which is not limited in this application.
Step S32: carrying out difference quotient calculation on the smooth power data set to obtain a power difference quotient data set;
specifically, the power data in the smoothed power data set is subjected to difference quotient calculation, a difference value of adjacent power points is obtained through staggered subtraction, the difference value is divided by a time period between the adjacent power points, power difference quotient data of the adjacent power points are obtained, and a power difference quotient data set is obtained, wherein the power difference quotient data can reflect whether power change is stable or not.
Step S33: and obtaining a stable power data set based on the power difference quotient data set and the power difference quotient threshold.
Specifically, the power difference quotient data set obtained in step S32 includes a plurality of power difference quotient data, when the power difference quotient data is smaller than the power difference quotient threshold, the power of the original power data set corresponding to the power difference quotient data forms a stable power data set, and the logic sequence corresponding to the stable power data set is 1; when the power difference quotient function is greater than the power difference quotient threshold, the power of the original power data set corresponding to the power difference quotient data is an unstable power data set, a logic sequence corresponding to the unstable power data set is 0, and the logic sequence is defined herein to facilitate subsequent computer algorithm calculation. Optionally, the power difference quotient threshold is 1.5kw · s -1 。
In one embodiment of the present application, step S40 includes:
and performing intersection processing on the power-on state data set and the stable power data set to obtain a power-on state stable power data set.
Specifically, the power data which belongs to the power-on state data set and conforms to the stable power data set is screened out to be used as the power-on stable power data set. Furthermore, when the computer performs the algorithm calculation, the logical sequence corresponding to the power data set in the startup state and the logical sequence corresponding to the stable power data set are subjected to and calculation, and power data which not only accord with the condition that the logical sequence corresponding to the power data set in the startup state is 1, but also accord with the condition that the logical sequence corresponding to the stable power data set is 1 is screened out to be used as the power data set in the startup stable power.
In an embodiment of the present application, the step S50 includes dividing the boot-state stable power data set into a plurality of boot-state stable power data segments according to time continuity, where each boot-state stable power data segment corresponds to an operating time, and the step S50 includes:
comparing the running time corresponding to each startup state stable power data segment with the time threshold;
and setting the corresponding startup state stable power data segment with the running time larger than the time threshold value as a final stable power data segment, wherein the final stable power data segments form a final stable power data set. Optionally, the time threshold is selected from 300s to 900 s.
Specifically, from the perspective of the logic sequence, the logic sequence corresponding to the final stable power data set is still 1, and the logic sequence corresponding to the power data in the power-on state stable power data set, which does not belong to the final stable power data set, is updated to 0.
In one embodiment of the present application, step S60 includes:
step S61: respectively calculating the average power of each final stable power data segment;
specifically, each final stable power data segment in the final stable power data set is numbered 1 to N in sequence, and the average power of each final stable power data segment is calculated respectively.
Step S62: calculating the power deviation between each average power and the standard power under each operation condition;
specifically, the standard power under each operating condition is known, for example, the power deviation between the average power of the final stable power data segment with the number 1 and the standard power under each operating condition is calculated; the same processing is performed on the final stable power data segments numbered 2 to N to obtain corresponding power deviations.
Step S63: and classifying each final stable power data segment into each operation working condition according to the power deviation to obtain a final stable power data set under each operation working condition.
Specifically, the final stable power data segment with the number 1 is compared with the power deviation of the standard power under each operation condition, and the operation condition corresponding to the minimum power deviation is selected as the operation condition to which the final stable power data segment with the number 1 is classified; and classifying the final stable power data sections with the numbers from 2 to N by the same method to obtain a final stable power data set under each operating condition.
One embodiment of the invention discloses a system for identifying stable operation conditions of a marine gas turbine, which comprises: the original power data set acquisition module is used for acquiring power data of the gas turbine in a historical period to obtain an original power data set; a power-on state data set acquisition module for acquiring a power-on state data set based on the original power data set and a power threshold; the stable power data set acquisition module is used for performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set; a startup state stable power dataset obtaining module, configured to obtain a startup state stable power dataset based on the startup state power dataset and the stable power dataset; a final stable power data set obtaining module, configured to obtain a final stable power data set based on the startup state stable power data set and a time threshold; and the final stable power data set acquisition module under each operating condition is used for acquiring a final stable power data set under each operating condition by combining the standard power under each operating condition based on the final stable power data set.
Compared with the prior art, the identification system for the stable operation working condition of the marine gas turbine provided by the application screens the original power data set to obtain the starting-state stable power data set by executing the identification method for the stable operation working condition of the marine gas turbine and considering the two aspects of obtaining the starting-state power data set and the stable power data set by using the power parameter of the gas turbine; further screening the starting-up state stable power data set by combining a time threshold to obtain a final stable power data set; and classifying the final stable power data set into each operation condition to obtain the final stable power data set under each operation condition. The technical scheme in the application is not limited by complicated control logic of the marine gas turbine, and the stable power data sets under each operation condition are obtained only through the power parameters in the historical data of the marine gas turbine, so that the efficiency and the accuracy of identifying the stable operation condition of the gas turbine are improved, and the identification of the stable operation condition of the gas turbine can be quickly, effectively and accurately realized.
In a specific embodiment of the present application, the boot-state power dataset acquisition module includes: a comparison module to compare the power of the raw power data set to the power threshold; and the power-on state power data set acquisition submodule is used for setting the set of powers which are greater than or equal to the power threshold value as a power-on state power data set.
In a specific embodiment of the present application, the stable power dataset acquisition module includes: the moving average calculation module is used for carrying out moving average on the original power data set to obtain a smooth power data set; the power difference quotient calculation module is used for carrying out difference quotient calculation on the smooth power data set to obtain a power difference quotient data set; and the stable power data set acquisition submodule is used for acquiring a stable power data set based on the power difference quotient data set and the power difference quotient threshold.
The embodiment of the application also provides an application example of the identification method for the stable operation condition of the marine gas turbine.
Specifically, a ship of a certain type is taken as an example for explanation:
the raw power data set, which is the power data obtained for the historical period of the gas turbine, is shown in FIG. 3.
Based on the original power data set and the power threshold, a power data set in the power-on state is obtained, as shown in fig. 4, an enlarged portion in fig. 4 represents the power data set in the power-on state, where the power threshold is 10kw, and the specific processing process refers to step S20, which is not described herein again.
The moving average and the difference quotient are calculated on the original power data set to obtain a stable power data set, as shown in fig. 5, a portion that is added with a thick mark in fig. 5 represents the stable power data set, and for the specific processing process, refer to step S30, which is not described herein again.
Based on the boot-state power dataset and the stable power dataset, a boot-state stable power dataset is obtained, as shown in fig. 6, the bold part in fig. 6 represents the boot-state stable power dataset, that is, the bold part in fig. 4 and fig. 5 is equivalent to performing intersection (and operation) processing on the bold part to obtain the boot-state stable power dataset.
Based on the power-on state stable power data set and the time threshold, obtaining a final stable power data set, where fig. 7 is a corresponding final stable power data set (a bold portion) when the time threshold is 360 s; fig. 8 shows the corresponding final stable power data set (bold) for a time threshold of 600 s.
And (4) identifying the stable operation condition of the gas turbine based on the final stable power data set of the FIG. 7 or the FIG. 8 and combining the specific processing procedure of the step S60.
The method embodiment and the system embodiment are realized based on the same principle, the related parts can be referred to each other, and the same technical effect can be achieved.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
While the invention has been described with reference to specific preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the following claims.
Claims (10)
1. A method for identifying stable operation conditions of a marine gas turbine is characterized by comprising the following steps:
acquiring power data of the gas turbine in a historical time period to obtain an original power data set;
obtaining a power-on state data set based on the original power data set and a power threshold;
performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set;
obtaining a power-on state stable power dataset based on the power-on state power dataset and the stable power dataset;
obtaining a final stable power data set based on the starting-up state stable power data set and a time threshold;
and obtaining a final stable power data set under each operation condition by combining the standard power under each operation condition based on the final stable power data set.
2. The method of claim 1, wherein obtaining the power-on state data set based on the raw power data set and a power threshold comprises:
comparing the power of the raw power data set to the power threshold;
setting a set of powers greater than or equal to the power threshold as an on-state power dataset.
3. The identification method of claim 2, wherein the performing a moving average and a difference quotient calculation on the original power data set to obtain a stable power data set comprises:
carrying out moving average on the original power data set to obtain a smooth power data set;
carrying out difference quotient calculation on the smooth power data set to obtain a power difference quotient data set;
and obtaining a stable power data set based on the power difference quotient data set and the power difference quotient threshold.
4. The method of claim 3, wherein obtaining a power-on state stable power data set based on the power-on state power data set and the stable power data set comprises:
and performing intersection processing on the power-on state data set and the stable power data set to obtain a power-on state stable power data set.
5. The method according to claim 4, wherein the dividing the steady-state power data set into a plurality of steady-state power data segments according to time continuity, each steady-state power data segment corresponding to an operating time, and the obtaining a final steady-state power data set based on the steady-state power data set and a time threshold comprises:
comparing the running time corresponding to each startup state stable power data segment with the time threshold;
and setting the corresponding startup state stable power data segment with the running time larger than the time threshold value as a final stable power data segment, wherein the final stable power data segments form a final stable power data set.
6. The identification method according to claim 5, characterized in that said time threshold is selected from 300s to 900 s.
7. The identification method according to claim 5, wherein the obtaining of the final stable power data set under each operating condition based on the final stable power data set in combination with the standard power under each operating condition comprises:
respectively calculating the average power of each final stable power data segment;
calculating the power deviation between each average power and the standard power under each operation condition;
and classifying each final stable power data segment into each operation working condition according to the power deviation to obtain a final stable power data set under each operation working condition.
8. A marine gas turbine steady operation condition identification system, characterized by comprising:
the original power data set acquisition module is used for acquiring power data of the gas turbine in a historical period to obtain an original power data set;
a power-on state data set acquisition module for acquiring a power-on state data set based on the original power data set and a power threshold;
the stable power data set acquisition module is used for performing moving average and difference quotient calculation on the original power data set to obtain a stable power data set;
a startup state stable power dataset obtaining module, configured to obtain a startup state stable power dataset based on the startup state power dataset and the stable power dataset;
a final stable power data set obtaining module, configured to obtain a final stable power data set based on the startup state stable power data set and a time threshold;
and the final stable power data set acquisition module under each operating condition is used for acquiring a final stable power data set under each operating condition by combining the standard power under each operating condition based on the final stable power data set.
9. The identification system of claim 8, wherein the power-on state power dataset acquisition module comprises:
a comparison module for comparing the power of the raw power data set with the power threshold;
and the power-on state power data set acquisition submodule is used for setting the set of powers which are greater than or equal to the power threshold value as a power-on state power data set.
10. The identification system of claim 9, wherein the stable power data set acquisition module comprises:
the moving average calculation module is used for carrying out moving average on the original power data set to obtain a smooth power data set;
the power difference quotient calculation module is used for carrying out difference quotient calculation on the smooth power data set to obtain a power difference quotient data set;
and the stable power data set acquisition submodule is used for acquiring a stable power data set based on the power difference quotient data set and the power difference quotient threshold.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002030944A (en) * | 2000-07-13 | 2002-01-31 | Toshiba Corp | Multi fuel combustion monitoring device and multi fuel combustion control device |
| CN106991508A (en) * | 2017-05-25 | 2017-07-28 | 华北电力大学 | A kind of running of wind generating set state identification method based on DBSCAN |
| CN107909677A (en) * | 2017-11-28 | 2018-04-13 | 中国船舶工业系统工程研究院 | A kind of marine mechanical and electrical equipment operation data stability extraction and method of discrimination |
| CN108052007A (en) * | 2017-12-18 | 2018-05-18 | 华润电力湖北有限公司 | Thermal power unit operation optimization method, device, equipment and computer storage media |
| CN109407506A (en) * | 2018-11-28 | 2019-03-01 | 深圳圣缘节能科技有限公司 | A kind of acquisition methods of the power plant units dynamic optimal value based on data mining |
| CN109523171A (en) * | 2018-11-20 | 2019-03-26 | 未必然数据科技(北京)有限公司 | A kind of gas turbine inlet air system health degree appraisal procedure based on SVDD |
| CN109753634A (en) * | 2018-12-29 | 2019-05-14 | 山东科技大学 | Dynamical system gain estimation method based on historical data steady-state value |
| CN110696990A (en) * | 2019-11-11 | 2020-01-17 | 中国船舶工业系统工程研究院 | Ship generator component influence identification method and system based on data driving |
-
2020
- 2020-12-09 CN CN202011429530.XA patent/CN112557047B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002030944A (en) * | 2000-07-13 | 2002-01-31 | Toshiba Corp | Multi fuel combustion monitoring device and multi fuel combustion control device |
| CN106991508A (en) * | 2017-05-25 | 2017-07-28 | 华北电力大学 | A kind of running of wind generating set state identification method based on DBSCAN |
| CN107909677A (en) * | 2017-11-28 | 2018-04-13 | 中国船舶工业系统工程研究院 | A kind of marine mechanical and electrical equipment operation data stability extraction and method of discrimination |
| CN108052007A (en) * | 2017-12-18 | 2018-05-18 | 华润电力湖北有限公司 | Thermal power unit operation optimization method, device, equipment and computer storage media |
| CN109523171A (en) * | 2018-11-20 | 2019-03-26 | 未必然数据科技(北京)有限公司 | A kind of gas turbine inlet air system health degree appraisal procedure based on SVDD |
| CN109407506A (en) * | 2018-11-28 | 2019-03-01 | 深圳圣缘节能科技有限公司 | A kind of acquisition methods of the power plant units dynamic optimal value based on data mining |
| CN109753634A (en) * | 2018-12-29 | 2019-05-14 | 山东科技大学 | Dynamical system gain estimation method based on historical data steady-state value |
| CN110696990A (en) * | 2019-11-11 | 2020-01-17 | 中国船舶工业系统工程研究院 | Ship generator component influence identification method and system based on data driving |
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