CN116561638B - Protective pressing plate non-correspondence checking method based on neural network learning and state evaluation - Google Patents
Protective pressing plate non-correspondence checking method based on neural network learning and state evaluation Download PDFInfo
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Abstract
The invention discloses a protective pressing plate non-correspondence checking method based on neural network learning and state evaluation, which comprises the following steps: collecting voltage and current real-time information of key sections of main equipment of a transformer substation; preprocessing the real-time information to obtain a correct section state z; according to the network topology structure of the transformer substation and related equipment parameters, solving the minimum value of the objective function to obtain the state evaluation value of the primary equipment of the whole transformer substation; and detecting the defective data of the measured data, determining specific defective data, eliminating the defective data, and repeating the steps until a final state evaluation value is obtained. According to the invention, through learning a large amount of data, the rules and the characteristics in the data are automatically extracted, so that the tasks of classifying and identifying unknown data are realized. The implementation of the invention improves the accuracy and efficiency of judgment and reduces the problem of missed judgment and misjudgment; the automatic and intelligent judgment and check can be realized, and the workload and the labor cost are greatly reduced.
Description
Technical Field
The invention relates to the technical field of non-correspondence checking of transformer substation protection pressing plates, in particular to a non-correspondence checking method of a protection pressing plate based on neural network learning and state evaluation.
Background
Substations are an important component in electrical power systems, mainly for the transmission, conversion and distribution of electrical power. Inside the substation are various devices, such as transformers, switching devices, protection devices, etc., which together form the core of the power system, directly affecting the operational safety and reliability of the power grid. The protection pressing plate is used as an important component of a protection device in a transformer substation and is mainly used for automatically disconnecting a power supply when the protection device fails, so that the damage of the power device caused by overload, short circuit and other faults is avoided. Since the protection pressing plate plays a very important role, the judgment of the correct installation and state of the protection pressing plate has very important significance for the safe operation of the transformer substation.
However, in actual operation of the transformer substation, the corresponding relationship between the operation state of the internal equipment of the transformer substation and the protection pressing plate often changes due to the huge number of equipment, complex structure and influence of external environment, and the conventional judging method mainly depends on experience and intuition of engineers, so that the following problems exist: firstly, the workload is large, a large amount of manpower and material resources are needed to be input, and the problems of human negligence, errors and the like are easy to occur; secondly, the efficiency is low, and a long time is required to finish checking; thirdly, the corresponding relation between the equipment operation state and the protection pressing plate is difficult to find, so that a method capable of automatically judging the corresponding relation between the equipment operation state and the protection pressing plate is needed. In addition, researchers realize judgment of the relationship between the equipment state and the protective pressing plate by adopting methods such as image processing and pattern recognition, and the like, and the method can reduce manual operation to a certain extent, but the traditional image processing method has a certain limitation in practical application because of high sensitivity to factors such as illumination, shielding and the like in images. Meanwhile, the method also needs a large amount of manual labeling and manual parameter adjustment, and is difficult to realize a full-automatic checking flow.
At present, the application of a machine learning technology gradually becomes one of research hotspots, and the technology can automatically extract rules and features in a large amount of data through learning, so as to further realize tasks of classifying, identifying and the like of unknown data. Therefore, the machine learning technology is applied to checking of the transformer substation protection pressing plate, and the aim of automatically judging the corresponding relation between the equipment operation state and the protection pressing plate is hopeful to be achieved. The machine learning-based method can automatically judge the corresponding relation between the equipment state and the protection pressing plate by learning a large amount of data, and has better robustness and accuracy compared with the traditional method.
Disclosure of Invention
The invention aims to provide a protective pressing plate non-correspondence checking method based on neural network learning and state evaluation, so as to solve the technical problems: firstly, the workload is large, a large amount of manpower and material resources are needed to be input, and the problems of human negligence, errors and the like are easy to occur; secondly, the efficiency is low, and a long time is required to finish checking; thirdly, the corresponding relation between the equipment operation state and the protection pressing plate is difficult to find.
In order to solve the technical problems, the invention adopts the following technical scheme: the method for verifying the non-correspondence of the protection pressing plate based on the neural network learning and the state evaluation comprises the following steps: and (5) evaluating the running state of primary equipment of the transformer substation and checking the protection pressing plate based on neural network learning.
Further, the specific steps of the operation state evaluation of the primary equipment of the transformer substation include:
Step S11, collecting voltage and current real-time information of key sections of main equipment of the transformer substation; on the basis, preprocessing the real-time information, filtering interference and error data, and obtaining a correct section state z;
Step S12, a measurement function equation h (x) and a measurement error variance array R are constructed according to the network topology structure of the transformer substation and related equipment parameters; constructing an evaluation objective function R=z-h (xp) by taking a global to-be-evaluated quantity xp of the transformer substation as a variable, and solving the minimum value of the objective function to obtain a state evaluation quantity of all primary equipment of the transformer substation;
And S13, carrying out bad data detection and specific bad data determination on the measured data based on the obtained primary equipment state evaluation value, and repeating the steps until a final state evaluation value is obtained after the bad data is removed.
Further, the specific step of verifying that the protective pressing plate based on neural network learning does not correspond to comprises the following steps:
step S21, dividing the historical state of the pressing plate of the transformer substation protection device and the data set of the primary equipment operation state of the corresponding transformer substation into training data and verification test data according to a certain proportion;
Step S22, the training data of the last step is brought into a learning model based on a neural network, the input weight of the learning model is randomly given, the state of a protective pressing plate corresponding to the training data is used as an initial output value of the learning model, and the model is trained to obtain the initial output weight;
Step S23, carrying in verification test data, optimizing the initial output weight to obtain the optimal output weight, so as to optimize verification model parameters and obtain a relation model between the equipment running state and the protection pressing plate;
And S24, carrying out state evaluation measurement on the total station primary equipment, and obtaining a final output result, namely a state evaluation result of the protection loop pressing plate by adopting a relation model after parameter optimization. And comparing the result with the current state of the protective pressing plate, and checking the non-corresponding relation.
Further, the main equipment comprises a bus, a feeder, a transformer and the like.
Further, the key section comprises a bus and a feeder of the main equipment.
Furthermore, the model based on neural network learning adopts a single hidden layer extreme learning machine model, and the built learning model based on L hidden layer nodes is as follows:
In the formula, g (x) is an activation function, the hyperbolic tangent Sigmoid function is adopted as the activation function, wi is an input weight, βi is an output weight, bi is a bias of an ith hidden layer unit, xj is a primary equipment operation state value, yj is a secondary protection pressing plate state value, and N is the total number of training samples;
The matrix can be expressed as: hβ=y:
wherein H represents hidden layer node output, and Y is desired output.
The beneficial effects are that: compared with the prior art, the method automatically extracts the rules and the characteristics of a large amount of data through learning, and further realizes the tasks of classifying and identifying unknown data. The implementation of the invention improves the accuracy and efficiency of judgment and reduces the problem of missed judgment and misjudgment; the automatic and intelligent judgment and check can be realized, and the workload and the labor cost are greatly reduced.
Drawings
FIG. 1 is a flow chart of the protection platen status verification of the present invention.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. The invention may be embodied in different forms and is not limited to the embodiments described herein. Rather, the embodiments are provided so that this disclosure will be thorough and complete.
The invention provides a protective pressing plate non-correspondence checking method based on neural network learning and state evaluation, which is suitable for intelligent substation secondary circuit pressing plate state checking. The protection pressing plate state verification flow chart provided by the invention is shown in fig. 1, and the method mainly comprises two parts: and (5) evaluating the running state of primary equipment of the transformer substation and checking the protection pressing plate based on neural network learning.
The method comprises the steps of evaluating the running state of primary equipment of the transformer substation, cleaning and processing collected real-time data of the transformer substation, extracting information such as key section current, voltage and power from the data, evaluating the state of primary equipment of the whole transformer substation, and inputting a relation model; the protection pressing plate based on neural network learning does not correspond to the protection pressing plate, historical operation data of internal equipment of the transformer substation are collected, the historical data are utilized for training, and a relation model between the equipment operation state and the protection pressing plate is built so as to perform the verification of the pressing plate.
The operation state evaluation of the primary equipment of the transformer substation provides input data for the subsequent protection pressing plate verification and comprises the following specific steps:
Step one: collecting voltage and current real-time information of key sections (bus, feeder) of main equipment (bus, feeder, transformer, etc.) of a transformer substation; on the basis, preprocessing the real-time information, filtering interference and error data, and obtaining a correct section state z;
Step two: constructing a measurement function equation h (x) and a measurement error variance matrix R according to the network topology structure of the transformer substation and related equipment parameters; constructing an evaluation objective function R=z-h (xp) by taking a global to-be-evaluated quantity xp of the transformer substation as a variable, and solving the minimum value of the objective function to obtain a state evaluation quantity of all primary equipment of the transformer substation;
Step three: and carrying out bad data detection on the measured data and determining specific bad data based on the obtained primary equipment state evaluation value, and repeating the steps until a final state evaluation value is obtained after the bad data is removed.
The protective pressing plate based on neural network learning does not correspond to the verification steps as follows:
Step one: dividing the historical state of the pressing plate of the transformer substation protection equipment and the data set of the primary equipment operation state of the corresponding transformer substation into training data and verification test data according to a certain proportion;
Step two: the training data of the last step is brought into a learning model based on a neural network, the input weight of the learning model is randomly given, the state of a protective pressing plate corresponding to the training data is used as an initial output value of the learning model, and the model is trained to obtain the initial output weight;
step three: carrying in verification test data, optimizing the initial output weight to obtain the optimal output weight, so as to optimize verification model parameters and obtain a relation model between the equipment running state and the protection pressing plate;
step four: and carrying out state evaluation on the total station primary equipment, and obtaining a final output result, namely a state evaluation result of the protection loop pressing plate by adopting a relation model after parameter optimization. And comparing the result with the current state of the protective pressing plate, and checking the non-corresponding relation.
The model based on neural network learning adopts a single hidden layer extreme learning machine model, and the built learning model based on L hidden layer nodes is as follows:
In the formula, g (x) is an activation function, the hyperbolic tangent Sigmoid function is adopted as the activation function, wi is an input weight, βi is an output weight, bi is a bias of an ith hidden layer unit, xj is a primary equipment operation state value, yj is a secondary protection pressing plate state value, and N is the total number of training samples;
The matrix can be expressed as: hβ=y:
wherein H represents hidden layer node output, and Y is desired output.
All functions may be implemented in the above embodiments, or some of the functions may be implemented as needed.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. The various possible combinations of the invention are not described in detail in order to avoid unnecessary repetition.
Claims (4)
1. The method for verifying the non-correspondence of the protective pressing plate based on the neural network learning and the state evaluation is characterized by comprising the following steps of: the method comprises the steps of evaluating the running state of primary equipment of a transformer substation and checking the non-correspondence of a protective pressing plate based on neural network learning;
The specific steps of the operation state evaluation of the primary equipment of the transformer substation comprise:
Step S11, collecting voltage and current real-time information of key sections of main equipment of the transformer substation; on the basis, preprocessing the real-time information, filtering interference and error data, and obtaining a correct section state z;
Step S12, a measurement function equation h (x) and a measurement error variance array R are constructed according to the network topology structure of the transformer substation and related equipment parameters; constructing an evaluation objective function R=z-h (xp) by taking a global to-be-evaluated quantity xp of the transformer substation as a variable, and solving the minimum value of the objective function to obtain a state evaluation quantity of all primary equipment of the transformer substation;
Step S13, based on the obtained primary equipment state evaluation value, carrying out bad data detection on the actual measurement data and determining specific bad data, and repeating the steps until a final state evaluation value is obtained after the bad data is removed;
the specific steps of the protective pressing plate non-corresponding verification based on the neural network learning include:
step S21, dividing the historical state of the pressing plate of the transformer substation protection device and the data set of the primary equipment operation state of the corresponding transformer substation into training data and verification test data according to a certain proportion;
Step S22, the training data of the last step is brought into a learning model based on a neural network, the input weight of the learning model is randomly given, the state of a protective pressing plate corresponding to the training data is used as an initial output value of the learning model, and the model is trained to obtain the initial output weight;
step S23, carrying in verification test data, optimizing the initial output weight to obtain the optimal output weight, so as to optimize the learning model parameters and obtain a relation model between the equipment running state and the protection pressing plate;
And S24, carrying out state evaluation on the total station primary equipment, obtaining a final output result, namely a state evaluation result of the protection loop pressing plate by adopting a learning model after parameter optimization, comparing the result with the current state of the protection pressing plate, and checking out no corresponding relation.
2. The neural network learning and state evaluation-based protective platen non-correspondence checking method according to claim 1, characterized in that: the main equipment comprises a bus, a feeder line and a transformer.
3. The neural network learning and state evaluation-based protective platen non-correspondence checking method according to claim 1, characterized in that: the key section comprises a bus and a feeder of main equipment.
4. The neural network learning and state evaluation-based protective platen non-correspondence checking method according to claim 1, characterized in that: the model based on neural network learning adopts a single hidden layer extreme learning machine model, and the built learning model based on L hidden layer nodes is as follows:
， （1）
In the formula, g (x) is an activation function, a hyperbolic tangent Sigmoid function is adopted as the activation function, wi is an input weight, βi is an output weight, bi is a bias of an ith hidden layer unit, xj is a primary equipment operation state value, yj is a secondary protection pressing plate state value, and N is the total number of training samples;
expressed as a matrix: hβ=y:
（2）
wherein H represents hidden layer node output, and Y is desired output.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104407577A (en) * | 2014-10-28 | 2015-03-11 | 国网山东省电力公司淄博供电公司 | Intelligent check anti-maloperation method based on real-time trend |
| CN104578012A (en) * | 2015-01-30 | 2015-04-29 | 国网北京经济技术研究院 | Method for evaluating safety margin of protection setting values on basis of sensitivity analysis |
| CN107203816A (en) * | 2017-06-09 | 2017-09-26 | 珠海市鸿瑞软件技术有限公司 | A kind of trouble hunting method and system of secondary equipment in power system |
| CN108468622A (en) * | 2018-02-09 | 2018-08-31 | 湖南工业大学 | Wind turbines blade root load method of estimation based on extreme learning machine |
| CN109086518A (en) * | 2018-08-01 | 2018-12-25 | 国网福建省电力有限公司 | A kind of method of intelligent substation power transmission and transformation primary equipment status assessment |
| CN109697570A (en) * | 2018-12-27 | 2019-04-30 | 北京科东电力控制系统有限责任公司 | Substation secondary device state evaluating method, system and equipment |
| CN111143630A (en) * | 2019-11-26 | 2020-05-12 | 南京国电南自电网自动化有限公司 | Method and device for checking maintenance safety measure execution state of intelligent substation |
| CN111416438A (en) * | 2020-03-18 | 2020-07-14 | 国网湖南省电力有限公司 | Method and system for monitoring and intelligently checking state of hard pressing plate at transformer station end |
| CN111563658A (en) * | 2020-04-10 | 2020-08-21 | 国网安徽省电力有限公司滁州供电公司 | Visual online checking method and device for pressing plate in secondary safety measure |
| CN113254545A (en) * | 2021-05-21 | 2021-08-13 | 国网河北省电力有限公司邯郸供电分公司 | Method for checking state of pressing plate of secondary equipment of intelligent substation |
| CN114022030A (en) * | 2021-11-23 | 2022-02-08 | 国网重庆市电力公司电力科学研究院 | Dynamic detection analysis and risk assessment method for bus state of transformer substation |
| CN114841627A (en) * | 2022-07-04 | 2022-08-02 | 国电南瑞南京控制系统有限公司 | Maintenance plan checking method, device, equipment and storage medium |
| CN115222104A (en) * | 2022-06-24 | 2022-10-21 | 南京电力设计研究院有限公司 | Intelligent substation secondary equipment state evaluation method based on extreme learning machine |
| CN115795276A (en) * | 2022-11-22 | 2023-03-14 | 南京电力设计研究院有限公司 | Secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230040444A1 (en) * | 2021-07-07 | 2023-02-09 | Daily Rays Inc. | Systems and methods for modulating data objects to effect state changes |
-
2023
- 2023-05-24 CN CN202310591837.7A patent/CN116561638B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104407577A (en) * | 2014-10-28 | 2015-03-11 | 国网山东省电力公司淄博供电公司 | Intelligent check anti-maloperation method based on real-time trend |
| CN104578012A (en) * | 2015-01-30 | 2015-04-29 | 国网北京经济技术研究院 | Method for evaluating safety margin of protection setting values on basis of sensitivity analysis |
| CN107203816A (en) * | 2017-06-09 | 2017-09-26 | 珠海市鸿瑞软件技术有限公司 | A kind of trouble hunting method and system of secondary equipment in power system |
| CN108468622A (en) * | 2018-02-09 | 2018-08-31 | 湖南工业大学 | Wind turbines blade root load method of estimation based on extreme learning machine |
| CN109086518A (en) * | 2018-08-01 | 2018-12-25 | 国网福建省电力有限公司 | A kind of method of intelligent substation power transmission and transformation primary equipment status assessment |
| CN109697570A (en) * | 2018-12-27 | 2019-04-30 | 北京科东电力控制系统有限责任公司 | Substation secondary device state evaluating method, system and equipment |
| CN111143630A (en) * | 2019-11-26 | 2020-05-12 | 南京国电南自电网自动化有限公司 | Method and device for checking maintenance safety measure execution state of intelligent substation |
| CN111416438A (en) * | 2020-03-18 | 2020-07-14 | 国网湖南省电力有限公司 | Method and system for monitoring and intelligently checking state of hard pressing plate at transformer station end |
| CN111563658A (en) * | 2020-04-10 | 2020-08-21 | 国网安徽省电力有限公司滁州供电公司 | Visual online checking method and device for pressing plate in secondary safety measure |
| CN113254545A (en) * | 2021-05-21 | 2021-08-13 | 国网河北省电力有限公司邯郸供电分公司 | Method for checking state of pressing plate of secondary equipment of intelligent substation |
| CN114022030A (en) * | 2021-11-23 | 2022-02-08 | 国网重庆市电力公司电力科学研究院 | Dynamic detection analysis and risk assessment method for bus state of transformer substation |
| CN115222104A (en) * | 2022-06-24 | 2022-10-21 | 南京电力设计研究院有限公司 | Intelligent substation secondary equipment state evaluation method based on extreme learning machine |
| CN114841627A (en) * | 2022-07-04 | 2022-08-02 | 国电南瑞南京控制系统有限公司 | Maintenance plan checking method, device, equipment and storage medium |
| CN115795276A (en) * | 2022-11-22 | 2023-03-14 | 南京电力设计研究院有限公司 | Secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning |
Non-Patent Citations (3)
| Title |
|---|
| Analysis of Substation Joint Safety Control System and Model Based on Multi-Source Heterogeneous Data Fusion;Bo Wu 等;《IEEE Access》;20230405;35281-35297 * |
| 智能变电站二次系统的可靠性及风险评估;刁兴华;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20170715(第(2017)07期);C042-331 * |
| 电力二次设备状态评估及检修策略的优化;伍兆恒;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20181215(第(2018)12期);C042-738 * |
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