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Low-voltage risk assessment system based on neural network identification and fuzzy analysis

A neural network identification and risk assessment system technology, applied in biological neural network models, data processing applications, instruments, etc., can solve problems such as lack of safety information, small voltage safety margin, and accident expansion

Pending Publication Date: 2020-12-29
HEBI POWER SUPPLY OF HENAN ELECTRIC POWERCORP
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the development of the power system and the practice of the power market, the characteristics of long lines, heavy loads and insufficient reactive power reserves are gradually becoming prominent, and the voltage safety margin of the system tends to become smaller and smaller, but the current system operators cannot accurately grasp The voltage safety state of the system
Therefore, when the accident occurred, the system operators lacked sufficient safety information to take corresponding measures, which led to the expansion of the accident. Several major blackouts at home and abroad have shown the importance of adopting effective evaluation methods

Method used

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  • Low-voltage risk assessment system based on neural network identification and fuzzy analysis
  • Low-voltage risk assessment system based on neural network identification and fuzzy analysis
  • Low-voltage risk assessment system based on neural network identification and fuzzy analysis

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Embodiment 1

[0025] Such as Figure 1-6 As shown, a low-voltage risk assessment system based on neural network identification and fuzzy analysis, which includes neural network identification system and fuzzy analysis system;

[0026] The neural network recognition system includes an input layer, a hidden layer and an output layer, and the input layer of the neural network recognition system is used to receive data from an external database, the various factors that have the greatest impact on the prediction results and the factors that exist in the sample Historical data is used as the input of the input layer, and the hidden layer is connected to the input layer for receiving the transmission signal of the input layer. The hidden layer node is composed of Gaussian function as the basis function, and the basis function in the hidden layer node is to the input The signal responds locally. When the input is close to the central range of the basis function, the hidden layer will generate a la...

Embodiment 2

[0034] Such as Figure 1-6 As shown, a low-voltage risk assessment system based on neural network identification and fuzzy analysis, which includes neural network identification system and fuzzy analysis system;

[0035] The neural network recognition system includes an input layer, a hidden layer and an output layer, and the input layer of the neural network recognition system is used to receive data from an external database, the various factors that have the greatest impact on the prediction results and the factors that exist in the sample Historical data is used as the input of the input layer, and the hidden layer is connected to the input layer for receiving the transmission signal of the input layer. The hidden layer node is composed of Gaussian function as the basis function, and the basis function in the hidden layer node is to the input The signal responds locally. When the input is close to the central range of the basis function, the hidden layer will generate a la...

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Abstract

The invention relates to a low-voltage risk assessment system based on neural network identification and fuzzy analysis, the neural network identification system comprises an input layer, a hidden layer and an output layer, the input layer of the neural network identification system is used for receiving data of an external database, and the hidden layer is connected with the input layer and is used for receiving a transmission signal of the input layer; the hidden layer node is formed by taking a Gaussian function as a primary function, the primary function in the hidden layer node locally responds to an input signal; when the input quantity is close to the central range of the primary function, the hidden layer generates relatively large output, the signal outputs a low-voltage risk index through the output layer, and the fuzzy analysis system comprises a fuzzy controller. The input end of the fuzzy controller is connected with the database, the output voltage indexes screen and output low-voltage risk indexes, and fuzzy objects are quantitatively described by using a membership function method; The system has the advantages of accurate evaluation and combination of a neural network identification system and a fuzzy analysis system.

Description

technical field [0001] The invention relates to the technical field of power system risk assessment, in particular to a low-voltage risk assessment system based on neural network identification and fuzzy analysis. Background technique [0002] With the development of the power system and the practice of the power market, the characteristics of long lines, heavy loads and insufficient reactive power reserves are gradually becoming prominent, and the voltage safety margin of the system tends to become smaller and smaller, but the current system operators cannot accurately grasp The voltage safety state of the system. Therefore, when the accident occurred, the system operation personnel lacked enough safety information to take corresponding measures, which led to the expansion of the accident. Several major blackouts at home and abroad showed the importance of adopting effective evaluation methods. [0003] The low-voltage risk of the domestic power system reflects the possibi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/02G06Q10/06G06Q50/06
CPCG06Q10/0635G06Q10/06393G06Q50/06G06N3/02
Inventor 王志立刘世伟李鑫路孙秀兵党明鹏邵利坤夏传鲲付文竹秦建新
Owner HEBI POWER SUPPLY OF HENAN ELECTRIC POWERCORP
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