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Energy storage power station intelligent monitoring method based on multilayer feedforward neural network

A technology of feedforward neural network and energy storage power station, applied in the field of intelligent monitoring of energy storage power station based on multi-layer feedforward neural network, can solve the problems of untimely accident discovery and insufficient monitoring of energy storage power station, and achieve short response time , high accuracy, and the effect of improving efficiency

Active Publication Date: 2022-07-29
广州兆和电力技术有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of this invention is to provide an intelligent monitoring method for energy storage power stations based on multi-layer feed-forward neural network, by monitoring the concentration changes of three main gases after thermal runaway Realize accurate early warning of thermal runaway of battery packs, and solve the problems of insufficient monitoring of existing energy storage power stations and untimely discovery of accidents

Method used

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  • Energy storage power station intelligent monitoring method based on multilayer feedforward neural network
  • Energy storage power station intelligent monitoring method based on multilayer feedforward neural network
  • Energy storage power station intelligent monitoring method based on multilayer feedforward neural network

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Experimental program
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Effect test

Embodiment 1

[0074] M×N high-precision sensors are arranged in a rectangular distribution in the energy storage power station, and each sensor is at t 0 ~t y-1 A total of y concentration data are collected at the moment

[0075]

[0076] where x=CO, CO 2 or H 2 , Indicates that the sensor located at row i and column j is at t 0 The concentration of x gas collected at time.

[0077] Perform data denoising processing:

[0078] Firstly, the corresponding Hankel matrix is ​​constructed based on the time series of the concentration signal, and then the singular values ​​of the matrix are extracted as the characteristics of discharge. The Hankel matrix is ​​a matrix in which the elements of each inverse diagonal line in the matrix are equal. When extracting, the elements located in the same position in the matrix of each page are constructed into a one-dimensional signal sequence.

[0079]

[0080] is the concentration time series collected by the sensor located in row i, column ...

Embodiment 2

[0120] Two neural networks are constructed to predict the number of rows and columns of leak locations in energy storage power plants. Taking a small energy storage power station with only 12 sensors as an example, the best model accuracy is achieved using a 12-30-30-1 architecture (number of neurons in each layer), as shown in Figure 5. The input layer uses concentration data from 12 sensors at specific time points as input data, and a neuron in the output layer represents the predicted result of row or column number.

[0121] The artificial neural network model can detect and locate the gas leak location after the training process. like Figure 5 The shown convergence of training error and accuracy shows that the minimum loss reaches 8.8 × 10 after 500 iterations -3 , with a maximum accuracy of 0.996.

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Abstract

The invention discloses an energy storage power station intelligent monitoring method based on a multilayer feedforward neural network, and relates to the technical field of energy storage power station monitoring. The method comprises the following steps: a sensor collects concentration signals of gas at different measuring points to form a space-time matrix of three gas concentrations; constructing a corresponding Hankel matrix according to the concentration time sequence of each sensor, and extracting a singular value of the matrix as a characteristic value of discharge; carrying out interpolation on the denoised two-dimensional concentration matrix on a specific time section, and carrying out normalization processing to obtain comprehensive concentration indexes of the three gases; and taking the normalized data as a test set to be input into the neural network, and judging a leakage position through the output line number and column number. According to the method, accurate early warning of thermal runaway of the battery pack is achieved by monitoring concentration changes of main gas after three kinds of thermal runaway, the Hankel matrix singular value decomposition method is adopted, the signal processing efficiency is improved while filtering is conducted, and the position of a leakage source is rapidly calculated.

Description

technical field [0001] The invention belongs to the technical field of energy storage power station monitoring, in particular to an intelligent monitoring method for energy storage power stations based on a multi-layer feedforward neural network. Background technique [0002] At present, the excessive consumption of traditional fossil energy, which provides the main energy for human society, has made it increasingly exhausted, and fossil energy has a significant negative impact on the environment. Therefore, changing the existing unreasonable energy structure has become a challenge for the sustainable development of human society. primary issue. At present, wind energy, solar energy, tidal energy, and geothermal energy, which are vigorously advocated by the state, are all renewable and clean energy sources. Due to their randomness and intermittency, if the electric energy generated by them is directly input into the power grid, it will have a great impact on the power grid. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/16G06N3/04G06Q10/06G06Q50/06G01R31/396G01M3/02G01N33/00
CPCG06F17/16G06Q10/0635G06Q50/06G01R31/396G01M3/02G01N33/0067G06N3/044
Inventor 李智欢刘淼伍兆恒张俊峰赵春太肖应辉瞿运武何珂陈衍恒
Owner 广州兆和电力技术有限公司