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Microgrid risk monitoring and early warning system

A risk monitoring and early warning system technology, applied in information technology support systems, biological neural network models, instruments, etc., can solve the problem of less monitoring and early warning of micro-grids, and achieve the effect of ensuring the safety of data sharing

Pending Publication Date: 2020-12-11
SHANGHAI DIANJI UNIV
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Problems solved by technology

[0007] Judging from the current research status at home and abroad, there are few studies on the accuracy of microgrid status monitoring research that need to be improved for microgrid fault warning. The application of blockchain in microgrid is mainly in market transactions and other aspects. There are few studies on early warning

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  • Microgrid risk monitoring and early warning system
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  • Microgrid risk monitoring and early warning system

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Embodiment

[0030] Such as figure 1 As shown, this application proposes a microgrid risk monitoring and early warning system, including microgrid 1, sensor 2, blockchain 3, data processing equipment 4, neural network prediction unit 5, threshold abnormality detection unit 6, data processing The equipment 4 is connected to the microgrid 1 through the manual control center 7, and the neural network prediction unit 5 is equipped with a CNN-GRU neural network model for predicting power generation. The data processing device 4 transmits the data collected by the sensor 2 to the neural network prediction unit 5 to predict the power generation, and then performs threshold abnormality analysis and evaluation through the threshold abnormality detection unit 6. When the evaluation result is abnormal, the data processing device 4 reports to the manual control center 7 sends out an alarm, and the manual control center 7 then makes corresponding adjustment measures to the microgrid 1 .

[0031] Based...

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Abstract

The invention relates to a microgrid risk monitoring and early warning system, and the system comprises a microgrid, a sensor, a blockchain, data processing equipment, a neural network prediction unitand a threshold anomaly detection unit which are connected in sequence, wherein the data processing equipment is connected with the microgrid through a manual control center; and a CNN-GRU neural network model used for predicting generated power is arranged in the neural network prediction unit. Compared with the prior art, the power prediction model is carried out based on the CNN-GRU neural network, monitoring and early warning of the microgrid are carried out in combination with the threshold anomaly analysis method, the function of predicting the generated power is achieved, and the generated power can be predicted according to various data of actual operation of the microgrid; and fault early warning is realized by adopting a threshold analysis method according to the prediction data, so potential abnormal conditions can be quickly found to be timely dealt with.

Description

technical field [0001] The invention relates to the technical field of micro-grid safety precautions, in particular to a micro-grid risk monitoring and early warning system. Background technique [0002] At present, in the research of microgrid status monitoring and fault early warning, literature (Wu Jianhui, Liu Wei, Yang Sumei, Meng Xiangnan. Remote monitoring method of smart grid status based on big data[J]. Automation and Instrumentation, 2020(03): 209-211. ) proposed a remote monitoring method for smart grid status based on big data adaptive immune particle swarm algorithm. This method first collects the status information of the smart grid by using measurement tools, and then processes the collected information, including data cleaning, data denoising, data reduction, and data standardization, and finally uses the adaptive immune particle swarm algorithm to realize intelligent Grid health assessment. However, due to the differences between the large grid and the mic...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06N3/04G06N3/08
CPCG06Q10/0635G06Q50/06G06N3/08G06N3/045Y04S10/50
Inventor 张智禹王致杰刘衡陶梦林渠省委王鸿曹荣斌
Owner SHANGHAI DIANJI UNIV
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