A smart grid fault diagnosis method with repairable telemetry under malicious information tampering

A technology of fault diagnosis and telemetry, which is applied in the direction of complex mathematical operations, biological neural network models, instruments, etc., which can solve fault diagnosis methods that lack fault alarm information, fault diagnosis methods that are difficult to diagnose, model self-adaptive update, and failure to use telemetry faults Diagnosis and other problems, to achieve good diagnostic accuracy and robustness, to achieve the effect of adaptive update

Active Publication Date: 2022-07-01
XIHUA UNIV +1
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

Although such a method makes full use of abundant power data to establish a fault diagnosis model, it will also face the following problems: (1) The diagnosis effect depends on the quality of the training samples, and in the actual situation, enough good training samples are obtained at one time It is very difficult, resulting in the best model at one time; (2) It is difficult for the fault diagnosis method to update the diagnosis model adaptively, in other words, it lacks the ability to learn from wrong diagnosis
[0006] 1. Fault diagnosis methods rarely take into account the impact of malicious tampering of fault alarm information. When fault alarm information is maliciously tampered with, existing fault diagnosis methods will cause serious misdiagnosis
[0007] 2. Most of the existing fault diagnosis models based on SNPS can only use remote signaling to realize fault diagnosis, but cannot use remote measurement to realize fault diagnosis
[0008] 3. It is difficult for the existing fault diagnosis methods to realize the self-adaptive update of the fault diagnosis model. When a fault is misdiagnosed this time, the next time the same fault scenario occurs in the power grid, the misdiagnosis will continue, and it is impossible to learn from the error.

Method used

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  • A smart grid fault diagnosis method with repairable telemetry under malicious information tampering
  • A smart grid fault diagnosis method with repairable telemetry under malicious information tampering
  • A smart grid fault diagnosis method with repairable telemetry under malicious information tampering

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Embodiment 1

[0137] like figure 1 As shown, the method for diagnosing faults in a smart grid that can repair telemetry under malicious tampering of information in this embodiment includes the following steps:

[0138] S1. When the target grid fault occurs, use the tie-line analysis method to determine the suspected faulty components;

[0139] S2. Identify and repair the maliciously tampered fault alarm information by using the fault alarm information malicious tampering and repairing method of the multi-layer optimal clustering number FCM;

[0140] S3, using the wavelet packet decomposition algorithm to extract fault features from the fault telemetry in the current fault alarm information;

[0141]S4. Based on the extracted fault telemetry features, a fault diagnosis model based on the growth inference spiking neural membrane system for each suspected fault element is respectively established, and a neuron inference algorithm is used to solve it to obtain a corresponding fault diagnosis r...

Embodiment 2

[0265] The smart grid fault diagnosis method provided by the present invention will be described in detail below with a specific experimental example.

[0266] Taking the IEEE-39 node standard bus system as the diagnosis object, in the first diagnosis, after executing the NGA algorithm, the image 3 The grSNPS fault diagnosis model shown.

[0267] figure 2 where F1-F48 represent conditional neurons σ of the grSNPS fault diagnosis model 1 ~σ 48 , the physical meaning is the fault feature of wavelet energy entropy, where σ 1 ~σ 16 is the fault feature of positive sequence wavelet energy entropy, σ17 ~σ 32 is the energy entropy feature of negative sequence wavelet, σ 33 ~σ 38 Zero-sequence wavelet energy entropy fault features, T1~T11 are the decision neurons ξ of the grSNPS fault diagnosis model 1 ~ξ 11 , its physical meaning is the fault state of the target power grid (in order: no fault, A-G fault, B-G fault, C-G fault, AB fault, AC fault, BC fault, AB-G fault, BC-G ...

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Abstract

The invention discloses a smart grid fault diagnosis method that can repair telemetry under malicious tampering of information. Based on the fuzzy C-mean algorithm, a fault alarm information repair method with a multi-layer optimal number of clusters FCM is designed to repair the power grid suffering from Telemetry of malicious tampering; in the process of fault diagnosis, the SNPS model is improved by combining information entropy theory, and a growth inference spiking neural membrane system and its corresponding algorithm are designed, which realizes that the fault alarm information is maliciously tampered with. , using fault telemetry for power grid fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of power grid fault diagnosis, and in particular relates to a smart grid fault diagnosis method capable of repairing telemetry under malicious tampering of information. Background technique [0002] The intelligent self-adaptation of models has always been the goal of modern artificial intelligence. Through self-adaptation, machines can aim at self-learning, learn lessons from mistakes, and realize self-adjustment of models. It is no exception to the on-site intelligent diagnosis method. If the fault diagnosis method can learn from errors and automatically adjust the model in time according to the actual situation, it will make the fault diagnosis method more intelligent. [0003] Judging from the existing fault diagnosis methods, most of the artificial intelligence-based fault diagnosis methods use training samples to train the model, and then use the trained model for fault diagnosis. Although such a meth...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/16G06K9/62G06N3/06
CPCG06N3/061G06F17/16G06F18/23
Inventor 王涛刘伟古世甫赵斌詹红霞陈孝天黄著张怡然程亮张浩博许喆
Owner XIHUA UNIV
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