Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching

Abstract

To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. Then, an intuitionistic fuzzy Petri Nets fault diagnosis model, with an excellent ability to process uncertain information from intuitionistic fuzzy sets, is constructed, and the initial place value of the model is corrected by the timing confidence. Finally, an application of the fault diagnosis model for the actual grid is established to analyze and verify the diagnostic results of the new method. The results for some test cases show that the new method can improve the accuracy and fault tolerance of fault diagnosis, and, furthermore, the abnormal state of the component can be inferred.

A Novel Intuitionistic Fuzzy Inhibitor Arc Petri Net With Error Back Propagation Algorithm and Application in Fault Diagnosis

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“…The deterministic value of the inter-layer identification value is processed by a Gaussian function and applied to the matrix derivation process, where ψ indicates identification value, ψ µ represents the identification value of the membership degree, and f ψ (x) is a Gaussian function [13]:…”

“…To fully use the alarm information and reduce the uncertainty of the information, a power grid fault diagnosis method based on the intuitionistic fuzzy Petri net was proposed, which enhances the description of the alarm information by considering the membership degree and non-membership degree of the information [12]. In [13], the time series information was introduced into Petri net power grid fault diagnosis, which was able to filter some uncertainty information and correct the error information, thereby further improving the accuracy of the fault diagnosis. In addition, the reasoning process based on matrix operations is given in this method, which makes the calculations more concise and efficient.…”

U-Model Based Adaptive Neural Networks Fixed-Time Backstepping Control for Uncertain Nonlinear System

“…Recently, neural networks control has increasingly attracted attention and intensive research has been performed in adaptive law for training neural networks weights and application in different fields [1][2][3]. Neural network technique is a typical data-driven modelling method [4][5][6], which used measured data to find proper control in reversion of some expected closed-loop performance [7][8][9]. U-model control [10,11] played an important role in some complex systems.…”

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

“…Hu et al [11] developed a wind turbine bearing fault diagnosis based on multi-masking EMD and fuzzy c-means clustering. Other approaches, such as k-nearest neighbor [12], naïve Bayes [13], linear discriminant analysis [14], fuzzy petri nets [15], extreme learning machines [16], have also been Although these intelligent fault diagnostics approaches have shown great progress, there are still some limitations. On the one hand, they rely on the identification of handcrafted features requiring expert knowledge or computationally demanding feature selection methods.…”