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.
The setting and adjustment of the weight parameters in the traditional fault diagnosis method depend entirely on personal experience, and the parameter setting lacks regularity. To reduce the fault diagnosis errors caused by human subjective factors and improve the speed and accuracy of power grid fault diagnosis, we propose a method for power grid fault diagnosis using intuitionistic fuzzy inhibitor arc Petri net (IFIAPN) with error back propagation (BP) algorithm. Firstly, according to the network topology analysis and relay protection configuration setting rules, the inhibitor arc (IA) tuple is introduced into the model structure of the intuitionistic fuzzy Petri net to reduce the ambiguity of protection and circuit breaker action. Then, the weight parameters in the model are trained using a BP neural network algorithm to enhance the objectivity of the parameters. Finally, a simulation of an IEEE-39 node system and a real case study using the Hou-zhong line local grid were used to verify the effectiveness of the fault diagnosis method. The results show that the method can effectively deal with the refusal and mis-operation of multiple circuit breakers and improve the diagnostic efficiency under complex data environment. INDEX TERMS Intuitionistic fuzzy set, inhibitor arc Petri net, BP algorithm, grid fault diagnosis.
A power grid harmonic signal is characterized as having both nonlinear and nonstationary features. A novel multifractal detrended fluctuation analysis (MFDFA) algorithm combined with the empirical mode decomposition (EMD) theory and template movement is proposed to overcome some shortcomings in the traditional MFDFA algorithm. The novel algorithm is used to study the multifractal feature of harmonic signals at different frequencies. Firstly, the signal is decomposed and the characteristics of wavelet transform multiresolution analysis are employed to obtain the components at different frequency bands. After this, the local fractal characteristic of the components is studied by utilizing the novel MFDFA algorithm. The experimental results show that the harmonic signals exhibit obvious multifractal characteristics and that the multifractal intensity is related to the signal frequency. Compared with the traditional MFDFA algorithm, the proposed method is more stable in curve fitting and can extract the multifractal features more accurately.
The adjustable parameters of the traditional fuzzy Petri net (FPN) are single and mostly depend on expert experience. This approach lacks the adaptability to the complex network of sensors, which will result in insufficient accuracy of fault diagnosis. We propose a method combining the FPN with an adaptive arc and deep belief network (DBN) and improved a fast Gibbs sampling (FGS) algorithm to realize sensor fault diagnosis. First, we present the concept of adaptive arcs with label-weights based on the confidence-weights of directed arcs, which is an important component of the sensor fault model. Then, the improved FGS algorithm optimizes the model layer-by-layer, and the adjustment of the transition threshold relies on the marginal distribution of a restricted Boltzmann machine (RBM). Finally, the optimized dualweights and dual-transition influence factors are applied to the forward and backward fuzzy reasoning of the model to achieve network adaptability. Our studies showed that this method has obvious advantages in terms of the accuracy and adaptability of complex networks compared to other FPN fault diagnosis methods. The fault reasoning confidence can provide an effective reference for maintenance personnel and improve maintenance efficiency, ensuring the reliable operation of sensors and related systems. INDEX TERMS Adaptive arc, fuzzy Petri net, deep belief network, fast Gibbs sampling, sensor fault diagnosis.
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