A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

Abstract

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

“…Energies 2019, 12, 4170 2 of 18 DGA interpretation methods [1], including key gas method [2,3], IEC three-ratio method [4,5], Duval triangle method [6], Rogers ratio method [7] and Dornenburg ratio method [8], Duval pentagon [9], Mansour pentagon method [10,11], etc., are available to identify the different types of faults occurring in operating transformers. Although the commonly used methods are simple and effective in transformer fault diagnosis, they suffer from defects such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which will affect the reliability of fault analysis [12].With the development of artificial intelligence (AI), machine learning and pattern recognition methods have been widely used in power transformer fault diagnosis, including artificial neural network (ANN) [13][14][15], support vector machine (SVM) [16][17][18][19][20][21][22][23][24], probabilistic neural network [25,26], Bayesian neural network [27], fuzzy logic [28][29][30], deep belief network [31], expert system [32,33], which make up for the shortcomings of the traditional DGA methods, directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new idea for high-precision transformer fault diagnosis. Although these methods have achieved good results, there are also some shortcomings.…”

“…Nine feature ratios was selected as input vectors of the SVM and the diagnostic accuracy of 87.18% was obtained, which verified the robustness and generalization ability of optimal dissolved gas ratios (ODGR). Yuan et al [22] proposed a transformer fault diagnosis model based on chemical reaction optimization (CRO) and twin support vector machine (TWSVM) which used restricted Boltzmann machine (RBM) for data preprocessing, cross-validation (CV) to ensure the reliability and generalization ability of the diagnostic model and CRO algorithm to…”

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

“…The 30 sets of forecasted results of dissolved gas in oil were input into the transformer fault diagnosis model in [88] for fault diagnosis. As a result, the diagnosis results of 24 sets of data were low-energy discharge fault, and the other six sets of data were normal state, in which five of these six sets of normal data appear in the first eight sets of data at an earlier time, and this shows that the fault state of the transformer is still in the incubation period and the fault characteristics are not obvious.…”

An Assessment of the Condition of Distribution Network Equipment Based on Large Data Fuzzy Decision-Making

“…Due to the wide distribution and large amount of distribution equipment, and the large amount of operational monitoring data without uniform evaluation standards, great difficulties have been brought to the assessment of distribution equipment [4][5][6][7][8][9][10]. As one of the most important pieces of equipment of a distribution network, power transformers have also been paid much attention.…”

“…For the weight of qualitative indicators, use the transformation method of Table 5 to convert it into an expression of triangular fuzzy numbers. General General (6,7,8) Low High (7,8,9) Very low Very high (9,10,10) Lowest Highest…”