Abstract
The method of Support Vector Machine (SVM) based on Dissolved Gas Analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings, such as the fuzzy boundaries of DGA data, and SVM parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and a hybrid improved Seagull Optimization Algorithm to optimize the SVM (TISOA-SVM). Firstly, KPCA is used to extract features from DGA feature quantities. In addition, TISOA is further proposed to optimize the SVM parameters to build the optimal diagnosis model based on SVM. For the SOA, three improvement methods are proposed. An improved tent map is used to replace the original population initialization to improve population diversity. In addition, the nonlinear inertia weight and random double helix formula are proposed to improve the optimization accuracy and efficiency of the SOA. Then, benchmarking functions are used to test the optimization performance of TISOA and six algorithms, and the results show that TISOA has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on KPCA and TISOA-SVM is obtained, and it is noteworthy that three examples are tested to verify the diagnostic performance of the proposed method. These results show that the proposed method has higher diagnostic accuracy, shorter diagnosis time, stronger significance and validity than other methods. Therefore, a research idea is provided for solving practical engineering problems in the field of fault diagnosis.
Highlights
The power transformer is one of the most important pieces of equipment in the power grid, and the failure of a power transformer will affect the stable operation of a power grid, resulting in very large economic losses and even endangering people's lives [1,2]
EXAMPLE DIAGNOSIS RESULTS The method based on TISOA-support vector machine (SVM) to diagnose the faults with the kernel principal component analysis (KPCA)-processed dissolved gas analysis (DGA) data in this paper
The results show that the fault diagnosis accuracy of improved gray wolf optimization (IGWO)-SVM is 93.33%, with 4 misjudgments; the fault diagnosis accuracy of IGWO-probabilistic neural network (PNN) is 93.33%, with 4 misjudgments; the fault diagnosis accuracy of HGWOLSSVM is 91.67%, with 5 misjudgments; the fault diagnosis accuracy of bat algorithm (BA)-SVM is 90%, with 6 misjudgments; and the fault diagnosis accuracy of improved krill algorithm (IKH)-SVM is 95%, with 3 misjudgments
Summary
The power transformer is one of the most important pieces of equipment in the power grid, and the failure of a power transformer will affect the stable operation of a power grid, resulting in very large economic losses and even endangering people's lives [1,2]. It is crucial to perform fault diagnosis of power transformers quickly and accurately to ensure safe and stable operation [4]. In the field of power transformer fault diagnosis, dissolved gas analysis (DGA) is the most versatile method [5]. The above methods mostly rely on manual experience and historical data. They encounter defects, such as incomplete coding and coding boundaries, so these methods cannot perform fault diagnosis of power transformers quickly and accurately. With the development of artificial intelligence, many new methods, such as artificial neural network (ANN) [10,11], support vector machine (SVM) [12,13], and fuzzy theory [14,15], have been proposed and applied in the field of power transformer fault diagnosis
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