Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

Abstract

Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified selfadaptive probabilistic neural networks 1 and 2) are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and selfadaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

“…In addition, we hope to show the performance of DCABC by Null Hypothesis Significance Testing (NHST) [35,36] in our future work. We only test the new algorithm on classical benchmark functions and have not used it to solve practical problems, such as fault diagnosis [37], path plan [38], Knapsack [39][40][41], multi-objective optimization [42], gesture segmentation [43], unit commitment problem [44], and so on. There is an increasing interest in prompting the performance of DCABC, which will be our future research direction.…”

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

“…In other words, it is essential to explore the principles, methods and means from various disciplines that are helpful to the fault diagnosis of transformers, so as to make the fault diagnosis technology interdisciplinary. Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37][38][39][40][41][42][43][44][45][46], expert system (EPS) [47][48][49][50][51], fuzzy theory [52][53][54][55][56][57][58], rough sets theory (RST) [36], grey system theory (GST) [59][60][61][62][63][64][65][66], and other intelligent diagnosis tools [5, such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, wavelet analysis (WA), optimized neural network, Bayesian network (BN), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiencies of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”

“…This method shows a better data fitting ability and more accurate prediction ability compared with SVM and grey model (GM) methods. In order to improve the accuracy of ANN applied in the transformer fault diagnosis, Yi et al [41] proposed a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, which can solve the transformer fault diagnosis problem and shows a more accurate prediction and better generalization performance when compared with other neural networks. Moreover, Meng et al [42] presented a novel hybrid self-adaptive training approach-based RBFNN for power transformer fault diagnosis, which clearly demonstrates the improved classification accuracy compared with other alternatives and shows that it can be employed as a reliable transformer fault diagnosis tool.…”

“…Therefore, when the development of EPS in transformer fault diagnosis using DGA meets with some technical obstacles, the research and application of ANN is developing rapidly, especially the new AI techniques, such as improved probabilistic neural network [41], self-adaptive radial basis function (RBF) neural network [42], knowledge discovery-based neural network [43], knowledge extraction-based neural network [44], fuzzy reasoning-based neural network [45], MLP neural network-based decision [46], back propagation (BP) neural network [103], recurrent ANN [104], deep learning (DL) based ANN [105], hybrid ANN and EPS [106], and generalized regression neural network (GRNN) [40,107]. Besides, the combination of ANN and mathematical morphology has been applied for the transformer fault diagnosis [108].…”

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

“…These algorithms have been used to successfully address many complicated engineering problems, such as ordinal regression 27 , classification 28 , data encryption 29 , possession 30 , scheduling 31 , test-sheet composition 32 , target assessment [33][34] , path planning [35][36][37] , directing orbits of chaotic systems 38 , feature selection 39 , and fault diagnosis 40 .…”