MLP neural network-based decision for power transformers fault diagnosis using an improved combination of Rogers and Doernenburg ratios DGA

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.…”

“…Recently, several other approaches or techniques have been proposed for fault diagnosis of transformers, such as Rigatos and Siano's [82] proposed neural modeling and local statistical approach to fault diagnosis for the detection of incipient faults in power transformers, which can detect transformer failures at their early stages and consequently can deter critical conditions for the power grid; Shah and Bhalja [85] and Bacha et al [5] both proposed support vector machine (SVM)-based intelligent fault classification approaches to power transformer DGA. Furthermore, the random forest technique-based fault discrimination scheme [84] for fault diagnosis of power transformers, as well as the multi-layer perceptron (MLP) neural network-based decision [46], vibration correlation-based winding condition assessment technique [86], and induced voltages ratio-based thermodynamic estimation algorithm [73] have been proposed consecutively. Besides, in order to develop more accurate diagnostic tools based on DGA, a large number of information processing-based algorithms have been extensively investigated, e.g., Abu-Siada and Hmood [88] proposed a new fuzzy logic algorithm to identify the power transformer criticality based on the dissolved gas-in-oil analysis; Illias et al [89] developed a hybrid modified evolutionary particle swarm optimizer (PSO) time varying acceleration coefficient-ANN for power transformer fault diagnosis, which can obtain the highest accuracy than the previous methods; Pandya and Parekh [90] presented how interpretation of sweep frequency response analysis traces can be done for open circuit and short circuit winding faults on the power transformer.…”

“…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. In addition, Souahlia et al [46] used an improved combination of Rogers and Doernenburg ratios DGA to make MLP neural network-based decisions for power transformers fault diagnosis. This developed pre-processing approach can significantly improve the diagnosis accuracies for power transformer fault classification.…”

“…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].…”

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

“…The MLP neural network is composed of input layer, output layer and hidden layer, and the training process of the MLP neural network [13,14] is expressed as follows: Step1: The connection weights of the MLP neural network are initialized. The inputted features are sent to the hidden layer, and the calculation of each neuron of the hidden layer is expressed as follows:…”