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This paper presents a hybrid machine‐learning method based on oil‐immersed power transformer fault diagnosis Probability Neural Network (PNN) optimized via a Multi‐Verse Optimizer (MVO) algorithm. PNN is a radial basis function prefeedback neural network based on Bayesian decision theory. It has strong fault tolerance and has significant advantages in pattern classification. However, the performance of PNN is greatly affected by the hidden‐layer unit‐smoothing factor, and the classification result is affected. MVO is a metaheuristic algorithm with strong global convergence. Therefore, the smoothing factor of MVO‐optimized PNN (MVO‐PNN) can effectively improve the fault diagnosis ability. Recent studies have demonstrated the MVO algorithm. We utilize an experiment about the oil data in the power transformer in Jiangxi Province, China. The results show that MVO‐PNN can significantly improve the accuracy of power transformer fault classification and is more efficient than the Cuckoo search algorithm, Bat algorithm, Genetic Algorithm optimization, and other algorithms capabilities in some cases. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

For fault detection and diagnosis in large‐scale industrial systems, traditional methods have a low classification accuracy, which is an issue. This paper proposes a fault diagnosis method based on the modified cuckoo search algorithm (MCS) to optimize the probabilistic neural network (PNN). The random forest treebagger (RFtb) is used to reduce the data feature and the PNN is trained for fault diagnosis and classification. However, in order to address the problem that the parameters of PNN easily fall into the local optimal value, the MCS algorithm is introduced to globally optimize the hidden layer element smoothing factor (σ) in the PNN. The MCS algorithm uses a parameters update and a better optimization mechanism to achieve excellent global convergence and to effectively improve the fault diagnosis capability of the model. During the testing process using the Tennessee Eastman (TE) process dataset, the performance of the proposed model is assessed by comparing the accuracy and the F1‐score of different methods. Graphs are presented that depict fault classification and diagnostic results for the different models. The results show that the MCS algorithm has a better optimization ability than the traditional optimization algorithm and the proposed combination method can significantly improve the accuracy of the TE process fault diagnosis. [ABSTRACT FROM AUTHOR]

This article presents an innovative approach for fault diagnosis based on an optimized generalized regression neural network (GRNN) by integrating with dissolved gas analysis, cuckoo search algorithm (CSA), and rough set theory (RS). In the proposed method, the high dimensioned data will be simplified and reduced by RS to generate better features or attributes for the GRNN input. Meanwhile, to enhance the network performance, the smoothing factor of GRNN is optimized by CSA with Levy flight, which leads to a good global convergence. As a consequence, CSA can provide a good solution to effectively improve the fault diagnosis performance. To validate and demonstrate the proposed method, we applied it to a real‐world fault diagnosis application, power transformer fault diagnosis, by comparing the results with those of other methods. From the experimental results obtained from the evaluation, it is obvious that the proposed fault diagnosis method enabled with RS‐CSA‐GRNN can provide a useful solution for power transformer fault diagnosis because it outperformed other GRNN‐based methods that deployed different optimizing algorithms such as the particle swarm optimization and genetic algorithm. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The burgeoning prognostic and health management (PHM) engineering technology with superior performance has lately received extensive attention in the academic circle. Nevertheless, the various types of faults of the power transformer often lead to less accurate predictions and the instability of the power system. To address these problems, a power transformer PHM model with a hybrid machine learning method-approach is proposed in this paper. The model uses intelligent sensors to obtain dissolved gas analysis (DGA) data for fault diagnosis of the power transformer system, so as to compress the complexity of features (gas types) in the power transformer. In particular, to enhance the robustness of the model, we adopt a modified differential evolution whale optimization algorithm (MDE-WOA) to optimize the probabilistic neural network (PNN), namely, the classification performance of the model is improved by updating the smoothing factor (σ) of PNN. In addition, compared with other optimization algorithms, the MDE-WOA algorithm has a lower complexity and more stable optimization process. Finally, we evaluate this model with real world data from the power transformer sensor in Jiangxi province, China. The results indicated that the proposed algorithm could achieve the highest diagnostic accuracy in the fourth iteration, its accuracy having reached 98.86%. Therefore, the proposed PNN parameter optimization meta heuristic algorithm could effectively enhance the accuracy and efficiency of the power transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

Prognostics and Health Management (PHM) is an emerging technique which can improve the availability and efficiency of equipment. A series of related optimization of the PHM system has been achieved due to the growing need for lowering the cost of maintenance. The PHM system highly relies on data collected from its components. Based on the theory of machine learning, this paper proposes a bio-inspired PHM model based on a dissolved gas-in-oil dataset (DGA) to diagnose faults of transformes in power grids. Specifically, this model applies Bat algorithm (BA), a metaheuristic population-based algorithm, to optimize the structure of the Back-propagation neural network (BPNN). Furthermore, this paper proposes a modified Bat algorithm (MBA); here the chaos strategy is utilized to improve the random initialization process of BA in order to avoid falling into local optima. To prove that the proposed PHM model has better fault diagnostic performance than others, fitness and mean squared error (MSE) of Bat-BPNN are set as reference amounts to compare with other power grid PHM approaches including BPNN, Particle swarm optimization (PSO)-BPNN, as well as Genetic algorithm (GA)-BPNN. The experimental results show that the BA-BPNN model has increased the fault diagnosis accuracy from 77.14% to 97.14%, which is higher than other power transformer PHM models. [ABSTRACT FROM AUTHOR]

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis. [ABSTRACT FROM AUTHOR]