Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

Pu, Yuan; Mao, Jianlin; Xiang, Fenghong; Lian, Liu; Zhang, Maoxing

doi:10.1051/matecconf/20152201050

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The number of illustrative applications of these solvers based on back propagation (BP) neural networks and genetic algorithms is seen in the literature, such as nonlinear optics problems [14], nonlinear nanofluidic systems of Jeffery-Hamel flow [15], the dynamics of nonlinear singular heat conduction model of the human head [16], nonlinear Painlev'e II systems in applications of random matrix theory [17], hermal analysis of porous fin model [18], Nonlinear Singular Thomas-Fermi Systems [19], credit evaluation for listed companies [20], vibration dynamics of rotating electrical machines [21], environmental quality assessment [22], grid fault diagnosis [23], wind speed soft sensor [24], prediction of postgraduate entrance examination [25], fault section locating in distribution net-work with DG [26], crude oil production prediction [27], prediction of junction temperature for high power LED [28].…”

Section: B Innovation Contributionmentioning

confidence: 99%

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Jiang

Huang²,

Huang³

et al. 2019

IEEE Access

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The artificial intelligence calculation method can effectively solve various nonlinear mapping relationships. The strength of these nonlinear solvers is exploited for the evaluation of power grid investment risk using back propagation (BP) neural network optimized by genetic algorithm. The mathematical model of the problem is constructed by selecting the transfer function of the neural network and defining the fitness function of genetic algorithm. BP neural network has good ability of self-learning, self-adaptation and generalization, which can overcome the drawbacks of traditional evaluation methods relying on experts' experience. For the characteristics of genetic algorithm global optimization, the genetic algorithm is used to optimize the weight and threshold of BP neural network, and BP neural network is trained to obtain the optimal evaluation model. The model fully exploits the local search ability of BP neural network and the global search ability of genetic algorithm. It has obtained good evaluation accuracy for the processing of multi-dimensional influence factor problem. And the model can be adapted to different power grids by changing the training data. However, the method cannot describe the specific relationship between each impact factor and the investment risk of the grid. The case study shows that the method can accurately and effectively evaluate power grid investment risk and improve the fault tolerance of the power grid investment risk evaluation.INDEX TERMS Power grid investment risk, risk evaluation, BP neural network, genetic algorithm.

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Section: B Innovation Contributionmentioning

confidence: 99%

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Jiang

Huang²,

Huang³

et al. 2019

IEEE Access

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“…GA is a global optimization method based on the principle of biological evolution, namely, survival of the fittest [35]. By optimizing the initial weights and thresholds, GA can move the neural network training process from the local optimal domain to the global optimal domain [36]. Therefore, for the walnut breaking process model, the initial weights and thresholds of the ANN can be optimized by the GA first, followed by ANN training with the optimized initial weights and thresholds, resulting in global optimal weights and thresholds.…”

Section: Optimization Of Artificial Neural Network Using Genetic Algo...mentioning

confidence: 99%

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Yang

Zheng

Vidyarthi

et al. 2023

Foods

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This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51–3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities.

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“…Only with large and comprehensive data can the accuracy and practicability of the whole diagnosis system be guaranteed. This method has the problems of low accuracy and low diagnosis accuracy with limited information [27][28][29]. Although the diagnostic methods mentioned above have achieved certain results in the diagnosis of transformer faults, the overall diagnostic accuracy is still insufficient.…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Fault Diagnosis Based on Improved BP Neural Network

Jin,

Wu,

Zheng

et al. 2023

Electronics

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Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids.

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Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

Cited by 3 publications

References 2 publications

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Power Transformer Fault Diagnosis Based on Improved BP Neural Network

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