Research on pump fault diagnosis based on pso-bp neural network algorithm

Sang, Jinguo

doi:10.1109/itaic.2019.8785590

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…Many recent studies by experts in the field have also explored methods for diagnosing faults in power transformers. For instance, Liu Chang et al [36] applied the bee algorithm to optimize the BP neural network for transformer fault diagnosis; Fu Baoying et al [37] used the particle swarm algorithm to optimize the BP neural network for transformer fault diagnosis; Han Qingchun [38] proposed a transformer fault diagnosis method based on the cuckoo algorithm, optimizing the BP neural network; Zeng Zhi et al [39] developed a transformer BP neural network fault diagnosis system based on the ant algorithm.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Power Transformer Fault Diagnosis Based on Improved BP Neural Network

Jin,

Wu,

Zheng

et al. 2023

Electronics

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“…Optimizing BP neural networks using intelligent algorithms has become a research hotspot. Researchers applied standard PSO algorithms to BP neural networks to effectively reduce learning time and improve computational accuracy, [21,22]. Li et al conducted virtual simulation experiments on the short-term power generation of photovoltaic power plants through three sets of models of BP, GA-BP, and PSO-BP.…”

Section: Introductionmentioning

confidence: 99%

An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm

Tang¹,

Feng²,

Li³

et al. 2023

Computers, Materials &Amp; Continua

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Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading to the MSSA-BP prediction model. The MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the algorithm. Two different datasets were selected for comparison experiments on three groups of models. Under the same conditions, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results of MSSA-BP were significantly reduced, and the convergence speed was significantly improved. MSSA-BP can effectively improve the prediction accuracy and has certain application value.

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“…In 2019, J. Sang [11] proposed a PSO-BP neural network algorithm aiming at the problems of slow convergence and unstable results of the traditional BP neural network algorithm; they designed the adjustment rules of the inertia weight and learning factor of the PSO algorithm and adjusted the weight coefficient of the output layer and the hidden layer of the BP neural network algorithm. In 2020, L. Zhang [12] used Freeman chain code and differential code to extract the characteristics of dynamometer card data of the pumping unit group.…”

Section: Introductionmentioning

confidence: 99%

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

Zhang,

Li,

Shan

et al. 2023

Processes

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Due to the complex underground environment, pumping machines are prone to producing numerous failures. The indicator diagrams of faults are similar to a certain degree, which produces indistinguishable samples. As the samples increase, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. To accurately and quickly judge the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the Rectifying linear unit activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared to the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity and can improve the generalization performance of the deep learning model and the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well-embedded into other neural networks.

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Research on pump fault diagnosis based on pso-bp neural network algorithm

Cited by 6 publications

References 1 publication

Power Transformer Fault Diagnosis Based on Improved BP Neural Network

Power Transformer Fault Diagnosis Based on Improved BP Neural Network

An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm

An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

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