Particle Swarm Optimization Based Probabilistic Neural Network for Power Transformer Protection

2006 ◽  
Author(s):  
Manoj Tripathy ◽  
R. P. Maheshwari ◽  
H. K. Verma
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power Transformer ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Transformer Protection

Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3804-3808
Author(s):  
Zhi Mei Duan ◽  
Jia Tang Cheng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Bp Network ◽  
Swarm Optimization ◽  
Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

Complex lithology prediction using mean impact value, particle swarm optimization, and probabilistic neural network techniques

2020 ◽  
Vol 68 (6) ◽  
pp. 1727-1752
Author(s):  
Yufeng Gu ◽  
Zhongmin Zhang ◽  
Demin Zhang ◽  
Yixuan Zhu ◽  
Zhidong Bao ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Lithology Prediction

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Training ◽  
Gradient Descent Algorithm ◽  
Ipso Algorithm

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

scholarly journals PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR

2021 ◽  
Vol 23 (3) ◽  
pp. 99
Author(s):  
Yoyok Dwi Setyo Pambudi
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Nuclear Reactor ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Severe Accident ◽  
Swarm Optimization

Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard  probabilistic neural network (PNN)  and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB.  The experiment results showed that supervised PNN PSO could classify severe accident of nuclear power plant better than the standar PNN.

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