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Yuhong Zhou
Agricultural University of Hebei
Computer Science & IT

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Fault Diagnosis of Power Network Based on GIS Platform and Bayesian Networks Yunfang Xie; Yuhong Zhou; Weina Liu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2750

Abstract

In order to determine the location of the fault components of the power network quickly and give troubleshooting solutions, this paper obtains a simplify structure of relay protection and circuit-breaker as key equipment by analyzing the power network topology of GIS platform and uses the Bayesian networks fault diagnosis algorithm and finally designs the power network fault diagnosis module based on GIS platform. Fault diagnosis algorithm based on Bayesian networks is a new method for power network fault diagnosis which deals with the power network fault diagnosis with incomplete alarm signals caused by the protection device’s and the circuit breaker’s malfunction or refusal to move, device failure of communication and other reasons in the use of Bayesian networks method.  This method establishes the transmission line fault diagnosis model by using Noisy-Or, Noisy-And node model and similar BP neural network back propagation algorithm, and obtains the fault trust degree of each component by using the formula, and finally determines the fault according to the fault trust degree. The practical engineering application shows that the search speed and accuracy of fault diagnosis are improved by applying the fault diagnosis module based on GIS platform and Bayesian network.
Application of Artificial Fish Swarm Algorithm in Radial Basis Function Neural Network Yuhong Zhou; Jiguang Duan; Limin Shao
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2752

Abstract

Neural network is one of the branches with the most active research, development and application in computational intelligence and machine study. Radial basis function neural network (RBFNN) has achieved some success in more than one application field, especially in pattern recognition and functional approximation. Due to its simple structure, fast training speed and excellent generalization ability, it has been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm intelligent optimization algorithm derived from the study on the preying behavior of fish swarm. This algorithm is not sensitive to the initial value and the parameter selection, but strong in robustness and simple and easy to realize and it also has parallel processing capability and global searching ability. This paper mainly researches the weight and threshold of AFSA in optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is significantly advantageous in global optimization capability and that it has outstanding global optimization ability and stability.
Co-Authors Jiguang Duan Limin Shao Weina Liu Yunfang Xie