An Electric Power System Fault Diagnosis Method Based on PSO and FCM

2012 ◽  
Vol 241-244 ◽  
pp. 347-350
Author(s):  
Jie Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power System ◽  
Electric Power ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Electric Power System ◽  
Swarm Optimization ◽  
New Approach ◽  
Diagnosis Method

For effectively analyzing electric power faults, exactly identifying failure type, and highly providing disposal measure, depending on PSO (particle swarm optimization) algorithm, a PSO-FCM (particle swarm optimization-fuzzy c-means) algorithm was constructed by the FCM improvement of fuzzy clustering to avoid get in local optimal state. On this basis, an electric power system fault diagnosis method was established by means of PSO and FCM. Finally, this method was validated by an example. Consequently, this method can intellectively diagnose and identify the fault of electric power system, and can provide a new approach to stably operation in electric power system.

Simulation for Automotive Engine Fault Diagnosis Method

2014 ◽  
Vol 687-691 ◽  
pp. 882-885
Author(s):  
Huan Xue Liu ◽  
Guang Dong Zhang ◽  
Zhen Zhong Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Accurate Data ◽  
Swarm Optimization ◽  
Automotive Engine ◽  
Diagnosis Method

For engine fault diagnosis problem, an engine fault diagnosis method based on particle swarm optimization algorithm is proposed. The velocity and spatial position of all the particles in the particle swarm are updated, in order to provide accurate data basis for the engine fault diagnosis. Particle swarm optimization method is utilized to process iteration for all particles, so as to determine whether failure exists in components of engine. Experimental results show that with the proposed algorithm to diagnose engine fault can effectively improve the accuracy of fault diagnosis, and achieved the desired results.

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

Intelligent Optimization of Hybrid Wind/PV/pumped-Storage Power System

2012 ◽  
Vol 512-515 ◽  
pp. 719-722
Author(s):  
Yan Ren ◽  
Yuan Zheng ◽  
Chong Li ◽  
Bing Zhou ◽  
Zhi Hao Mao
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Hybrid System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Pumped Storage

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. Particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.

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