A MOPSO based faulty section location method for distribution networks with PVs

The distributed generations (DGs) make the establishment of switch function complex, and the faulty section location methods for traditional distribution networks are no longer applicable. In order to improve the rapidity and accuracy of faulty section location, a Multi-Objective Particle Swarm Optimization (MOPSO) based faulty section location method for distribution networks with photovoltaic (PV) generations is proposed. The influence of fault current characteristics of PV generations under different light intensities is taken into account. Switch function for dynamic switching of PV generations is proposed. Since the single objective optimization intelligence algorithms easily cause the premature convergence and the computational complexity of the NSGA-II algorithm is high, the MOPSO algorithm is used to solve the problem, which can avoid the determination of the weighting factors. The simulation results show that the proposed method improves the rapidity and accuracy of location effectively, and has superior fault-tolerance to distortion information.

[1]  Zheng Ta Fast and robust fault location for distribution systems , 2014 .

[2]  Csg Guangzhou Harmony Search Algorithm for Solving Fault Location in Distribution Networks with DG , 2013 .

[3]  L.F. Ochoa,et al.  Evaluating distributed generation impacts with a multiobjective index , 2006, IEEE Transactions on Power Delivery.

[4]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[5]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[6]  Miao Liheng Pareto evolutionary algorithm for multi-objective fault location of distribution network , 2012 .

[7]  Zhengyou He,et al.  Fault-section estimation in power systems based on improved optimization model and binary particle swarm optimization , 2009, 2009 IEEE Power & Energy Society General Meeting.

[8]  Maofa Gong,et al.  Fault-section location of distribution network containing distributed generation based on the multiple-population genetic algorithm of chaotic optimization , 2017, 2017 Chinese Automation Congress (CAC).

[9]  J B A London,et al.  Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration , 2010, IEEE Transactions on Power Systems.

[10]  Zhang Zhihua Simulation Analysis on Influences of Distributed Photovoltaic Generation on Short-Circuit Current in Distribution Network , 2013 .

[11]  C. Su,et al.  Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems , 2005 .

[12]  Deng Bo Analysis of Impact of DGs on Line Protection of Distribution Networks , 2009 .

[13]  Du Hong FAULT SECTION DIAGNOSIS AND ISOLATION OF DISTRIBUTION NETWORKS BASED ON GENETIC ALGORITHM , 2000 .

[14]  Xu Zhi Fault-section location for distribution networks with DG based on a hybrid algorithm of particle swarm optimization and differential evolution , 2013 .

[15]  Newton Bretas,et al.  Node-depth encoding and multiobjective evolutionary algorithm applied to large-scale distribution system reconfiguration , 2011, 2011 IEEE Power and Energy Society General Meeting.

[16]  Adly A. Girgis,et al.  Automated fault location and diagnosis on electric power distribution feeders , 1997 .