Intelligent localisation of multiple non-linear loads considering impact of harmonic state estimation accuracy

The localisation and harmonic level determination of non-linear loads (NLLs) are the first steps in solving harmonic problems in power system. To cope with this challenge, this study presents an intelligent approach based on fuzzy logic. The location and relative level of harmonic sources are determined in the fuzzy stage by interpreting the magnitude and sign of harmonic powers and network reactances. The accuracy of harmonic state estimation (HSE) is considered, besides the number of harmonic meters (HMs) and observability redundancy, in the optimal allocation of HMs. A modified adaptive binary imperialist competitive algorithm for the optimal allocation of HMs is presented. Also, the HSE accuracy is investigated on the accuracy of intelligent localisation of multiple NLLs in the presence of measurement errors. The efficiency of the proposed approach is verified on IEEE 18-bus and IEEE 69-bus test systems under different scenarios.

[1]  Ekrem Gursoy,et al.  Harmonic Load Identification Using Complex Independent Component Analysis , 2009 .

[2]  S. N. Singh,et al.  Identification of Multiple Harmonic Sources in Power System Using Optimally Placed Voltage Measurement Devices , 2014, IEEE Transactions on Industrial Electronics.

[3]  Neville R. Watson,et al.  Identification of harmonic sources of power systems using state estimation , 1999 .

[4]  J. Arrillaga,et al.  Error analysis in static harmonic State estimation: a statistical approach , 2005, IEEE Transactions on Power Delivery.

[5]  A.C. Liew,et al.  Neural-network-based signature recognition for harmonic source identification , 2006, IEEE Transactions on Power Delivery.

[6]  Shahriar Lotfi,et al.  Social-Based Algorithm (SBA) , 2013, Appl. Soft Comput..

[7]  Gustavo Adolfo,et al.  Uncertainty and State Estimation of Power Systems , 2012 .

[8]  Jianfeng Guo,et al.  Multi-objective optimal PMU placement using a non-dominated sorting differential evolution algorithm , 2010 .

[9]  Weidong Xiao,et al.  Optimal penetration levels for inverter-based distributed generation considering harmonic limits , 2013 .

[10]  Y. Liu,et al.  Test systems for harmonics modeling and simulation , 1999 .

[11]  Amir Moradifar,et al.  A Fuzzy Based Solution for Allocation and Sizing of Multiple Active Power Filters , 2012 .

[12]  Gabriele D'Antona,et al.  Localization of Nonlinear Loads in Electric Systems Through Harmonic Source Estimation , 2009, IEEE Transactions on Instrumentation and Measurement.

[13]  Fushuan Wen,et al.  Fuzzy logic approach in power system fault section identification , 1997 .

[14]  Y. Liu,et al.  An Investigation on the Validity of Power Direction Method for Harmonic Source Determination , 2002, IEEE Power Engineering Review.

[15]  Peter Minns,et al.  Power quality disturbance source identification using self-organising maps , 2010 .

[16]  Jaydev Sharma,et al.  Simple technique for placement of meters for estimation of harmonics in electric power system , 2005 .

[17]  Whei-Min Lin,et al.  Multiple harmonic source detection and equipment identification with cascade correlation network , 2005 .

[18]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[19]  Dejan Stevanovic,et al.  The Efficient Technique for Harmonic Sources Detection AT Power Grid , 2012 .

[20]  José Carlos de Oliveira,et al.  The sharing of responsibility between the supplier and the consumer for harmonic voltage distortion : A case study , 2008 .

[21]  R. Vinter,et al.  Measurement Placement in Distribution System State Estimation , 2009, IEEE Transactions on Power Systems.