Chaotic mutation immune evolutionary programming for voltage security with the presence of DGPV

Due to environmental concern and certain constraint on building a new power plant, renewable energy particularly distributed generation photovoltaic (DGPV) has becomes one of the promising sources to cater the increasing energy demand of the power system. Furthermore, with appropriate location and sizing, the integration of DGPV to the grid will enhance the voltage stability and reduce the system losses. Hence, this paper proposed a new algorithm for DGPV optimal location and sizing of a transmission system based on minimization of Fast Voltage Stability Index (FVSI) with considering the system constraints. Chaotic Mutation Immune Evolutionary Programming (CMIEP) is developed by integrating the piecewise linear chaotic map (PWLCM) in the mutation process in order to increase the convergence rate of the algorithm. The simulation was applied on the IEEE 30 bus system with a variation of loads on Bus 30. The simulation results are also compared with Evolutionary Programming (EP) and Chaotic Evolutionary Programming (CEP) and it is found that CMIEP performed better in most of the cases.

[1]  Amin Khodabakhshian,et al.  A comprehensive review of the voltage stability indices , 2016 .

[2]  Gevork B. Gharehpetian,et al.  Optimal allocation and sizing of DG units considering voltage stability, losses and load variations , 2016 .

[3]  Parimal Acharjee,et al.  Chaotic particle swarm optimization based robust load flow , 2010 .

[4]  Leandro dos Santos Coelho,et al.  Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches , 2008 .

[5]  B. Ravindhranath Reddy,et al.  Reduction of Real Power Loss and Safeguarding of Voltage Constancy by Artificial Immune System Algorithm , 2015 .

[6]  Chang Liu,et al.  Optimization of hysteretic chaotic neural network based on fuzzy sliding mode control , 2016, Neurocomputing.

[7]  Dheeraj Kumar Khatod,et al.  Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks , 2013 .

[8]  Abhijit R. Abhyankar,et al.  Loss allocation of radial distribution system using Shapley value: A sequential approach , 2017 .

[9]  T. Kapitaniak.  Chaos for Engineers: Theory, Applications, and Control , 2012 .

[10]  L. Coelho,et al.  A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch , 2009 .

[11]  B. Kendall.  Nonlinear Dynamics and Chaos , 2001 .

[12]  Kwok-Wo Wong,et al.  An improved particle swarm optimization algorithm combined with piecewise linear chaotic map , 2007, Appl. Math. Comput..

[13]  Sarat Kumar Sahoo,et al.  Renewable and sustainable energy reviews solar photovoltaic energy progress in India: A review , 2016 .

[14]  Bo Liu,et al.  Improved particle swarm optimization combined with chaos , 2005 .

[15]  David B. Fogel,et al.  Preliminary experiments on discriminating between chaotic signals and noise using evolutionary programming , 1996 .

[16]  Sriparna Roy Ghatak,et al.  Optimal allocation of Distributed Generation (DGs) and static VAR compensator (SVC) in a power system using Revamp Voltage Stability Indicator , 2016, 2016 National Power Systems Conference (NPSC).

[17]  Swagatam Das,et al.  Adaptive evolutionary programming with p-best mutation strategy , 2013, Swarm Evol. Comput..

[18]  Xiaolu Wang,et al.  Chaotic CPG based locomotion control for modular self-reconfigurable robot , 2016 .

[19]  Cheng Bo,et al.  Parallel Chaos Immune Evolutionary Programming , 2006, Australian Conference on Artificial Intelligence.

[20]  Leandro dos Santos Coelho,et al.  Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization , 2008, Expert Syst. Appl..

[21]  Mohammad Reza Mohammadi,et al.  Optimal placement of multitypes DG as independent private sector under pool/hybrid power market using GA-based Tabu Search method , 2013 .

[22]  N. Zareen,et al.  A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system , 2016 .

[23]  Ismail Musirin,et al.  On-line voltage stability based contingency ranking using fast voltage stability index (FVSI) , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[24]  Mohammad Salehi Male,et al.  Optimal Determination of Size and Site of DGs in Mesh System Using PSO , 2014 .

[25]  Majid Jamil,et al.  Optimal sizing and location of SPV (solar photovoltaic) based MLDG (multiple location distributed generator) in distribution system for loss reduction, voltage profile improvement with economical benefits , 2016 .

[26]  Jeng Shiun Lim,et al.  Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods , 2017 .

[27]  Ying-Yi Hong,et al.  Interactive multi-objective active power scheduling considering uncertain renewable energies using adaptive chaos clonal evolutionary programming , 2013 .

[28]  Sakti Prasad Ghoshal,et al.  Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints , 2011 .

[29]  H. Hashim,et al.  A methodology to determine suitable placement of solar photovoltaic sources in the transmission system taking into account voltage stability index (VSI) , 2014, 2014 IEEE International Conference on Power and Energy (PECon).

[30]  Juntao Li,et al.  Chaos control for a unified chaotic system using output feedback controllers , 2017, Math. Comput. Simul..

[31]  Yun-feng Wang,et al.  Research on a Piecewise Linear Chaotic Map and Its Cryptographical Application , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[32]  S. Boccaletti,et al.  The control of chaos: theory and applications , 2000 .

[33]  T. Ananthapadmanabha,et al.  Optimal Allocation of Combined DG and Capacitor Units for Voltage Stability Enhancement , 2015 .