Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm

The present work approaches a novel quasi-oppositional harmony search (QOHS) algorithm, as an optimization technique, for its optimum performance in the subject area of automatic generation control (AGC) of power system. The proposed QOHS algorithm is applied with an aim to converge rapidly towards the optimal solution(s) that houses both the characters of two guesses, i.e. opposite-point and quasi-opposite point. The area of concern of this study is to discuss the multi-objective problems of an interconnected power system for the benefits of AGC. The proposed QOHS algorithm is, individually, applied to single-area, precede to two-area considering the non-linearity effects of governor dead band and generation rate constraint and, finally, extended to four-area power system showing the consequences of multiple load disturbances. A case of robustness and stability analysis are also investigated for the studied two-area power system model. The control strategy, for the dynamic power system model, is based on area control error. The simplicity of the structure and acceptability responses of the well-known proportional–integral–derivative controller enforces to implement as a controller in this work. The comparative evaluation of the proposed QOHS algorithm is carried out by the way of comparing the dynamic performances of the studied power system model with those offered by other algorithms reported in the recent state-of-the-art literature. The simulation works, presented in the paper, reveal that the proposed QOHS algorithm may be effectively utilized for the purpose of AGC study of power system having varying degrees of complexities and non-linearities. Moreover, the proposed QOHS based control strategy adopted in this work provides a robust and stable speed control mechanism.

[1]  E. S. Ali,et al.  BFOA based design of PID controller for two area Load Frequency Control with nonlinearities , 2013 .

[2]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[3]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[4]  Charles E. Fosha,et al.  The Megawatt-Frequency Control Problem: A New Approach Via Optimal Control Theory , 1970 .

[5]  M. Teshnehlab,et al.  Load frequency control in interconnected power system using multi-objective PID controller , 2008, 2008 IEEE Conference on Soft Computing in Industrial Applications.

[6]  Issarachai Ngamroo,et al.  Design of Optimal Fuzzy Logic based PI Controller using Multiple Tabu Search Algorithm for Load Frequency Control , 2006 .

[7]  Mahamed G. H. Omran,et al.  Global-best harmony search , 2008, Appl. Math. Comput..

[8]  Somanath Majhi,et al.  A new control scheme for PID load frequency controller of single-area and multi-area power systems. , 2013, ISA transactions.

[9]  Vichit Avatchanakorn,et al.  Genetic algorithms based on an intelligent controller , 1996 .

[10]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[11]  Seyed Abbas Taher,et al.  Optimal Decentralized Load Frequency Control Using HPSO Algorithms in Deregulated Power Systems , 2008 .

[12]  Sidhartha Panda,et al.  Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems , 2013, Appl. Soft Comput..

[13]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[14]  Sidhartha Panda,et al.  Automatic generation control of multi-area power system using multi-objective non-dominated sorting genetic algorithm-II , 2013 .

[15]  Haluk Gozde,et al.  Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system , 2012 .

[16]  E. S. Ali,et al.  Bacteria foraging optimization algorithm based load frequency controller for interconnected power system , 2011 .

[17]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[18]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[19]  Sakti Prasad Ghoshal,et al.  An opposition-based harmony search algorithm for engineering optimization problems , 2014 .

[20]  P. Kundur,et al.  Power system stability and control , 1994 .

[21]  Haluk Gozde,et al.  Automatic generation control application with craziness based particle swarm optimization in a thermal power system , 2011 .

[22]  Ibraheem,et al.  Recent philosophies of automatic generation control strategies in power systems , 2005, IEEE Transactions on Power Systems.

[23]  S. Mishra,et al.  Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control , 2009, IEEE Transactions on Power Systems.

[24]  Takashi Hiyama,et al.  Intelligent Automatic Generation Control , 2011 .

[25]  Wen Tan,et al.  Unified Tuning of PID Load Frequency Controller for Power Systems via IMC , 2010, IEEE Transactions on Power Systems.

[26]  Behrooz Vahidi,et al.  A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. , 2013, ISA transactions.

[27]  Bijaya K. Panigrahi,et al.  Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  Shahryar Rahnamayan,et al.  Opposition versus randomness in soft computing techniques , 2008, Appl. Soft Comput..

[29]  Marialena Vagia PID Controller Design Approaches - Theory, Tuning and Application to Frontier Areas , 2012 .

[30]  Giancarlo Mauri,et al.  Learning fuzzy rules with tabu search-an application to control , 1999, IEEE Trans. Fuzzy Syst..

[31]  H. Shayeghi,et al.  Multi-stage fuzzy load frequency control using PSO , 2008 .

[32]  Haiping Ma,et al.  Oppositional ant colony optimization algorithm and its application to fault monitoring , 2010, Proceedings of the 29th Chinese Control Conference.

[33]  P. S. Nagendra Rao,et al.  A reinforcement learning approach to automatic generation control , 2002 .

[34]  S. P. Ghoshal Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control , 2004 .

[35]  Jing J. Liang,et al.  A self-adaptive global best harmony search algorithm for continuous optimization problems , 2010, Appl. Math. Comput..

[36]  Z. Geem Music-Inspired Harmony Search Algorithm: Theory and Applications , 2009 .

[37]  Nathan Cohn,et al.  Some Aspects of Tie-Line Bias Control on Interconnected Power Systems [includes discussion] , 1956, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[38]  Sakti Prasad Ghoshal,et al.  Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm , 2014 .

[39]  Zong Woo Geem,et al.  Recent Advances in Harmony Search , 2008 .

[40]  S. P. Ghoshal,et al.  Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system , 2004 .