A novel hybrid many optimizing liaisons gravitational search algorithm approach for AGC of power systems

A hybrid Many Optimizing Liaisons Gravitational Search Algorithm (hMOL-GSA)-based fuzzy PID controller is proposed in this work for Automatic Generation Control problem. MOL is a simplified version of particle swarm optimization which ignores the particle best position consequently simplifying the algorithm. The proposed method is employed to tune the fuzzy PID parameters. The outcomes are equated with some newly proposed methods like Artificial Bee Colony (ABC)-based PID for the identical test systems to validate the supremacy of GSA and proposed hMOL-GSA techniques. Further, the design task has been carried out in a three-area test system and the outcomes are equated with newly proposed Firefly Algorithm (FA) optimized PID and Teaching Learning-Based Optimization (TLBO) tuned PIDD controller for the identical system. Better system response has been observed with proposed hMOL-GSA method. Finally, sensitivity study is being carried out and robustness of the proposed method is established.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

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

[3]  Rabindra Kumar Sahu,et al.  A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems , 2014 .

[4]  Rabindra Kumar Sahu,et al.  A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system , 2015, Appl. Soft Comput..

[5]  Rabindra Kumar Sahu,et al.  Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm , 2016 .

[6]  J. Nanda,et al.  Some new findings on automatic generation control of an interconnected hydrothermal system with conventional controllers , 2006, IEEE Transactions on Energy Conversion.

[7]  Rabindra Kumar Sahu,et al.  Teaching learning based optimization algorithm for automatic generation control of power system using 2-DOF PID controller , 2016 .

[8]  Lalit Chandra Saikia,et al.  Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system , 2011 .

[9]  Olle I. Elgerd,et al.  Electric Energy Systems Theory: An Introduction , 1972 .

[10]  Yogendra Arya,et al.  Automatic generation control of two-area electrical power systems via optimal fuzzy classical controller , 2018, J. Frankl. Inst..

[11]  Rajani K. Mudi,et al.  A robust self-tuning scheme for PI- and PD-type fuzzy controllers , 1999, IEEE Trans. Fuzzy Syst..

[12]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[13]  Hassan Bevrani,et al.  Robust Power System Frequency Control , 2009 .

[14]  Subranshu Sekhar Dash,et al.  A hybrid stochastic fractal search and local unimodal sampling based multistage PDF plus (1 + PI) controller for automatic generation control of power systems , 2017, J. Frankl. Inst..

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

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

[17]  Rajani K. Mudi,et al.  A self-tuning fuzzy PI controller , 2000, Fuzzy Sets Syst..

[18]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[19]  H. Shayeghi,et al.  Load frequency control strategies: A state-of-the-art survey for the researcher , 2009 .

[20]  S. M. Abd-Elazim,et al.  Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm , 2016, Neural Computing and Applications.

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

[22]  Andrew J. Chipperfield,et al.  Simplifying Particle Swarm Optimization , 2010, Appl. Soft Comput..

[23]  Rabindra Kumar Sahu,et al.  DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity , 2013 .

[24]  Nilanjan Dey,et al.  Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity , 2017, Neural Computing and Applications.

[25]  Narendra Kumar,et al.  Optimal control strategy–based AGC of electrical power systems: A comparative performance analysis , 2017 .

[26]  Dipayan Guha,et al.  Load frequency control of interconnected power system using grey wolf optimization , 2016, Swarm Evol. Comput..

[27]  Rabindra Kumar Sahu,et al.  Application of Firefly Algorithm for Load Frequency Control of Multi-area Interconnected Power System , 2014 .

[28]  Rabindra Kumar Sahu,et al.  A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems , 2015 .

[29]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.

[30]  Nand Kishor,et al.  A literature survey on load–frequency control for conventional and distribution generation power systems , 2013 .