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23-11-2020 | Research Paper

Application of grey wolf optimization algorithm for load frequency control in multi-source single area power system

Authors: Nikhil Paliwal, Laxmi Srivastava, Manjaree Pandit

Published in: Evolutionary Intelligence | Issue 1/2022

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Abstract

In this paper, application of evolutionary intelligence-based Grey Wolf Optimizer (GWO) algorithm has been presented to estimate the optimal parameters of Proportional-Integral-Derivative (PID) controller for load frequency control (LFC) in multi-source single area power network. The multi-source single area power network which is considered here is comprised of reheat turbine thermal power plant, gas power plant and hydro power plant with the mechanical hydraulic governor. In this paper, the parameters of PID controller for LFC of under study system are optimized using GWO algorithm by considering four important performance indices (instead of considering one or two) namely Integral of Time-weighted Squared Error (ITSE), Integral of Time-weighted Absolute Error (ITAE), Integral of Squared Error (ISE) and Integral of Absolute Error (IAE). The implemented technique has been compared with Genetic Algorithm (GA) technique for 1%, 2% and 5% load disturbances to show the effectiveness and applicability for LFC in multi-source single area power network.

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Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278. https://doi.org/10.1016/j.tcs.2005.05.020MathSciNetCrossRefMATH

Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1:33–57. https://doi.org/10.1007/s11721-007-0002-0CrossRef

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471. https://doi.org/10.1007/s10898-007-9149-xMathSciNetCrossRefMATH

Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46. https://doi.org/10.1016/j.swevo.2013.06.001CrossRef

Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider Monkey Optimization algorithm for numerical optimization. Memetic Comput 6:31–47. https://doi.org/10.1007/s12293-013-0128-0CrossRef

Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169–174. https://doi.org/10.1007/s00521-013-1367-1CrossRef

Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010CrossRef

Yu JJQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput J 30:614–627. https://doi.org/10.1016/j.asoc.2015.02.014CrossRef

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007CrossRef

10.

Gupta E, Saxena A (2015) Robust generation control strategy based on Grey Wolf Optimizer. J Electr Syst 11:174–188

11.

Chaman-Motlagh A (2015) Superdefect Photonic Crystal Filter Optimization Using Grey Wolf Optimizer. IEEE Photonics Technol Lett 27:2355–2358. https://doi.org/10.1109/LPT.2015.2464332CrossRef

12.

Sulaiman MH, Mustaffa Z, Mohamed MR, Aliman O (2015) Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl Soft Comput J 32:286–292. https://doi.org/10.1016/j.asoc.2015.03.041CrossRef

13.

El-Fergany AA, Hasanien HM (2015) Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms. Electr Power Components Syst 43:1548–1559. https://doi.org/10.1080/15325008.2015.1041625CrossRef

14.

Song X, Tang L, Zhao S et al (2015) Grey Wolf Optimizer for parameter estimation in surface waves. Soil Dyn Earthq Eng 75:147–157. https://doi.org/10.1016/j.soildyn.2015.04.004CrossRef

15.

Komaki GM, Kayvanfar V (2015) Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. J Comput Sci 8:109–120. https://doi.org/10.1016/j.jocs.2015.03.011CrossRef

16.

Mirjalili S (2015) How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 43:150–161. https://doi.org/10.1007/s10489-014-0645-7CrossRef

17.

Zhang S, Zhou Y, Li Z, Pan W (2016) Grey Wolf optimizer for unmanned combat aerial vehicle path planning. Adv Eng Softw 99:121–136. https://doi.org/10.1016/j.advengsoft.2016.05.015CrossRef

18.

Pradhan M, Roy PK, Pal T (2016) Grey wolf optimization applied to economic load dispatch problems. Int J Electr Power Energy Syst 83:325–334. https://doi.org/10.1016/j.ijepes.2016.04.034CrossRef

19.

Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/j.eswa.2015.10.039CrossRef

20.

Jayakumar N, Subramanian S, Ganesan S, Elanchezhian EB (2016) Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Int J Electr Power Energy Syst 74:252–264. https://doi.org/10.1016/j.ijepes.2015.07.031CrossRef

21.

Nuaekaew K, Artrit P, Pholdee N, Bureerat S (2017) Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer. Expert Syst Appl 87:79–89. https://doi.org/10.1016/j.eswa.2017.06.009CrossRef

22.

Ali M, El-Hameed MA, Farahat MA (2017) Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer. Renew Energy 111:455–462. https://doi.org/10.1016/j.renene.2017.04.036CrossRef

23.

Şimşir Ş, Taşpınar N (2017) Pilot tones design using Grey Wolf Optimizer for OFDM–IDMA system. Phys Commun 25:259–267. https://doi.org/10.1016/j.phycom.2017.08.010CrossRef

24.

Zhang S, Zhou Y (2017) Template matching using grey wolf optimizer with lateral inhibition. Optik (Stuttg) 130:1229–1243. https://doi.org/10.1016/j.ijleo.2016.11.173CrossRef

25.

Liu H, Wu H, Li Y (2018) Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction. Energy Convers Manag 161:266–283. https://doi.org/10.1016/j.enconman.2018.02.006CrossRef

26.

Gujarathi PK, Shah VA, Lokhande MM (2018) Grey wolf algorithm for multidimensional engine optimization of converted plug-in hybrid electric vehicle. Transp Res Part D Transp Environ 63:632–648. https://doi.org/10.1016/j.trd.2018.06.003CrossRef

27.

Dehghani M, Seifi A, Riahi-Madvar H (2019) Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization. J Hydrol 576:698–725. https://doi.org/10.1016/j.jhydrol.2019.06.065CrossRef

28.

Rana KPS, Kumar V, Sehgal N, George S (2019) A Novel [Formula presented] feedback based control scheme using GWO tuned PID controller for efficient MPPT of PEM fuel cell. ISA Trans 93:312–324. https://doi.org/10.1016/j.isatra.2019.02.038CrossRef

29.

Nayak B, Mohapatra A, Mohanty KB (2019) Parameter estimation of single diode PV module based on GWO algorithm. Renew Energy Focus 30:1–12. https://doi.org/10.1016/j.ref.2019.04.003CrossRef

30.

Manikandan K (2019) Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization. Pattern Recognit Lett 125:432–438. https://doi.org/10.1016/j.patrec.2019.06.005CrossRef

31.

Shabeerkhan S, Padma A (2020) A novel GWO optimized pruning technique for inexact circuit design. Microprocess Microsyst 73:102975. https://doi.org/10.1016/j.micpro.2019.102975CrossRef

32.

Kalemci EN, İkizler SB, Dede T, Angın Z (2020) Design of reinforced concrete cantilever retaining wall using Grey wolf optimization algorithm. Structures 23:245–253. https://doi.org/10.1016/j.istruc.2019.09.013CrossRef

33.

Xianming L, Ping L, Baocun Z et al (2020) Localization of multiple leaks in a fluid pipeline based on ultrasound velocity and improved GWO. Process Saf Environ Prot 137:1–7. https://doi.org/10.1016/j.psep.2020.02.006CrossRef

34.

Ibraheem A, Kumar P, Kothari DP (2005) Recent philosophies of automatic generation control strategies in power systems. IEEE Trans Power Syst 20:346–357CrossRef

35.

Parmar KPS, Majhi S, Kothari DP (2014) LFC of an interconnected power system with multi-source power generation in deregulated power environment. Int J Electr Power Energy Syst 57:277–286. https://doi.org/10.1016/j.ijepes.2013.11.058CrossRef

36.

Barisal AK (2015) Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems. Int J Electr Power Energy Syst 66:67–77. https://doi.org/10.1016/j.ijepes.2014.10.019CrossRef

37.

Mohanty B, Panda S, Hota PK (2014) Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. Int J Electr Power Energy Syst 54:77–85. https://doi.org/10.1016/j.ijepes.2013.06.029CrossRef

38.

Tan W (2009) Tuning of PID load frequency controller for power systems. Energy Convers Manag 50:1465–1472. https://doi.org/10.1016/j.enconman.2009.02.024CrossRef

39.

Hassanat A, Almohammadi K, Alkafaween E et al (2019) Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Inf 10:1–36. https://doi.org/10.3390/info10120390CrossRef

40.

Bhatt P, Roy R, Ghoshal SP (2011) Comparative performance evaluation of SMES-SMES, TCPS-SMES and SSSC-SMES controllers in automatic generation control for a two-area hydro-hydro system. Int J Electr Power Energy Syst 33:1585–1597. https://doi.org/10.1016/j.ijepes.2010.12.015CrossRef

41.

García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci (Ny) 180:2044–2064. https://doi.org/10.1016/j.ins.2009.12.010CrossRef

42.

Friedman M (1940) A Comparison of Alternative Tests of Significance for the Problem of m Rankings. Ann Math Stat 11:86–92. https://doi.org/10.1214/aoms/1177731944MathSciNetCrossRefMATH

43.

Friedman M (1937) The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. J Am Stat Assoc 32:675–701. https://doi.org/10.1080/01621459.1937.10503522CrossRefMATH

44.

Davenport JM (1980) Approximations of the critical region of the friedman statistic. Commun Stat - Theory Methods 9:571–595. https://doi.org/10.1080/03610928008827904CrossRefMATH

Title: Application of grey wolf optimization algorithm for load frequency control in multi-source single area power system
Authors: Nikhil Paliwal
Laxmi Srivastava
Manjaree Pandit
Publication date: 23-11-2020
Publisher: Springer Berlin Heidelberg
Published in: Evolutionary Intelligence / Issue 1/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-020-00530-5

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