Optimal Placement of FACTS Devices for Voltage Stability Improvement with Economic Consideration Using Firefly Algorithm

Author(s): S. Rajasekaran*, S. Muralidharan.

Journal Name: Current Signal Transduction Therapy

Volume 14 , Issue 1 , 2019

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Abstract:

Background: Increasing power demand forces the power systems to operate at their maximum operating conditions. This leads the power system into voltage instability and causes voltage collapse. To avoid this problem, FACTS devices have been used in power systems to increase system stability with much reduced economical ratings. To achieve this, the FACTS devices must be placed in exact location. This paper presents Firefly Algorithm (FA) based optimization method to locate these devices of exact rating and least cost in the transmission system.

Methods: Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensator (SVC) are the FACTS devices used in the proposed methodology to enhance the voltage stability of power systems. Considering two objectives of enhancing the voltage stability of the transmission system and minimizing the cost of the FACTS devices, the optimal ratings and cost were identified for the devices under consideration using Firefly algorithm as an optimization tool. Also, a model study had been done with four different cases such as normal case, line outage case, generator outage case and overloading case (140%) for IEEE 14,30,57 and 118 bus systems.

Results: The optimal locations to install SVC and TCSC in IEEE 14, 30, 57 and 118 bus systems were evaluated with minimal L-indices and cost using the proposed Firefly algorithm. From the results, it could be inferred that the cost of installing TCSC in IEEE bus system is slightly higher than SVC.For showing the superiority of Firefly algorithm, the results were compared with the already published research finding where this problem was solved using Genetic algorithm and Particle Swarm Optimization. It was revealed that the proposed firefly algorithm gives better optimum solution in minimizing the L-index values for IEEE 30 Bus system.

Conclusion: The optimal placement, rating and cost of installation of TCSC and SVC in standard IEEE bus systems which enhanced the voltage stability were evaluated in this work. The need of the FACTS devices was also tested during the abnormal cases such as line outage case, generator outage case and overloading case (140%) with the proposed Firefly algorithm. Outputs reveal that the recognized placement of SVC and TCSC reduces the probability of voltage collapse and cost of the devices in the transmission lines. The capability of Firefly algorithm was also ensured by comparing its results with the results of other algorithms.

Keywords: Economic analysis, FACTS devices, Firefly Algorithm (FA), IEEE bus systems, optimal placement, Particle Swarm Optimization (PSO).

[1]
Hingorani G, Gyugyi I. Understanding FACTS: Concepts and technology of Flexible AC Transmission Systems. New York: IEEE Press 2000.
[2]
Mathur RM, Varma RK. Thyristor-based FACTS controllers for electrical transmission systems. Piscataway: IEEE Press 2002.
[3]
Ambriz-perez H, Acha E, Fuerte-Esquivel CR. Advanced SVC models for newton raphson load flow and newton optimal power flow studies. IEEE Trans Power Syst 2000; 15: 129-36.
[4]
Orfanogianni T. A Flexible software environment for steady state power flow optimization with series FACTS devices DSc Tech Diss 2000; ETH, Zurich.
[5]
Larsen EV, Clark K, Miske SA, Urbanek J. Characteristic and rating considerations of thyristor controlled series compensation. IEEE Trans Power Deliv 1994; 9: 992-1000.
[6]
Gyugyi L. Unified power flow controller concept for flexible AC transmission system. IEE Proc 1994; 139(4): 323-31.
[7]
Yang XS. Nature-Inspired Meta-Heuristic Algorithms 2nd ed., Beckington, Luniver Press 2010.
[8]
Yang XS. Firefly algorithms for multimodal optimization, stochastic algorithms: Foundations and applications, SAGA 2009. Lect Notes Comput Sci 2009; 5792: 169-78.
[9]
Gerbex S, Cherkaoui R, Germond AJ. Optimal location of multi type FACTS devices in power systems by means of genetic algorithms. IEEE Trans Power Syst 2001; 16(3): 537-44.
[10]
Saravanan M, Slochanal SM, Venkatesh P, Abraham JP. Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electr Power Syst Res 2007; 77: 276-83.
[11]
Mondal D, Chakrabarti A, Sengupta A. Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability problem. Int J Electr Power Energy Syst 2012; 42(1): 334-40.
[12]
Idris RM, Khairuddin A, Mustafa MW. Optimal allocation of FACTS devices for ATC enhancement using Bees algorithm. World Acad Sci Eng Technol 2009; 3(6): 313-20.
[13]
Tripathy M, Mishra S. Bacteria foraging based solution to optimize both real power loss and voltage stability limit. IEEE Trans Power Syst 2007; 22(1): 240-8.
[14]
Senthil KM, Renuga P. Application of UPFC for enhancement of voltage profile and minimization of losses using Fast Voltage Stability Index (FVSI). Arch Electr Eng J 2012; 6(2): 239-50.
[15]
Rajasekaran S, Muralidharan S, Preethi VA. Performance comparison of genetic algorithm and particle swarm optimization based techniques for power system voltage stability problem using facts devices. Int Rev Modell Simulat 2012; 5(4): 1603-11.
[16]
Rajasekaran S, Muralidharan S. Firefly algorithm in determining maximum load utilization point and its enhancement through optimal placement of FACTS device. Circuit Syst 2016; 7: 3081-94.
[17]
Apostolopoulos T, Vlachos A. Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int J Combin 2011; 523806: 23.
[18]
Taher N, Azizipanah-Abarghooee R, Roosta A. Reserve constrained dynamic economic dispatch: A new fast self-adaptive modified firefly algorithm. IEEE Syst J 2012; 6(4): 635-46.
[19]
Yang XS, Hosseini SS, Gandomi AH. Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 2012; 12(3): 180-6.
[20]
Falcon R, Almeida M, Nayak A. Fault identification with binary adaptive fireflies in parallel and distributed systems. IEEE Congress on Evolutionary Computation (CEC) 2011; 1359-66.
[21]
Chandrasekaran K, Simon SP. Demand response scheduling in SCUC problem for solar integrated thermal system using firefly algorithm. Proceedings of IET Conference on Renewable Power Generation 2011; 1-8.
[22]
Chandrasekaran K, Simon SP. Network and reliability constrained unit commitment problem using binary real coded firefly algorithm. Int J Electr Power Energy Syst 2012; 43(1): 921-32.
[23]
Balamourougan V, Sidhu TS, Sachdev MS. Technique for online prediction of voltage collapse. IEEE Proc Generat Trans Distribut 2004; 151: 453-60.
[24]
Wang Y, Wang C, Lin F, Li W, Wang LY, Zhao J. Incorporating generator equivalent model into voltage stability analysis. IEEE Trans Power Syst 2013; 28(4): 4857-66.
[25]
Hadi K, Hamid A. Voltage stability improvement by using FACTS elements with economic consideration. Ciênc Nat 2015; 37(2): 162-7.
[26]
Janke A, Mouatt J, Sharp R, et al. SVC operation & reliability experiences InPower and Energy Society General Meeting IEEE Press: Providence 2010.
[27]
Hingorani NG, Gyugyi L. Understanding FACTS concepts and technology of flexible AC transmission systems. Hoboken: Wiley-IEEE Press 2000.
[28]
Kumari MS, Priyanka G, Sydulu M. Comparison of genetic algorithms and particle swarm optimization for optimal power flow including FACTS devices. Power Tech 2007; 2007: 1105-10.
[29]
Selvaperumal S, Rajan CCA, Muralidharan S. Stability and performance investigation of a Fuzzy-Controlled LCL resonant converter in an RTOS environment. IEEE Trans Power Electron 2012; 28(4): 1817-32.
[30]
Manikandan S, Natesan K. A novel approach for the reduction of 50 Hz noise in electrocardiogram using variational mode decomposition. Curr Sig Trans Ther 2017; 12(1): 39-48.
[31]
Dehai X, Jianqiao Z, Gao X. A new approach for skin-derived precursors to ameliorate skin photodamage through activation of Nrf2 signaling pathway. Curr Sig Trans Ther 2017; 12(1): 34-8.
[32]
Anbarasi S, Muralidharan S. Enhancing the transient performances and stability of AVR system with BFOA tuned PID controller. J Contr Eng Appl Inform 2016; 18(1): 20-9.
[33]
Muralidharan S, Anbarasi S. Intelligent tuning of proportional integral derivative controller using hybrid bacterial foraging particle swarm optimization. Rev Roum Sci Techn Électrotechn ET Énerg 2017; 62(3): 325-33.
[34]
Rajan CC, Surendra K, Reddy BR, Shobana R, Reddy YS. A refined solution to classical unit commitment problem using IWO algorithm. Int J Res Eng Technol 2014; 3(7): 327-35.
[35]
Vijayakumar K, Kumaresan N, Ammasai Gounden N. Operation of inverter assisted wind-driven slip ring induction generator for standalone power supplies. IET Electr Power Appl 2013; 7(4): 256-69.


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Article Details

VOLUME: 14
ISSUE: 1
Year: 2019
Page: [5 - 11]
Pages: 7
DOI: 10.2174/1573411014666180320112127
Price: $58

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