Abstract
Optimal power flow (OPF) plays an important role in power system operation and control. The OPF mainly aims to optimize the certain objective function such as minimizing the generation fuel cost, while at the same time satisfying load balance constraints and bound constraints. Particle swarm optimization (PSO) technique is an artificial intelligence-based technique. PSO technique is used to optimize the parameters like bus voltages, angles, real power generation fuel cost and the reactance values of TCSC. In this paper, the TCSC is incorporated using reactance model at fixed locations and power flow studies are carried out using Newton–Raphson method. The proposed approach is examined on modified IEEE 14-bus test system with and without TCSC device. The results are compared to the performance of the overall power network in the presence and absence of TCSC, and it is representing an analysis in order to show effectiveness of presented work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Christie R, Wollenberg B, Wangensteen I (2000) Transmission management in the deregulated environment. Proc IEEE 88(2):170–195
Wood AJ, Wollenberg BF (2005) Power Generation, Operationn and Control, 2nd edn. Wiley, Singapore
Pandya KS, Joshi SK (1991) Survey of optimal power flow methods. J Theor Appl Inf Technol
Man K, Tang K, Kwong S (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Ind Electron 43(5):519–534
Nayak MR, Krishnanand KR, Rout PK (2011) Modified differential evolution optimization algorithm for multi-constraint optimal power flow. In: International conference on energy, automation, and signal (ICEAS) Dec 2011, pp 1–7
Zhang G (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C: Appl Rev 30(4):451–462
Sumpavakup C, Srikun I, Chusanapiputt S (2010) A solution to the optimal power flow using artificial bee colony algorithm. In: Proceeding international conference power system technology, pp 1–5
Kumar R, Anand S, Sydulu M (2011) A new multi agent based PSO approaches for optimal power flows with security constraints using different cost functions. In: IEEE international conference on intelligent agent & multi-agent systems, pp 67–72
Mithulananthan N, Acharya N (2007) A proposal for investment recovery of FACTS devices in deregulated electricity markets. Electr Power Syst Res 77:695–703
Smita P, Vaidya BN (2012) Particle swarm optimization based optimal power flow for reactive loss minimization. In: IEEE conference on electrical, electronics and computer science, pp 4673–1515
Karthik M, Reddy A (2014) Particle swarm optimization to solve economic dispatch considering generator constraints. Int J Eng Sci, pp 94–100
Verma KS, Singh SN, Gupta HO (2001) Location of unified power flow controller for congestion management. Electr Power Syst Res 58:89–96
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Monal, P., Heistrene, L., Pandya, V. (2020). Optimal Power Flow in Power Networks with TCSC Using Particle Swarm Optimization Technique. In: Mehta, A., Rawat, A., Chauhan, P. (eds) Advances in Electric Power and Energy Infrastructure. Lecture Notes in Electrical Engineering, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-15-0206-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-0206-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0205-7
Online ISBN: 978-981-15-0206-4
eBook Packages: EngineeringEngineering (R0)