Abstract
This paper discusses the implementation of Load Frequency Control (LFC) in restructured power system using Hybrid Fuzzy controller. The formulation of LFC in open energy market is much more challenging; hence it needs an intelligent controller to adapt the changes imposed by the dynamics of restructured bilateral contracts. Fuzzy Logic Control deals well with uncertainty and indistinctness while Particle Swarm Optimization (PSO) is a well-known optimization tool. Abovementioned techniques are combined and called as Hybrid Fuzzy to improve the dynamic performance of the system. Frequency control of restructured system has been achieved by automatic Membership Function (MF) tuned fuzzy logic controller. The parameters defining membership function has been tuned and updated from time to time using Particle Swarm Optimization (PSO). The robustness of the proposed hybrid fuzzy controller has been compared with conventional fuzzy logic controller using performance measures like overshoot and settling time following a step load perturbation. The motivation for using membership function tuning using PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes is tested on a two-area restructured power system.
Highlights
Frequency regulation service in energy market is one of the profitable ancillary services
Consider a system where all Generation Companies (GENCOs) in each area participate in Load Frequency Control (LFC), i.e. area control error (ACE) participation factors are α1 = 0.75, α2 =1 − α1 =0.25 ; α3 = 0.5, α4 =1 − α3 =0.5
Automatic tuning of membership function of fuzzy controller using Particle Swarm Optimization (PSO) is applied to bilateral LFC scheme
Summary
Frequency regulation service in energy market is one of the profitable ancillary services. How to cite this paper: Anitha, P. and Subburaj, P. (2016) Hybrid Fuzzy Controller Based Frequency Regulation in Restructured Power System. Subburaj frequency control in a complex and competitive environment is a tedious process, the control technique must be more intelligent and adaptive for a changing environment. More research work has been carried on Frequency control in restructured power system [1]-[7]. Possible issues in Frequency control in energy market were discussed in [1]. A decentralized approach based on control theory was formulated and analyzed in [5]. Some researchers have proposed a frequency control logic based on fuzzy logic controller [6], reinforcement learning [7] and artificial neural network [8] in interconnected power systems
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