Robust Optimization Algorithms for Solving Automatic Generation Control of Multi-Constrained Power System: Robustness Study of AGC Problem in Power System

Robust Optimization Algorithms for Solving Automatic Generation Control of Multi-Constrained Power System: Robustness Study of AGC Problem in Power System

Dipayan Guha (Dr. B. C. Roy Engineering College, India), Provas Kumar Roy (Kalyani Government Engineering College, India) and Subrata Banerjee (National Institute of Technology Durgapur, India)
Copyright: © 2018 |Pages: 40
DOI: 10.4018/978-1-5225-3935-3.ch003


This chapter presents four effective evolutionary methods, namely grey wolf optimization (GWO), symbiotic organism search (SOS), JAYA, and teaching-learning-based optimization (TLBO), for solving automatic generation control (AGC) problem in power system. To show the effectiveness, two widely used interconnected power plants are examined. To extract maximum possible generation, distinct PID-controllers are designed employing ITAE-based fitness function. Further, to enhance the dynamic stability of concerned power systems, 2DOF-PID controllers are proposed in LFC area and optimally designed using aforesaid algorithms. To demonstrate the supremacy, obtained results are compared with some existing control algorithms. Moreover, robustness of the designed controller is believed under the action of random load perturbation (RLP). Finally, sensitivity analysis is carried out to show the stability of the designed system under loading and parametric disturbance conditions.
Chapter Preview

1. Introduction

Balance between total power generation and load consumption is always an intricate task particularly at peak load. Hence this is assigned as serious issue to ensure stable and reliable operation of power system at dynamic period. Since the term frequency is directly correlated with system loading, thus any abrupt change in loading cause serious deviation of system frequency from its tolerance value. Owing to load variations, the oscillation in frequency and interchange tie-line power persist for long time. Primary controller served by speed governor would be unable to nullify these fluctuations after load perturbation. An ample variation of frequency may severely affects the equilibrium states of the system and causes high flow of magnetizing current in transformers, induction motors etc (Arya, 2017). Hence, continuous monitoring of power generation and loading demand is needed. This is accomplished by automatic generation controller (AGC). AGC continuously monitor the generator output to meet load demand so as to regulate the frequency error down to zero value. The AGC scheme is not only maintaining the frequency level, it offers steady flow of power through tie-line between the nearby control areas.

Sudden load fluctuation due to variation of load is the key source of disturbance in power system. In this connection, AGC play a significant role to ensure system stability under the action of load perturbation (Naidu et al., 2017). In the literature an extensive works have been undertaken for continuous improvement of AGC performance. The implementation and impact of AGC in power system has been documented by Chon (Chon, 1956). However, the success of optimal control theory in LFC has been shown by Elgerd and Fosha (Fosha & Elgerd, 1970). An extensive review of literature of LFC for conventional and deregulated power systems has been available in (Kumar and Kothari, 2005). Gradual expansion in dimension, change in structure and increasing load demand in power system necessitated establishment of new control strategies. The traditional control strategies may be inept to takeover such abrupt changes in AGC. Some of the recently addressed control strategies in the literature are dual mode linguistic hedge fuzzy logic (Ansari & Velusami, 2010), dual mode fuzzy logic (Kumar et al., 2015), feedback controller (Ahmadi & Aldeen, 2017), fractional order fuzzy proportional-integral-derivative (PID) controller (Arya, 2017), 3 degree-of-freedom integral-derivative (3DOF-ID) controller (Rahman et al., 2015). Load frequency control in deregulated environment via active disturbance rejection scheme is illustrated in (Tan et al., 2015). The infliction of PID controller or its variants in AGC analysis has been appeared in the literature because of its numerous practical amenities (Padhan et al., 2014, Shankar & Mukherjee, 2016, Dhillon et al., 2016, Guha et al., 2016a). Recently in (Guha et al., 2017) an optimized PID-controller is designed to resolve the load frequency control (LFC) issue of an interconnected power system.

Key Terms in this Chapter

Load Frequency Control: Load frequency control (LFC) is a system to maintain reasonably uniform frequency, to divide the load between the generators, and to control the tie-line interchange schedules.

Robustness: Robustness is the ability of a closed loop system to be insensitive while system parameters are varied over a wide range. Trade-offs between robustness and speed of response is a key issue for control system design.

Optimization: The word optimization is originated from Latin word optimus , meaning “the best or most favorable point.” Optimization is the process of making something better. In other words, optimization refers to the process of exploring the best possible solutions for a given problem.

Describing Function: Describing function approach was coined by M. F. Kryloy and N. Bogoliubov in the year 1930 and then forwarded by R. Kochenburgen. Describing function method is based on first harmonic approximation and only valid if the linear part of the separable system possesses low pass filter characteristic.

Time Moments: Time moments are defined as the coefficients of the series when the transfer function is expanded as series at the nearby of s = 0. If the time moments of two LTI-systems are close, their low frequencies are matched.

Two Degrees of Freedom: Degree of freedom (DOF) in control system is defined as number of closed loop transfer function that can be handled independently. It has two main objectives (i.e., set point tracking and disturbance rejection). 2DOF system mainly has two components, namely serial or main compensator and feed-forward controller.

Sensitivity: In control system application, the controller parameters are selected to match the process characteristics. However, in the due course of disturbance, process may changed. Therefore, controller parameters must be selected such that closed-loop response is less sensitive to these disturbances. Lower the sensitivity factor, higher the degree of relative stability of the system.

Dead-Band: Dead-band is defined as a total magnitude of sustained speed change within which there is no resulting change in the valve position. Dead-band possesses non-minimum phase behavior and thereby causes destabilizing effect to the system.

Complete Chapter List

Search this Book: