A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system
Introduction
Frequency control is one of the most important services in power system. It is achieved through governor response and load frequency control (LFC) [1]. The basic role of LFC is to
- i.
Provide the desired real power output from a generator to meet the change in load.
- ii.
Control the frequency of a large interconnected power system.
- iii.
Maintain the interchange of power between control areas through tie-lines at pre-specified values.
The main objective of LFC is to make zero steady state errors in frequency variations and tie-line power variations of different control areas. It also reduces damping of frequency oscillations and decreases overshoot and undershoot of the disturbance so as to improve the power system stability [1]. A multi-source power system is the interconnection of several lower-order subsystems in each area.
Literature survey reveals that works on AGC was first of all introduced by Cohn [2]. Khuntia and Panda [3] have employed AFSIS controller for a multi-area hydrothermal system. Abraham et al. [4] have used SMES units in a hydro-thermal system to improve its dynamic performance. Ali and Abd-Elazim [5] have proposed a BFOA based PI controller to enhance the performance of a two-area thermal power system. Sabahi et al. [6] have used multi-objective PID controller based on adaptive weighted PSO for AGC of an interconnected system. Lee et al. [7] have employed a gain scheduling control strategy for AGC of interconnected power system. Nanda et al. [8] have suggested gain scheduling of PI-controller optimized by bacterial foraging optimization technique. Lu et al. [9] have improved the performance of load frequency control using battery energy storage system. In Ref. [10], the authors have employed neural network based controller to enhance the performance of LFC of a two-area power system. Ghosal [11] has reported PSO, hybrid PSO-SA and hybrid GA-SA optimization technique in order to tune the gains of PID controller for a 3-area thermal power system. Kocaarslan and Cam [12] have proposed the fuzzy gain scheduling PI controller to improve the dynamic performance of a two-area interconnected power system. Rerkpreedapong et al. [13] have compared the performance of an controller using linear matrix inequalities (LMI) method and a GA optimized PI controller for LFC of a 3-area power system. Naidu et al. [14] have used artificial bee colony optimization technique to tune the parameters of PID controller for LFC of a two-area interconnected system. Sathya and Ansari [15] have used bat algorithm based PI controller for LFC of an interconnected power systems. Mohanty et al. [16] have presented Differential Evolution (DE) based PI and PID controller for LFC of multi-area interconnected power systems.
It is verified by many researchers that the fuzzy logic controller (FLC) can safely handle all the changes in operating point by online updating of the controller parameters [17], [18]. But there is no specific rule which can be followed to decide the fuzzy parameters (such as inputs, scaling factors, membership functions, and rule base). Usually these parametric values are selected by trial and error method. But trial and error method may not yield better performance.
In this paper scaling factors of fuzzy-PID controller are optimized using a novel hybrid optimization technique called LUS–TLBO algorithm. It is the hybridization of a local search algorithm (LUS) [19], [20], [21] and a global search algorithm (TLBO) [22], [23], [24]. Any local search algorithm can beautifully exploit the search in local area but are not helpful in exploring over wider area. Whereas, global search algorithms are good in exploring search space over wider area but gives an optimal/near optimal solution. Therefore in this paper a maiden attempt has been made in hybridizing LUS and TLBO algorithm to optimally tune the parameters of fuzzy-PID and conventional PID controller for LFC of a two-area interconnected multi-source power system with and without HVDC link. The superiority of the proposed LUS–TLBO optimized fuzzy-PID controller is proved by comparing the results with a recently published article for the same power system based on DE algorithm [25]. Finally it is observed that LUS–TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of undershoot, overshoot and settling time.
Section snippets
System investigated
In this paper LFC issue in a two area interconnected multi source power system is presented. Each area of the power system comprises of a reheat thermal unit, a hydro unit and a gas unit. A step load change of 0.01 pu (1%) is applied in area1 to study the dynamic behavior of both the power systems (with and without HVDC link). Each area is equipped with a fuzzy-PID controller to control the frequency and tie-line power deviations. The linearized transfer function model of two area interconnected
The proposed hybrid optimization algorithm
In this paper a novel hybrid optimization technique is used to optimize the gains of a fuzzy PID controller. The optimization technique used in this paper is hybridization of a local and a global optimization technique. The local optimization technique is Local Unimodal Sampling (LUS) and the global optimization technique is Teaching Learning Based Optimization (TLBO). Therefore it is named as LUS–TLBO optimization technique.
Control strategy
In this paper three fuzzy-PID controllers are used, 1st one for thermal units, 2nd one for hydro units and the 3rd one for gas units. Many of the previous research works in different areas related to fuzzy-PID controller dealt with selection of input and output scaling factors of the controller with several hit and trial runs. The performance of the controller mainly depends on the selection of these parameters and it is very difficult to get the optimum values using hit and trial method. The
Design of LUS–TLBO optimized fuzzy-PID controller for AGC system
For any controlled process quick response and excellent stability are desirable, but for any practical system both the desires can never be achieved simultaneously. So there is always a compromise between stability and faster response. This can be achieved through properly selecting a controller and designing it by minimizing a suitably defined objective function with the help of an optimization technique. In this paper a fuzzy-PID controller is designed for AGC of the interconnected
Conclusion
In this paper, for the first time in literature, a hybrid LUS–TLBO algorithm is used to optimize the gains of the proposed fuzzy-PID controller and conventional PID controller in order to tackle the load frequency control problem of a two-area interconnected multi-source power system. The proposed algorithm is hybridization of Local Unimodal Sampling (LUS) algorithm and Teaching Learning Based Optimization (TLBO) algorithm. LUS is a local search algorithm and TLBO is global search. The proposed
References (30)
- et al.
Simulation study for automatic generation control of a multi-area power system by ANFIS approach
Appl Soft Comput
(2012) - et al.
Automatic generation control of an interconnected hydrothermal power system considering superconducting magnetic energy storage
Electr Power Energy Syst
(2007) - et al.
Bacteria foraging optimization algorithm based load frequency controller for interconnected power system
Electr Power Energy Syst
(2011) - et al.
Self-tuning algorithm for automatic generation control in an interconnected power system
Electr Power Syst Res
(1991) - et al.
Load frequency control: a generalized neural network approach
Electr Power Energy Syst
(1999) - et al.
Fuzzy logic controller in interconnected electrical power systems for load frequency control
Electr Power Energy Syst
(2005) - et al.
Multi-objective optimization using weighted sum artificial bee colony algorithm for load frequency control
Electr Power Energy Syst
(2014) - et al.
Differential evolution algorithm based automatic generation control for interconnected power systems with non-linearity
Alexandria Eng J
(2014) - et al.
Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system
Appl Soft Comput
(2015) - et al.
Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems
Inf Sci
(2012)