A Novel Fuzzy RPID Controller for Multiarea AGC with IABC Optimization

Abstract

An Interactive Artificial Bee Colony (IABC) Optimization based fuzzy (IABCF) to tune optimal gains of a Robust Proportional Integral Derivative (RPID) controller is proposed for the solution of multiarea automatic generation control (AGC) simulation problem in a restructured power system. One of the important problems in the proposed method is the exact tuning of the RPID parameters for achieving the desired level of robust performance. The problem of robustly tuning of RPID based AGC design is formulated as an optimization problem according to the time domain-based objective function, which is solved by the IABC technique that has a strong ability to find the most optimistic results. The robustness and effectiveness of the proffered method are shown on a two and four areas deregulated power system with possible contracted scenarios under large load demand and area disturbances in comparison with the other methods through FD and ITAE performance indices. The evaluation results show that the proposed control strategy achieves good robust performance for worldwide experience of automatic generation control in restructured systems parameters and load changes in the presence of system nonlinearities.

A new metaheuristic algorithm based on shark smell optimization

“…where R3 is a random number with uniform distribution in the range of [21,11]; M indicates number of points in the local search of each stage. As this operator implements a local search around Y k11 i , the range of random number generation of R3 is [21,11].…”

“…where R3 is a random number with uniform distribution in the range of [21,11]; M indicates number of points in the local search of each stage. As this operator implements a local search around Y k11 i , the range of random number generation of R3 is [21,11]. The M points of the local search, that is, Z k11;m i , are in the vicinity of Y k11 i (e.g., if zero is generated by the random number generator, Y k11 i is obtained) and by connecting these M points, a closed contour similar to the rotational movement of shark can be obtained.…”

“…The shape of Rastrigin benchmark function is shown in Figure 4, which illustrates the large number of its local optima. The results obtained from SSO algorithm for the test case are shown in Table 1 and compared with the results obtained from 32 other metaheuristic optimization approaches including EA [17], GA [37], improved GA (IGA) [37], DE [18], PSO [6], iteration PSO (IPSO) [38], chaotic PSO (CPSO) [32], APSO [32], PSO with time varying acceleration coefficients (PSOTVAC) [6], PSO with improved inertia weight (PSOIIW) [39], hybrid GA-PSO (HGAPSO) [40], dynamic PSO (DPSO) [41], fuzzy PSO (FPSO) [42], harmony search algorithm (HSA) [43], improved HSA (IHSA) [43], ACO [7], chaotic ACO (CACO) [8], bacterial foraging algorithm (BFA) [13], GSA [20], IGSA [21], seeker optimization algorithm (SOA) [44], imperialist competitive algorithm (ICA) [45], ABC [12], improved ABC (IABC) [11], chaotic ABC (CABC) [46], parallel ABC (PABC) [36], discrete ABC (DABC) [47], rosenberg ABC (RABC) [48], modified ABC (MABC) [48], HBMO [9], improved HBMO (IHBMO) [10], and honey bee optimization (HBO) [49]. All of these 32 benchmark methods have frequently been used in engineering applications and, for this reason, have been considered here for comparison with the proposed SSO algorithm.…”

“…where the minimum and maximum values of the decision variables are usually taken as 0.001 and 10, respectively [11,12,55]. Schematic representation of the PID controller is shown in Figure 8.…”

“…Many of real-world optimization problems involve with complexities such as nonlinearity, nonconvexity, nonsmoothness, nondifferentiability, mixed integer nature, and discontinuous domain, which challenge the numerical optimization methods [3]. Accordingly, to tackle the mentioned complexities, several metaheuristic optimization techniques have been proposed in the literature in the recent decades such as genetic algorithm (GA) [4], particle swarm optimization (PSO) [5,6], ant colony optimization (ACO) [7,8], honey bee mating optimization (HBMO) [9,10], artificial bee colony (ABC) [11,12], bacterial foraging (BF) [13], clonal selection algorithm (CSA) [14], invasive weed optimization (IWO) [15], shuffled frog leaping (SFL) [16], evolutionary algorithm (EA) [17], differential evolution (DE) [18], Correspondence to: O. Abedinia, E-mail: abediniaoveis@ gmail.com simulated annealing (SA) [19], and gravitational search algorithm (GSA) [20,21]. Due to their high flexibility, simplicity and modeling efficiency, these optimization methods have been widely used in many scientific and engineering areas.…”