A gray wolf optimized FPD plus (1+PI) multistage controller for AGC of multisource non-linear power system

Abstract

Purpose The paper has proposed to implement gray wolf optimization (GWO)-based filter-type proportional derivative with (FPD) plus (1+ proportional integral) multistage controller in a three-area integrated source-type interlinked power network for achieving automatic generation control. Design/methodology/approach For analysis, a three area interconnected power system of which each area comprises three different generating units where thermal and hydro system as common. Micro sources like wind generator, diesel generator and gas unit are integrated with area1, area2 and area3 respectively. For realization of system nonlinearity some physical constraints like generation rate constraint, governor dead band and boiler dynamics are effected in the system. Findings The supremacy of multistage controller structure over simple proportional integral (PI), proportional integral, derivative (PID) and GWO technique over genetic algorithm, differential evolution techniques has been demonstrated. A comparison is made on performances of different controllers and sensitivity analysis on settling times, overshoots and undershoots of different dynamic responses of system as well as integral based error criteria subsequent a step load perturbation (SLP). Finally, sensitive analysis has been analyzed by varying size of SLP and network parameters in range ±50 per cent from its nominal value. Originality/value Design and implementation of a robust FPD plus (1 + PI) controller for AGC of nonlinear power system. The gains of the proposed controller are optimized by the application of GWO algorithm. An investigation has been done on the dynamic performances of the suggested system by conducting a comparative analysis with conventional PID controller tuned by various optimization techniques to verify its supremacy. Establishment of the robustness and sensitiveness of the controller by varying the size and position of the SLP, varying the loading of the system randomly and varying the time constants of the system.

Grasshopper optimization algorithm optimized multistage controller for automatic generation control of a power system with FACTS devices

“…Sensitivity investigation observed the robustness of the most favorable controller gains [21][22][23]. Similar investigation has been carried out for the optimum controller in the presence of FACTS devices in AGC, and the most convenient objective function for optimum controller and FACTS devices is investigated.…”

“…TCSC can be employed in multi-transmission lines to effectively introduce series compensation for dynamic change in transmission line reactance. The transfer function of TCSC is presented in [10,11] while the criteria of TCPS, SSSC, TCSC and IPFC are taken from [5,[7][8][9][10][11]21]. As shown in Fig.…”

“…The fuzzy-PID controller was preferred to establish cohesion in power system dynamic behavior in [16][17][18], while cascaded PD-PID and PI-PD controllers were used in [19,20] because of their adequate results, simplicity, reliability and performance. A 2DOF controller was implemented in [19] to minimize the steady-state error and to establish stability in the system, while [21,22] satisfied a system necessity by introducing a metamorphic modern PDF+(1 + PI) controller which helped in developing the responses of transient and steady-state error to diminish with different dynamic responses with various parts.…”

“…Many methods have been used to try to address these problems, e.g., firefly algorithm (FA) [12], Symbiotic Organisms Search SOS [16], BAT [19], Grey Wolf Optimization GWO [20], and Cuckoo Search (CS) [23]. The efficacy of the sine cosine algorithm in the AGC system with FACTS devices was explored in [24], while better dynamic performance using GWO was obtained in [21] and in [22], the primacy of GOA with a 2stage controller was explored.…”

Generation control of isolated small hydro power plant by using DECSA optimized FPI controller

“…The different consolidations of FLC and PID controller were used in numerous fields to enhance the system proficiency (Nayak et al , 2017; Nayak et al , 2018a, 2018b). The fuzzy PID and FPD plus (1+PI) controllers optimized by hybrid Local Unimodal Sampling Teaching Learning-Based Optimization (LUS-TLBO) and Grey Wolf Optimizer (GWO) respectively were implemented in power system to regulate frequency (Sahu et al , 2016; Sahu et al , 2019). Jain et al (2018) and Pati et al (2018) had applied FLC to improve twin-rotor MIMO system and the performance of STATCOM, respectively.…”