Fitness Dependent Optimizer-Based Automatic Generation Control of Multi-Source Interconnected Power System With Non-Linearities

Abstract

This paper proposed an improved structure of Proportional Integral Derivative (PID) controller called as Integral Proportional Derivative (I-PD), applied for Automatic Generation Control (AGC) of Multi-Source Interconnected Power System (IPS). The parameters of the proposed controller are optimized with a newly developed, powerful, nature-inspired meta-heuristic technique known as Fitness Dependent Optimizer (FDO). To show the efficacy of the proposed controller and the technique used, they have been tested on three different system models. Initially, a two-equal area of diverse source generation including reheat-thermal, gas, and hydro power system is considered. In the second scenario, the same power system model is used with addition of two non-linearities; Generation Rate Constraint (GRC) and Governor Dead Band (GDB). Lastly, multiple non-linearities including Governor Dead Band (GDB), Time Delay (TD), Generation Rate Constraint (GRC), and Boiler Dynamics (BD) have been considered to make the initial system more realistic and practical. The outcome from the proposed techniques is also compared with some recently meta-heuristic algorithms such as Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). From the results, it has been perceived that the proposed technique shows superior performance in respect of Overshoot (Osh), Undershoot (Ush) and Settling Time (Ts) of the system frequency.

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

“…Many novel algorithms such as Fitness Dependent Optimizer, Coyote Optimization, Manta Ray Foraging, Student Psychology-Based Optimization, Slime Mould, Barnacles Mating Optimizer, Falcon Optimization etc., especially inspired by nature and based on the flawless properties of biological systems, have been developed. [4][5][6][7][8][9][10] These algorithms have yielded effective results in global optimization problems.…”

Improved-Fitness Dependent Optimizer Based FOI-PD Controller for Automatic Generation Control of Multi-Source Interconnected Power System in Deregulated Environment

“…An instant change in the reference input will not affect the derivative and proportional gains, since these two gains work on the output process Y(s) [44,45]. The Transfer Function (TF) of the closed-loop system by considering a plant Gp(s) with FOPID and FOI-PD controllers are given by Eq (29) and (30) respectively.…”

“…Eq (30) shows that there are two zeros with FOPID controller which is tough to adjust the response of the system with these zeros. Their impact takes place as higher overshoot or an earlier peak.…”

“…This algorithm has been tested on 19 classical benchmark functions and three practical problems which shows outstanding performance as compared to other recent techniques [46]. Furthermore, the efficiency of the algorithm has also been evaluated on AGC problem [30] and also for the optimization of one-dimensional bin packing combinatorial problem [47]. FDO algorithm has fewer parameters comparing to other algorithms, this makes FDO much simpler, less complex, and faster.…”

“…Hasanien in [26] has used the PID controller tuned with Whale Optimization Algorithm (WOA) considering a new model of renewable energy sources and conventional power system with the inclusion of GRC and GDB nonlinearities. Similarly, some other meta-heuristic techniques have also been used to study the AGC of interconnected power system such as Sine Cosine Algorithm (SCA) [27], Imperialist Competitive Algorithm (ICA) [28], Modified Group Search Optimization (MGSO) [29], Fitness Dependent Optimizer (FDO) [30], Improved Ant Colony Optimization (IACO) [31], Salp Swarm Algorithm (SWA) [32], PSO hybridized with Gravitational Search Algorithm (hPSO-GSA) [33], Improved Gray Wolf Optimization (IGWO) [34], and Volleyball Premier League (VPL) [35]. Therefore, it is worth to further study the application of meta-heuristic techniques to solve problems related to AGC.…”