Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller

Abstract

A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system.

Effect of electric vehicles on load frequency control in interconnected thermal and hydrothermal power systems utilising CF‐FOIDF controller

“…10. 1 Nominal system parameters (a) 2-ANTPS [16,[22][23][24][25]27]: P r = 2000 MW, P n = 50%, R i = 2.4 Hz/puMW, F 0 = 60 Hz, β i = 0.425 puMW/Hz, T Gi = 0.08 s, K PSi = 120 Hz/puMW, T PSi = 20 s, T Ti = 0.3 s, 2πT 12 = 0.545 puMW/Hz, ΔP D1 = 0.01 puMW, α 12 = −1, ΔF UL = 10 mHz, ΔF LL − 10 mHz, R AG = 2.4 Hz/puMW, [17-1λ, 26, 34]…”

“…Via suitable control arrangements, LFC reinstate the system stability and preserve the frequency/power at anticipated values. Various optimal, robust and intelligent control methodologies as stated few above are utilised as potential solutions to get a robust performance and stability of real PSs [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. These include hybrid human brain emotional learning PI [12], sine-cosine algorithm based on wavelet mutation (SCAWM) based model-free non-linear sliding mode controller (MFNSMC) [13], hybrid SCA-HS algorithm based FO-SMC [14], firefly algorithm-pattern search (hFA-PS) tuned PI/PID [15], hybrid invasive weed optimisation-PS (hIWO-PS) tuned PI/2-DOF-PID [16], multi-objective genetic algorithm (MOGA)/GA tuned PI/PID [17,18], modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC) tuned PID [19], dragonfly algorithm (DA) tuned PID/2DOF-PID [20], blended biogeography based optimisation (BBBO) tuned PID [21], grey wolf optimisation (GWO)/ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) tuned PI/PID [22], hybrid gravitational search algorithm-PS (hGSA-PS) tuned PI/PID with filter (PIDF) [23], salp swarm algorithm (SSA) tuned PIDF/ tilt IDF (TIDF)/cascade control-TIDF (CC-TIDF) [24], differential evolution (DE) tuned PID/TIDF [25] and lozi map-based chaotic optimisation algorithm (LCOA) tuned PID [26] controllers applied on different PS configurations.…”

“…In(16), min and max represent the minimum and maximum values of CF-FOIDF controller parameters in i-area. For CF-FOIDF controller, limits of the optimisedK Pi , K Ii , i , K I1i , 1 i , K Di , i and F…”

Cascade‐I λ D μ N controller design for AGC of thermal and hydro‐thermal power systems integrated with renewable energy sources

“…To boost AGC outcome, different control and tuning approaches are available in the recent literature. Some of them implemented in various traditional and restructured systems are classical [1], minoritycharge carrier inspired algorithm optimized I/PI/ID [1], biogeography based optimisation based 3-degree-of-freedom-PID (3DOF-PID) [2], genetic algorithm (GA) based integral minus proportional derivative (IPD)/PI/PID [3,4], imperialist competitive algorithm (ICA) based fuzzy-tilt-I-D with filter-double integral (FTIDN-II) [5], sine-cosine algorithm (SCA) tuned cascade fractional order (FO) PI-FOPIDN [6], SCA tuned cascade FOPI-FOPDN [7], wolf pack hunting strategy based control [8], multi-agent double deep Q network-action discovery (AD) based control [9], deep policy dynamics based win or learn fast-policy hill climbing (PDWoLF-PHC ) network based control [10], PDWoLF-PHC(λ) strategy [11,12], deep-reinforcement-learning-based three-network double-delay actor-critic control strategy [13], SCA optimized proportional derivative-proportional integral derivative with double derivative filter (PDPID + DDF) [14], grey wolf optimization (GWO) optimized PI/PID [15], artificial bee colony algorithm (ABCA) optimized PI/PID [16], hybrid firefly algorithmpattern search (hFA-PS) technique optimized PI/PID [17], ICA optimized PID [18], jaya algorithm-invasive weed optimization (JA-IWO) optimized PID [19], bacterial swarm optimization (BSO) optimized PID/FOPID [20], optics inspired optimization (OIO) optimized PID [21], symbiotic organisms search (SOS) algorithm optimized PID/PIDN [22,23], quasioppositional differential search algorithm (QODSA) optimized PI/PID [24], hybrid IWO-PS (hIWO-PS) tuned 2DOF-PID [25] and whale optimization algorithm (WOA) optimized cascade PIDN-FOPD [26]. Since, the operating conditions of power system are liable to vary widely over the time due to wearing out of the components, the conventional controllers optimized for fixed operating condition might work inappropriately in changed operating condition.…”