Design, analysis, and real‐time validation of type‐2 fractional order fuzzy PID controller for energy storage–based microgrid frequency regulation

Abstract

Summary The indeterminate characteristics of solar, wind sources, and load make modern AC microgrids highly complex and cause frequency fluctuations. It is very much required to maintain power equilibrium among generation and demand by planning a suitable frequency regulation controller. Henceforth, this research presents a maiden application of a hybrid Harris hawks optimization and pattern Search s (hHHO‐PS)‐based type‐2 fractional order fuzzy PID (T2FFOPID) controller for frequency regulation of an AC microgrid. Initially, a PID controller is considered, and the effectiveness of hHHO‐PS technique is compared with original Harris hawks optimization (HHO) as well as genetic algorithm (GA) and particle swarm optimization (PSO) techniques. In the next stage, superiority of proposed T2FFOPID over PID, type‐1 fuzzy PID (T1FPID), and fuzzy fractional order PID (T1FFOPID) controller is demonstrated. To authenticate the effectiveness of the suggested control method, different load disturbances as well as various rates of solar and wind sources penetration have been considered. The suggested control approach displays significant improvement in dynamic responses with fast damping of oscillations. Investigations of results affirm the better performance of T2FFOPID over other controllers regarding minutest error criteria and undershoot/overshoot of frequency deviations at various disturbances. Additionally, sensitivity analysis validates the robust conduct of T2FFOPID controller under variations parameters and communication delay. Finally, the MATLAB simulation results are validated in the OPAL‐RT OP5700 RCP/HIL FPGA real‐time simulator for practical feasibility of the proposed work.

Driving an asynchronous motor powered by PV‐MPPT using equivalent RST controller of fractional order PI and IP, based on generalized predictive control

“…e k+1 = r k+1 − y k+1 (11) 𝜆 is a weighting coefficient, r k+1 , y k+1 are reference and actual states respectively.…”

“…Other recent papers have used heuristic optimization techniques due to their usefulness in tuning controller parameters [11][12][13], but it is worth to be noted that all optimization techniques only provide optimal results that are mostly satisfactory.…”

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

“…Besides, forecasting approaches have been applied for intermittent energy sources and microgrids were investigated 27 . The applications of numerous metaheuristic approaches in LF and DSM like grey wolf optimization (GWO), 28,29 grasshopper optimization algorithm (GOA), 30,31 salp‐swarm algorithm (SSA), 32 and Harris hawks optimization (HHO) 33 were analysed. ANN‐based models have attracted the most for solving the buildings' LF problem among all existing forecasting approaches.…”

Design and implementation of a novel FO‐hPID‐FPID controller for AGC of multiarea interconnected nonlinear thermal power system

“…LFC issues in multi‐area multi‐unit power system are addressed by implementing fuzzy PI λ DF controller and its performance efficacy is verified 37 . Frequency regulation of AC microgrid with energy storage is investigated by implementation of type‐2 FO fuzzy PID (T2FFOPID) controller and the superiority of the proposed controller is verified over type‐1 fuzzy PID (T1FPID) and fuzzy FO PID (T1FFOPID) controller 38 . Frequency control in a micro grid system is demonstrated in real‐time simulator by employing type‐2 fuzzy fractional‐order PD‐PI (T2FFOPDPI) controller and its performance is compared with FPID and T2FPID controller to verify the dominance of the proposed controller 39…”

“…The stability and dynamic response of the considered power system is also investigated with conventional PID, fuzzy‐PID (FPID), hybrid PID‐FPID (hPID‐FPID) controllers and their counterpart FO controllers like FOPID, FOFPID along with the proposed FO‐hPID‐FPID controllers tuned by same hPSO‐MSCA technique. Literature review reveals the comparative performance analysis of different IO and FO based conventional/fuzzy logic‐based controllers for AGC in different scenarios 22‐39 . But so far, there is no study available in the literature describing the comparison of control performance between the six different controllers as mentioned above.…”