The polymer electrolyte membrane (PEM) fuel cell or proton exchange membrane fuel cell (PEMFC) has sparked a lot of interest in renewable electricity generation due to high efficiency, quick start up time, and low operating temperature. However, they exhibit highly nonlinear behavior with degraded power quality under uncertain input conditions. The hydrogen, oxygen, and air feed determine the PEMFC system’s performance. Controlling the manifold pressure at the cathode side is essential in preventing the problem of oxygen starvation. In this paper, a nonlinear dynamic model of the PEMFC is considered to control the supply manifold pressure. The performance of the nonlinear PEMFC with various classical controllers (PI, PD, PID, and PID with filter derivative), Smithpredictor, and Fractional Order PI (FOPI) controllers are compared. The optimization of classical controllers is performed by PID tuner. On the other hand, Smith predictor and FOPI controller are tuned by artificial intelligence technique, namely, Particle Swarm Optimization (PSO). The FOPI controller is also tuned with Genetic Algorithm (GA). The PEMFC output is compared in terms of performance evaluation parameters, i.e. settling time, overshoot, and steady state error. The computer simulations suggest that in comparison with GA-FOPI [settling time (t_s) = 1.374 s, overshoot (M_p) = 18.452%, steady state error (e_ss) = 0.1%] and PSO-Smith predictor (t_s = 1.262 s, M_p = 5.581%, e_ss = 0%), the PSO-FOPI controller results in much better settling time (t_s = 1.008 s), overshoot (M_p = 2.577%), and steady state error (e_ss = 0%). Thus, PSO-optimized FOPI controller with nonlinear PEMFC outperforms the classical, PSO-Smith predictor, and GA-FOPI control schemes.

With renewable energy having the potential to be the chief source of energy for the coming generations, wind energy will have a vital role to play. Obtaining good quality electricity from wind turbine system under varying wind conditions is crucial. An effective control design is essential to yield the desired results. This study is focused on achieving the desired rotor speed and frequency control for Doubly Fed Induction Generator (DFIG) wind turbines using different controllers. Three design techniques namely Genetic Algorithm (GA), Simulink Response/Design Optimization (SRO or SDO) and PID Tuner GUI available in MATLAB are explored to tune the PID controller parameters. Further, Genetic Algorithm (GA) has been used to tune the Fuzzy logic controller (FLC) scaling parameters. The quantitative comparisons of all control schemes in terms of time domain specifications show that GA optimized PID controller yields in the best frequency control.

This paper presents an investigation on automatic generation control (AGC) in restructured power systems with multiple interconnected areas and multiple machines. A particle swarm optimized combined optimal fuzzy controller has been proposed. The results obtained with the proposed controller have been compared with the conventional PI controller and optimal controller to exhibit its robust performance. Furthermore, the proposed controller aims to rapidly achieve zero deviations in frequency and tie line power, thereby maintaining system synchronism. The PSO-optimized combined optimal fuzzy controller demonstrates superior performance in terms of peak transient deviation, settling periods, and dynamic oscillations compared to other controllers.

Owing to the proven footmark for the clean, green energy, compact in size, cost effective and ease of availability of sunlight, applications of photovoltaic (PV) panels are continuously on rise. However, the low conversion efficiency is a major concern for researchers. The conversion efficiency may be improved significantly if PV panels are operating on maximum power point. Solar irradiations received by PV panel are not constant always. The irradiation may be linear and nonlinear, fast, slow and sudden varying in nature. Thus, tracking of maximum power point is a huge challenge. This irregular behaviour of incident irradiations may result in oscillations in output and low conversion efficiency. In order to enhance the performance, a suitably designed controller, operating at maximum power point is needed. This paper highlights maximum power point tracking under varying irradiation conditions (viz. Step Variable Irradiance and Ropp’s irradiance profile) using two control techniques namely Perturb and Observe (P&O) and Incremental Conductance (IC). The simulation results obtained for two control techniques are compared for system efficiency, tracking of maximum power point and enhancement of voltage. The comparison of results reveals that there is far better improvement in overall efficiency, output power, output voltage and faster regaining of the maximum power point path with IC technique as compared to P&O technique. Thus, IC control technique excellently tracks maximum power point with reduced probability of deviation and better oscillation damping under varying incident irradiations.