This paper presents a maximum power point tracking (MPPT) design for a photovoltaic (PV) system using a grey wolf optimization (GWO) technique. The GWO is a new optimization method which overcomes the limitations such as lower tracking efficiency, steady-state oscillations, and transients as encountered in perturb and observe (P&O) and improved PSO (IPSO) techniques. The problem of tracking the global peak (GP) of a PV array under partial shading conditions (PSCs) is attempted employing the GWO-based MPPT technique. The proposed scheme is studied for a PV array under PSCs which exhibits multiple peaks and its tracking performance is compared with that of two MPPT algorithms, namely P&O-MPPT and IPSO-MPPT. The proposed GWO-MPPT algorithm is implemented on a PV system using MATLAB/SIMULINK. Furthermore, an experimental setup is developed to verify the efficacy of the proposed system. From the obtained simulation and experimental results, it is observed that the proposed MPPT algorithm outperforms both P&O and IPSO MPPTs.

This paper proposes a new Hybrid MPPT algorithm combining Grey Wolf Optimization (GWO) and Perturb & Observe (P&O) technique for efficient extraction of maximum power from a Photovoltaic (PV) system subjected to rapid variation of solar irradiance and partial shading conditions (PSCs). GWO handles the initial stages of maximum power point (MPP) tracking followed by application of the P&O algorithm at the final stage in view of achieving faster convergence to the global peak (GP). This MPPT thus overcomes the computational overhead as encountered in the case of a GWO based MPPT algorithm reported earlier [15]. The idea behind using the hybrid technique is to scale down the search space of GWO which helps to speed up for achieving convergence towards the GP. The proposed MPPT algorithm is first implemented using MATLAB/SIMULINK and subsequently an experimental setup is prepared for its practical implementation. From the obtained results, it is confirmed that the proposed MPPT provides superior tracking performance in any weather conditions compared to both GWO and PSO+PO based MPPT algorithms.

Increasing awareness for green energy and sustainable energy management has accelerated the popularity for the incorporation of distributed energy resources and distributed energy storage into the distribution network and microgrid. This has proliferated the use of power electronic-based devices giving rise to a serious issue of deteriorating power quality (PQ) in the distribution system. In this context, this article presents a photovoltaic (PV) integrated unified power quality conditioner (UPQC) operating with an adaptive compensating technique based on variable leaky least mean square (VLLMS) algorithm. It is a soft computing-oriented method that offers quicker convergence to the desired condition in an iterative approach keeping the weight of the updating parameters within the specified limit. The VLLMS-based algorithm eliminates the use of low pass or moving average filter for the extraction of fundamental components from polluted source voltage and load current to generate reference signal for the switching of shunt as well as series voltage source converters (VSC) of the UPQC. Due to the involvement of feed-forward component of PV in the compensating technique of shunt VSC, it efficiently and smoothly manages power balance between grid, load, and PV besides resolving the PQ issues of current harmonics and poor power factor at PCC. It also ensures the regulation of dc-link voltage. The series converter maintains pure sinusoidal voltage at the load terminal irrespective of sag/swell and harmonics present in the grid voltage. The effectiveness of the proposed system is verified through simulation as well as hardware implementation under different static and dynamic operating conditions.

In this paper, a new optimization technique namely JAYA is proposed to optimize the coefficients of proportional plus integral controller and filter parameters of photovoltaic fed distributed static compensator (PV-DSTATCOM). The JAYA is a new optimization tool whose performance is independent of the algorithm-specific control parameters. It overcomes the limitations of others in terms of attaining global optima with reduced computational effort. From the obtained simulation and experimental results, the performance of JAYA optimized PV-DSTATCOM has been found quite satisfactory and stimulating as compared to grenade explosion method and teaching-learning based optimized PV-DSTATCOM.

The growing use of power-electronics-based components and nonlinear loads is increasing the presence of harmonics in power system signals. In this scenario, proper estimation of such harmonics is intended to maintain power quality and improved operation of the system. It is also desirable that the estimation technique should be computationally efficient while being accurate. From this viewpoint, this paper proposes a nonlinear state estimation technique based on ensemble Kalman filtering for estimation of harmonics, interharmonics, and subharmonics, all using a single framework and at a time, from distorted power system signal. The proposed technique is computationally efficient compared to conventional Kalman filtering leading to less computational cost and hardware requirement. It is observed from both simulation and experimental studies that the proposed ensemble Kalman filter (KF) approach to estimation of harmonics, interharmonics, and subharmonics in a distorted power system signal exhibits superior estimation performance in terms of tracking time and accuracy as compared to performances of some of the existing techniques such as recursive least square, recursive least mean square, and KF algorithms. The proposed technique is also found to be robust and gives accurate estimates even in the presence of amplitude variations in the measured signal.