This article formulates a fully-distributed and delay-tolerant secondary control scheme for droop-controlled AC microgrids. The proposed strategy is inspired by the cooperative control concept of multi-agent systems (MASs). It considers the hierarchical control structure of the distributed energy resource (DER) units. It ensures equal active power sharing between three DER units, where each unit tracks the time-varying average load, with a finite-time convergence. As a result, the frequencies of the DER units are regulated to their nominal values. Furthermore, it offers plug-and-play capability for DER units, demonstrates significant robustness against load disturbances, and successfully tolerates, small as well as large, communication time-delays. Due to the fully-distributed configuration of the proposed control strategy, each DER unit in the test microgrid requires only its own information and information of its neighbors. The benefit is two-fold: not only this configuration assists in minimizing the overall bandwidth requirement, and cost of the corresponding communication network, but also it increases the reliability of the microgrid operation. The performance and effectiveness of the proposed technique is supported by exhaustive numerical simulations performed in Matlab/Simulink on an AC microgrid testbench comprising three DER units. The proposed strategy renders the superior performance in several aspects as compared to the existing developments in the literature in this area.

The energy demand in the world has increased rapidly in the last few decades. This demand is arising the need for alternative energy resources. Solar energy is the most eminent energy resource which is completely free from pollution and fuel. However, the problem occurs when it comes to efficiency under different atmospheric conditions such as varying temperature and solar irradiance. To achieve its maximum efficiency, an algorithm of maximum power point tracking (MPPT) is needed to fetch maximum power from the photovoltaic (PV) system. In this article, a nonlinear backstepping terminal sliding mode control (BTSMC) is proposed for maximum power extraction. The system is finite-time stable and its stability is validated through the Lyapunov function. A DC-DC buck-boost converter is used to deliver PV power to the load. For the proposed controller, reference voltages are generated by a radial basis function neural network (RBF NN). The proposed controller performance is tested using the MATLAB/Simulink tool. Furthermore, the controller performance is compared with the perturb and observe (P&O) MPPT algorithm, Proportional Integral Derivative (PID) controller and backstepping MPPT nonlinear controller. The results validate that the proposed controller offers better tracking and fast convergence in finite time under rapidly varying conditions of the environment.

The formulation of a maximum power point tracking (MPPT) control strategy plays a vital role in enhancing the inherent low conversion efficiency of a photovoltaic (PV) module. Keeping in view the nonlinear electrical characteristics of the PV module as well as the power electronic interface, in this paper, a hybrid nonlinear sensorless observer based robust backstepping super-twisting sliding mode control (BSTSMC) MPPT strategy is formulated to optimize the electric power extraction from a standalone PV array, connected to a resistive load through a non-inverting DC–DC buck-boost power converter. The reference peak power voltage is generated via the Gaussian process regression (GPR) based probabilistic machine learning approach that is adequately tracked by the proposed MPPT scheme. A generalized super-twisting algorithm (GSTA) based differential flatness approach (DFA) is used to retrieve all the missing system states. The Lyapunov stability theory is used for guaranteeing the stability of the proposed closed-loop MPPT technique. The Matlab/Simulink platform is used for simulation, testing and performance validation of the proposed MPPT strategy under different weather conditions. Its MPPT performance is further compared with the recently proposed benchmark backstepping based MPPT control strategy and the conventional MPPT strategies, namely, sliding mode control (SMC), proportional integral derivative (PID) control and the perturb-and-observe (P&O) algorithm. The proposed technique is found to have a superior tracking performance in terms of offering a fast dynamic response, finite-time convergence, minute chattering, higher tracking accuracy and having more robustness against plant parametric uncertainties, load disturbances and certain time-varying sinusoidal faults occurring in the system.

Majority of the contemporary hierarchical control strategies for microgrids are either centralized or distributed, relying on leader-follower (or master-slave) consensus for secondary frequency and/or voltage regulation. Thus, in either case, these strategies are susceptible to single-point-failure (SPF). This potential research gap motivated the authors to propose a distributed three-layered hierarchical control strategy applicable to droop-controlled islanded AC microgrids. The proposed strategy can simultaneously ensure (i) frequency and voltage regulation of DGs within the prescribed frequency and voltage deviation limits as per IEC 60034–1 standard (i.e., ±2% and ±5%, respectively) without relying on leader-follower consensus at the secondary level, (ii) distributed economic dispatch of active power with minimum error, and (iii) distributed reactive power dispatch with plausible error. The proposed technique is fully distributed and shares the computation and communication burden among the neighboring nodes using a sparse communication network, thus, it is insusceptible to SPF. The feasibility of the proposed technique is guaranteed through various time-domain based numerical simulations executed in Matlab/Simulink under different loading conditions and microgrid expansion.

The technological development in wind energy conversion systems (WECSs) places emphasis on the injection of wind power into the grid in a smoother and robust way. Sliding mode control (SMC) has proven to be a popular solution for the grid-connected WECS due to its robust nature. This paper reviews the enhancement trends in the integer-order SMC (IOSMC) and fractional-order SMC (FOSMC) schemes reported in reputed journals over the last two decades. This work starts with a mathematical description of the wind turbine, generators, grid, and SMC and its variants available in literature. A comprehensive literature review is tabulated that includes the proposed errors, sliding surfaces, typologies, and major outcomes. Moreover, a comparative analysis of the integer-order and fractional-order SMC and its variants is also presented in this paper. This paper will provide insight for the researcher working in the WECS and will serve them in the selection and exploration of the most appropriate control schemes for quality wind power extraction. The concise mathematical proofs of the IOSMC, FOSMC and their variants will also serve the researchers in selecting the relevant sliding surfaces control laws for their research tasks. This paper also provides a comparative analysis of IOSMC, FOSMC, and fuzzy-FOSMC in terms of chattering reduction, robustness, and computational complexities using mathematical theories, simulation carried out in Matlab/Simulink, and a processor in the loop (PIL)-based experimental environment.

In this work, a meta-heuristic optimization based method, known as the Firefly Algorithm (FA), to achieve the maximum power point (MPP) of a solar photo-voltaic (PV) system under partial shading conditions (PSC) is investigated. The Firefly Algorithm outperforms other techniques, such as the Perturb & Observe (P&O) method, proportional integral derivative (PID, and particle swarm optimization (PSO). These results show that the Firefly Algorithm (FA) tracks the MPP accurately compared with other above mentioned techniques. The PV system performance parameters i.e., convergence and tracking speed, is improved compared to conventional MPP tracking (MPPT) algorithms. It accurately tracks the various situations that outperform other methods. The proposed method significantly increased tracking efficiency and maximized the amount of energy recovered from PV arrays. Results show that FA exhibits high tracking efficiency (>99%) and less convergence time (<0.05 s) under PSCs with less power oscillations. All of these methods have been validated in Matlab simulation software.