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To augment the photovoltaic (PV) power generation conversion, a Maximum Power Point Tracking (MPPT) technique plays a very significant role. This paper introduces a hybrid MPPT-algorithm integrating of Modified Invasive Weed Optimization (MIWO) and Perturb & Observe (P&O) technique under rapid weather change and partial shading scenarios for efficient extraction of the maximum power from the standalone PV-based hybrid system. MIWO handles the initial stages of MPPT followed by the application of the P&O algorithm at the final stages in view of acquiring rapid global peak (GP) and maximal PV power. The studied microgrid comprises of the PV system, battery, electrolyzer, fuel cell, and load. A coordinated DC-voltage regulation and power management strategy between each subsystem of the hybrid microgrid is implemented to save the battery from undesirable charging/discharging operation. Additionally, with the monitoring of DC-voltage, the DC/DC converter associated between the battery and DC-link plays as an MPPT-circuit of the PV without the requirement of an extra dedicated circuit. Takagi-Sugeno (TS)-fuzzy controller is adopted for suppressing/mitigating the voltage oscillations of the microgrid during the variations in solar irradiance/temperature and power demand. The results clearly exhibit the superior performance of the proposed methodology compared to some of the existing techniques.

The large-scale wind energy conversion systems (WECSs) based on doubly-fed induction generators (DFIGs) are very popular in recent years due to the numerous technical and economic benefits. With the increasing penetration level of wind energy, the latest grid codes require the DFIG-based WECSs to remain connected to the grid under grid fault scenarios and deliver the required reactive power into the grid. However, the direct connection of the stator of the DFIG to the grid makes it prone to grid disturbances, especially to voltage sag. This study proposes a modified demagnetisation control strategy to enhance the low-voltage ride-through (LVRT) capability of the DFIG under grid faults. The proposed control strategy is implemented in a coordinated approach by using the existing demagnetisation control and the addition of an external resistance in the stator side of the DFIG. The demagnetisation control damps the direct current component of the stator flux and the external resistance accelerates the damping of the transient flux by decreasing the time constant and hence, enhancing the LVRT capability of DFIG. The effectiveness of the proposed control strategy is demonstrated under both symmetrical and asymmetrical grid faults simulated system through MATLAB/Simulink®. The comparative results justify the merits of the proposed methodology. s time constant, s ω r angular speed of rotor flux, r.p.m. ω s angular speed of stator flux, r.p.m. *r *superscript denoted for the rotor reference frame *1,2,0 *subscript denoted for positive-, negative-, and zerosequence components, respectively

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Solar photovoltaic (PV) power has several advantages such as free availability, absence of rotating parts, can be easily integrated with building architecture, and need little maintenance. However, the PV cell current–voltage (I–V) characteristics are non-linear and power generated from a PV array depends on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, partial shading, and the presence of system uncertainties and load variation conditions of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is realized that the RIO algorithm has improved search performance in terms of finding the optimal solution and faster convergence characteristics than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO, perturb and observe (P&O), and incremental conductance (IC)-based MPPT schemes.