This paper proposes a fuzzy logic-based battery energy management system in hybrid renewable system. The novel topology consists of solar and wind energy system-based input sources and a battery bank to store the energy when in excess. The PV-Wind source is equipped with unidirectional boost converter whereas, the battery storage system is connected to the system with a bi-directional DC/DC converter. The main novelty of this research is the fuzzy logic-based battery management system which charges and discharges into the DC bus system based on the supply-load demand. The fuzzy logic controller (FLC) based maximum power point tracking (MPPT) is used in the PV and wind energy conversion system (WECS) to track the maximum available power for the different irradiance and wind velocity respectively. The obtained results are compared to conventional P&O MPPT control algorithm to find the effectiveness of the system. A 500 W PV system and a 500 W Permanent magnet synchronous generator (PMSG) based WECS is implemented for its simplicity and high efficiency. The proposed control topology is designed and tested using MATLAB/Simulink

<p>This paper proposes a power management strategy of parallel inveters based system, to enhance the power generation capacity of the existing system with distributed energy sources one has to choose DG source based inverter connected in parallel with the existing system.Two DG sources PV, Fuel cells feeds the DC voltage to two parallel inverters connected to the grid. Fixed band hysteresis current control with Instantaneous p-q power theory is adopted to create an artificial environment. Two parallel inverters are able to deliver the harvested power from PV, FC to grid and able to balance the load Without communication between parallel inverters this controller having the capability of load following, the harmonic components of currents at output of inverter are also very low; this will automatically reduces the circulating currents between parallel inverters. Simulation studies are carried out to investigate the results of PV, FC systems connected to the utility grid.</p>

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This study presents a simplified model predictive control (SMPC) strategy for three-phase T-type neutral-pointclamped (NPC) inverters to reduce the computational effort while achieving the current control, capacitor voltage equalisation and common-mode voltage (CMV) minimisation targets. The approach involves three stages. In the first stage, the number of switching states is reduced from 27 to 19 for reducing the peak CMV. In the second stage, a graphical approach is proposed to identify candidate control set (CCS) based on the present switching state, which helps in reducing the number of computations further. The number of elements in CCS may be 7, 10, 13 or 19. A look-up table with present applied state and its corresponding CCS is formed based on this graphical approach for all the 19 switching states. The third stage involves modifications to the MPC algorithm. Two versions of SMPC schemes (SMPC-I and SMPC-II) are proposed. SMPC-I can restrict the CMV to onesixth of DC input voltage. SMPC-II can achieve near zero CMV. The performance of the SMPC strategy is analysed with parameter uncertainty. Computational effort of SMPC schemes is compared with other MPC methods. The feasibility of SMPC algorithm is verified experimentally on T-type NPC inverter prototype.

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