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An hybrid renewable energy sources consisting of solar photovoltaic, wind energy system, and a microhydro system is proposed in this paper. This system is suitable for supplying electricity to isolated locations or remote villages far from the grid supply. The solar photovoltaic system is modeled with two power converters, the first one being a DC-DC converter along with an maximum power point tracking to achieve a regulated DC output voltage and the second one being a DC-AC converter to obtain AC output. The wind energy system is modeled with a wind-turbine prime mover with varying wind speed and fixed pitch angle to drive an self excited induction generator (SEIG). Owing to inherent drooping characteristics of the SEIG, a closed loop turbine input system is incorporated. The microhydro system is modeled with a constant input power to drive an SEIG. The three different sources are integrated through an AC bus and the proposed hybrid system is supplied to R, R-L, and induction motor loads. A static compensator is proposed to improve the load voltage and current profiles; it also mitigates the harmonic contents of the voltage and current. The static synchronous compensator is realized by means of a three-phase IGBT-based currentcontrolled voltage source inverter with a self-supporting DC bus. The complete system is modeled and simulated using Matlab/Simulink. The simulation results obtained illustrate the feasibility of the proposed system and are found to be satisfactory. a senior member of IEEE. His areas of interest are electrical machines, energy, distributed generation and power quality. PUBLIC INTEREST STATEMENTThis paper details the possible ways of integrating renewable energy sources which may be called a hybrid renewable energy system (HRES). The recent research in the field of HRES shows the possibility of integrating either photovoltaic (PV)-wind or PV-microhydro or wind-microhydro as a hybrid system. In this paper, the possibility of integrating three renewable energy sources namely, PV-windmicrohydro power generation system is presented. It aims at supplying electricity to remote villages which are far from the grid supply. Since power quality is a major issue in such a system, the same is analyzed. MATLAB/Simulink is used for modeling the system and studying various performance characteristics of the system under different electric loads. A static synchronous compensator (STATCOM) is incorporated to improve the voltage profile at the load end thereby achieving an improved power quality of the supply.

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The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.

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