Day to day there is an increase in installations of new Solar Photovoltaic Power plants (SPVPP’s) which directly reflects the grid. As the SPVPP’s output power is fluctuating in nature, which is directly injected to the utility grid faces the challenges like overloading. Therefore, to overcome the overloading situations of grid, its infrastructure should be replaced by higher ratings which will increase the system cost. Due to this there is a need to limit the grid injected power from SPVPP’s to overcome the overloading situations. In this article a Constant power injection (CPI) algorithm is used to control the feed in power to grid as per grid regulations. Based on the instantaneous SPVPP’s output power and power limiting value this strategy will controls the output power by switching between the incremental conductance maximum power point tracking (InC-MPPT) algorithm and CPI algorithm. The proposed InC-CPI algorithm is applied to the single phase two-stage (SPTS) systems with the working region is on the left side of maximum power point (MPP) for maintaining the system to be in stable conditions. In this article the InC-CPI and the perturb and observe (P&O)-CPI algorithms were compared, and results were analyzed. The proposed control algorithm is designed using Matlab/Simulink and the results taken for both clear and cloudy days with wide varying climatic conditions.

In the field of Solar systems, it is necessary for every engineer to start with the solar photovoltaic module (SPVM) design, this paper provides a complete mathematical design specification of the SPVM. The design and development of the SPVM are done to extract its electrical characteristics that are subjective to solar irradiance (G) and temperature (T). This paper model the SPVM with the datasheet of IB Solar-36 series and these modules are connected in parallel to form the Solar Photovoltaic Array (SPVA) is considered for the result verifications. To match the simulation performance of the system accurately with the practical model, this paper uses a novel approach for formulating the equations to find the exact values of shunt resistance (R sh) and series resistance (R s) called parasitic effects. The inverse slope method is used to formulate the Parasitic effects from the datasheets, which will extract the exact performance curves of the SPVA. These design principles can be applied to simulate the behavior of any large scale SPVA's which are present in the system. The simulation and experimental verification using IB Solar-36 polycrystalline modules with varying T and G values for the SPVA are presented.

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The energy demand on the electricity grids increased rapidly due to that non-conventional energy sources (NCES) like PV, wind power plants are encouraged to establish and operate with the grid. Out of the available NCES, Photovoltaic generating systems (PVGS) are widely penetrated to the grids. As the output power extracted from the PVGS is non-linear, it becomes fluctuating depending on the available Irradiance (G), Temperature (T), and partial shading conditions (PSC). So, there is a need for the development of maximum power point tracking (MPPT) algorithms in the PVGS for maximizing the output power and minimizing the fluctuations. In this article, a comparative analysis of two advanced MPPT algorithms namely particle swarm optimization algorithm (PSOA) and cuckoo search algorithm (CSA) is presented. These two algorithms are used to control the duty cycle of the boost converter to maximize the PVGS output power. The proposed design is modeled using Matlab/Simulink software and the results were obtained and analyzed.

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