MPPT Control Strategies For Partially Shaded PV Systems Based On GHO And HHO

The increase in global warming,depletion of fossil fuels and advancement in cost-effective manufacturing technology has enabled renewable energy resources to emerge as a dependable source of energy.Fuel cells,geothermal,wind,hydro,biomass,and solar are the leading renewable energy resources.Among these the most promising is solar power.Solar energy is directly converted into electrical energy using photovoltaic technology.The integration of solar panels with houses,cars,charging stations and mobile platforms,water pumping stations,etc.provides a wide range of applications in the real world.The main advantages of solar energy are its low cost,scalability,least carbon footprint,least maintenance,minimum mechanical fatigue,quick installation and being noise and pollution-free.Although the PV systems provide a promising future but its intrinsic nonlinear nature,sensitivity towards operating condition,varying irradiance and temperature make the task challenging.A major loss of available power is caused by partial shading(PS).The most effective way to increase the productivity of the PV systems is by the introduction of a control system to force the PV system to operate on the maximum power point(MPP).This technique is called maximum power point tracking(MPPT).The MPPT techniques are classified into many categories and have their pro and cons.Merits of MPPT controller are fast-tracking of MPP,Global Maxima(GM)detection and robustness.In this dissertation,two highly effective bio-inspired based MPPT control techniques are proposed.The main objective of this study is to develop control strategies for the PV system to overcome the drawbacks of existing MPPT control techniques.At first,to tackle MPPT issues,a novel swarm-intelligence(SI)based grasshopper optimization(GHO)technique is implemented on the MPPT controller under fast varying irradiance and PS conditions.A comprehensive comparison is made with MPPT control techniques,such as perturb and observe(P&O),artificial bee colony(ABC),particle swarm optimization(PSO),dragonfly optimization(DFO),PSO-gravitational search(PSOGS),and cuckoo search(CS)optimization algorithm under different cases of weather conditions.The shortcomings of existing techniques are exposed under complex partial shading(CPS).These are power loss from oscillations,random fluctuations,and slow tracking.The search and skip method is incorporated with GHO to enhance the robustness of the proposed technique.A detailed comparative analysis is made to establish the effectiveness of the proposed GHO based MPPT technique.Statistical analysis and results are used to solidify the feasibility of this study.The analysis confirms the effectiveness of proposed GHO over existing bio-inspired MPPT techniques.Results show that the proposed GHO is highly robust with the tracking efficiency of up to 99.5%.The oscillation reduction of up to 85%is achieved along with 14-60%faster tracking comparatively.Thus significant improvement is achieved in undertaking the existing drawbacks of MPPT techniques.Secondly,a novel Harris hawk optimization algorithm(HHO)is introduced to effectively track maximum power.The effectiveness of work is supplemented by a comparative study with perturb and observe algorithm(P&O),particle swarm optimization(PSO),cuckoo search(CS)and grey wolf optimization(GWO).The analytical and statistical analysis is made based on four different cases including fast varying irradiance,PS,complex-PS(CPS),and field atmospheric data of Hefei city of China.The results show superior performance in tracking maximum power and faster convergence at the GM.The improvement of 15-20%in tracking time and reduction of random oscillations by 94%are the main achievements of the proposed study.