In recent decades, depletion of fossil fuels and its demand emerge as renewable energy in the field of power generation. Amid eco-friendly based renewable energy, wind and photovoltaic play a vital role in power generation. Nonetheless, this form of power generation needs more advancement to retrieve optimal power flow under economical conditions. This paper aims to predict optimal sizing for hybrid wind and photovoltaic (PV) power generation under minimized cost. This optimal sizing of hybrid Wind-PV is accomplished by satisfying the average annual load demand. This process happens via opposition based genetic algorithm with Cauchy mutation (OGA-CM) and the proposed OGA-CM performance measure is compared with Opposition based Genetic Algorithm and Genetic Algorithm. The result shows that our proposed OGA-GA produces superior result than those of the other two. The overall computation process is done in the working platform of MATLAB R2013.

The significance intention of the proposed method is to eliminate total harmonics distortion (THD) in solar photovoltaic cells powered cascaded H-bridge multilevel inverter (CHMLI). This purpose incorporates Newton-Raphson (NR), particle swarm optimisation (PSO), artificial bee colony (ABC), genetic algorithm (GA), cuckoo search (CS) and social spider optimisation (SSO)-based selective harmonic elimination technique for solving a non-linear transcendental equation to obtain optimal switching angle to achieve minimise THD. The foremost goal to reduce THD value beyond IEEE standard allowable limit is to predict optimal switching angle. This prediction is done in offline then it utilises for online simulation process to achieve minimised THD. Amid, aforementioned techniques incorporate with CHMLI; SSO behaves literally better than all other comparative techniques to attain 3.6% of THD in line voltage from the modulation index of 0.95. This customised product development would enhance quality power flow in this power generation.

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