The growing concern about environmental issues and proliferating demand for green energy harvesting has diverted the attention of power industries for innovation and development in renewable energy technologies (RETs) to mitigate the energy crises and reduce the environmental impacts.To develop new competitive and efficient energy solutions, manufacturers of components (solar panels, inverters, MPPTs etc.) and industrial sectors are facing problems of optimization, control and durability.In addition, renewable energy sources (RESs) depends on several uncontrollable factors (geographical location, weather, etc.).Among all the RETs solar photovoltaic is considered as one of the most important energy technology because of abundant sun light.To harvest the maximum power from the PV it is necessary to implement a control strategy to identify the PV operating point characterized by the maximum power point (MPP).Therefore maximum power Point tracking (MPPT) is considered as an essential part of PV generation system and is one of the key issue for researchers to reduce the effects of nonlinear characteristics of PV array.　　So far different MPPT algorithms have been proposed for optimization of PV output power, such as Perturb & Observe (P&O), Incremental Conductance (INC), hill climbing, neural network, fuzzy logic theory and genetic algorithm.　　Among all these MPPT algorithms, INC and P&O are commonly used for small and large scale PV power plants.Since fast tracking response and accuracy conflict one from other, the mentioned tracking methods cannot satisfy, simultaneously, both of them.Because both the algorithms operates in accordance with power against voltage (P-V) curve of PV module and tune the duty cycle of converter to ensure the next MPP point accordingly.In P&O steady state oscillation occurred due to perturbations continuous changes in both the direction to maintain MPP under rapidly changing solar irradiance.This leads to a less efficient system having more power losses.However, the conventional incremental conductance method determines the slope of PV curve by varying the converter duty cycle in fixed or variable step size until the MPP is achieved.In this way oscillation under non-uniform solar irradiance is reduced with greater efficiency but due to complicated algorithm speed is slow.As well under faster changing solar irradiation INC produces some oscillation in PV voltage (VPV) which causes power losses and not only reduces overall system efficiency but also bus voltage instability.Some authors have proposed complex MPPT algorithms, based on fuzzy logic and neural network, in order to accomplish fast tracking response and accuracy in a single system.These proposals, nevertheless, present some disadvantageous: needed for high processing capacity, complexity, cost elevation and, in some cases, employment of extra sensors.　　Therefore, an efficient MPPT system need to be composed by the integration of an adequate dc dc converter (hardware) and proper tracking algorithm (software), resulting in some desired aspects:　　Fast tracking response (dynamic analysis);　　Accuracy and no oscillation around the MPP (stead-state analysis);　　Capacity to track the MPP for wide ranges of solar radiation and temperature;　　Simplicity of implementation;　　Low cost.　　The aim of this thesis is to find out the solutions for aforementioned limitations of traditional maximum power point techniques MPP tracking speed with better accuracy to optimize the PV array output power PPV with stable output voltage VPV.　　This thesis proposes a new simple moving voltage averaging (SMVA) technique with fixed step direct control incremental conductance method to reduce VPV oscillations and improve not only overall system efficiency interims of power losses as well bus voltage stability under non uniform solar irradiation for both the centralized (CMPPT) and distributed (DMPPT) methods.A MATALB/Simulink model was developed to perform the simulation and validate the proposed SMVA technique.Simulation results theoretical analysis reveals that proposed technique works more accurately and faster during dynamic and steady state conditions and improve the overall system efficiency.　　Additionally, the proposed maximum power point tracking algorithms provide more robustness and faster convergence under environmental variations than other maximum power point trackers.