A Novel Adaptive Filtering Technique for Mitigating Disturbances Arising Due to Integration of Renewable Resources and Non-Linear Loads

Over the last two decades renewable energy resources have become much popular choice of power because of their huge availability and other inherent benefits. However, the growing penetration of these resources has also led to enlarged application of equipment pertaining to power electronics resulting in non-linearity in electrical networks. The situation gets even worsen by introduction of non-linear loads at the distribution end. Thus, there has also been a rising challenging issue with regard to its deteriorating effects on the quality of power (PQ) being fed to electrical system provoking new interests in the filtering techniques. Here, this work focuses on description of an adaptive filtering scheme based on Least Mean Square (LMS) for a 3-phase grid system. The harmonics have been analyzed for various sets and combinations of loads consisting of linear type and non-linear type which have been connected at the point of common coupling (PCC). The reference value of source current is used to generate the pulses to switch on and off the converters. This reference value in turn is deduced by processing the currents of load so as to extract units pertaining to active weights. The applied LMS algorithm in the proposed topology of three-phase network enhances the PQ with a simpler approach where harmonics have been reduced significantly for both source voltage and source current. A comprehensive comparison work is presented where linear R-L load and induction motor as non-linear load have been analyzed in MATLAB. Further, to establish distinct features of proposed technique, responses are also tested for power factor variations for both types of load and the obtained results have been tabulated in detail. The effectiveness of the applied algorithm in reducing the complexity and limiting the system harmonics have been thoroughly presented and discussed by plotting the obtained graphs.