ABSTRACT

Nowadays Alternating Current (AC) electrical power system is facing voltage and current harmonics due to non-linear loads present in the power system such as rectifiers. power electronics convertors AC regulators. battery chargers. Direct Current (DC) motors drives and adjustable speed drives. These devices generate significant distortions in the electrical networks and cause power quality problems such as harmonics and waveform distortion. Various solutions such as filters are used to eliminate these power quality problems to the level of international standards. One of the solution celebrated as Shunt Active Power Filter (SAPF) based on Feed forward Multilayer Neural Network (MNN) is the most common and appropriate used technique to compensate the harmonic components in the power system. The purpose of this research is to provide an in-depth understanding on realizing multilayer perceptron neural network-based control algorithms for SAPF. In this
thesis a procedure to implement the MNN based technique using MATLAB/Simulink environment. Moreover, provides the detailed analysis on the performance. limitations, and advantages of the MNN based SAPF. The performance of SAPF based on Multi layer Neural Network (MNN) compensation algorithms is
found to be effective and satisfactory to compensate harmonics and reactive power factor.