Evaluation of Reduced-Switch Seven-Level Converter-Based upqc with Hybrid Fuzzy-Feedforward Neural Network Algorithm

Abstract To improve the performance of end-user equipment in an electric utility distribution network, it is necessary to improve power quality (PQ). This paper presents PQ improvement in electric distribution network using seven-level five switch converter based Unified Power Quality Conditioner (7LFSC-UPQC). Nowadays, power quality plays a vital role in the power distribution sector in the use of highly non-linear loads. Industrial non-linear loads produce electrical imbalances, thereby causing various voltage and current quality related problems, large amounts of harmonics, voltage swell, voltage sag, voltage interruption, etc. This paper proposes the design of a 7LFSC -UPQC based fuzzy-multilayer feed forward multi-layer neural network (FFMLNN) controller to mitigate PQ issues such as voltage sags, swells and current harmonics. A multi-layer feed forward neural network, in which a back-propagation algorithm is used to generate the appropriate reference voltage and current signals, and a shunt compensator of a seven-level unified power quality conditioner (UPQC). This proposed control scheme also controls the DC-link voltage and terminal voltage using an intelligent fuzzy controller. The performance of the proposed FFMLN-fuzzy based 7LFSC-UPQC configuration for simultaneous voltage swell, sag and total harmonic distortion is demonstrated and a comparative analysis of 7LFSC-UPQC and five level UPQC with PI-controller, fuzzy-controller and FFMLNN-controller is conducted using MATLAB/SIMULINK software.