Feed Forward Neural Network Based DSTATCOM for Compensating Load Demand and Mitigate Harmonics in Distributed System

Abstract

Power Quality (PQ) has become a critical requirement in modern power systems due to its impact on industrial automation, computer-based technologies, and controlled electrical systems. Accurate identification and mitigation of electrical disturbances are essential for improving system reliability and efficient power delivery. However, conventional approaches often face limitations in optimizing power flow at the end-user level. In this paper, a superior controller to a Distributed Static Compensator (DSTATCOM) was developed with intentions of optimizing power quality in a traditional bus system. A baseline DSTATCOM model of a basic bus setup had first been set up, followed by gathering real-time data over a range of disturbance conditions to power quality. This dataset was then trained in a Feed Forward Neural Network (FFNN) controller, creating a DSTATCOM pulse signal to be created in the analysis of bus voltage measurements. The proposed controller efficiency was estimated using case studies based on IEEE 33 and 13 bus systems. Performance validation was conducted at a number of disturbances such as interruption, swell, harmonic content, and sag. In the case of the IEEE 13-bus system, the model proposed meets the total harmonic distortion (THD) of 2.93, 1.81, and 0.02, under sag, swell, and interruption conditions, respectively. In the same manner, in the case of the IEEE 33-bus system, the THD of sag, swell and interruption are 0.58% 0.26% and 0.33%. The results confirm that the proposed FFNN-based DSTATCOM controller effectively mitigates a wide range of power quality disturbances while enhancing system stability and reliability.