Intelligent Bootstrapped LSTM-Controlled 9-Switch Unified Power Quality Conditioner for Enhancing Reliability of Grid-Integrated Systems under Nonlinear Loads

Abstract

The issue of power quality (PQ) in modern electrical systems is a significant concern that affects both utilities and consumers. On the distribution side of the utility grid, PQ is shaped by advancements in the power electronic devices used to control nonlinear loads.. The integration of back-to-back converters through a DC connection element using a Unified Power Quality Conditioner (UPQC), a nine-switch Flexible AC transmission system (FACTS) device, improves PQ on the distribution side. For the 9-switch UPQC system, traditional mathematically based statistical controllers perform suboptimally and insecurely during transient oscillations. Therefore, the proposed work introduces an advanced neural approach to manage the 9-switch UPQC to ensure a stable power supply for end users. In this context, the DC link of the UPQC is configured with a photovoltaic (PV) panel and a battery within a specified range. Conversely, the Bootstrapping Long Short-Term Memory (Boot-LSTM) controller mechanism was employed to execute the switching pulse. To train the boot-LSTM, a dataset is prepared under a range of fault conditions. The model interprets load voltage and current data to generate switching commands for the nine-switch topology, and its performance is validated using graphical results across various scenarios through graphical representation under PQs. According to the nine-switch UPQC results, under sag, swell, and interruption conditions, the compensator maintains very low load voltage THD values of 0.07%, 0.09%, and 0.30%, respectively, which demonstrate that the proposed controller operates in a safe, reliable, and efficient manner.

Keywords

• Power quality issues
• 9 switch UPQC
• PV and battery
• Boot-LSTM

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