Construction and Optimization of Power Quality Risk Spillover Trend Prediction Model Based on Deep Learning

Abstract

This study aims to establish and refine a deep learning-based predictive model for power quality risk spillover trends, thereby addressing the challenges posed by the escalating diversity and adaptability of power systems in the evolution of the renewable energy industry. Initially, the study introduces the concept of power quality risk spillover, referring to the influence exerted by power quality issues in one region on other regions. This influence initiates a chain reaction, ultimately leading to a more widespread deterioration in power quality. Then, the advantages and applications of the deep learning model in power quality risk spillover trend prediction are expounded. During the model development process, we employed deep learning algorithms such as the multilayer perceptron and extended short-term memory network, tailored specifically to the unique characteristics of power quality data. To enhance the model's predictive accuracy and stability, we implemented optimization strategies encompassing data preprocessing, feature engineering, and model integration. Our experimental findings demonstrate that the optimized model exhibits remarkable precision and stability in anticipating power quality risk spillover trends, thereby offering robust support for risk management and ensuring the stable operation of power systems. This study provides a valuable exploration and practice for the application and development of a power quality risk spillover trend prediction model based on deep learning.