Assessment algorithm of power supply reliability for customers in rural healthcare centres

Abstract

Power supply interruptions in low-voltage customers, caused by blackouts and other factors, can significantly impact the functioning of rural healthcare centres. To address this issue, the development of a predictive system that ensures a reliable power supply becomes essential. Artificial Neural Networks (ANNs) emerge as promising predictive systems due to their efficacy in various artificial intelligence and computer science problems, such as pattern recognition and diagnosis. In this study, we explore the application of three different ANN models: Nonlinear Multilayer Perceptron (MLP), Radial Basis Function Network (RBF Net), and Hopfield Neural Network (HNN), to enhance the prediction of power supply reliability for low-voltage customers. In addition to ANNs, an optimization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to identify obstacle hazards. The traditional DBSCAN algorithm's limitations in adapting to unmanned obstacle data processing are addressed through improvements. By adjusting the fixed cluster neighbourhood radius to an adaptive clustering parameter that varies with the target distance, the clustering effect of the optimized DBSCAN algorithm is significantly enhanced. Through experimental verification and analysis, we demonstrate the effectiveness of the ANN models and the improved DBSCAN algorithm in enhancing power supply reliability assessment and obstacle hazard identification. The combination of advanced predictive systems and optimized clustering algorithms promise to ensure an uninterrupted and reliable power supply for rural healthcare centers, improving the quality of healthcare services in underserved areas.