Analysis of distribution network reliability based on distribution automation technology

Abstract

The growing complexity and need for electricity in contemporary grids have resulted in an increased dependence on Distribution Automation Technology (DAT) to improve the effectiveness and reliability of distribution networks. Automation technologies, like smart sensors and fault detection systems, are critical for enhancing operational efficiency and lowering power outages in distribution networks. This study investigates the influence of distribution automation on the dependability of electricity networks, concentrating on important functional metrics and their relationship with network efficiency. Objectives: The main objective of this research is to examine the factors that influence the reliability of distribution networks, with a focus on distribution automation technology. This study uses a variety of efficiency indicators, like automation coverage, fault detection time, and consumer complaints, to discover the primary factors of network reliability. This paper introduced the Reliability-Optimized Meta-Learning Ensemble (ROME) algorithm, which seeks to predict the reliability category of various areas using these indicators. Methodology: This study utilizes the Distribution Network Reliability Dataset, which includes several areas with a variety of characteristics such as network age, automation coverage, smart sensor installation, power outages, fault detection time, and other operational metrics. The ROME algorithm is used, which integrates numerous base models (SVM, Random Forest, MLP) and a meta-learner (Gradient Boosting) to predict each region’s Reliability Category (High, Medium, Low). The dataset is thoroughly preprocessed, which includes mean and mode imputation, label encoding, standardization, and SMOTE balancing. Recursive Feature Elimination (RFE) is used for feature selection. Results: The findings show a strong correlation between automation coverage, fault detection time, and reliability category. When compared to traditional classification techniques, the ROME algorithm surpassed SVM, RF, MLP, and GB models with 94.7% accuracy, 0.18 Log-Loss, 91.2% Jaccard Index, 0.08% fall-out, and 95.3% specificity. Conclusion: This research emphasizes the value of distribution automation in improving network reliability. Utilities and grid operators can use the ROME algorithm to better predict and enhance network reliability. The results highlight the requirement for targeted investments in automation technologies, particularly in regions with lower reliability scores, to guarantee sustainable and effective electricity distribution.