Enhancing Smart Grid Management: Load Forecasting, Power Grid Stability Assessment, and Fault Detection using Artificial Neural Networks
Sunkara Yamini, Hanumantha Rao Sistla, Thakur Ashutosh Suman, D Sravani, N Rajeswaran, M. Deenababu
Abstract
As the needs of electricity customers grow, the smart grid has emerged as an innovative technology for updating power systems. To ensure the smart grid's dependable and sustainable operation, effective management is essential. The current research introduces a novel method for managing the smart grid that combines load forecasting, evaluation of the grid's stability, and defect finding. In order for utilities to optimize energy generation and distribution, load forecasting is a crucial part of smart grid management. Accurate projections of future electricity demand are made possible by this. In this research, ANNs are used to forecast and simulate load patterns based on historical data, environmental parameters, and other pertinent variables. The resource allocation and energy efficiency of the ANN-based load forecasting model are enhanced. Assessment of the stability of the power grid is crucial for avoiding power outages and ensuring the system's resistance to disruptions. ANNs are used to analyze the power grid's dynamic behavior, spot possible stability problems, and forecast voltage and frequency fluctuations. Operators can promptly execute corrective actions and ensure a robust and dependable power supply by continuously monitoring grid stability. The smart grid's fault detection is yet another crucial component of system management. ANNs are used as effective pattern recognition tools to find anomalies and deviations in the behavior of the power system. The ANN-based fault detection system can quickly identify and isolate issues, minimizing downtime and boosting grid resilience, by comparing real-time data with past trends.