Predictive Model for Grid Stability Analysis Using Supervisory Control and Data Acquisition (SCADA) System

This paper presents a Predictive model for grid stability analysis using SCADA system. It has been a norm of instability recorded in Electrical power system in Nigeria. Fluctuations in grid parameters such as voltage and frequency have always been the issues. This has contributed to frequent power outage which has crippled businesses and make life miserable. This research is aimed at stabilizing the grid system using artificial intelligent technique in order to promote constant electricity supply. The grid system comprises of renewable energy source, substations at different voltage levels, and overhead electrical conductors, then consumers. In order to develop a sustainable model, data were gathered from an existing substation and used for the model training. The Supervisory Control and Data Acquisition (SCADA) system was designed and simulated using Intouch software and Allen Brandley micrologix 1000, the Programmable Logic Controller (PLC). The graphical user interface (GUI) was developed using web application tools for the model testing. Training and validation were conducted using extensive datasets from SCADA-monitored grids, with the model achieving an overall accuracy of 98.65% in predicting stability-related incidents. The results of the investigation showcase that the suggested predictive model significantly enhances the functionality of SCADA systems by providing them with foresight on grid instability and problems. This model provides proactive and useful grid management in advance times with precise forecasting for improving electrical power network effectiveness and reliability. This research contributes to the advancement of smart grid technology, offering a scalable solution for maintaining grid reliability in the face of evolving energy demands