A Hybrid Model of SCADA Development with the need for Data Analytics & Time Series Analysis for Effective Load Forecasting, Data Security (DDoS) Attack Analysis & its Application in the Science & Engineering Applications

DOI: https://doie.org/10.1127/Jbse.2024847927

Dr. R. Balakrishna, Dr. J.V. Muruga Lal Jeyan

Keywords:

SCADA, DMS, EMS, ARIMA, Time Series Analysis, SCADA, Time Series, AMI, Smart Grid, Communication, Application.

Abstract:

In this research article, the work is divided into 2 folds, viz., (i) SCADA – Need for Data Analytics & Time Series Analysis for Effective Load Forecasting & (ii) SCADA – Data Security : DDoS Attack Analysis Using Time Series & its Application in the Communication Sector using Non-Linear Methods. In the first case (i), the SCADA systems, particularly Power SCADA, are essential for preserving the stability and dependability of the electrical grid in the context of power generation and delivery. Power SCADA systems make it easier to monitor electrical parameters like voltage, current, and frequency in real time. For such analysis, methods such as ARIMA, SARIMA, or machine learning models are frequently employed in order to guarantee effective energy distribution and make well-informed decisions. In this Paper effort is made to study the Load Forecasting Determination using Time Series Analysis. The results show the efficiency of the method developed by us.  This paper discusses the integration of data analytics in Power SCADA systems and its crucial role in optimizing power grid operations. The primary focus is on enhancing the real-time monitoring and control capabilities of Power SCADA through advanced analytical techniques. The paper is illustrated with three figures: the first highlights the necessity of data analytics for efficient grid management, the second showcases a short-term load forecast using the ARIMA model over a six-hour period, and the third evaluates the performance metrics of forecasting models. Through this examination, the paper demonstrates how predictive analytics and machine learning can significantly improve decision-making processes, grid stability, and energy distribution efficiency, particularly in the context of integrating renewable energy sources. The evaluation of model performance through established metrics ensures the reliability and accuracy of forecasts, crucial for proactive grid management and operational planning. In the 2nd case (i), the SCADA systems, particularly Power SCADA, are essential for preserving the stability and dependability of the electrical grid in the context of power generation and delivery. Power SCADA systems make it easier to monitor electrical parameters like voltage, current, and frequency in real time, where the time series analysis allows for the detection of temporal patterns and trends in network traffic. DDoS attacks often exhibit distinct patterns over time, such as sudden spikes or sustained high-volume traffic. By analyzing these patterns, time series techniques can help differentiate between normal traffic and anomalous behavior indicative of a DDoS attack in Smart Grid and SCADA.

PDF