Design and Simulation of an Intelligent Fault Protection Scheme for Smart Grid Applications using Fuzzy Inference and Rate-of-Change Analysis
DOI:
https://doi.org/10.71426/jasm.v1.i1.pp51-59Keywords:
MATLAB/Simulink, Fuzzy logic, System protection, Supervisory Control and Data Acquisition (SCADA), Rate of Change (RoC), Smart grid, Power systemAbstract
With the increasing deployment of smart electrical grids and interoperable digital control infrastructures, intelligent and adaptive fault protection schemes are essential for ensuring system reliability, stability, and rapid fault isolation. This paper presents the design and simulation of an intelligent logic-based fault protection scheme for smart grid applications using fuzzy inference and Rate of Change (RoC) analysis. The proposed system employs a fuzzy logic controller to detect and classify faults by continuously monitoring voltage, current, frequency, and their respective rates of change within an independent electricity distribution network. Unlike traditional protection systems that rely solely on fixed threshold values, the integration of RoC analysis enables faster detection of sudden disturbances, while fuzzy inference provides adaptive, rule-based decision-making under uncertain and dynamic operating conditions. Triangular membership functions are used to evaluate input parameters and generate appropriate protective responses, closely emulating the behavior of modern digital relays. The system is designed to support seamless integration with smart grid communication frameworks, including IEC 61850 protocols, Supervisory Control and Data Acquisition (SCADA), and Generic Object-Oriented Substation Event (GOOSE) messaging. Simulation results obtained using MATLAB/Simulink demonstrate the system’s ability to accurately distinguish between normal and fault conditions and to achieve rapid fault isolation with improved sensitivity and reliability. Comparative analysis confirms that the combined fuzzy inference and RoC-based approach significantly enhances protection speed and adaptability compared to conventional methods. The proposed framework provides a scalable and practical solution for intelligent protection in modern smart grid environments and supports potential real-time implementation on embedded and industrial control platforms.
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