DEVELOPMENT OF ALGORITHMS FOR DETECTING AND AUTOMATICALLY ELIMINATING FAULTS IN POWER SUPPLY SYSTEMS

Аннотация

Power supply systems are critical infrastructures that require high reliability and continuous operation. Faults such as short circuits, open circuits, and transient disturbances can compromise system stability, causing power outages and equipment damage. This paper presents the development of advanced algorithms designed to detect faults promptly and implement automatic elimination or isolation techniques to maintain system integrity. By integrating real-time monitoring, signal processing, and intelligent decision- making, the proposed algorithms aim to minimize fault impacts, improve system resilience, and reduce downtime. Simulation and experimental results validate the effectiveness of the developed methods in various fault scenarios. Keywords: Power supply systems, fault detection, automatic fault elimination, real-time monitoring, signal processing, power system protection Introduction: In today’s interconnected and technology-driven world, the demand for a reliable and uninterrupted power supply has never been greater. Power supply systems form the critical infrastructure that powers homes, industries, hospitals, and communication networks, making their stability and resilience a top priority. However, these systems are vulnerable to a range of faults — from short circuits caused by equipment failure or external disturbances, to open circuits and grounding issues that disrupt normal operation. Such faults not only cause inconvenience due to power outages but can also lead to severe damage to electrical equipment and pose safety risks. Traditionally, power system faults have been managed through protective devices like circuit breakers, relays, and fuses, which detect abnormal conditions and isolate the affected sections to prevent further damage. While these devices have been effective for decades, the complexity of modern power grids— characterized by the integration of renewable energy sources, distributed generation, and increasing loads—has exposed limitations in conventional fault management. These include slower response times, limited adaptability, and dependency on manual intervention or preset thresholds that may not cover all fault scenarios adequately. With advancements in digital technologies, communication systems, and data processing capabilities, new opportunities have emerged to enhance fault detection and mitigation. The concept of 148 CURRENT APPROACHES AND NEW RESEARCH IN MODERN SCIENCES International scientific-online conference automation in fault management involves continuously monitoring system parameters, rapidly analyzing data to identify faults, and autonomously executing control actions to isolate or correct the problem. This shift from reactive to proactive and intelligent fault handling promises to reduce outage durations, improve power quality, and increase overall system reliability. The development of sophisticated algorithms lies at the heart of this transformation. By leveraging techniques from signal processing, pattern recognition, and artificial intelligence, these algorithms can detect subtle fault signatures that traditional methods might miss, classify fault types with high accuracy, and coordinate complex control responses in real time. Moreover, automation facilitates better integration with smart grid technologies, enabling more flexible, resilient, and self-healing power networks. Literature review Power system fault detection and mitigation have been critical areas of research due to their direct impact on the reliability and safety of electrical networks. Early foundational work in power system protection, such as that by Kimbark [1], laid the groundwork for understanding fault characteristics and the role of protective relays. Traditional protective devices operate primarily on threshold-based measurements of current and voltage, isolating faulted sections by tripping circuit breakers when abnormal conditions are detected [2]. However, these methods are often rigid and can struggle with the increasingly complex and dynamic nature of modern power systems. Advances in signal processing have enabled more nuanced fault detection strategies. The use of wavelet transforms for transient fault detection was introduced by Singh et al. [3], demonstrating superior performance over Fourier-based methods in capturing fault signatures. Wavelet analysis provides a time-frequency representation that is particularly effective for detecting sudden changes in signals caused by faults. Similarly, Al-Haddad et al. [4] proposed real-time spectral analysis techniques that enhance fault detection speed and accuracy, especially in environments with significant noise and harmonic distortion. The classification of fault types has also evolved, with rule- based and pattern recognition approaches gaining prominence. Lee and Park [5] developed adaptive protection schemes that adjust their operational thresholds based on changing network conditions, improving fault isolation without