Smart Grid Monitoring System Using AI for Real-Time Fault Detection and Load Management

Abstract

The proposed invention, a Smart Grid Monitoring System utilizing Artificial Intelligence (AI), enhances real-time fault detection and load management within power grids. Designed to address the increasing demand for resilient and efficient energy distribution, this system employs machine learning algorithms and predictive analytics to monitor grid components continuously, identify potential faults, and preemptively schedule maintenance. Its adaptive load management capabilities enable real-time adjustments based on supply and demand, supporting integration with renewable energy sources and promoting optimized energy distribution. Enhanced with AI-driven cybersecurity, the system protects grid infrastructure from cyber threats, while intelligent load balancing algorithms ensure stability by adjusting power flow in response to fluctuations. The scalable design of this system allows deployment across various grid environments, from urban networks to rural and remote grids, enabling seamless expansion, remote fau

Specification

Description:The field of invention for this proposed Smart Grid Monitoring System lies at the intersection of artificial intelligence, electrical engineering, and smart grid management. It addresses the need for real-time fault detection and load management in modern power systems. Traditional grid infrastructures often face challenges with efficiency, fault resilience, and optimal power distribution, especially in the face of rising energy demands and integration of renewable energy sources. This invention aims to harness AI-driven analytics, machine learning, and IoT sensors to enhance the grid's responsiveness, reliability, and efficiency. By leveraging AI algorithms for fault prediction and anomaly detection, the system enables immediate identification and response to faults, minimizing service disruptions and energy loss. Additionally, AI optimizes load distribution across the grid to prevent overloading and enhance overall stability, creating a dynamic, self-regulating grid system.
Background of the invention:
The , Claims:1. The system provides real-time monitoring of grid components through a network of sensors, continuously collecting data on voltage, current, and temperature to maintain operational stability.
2. The system utilizes AI algorithms to identify and analyze data patterns, enabling early detection of anomalies and potential faults to facilitate predictive maintenance and reduce downtime.
3. The system of Claim 1 autonomously adjusts load distribution across the grid in real time based on data-driven insights, optimizing power flow and reducing stress on critical components.
4. The system of Claims 1 and 2 includes an adaptive load management feature that dynamically balances power distribution in grids with high renewable energy integration, adjusting supply to demand.
5. The fault detection mechanism leverages machine learning models to isolate and pinpoint fault locations, enabling rapid, localized responses and minimizing disruptions across the grid.
6. The system of Claim 5 integrates cybersecurity measures,

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