Intelligent Fault Analysis in Electrical Power Grids

TL;DR

This paper presents an AI-based system utilizing machine learning and formal models to analyze and predict the health of electrical power grids, aiming to prevent failures through early fault detection.

Contribution

It introduces a novel AI framework combining formal models and machine learning techniques like RNNs for real-time power grid health analysis.

Findings

High accuracy in fault detection and grid health assessment

Effective simulation of grid conditions using Siemens PSS/E

Scalable approach for complex power grid architectures

Abstract

Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and in some cases even life. Thus, it is imperative to design a system which can detect the health of the power grid and take protective measures accordingly even before a serious anomaly takes place. To achieve this objective, we have set out to create an artificially intelligent system which can analyze the grid information at any given time and determine the health of the grid through the usage of sophisticated formal models and novel machine learning techniques like recurrent neural networks. Our system simulates grid conditions including stimuli like faults, generator output fluctuations, load fluctuations using Siemens PSS/E software and this data is trained using various classifiers like SVM, LSTM and subsequently tested. The results are excellent with our methods giving very high accuracy for the data. This model can easily be scaled to handle larger and more complex grid architectures.