This project presents the concept of fault detection and location in a Power Microgrid making use of the machine learning concepts like Artificial Neural Network. The electronic equipment used in microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. A fault occurs when two or more conductors come in contact with each other or ground. Ground faults are considered as one of the main problems in power systems and account for more than 80% of all faults. An effective method to detect, isolate, and protect the power microgrid system against the effects of short circuit faults is extremely important. In this project we worked on a highly effective new method to protect the microgrid system using an Artificial Neural Network (ANN) that will detect and find the location of the fault before it affects other parts of the system. It would, therefore, be more dependable for microgrid protection. This protection network is distributed all along the power microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. This project focuses on detecting faults and identifying the location of the faults on electric power transmission lines in the power microgrid network.

Developing multiple ML Classifiers using SVM, PCA, Decision Tree, Random Forest, GMM & MLP Models for detecting Faults in Power Systems and comparing their accuracies & performances.

This project aims to detect and classify faults in electric power systems using machine learning algorithms. Fault detection is crucial for ensuring the reliability and safety of electric power systems. The project uses a publicly available dataset to train and test various machine learning models for fault detection.

Physics-informed machine learning system for anomaly detection and fault diagnosis in nuclear power plants, combining thermodynamic laws with AI to improve safety, reliability, and early fault prediction.

The project uses Machine Learning algorithms (YOLOv8 and other versions) to train the system for power-line faults detection and classification. Presented the Project at SJSU Engineering Expo 2023. Group G06 (https://www.sjsu.edu/cmpe/research/project-expo/2023-fall.php)

Unveiling the Power of Data-Driven Fault Detection in HVAC Systems: A Journey Through Advanced Machine Learning

A machine learning model to detect and classify the fault in a power system using the Electrical Fault detection and classification dataset.

Machine learning-based fault detection in power systems using electrical signal analysis

⚡ IBM Cloud ML Project – Power System Fault Detection This repository contains the Power System Fault Detection project built using IBM Cloud Machine Learning services. The aim of this project is to detect and classify faults in a power system using data analysis and machine learning models.

Power System Fault Detection and Classification using Machine Learning is a project or study focused on applying machine learning techniques to identify and categorize faults in electrical power systems. It involves analyzing real-time or historical data detect abnormalities and classify the type of fault .

Machine Learning model build as an assignment for IBM (AICTE) Internship exploring emerging technologies like AI and Cloud . The assignment focuses on Machine Learning model entitled "Power System Fault Detection System".

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This project implements a machine learning-based system to detect and classify faults in a power distribution network using electrical measurement data such as voltage and current phasors. The model is trained to distinguish between normal conditions and various types of faults.

Design a machine learning model to detect and classify different types of faults in a power distribution system.

No description available

No description available

Power System Fault Detection and Classification – Machine Learning Project (IBM Edunet)

Power System Fault Detection and Classification – Machine Learning Project (IBM Edunet)

This is a Django power system fault detection with machine learning and fastapi.

Power System Fault Detection and Classification-Machine learning model to detect and classify different types of faults in a power .

A power system fault detection and classification machine learning model using IBM cloud watsonx

T4Tech: A Machine Learning Framework for Faults and Cyber-Attacks Detection and Classification in Power Systems

Machine Learning-based detection and classification of power system faults using voltage and current phasors, with IBM Cloud deployment.

AI-powered Predictive Maintenance system using IoT sensor data, Machine Learning for fault detection, and Power BI dashboards for real-time monitoring.

Undergraduate Thesis | Title: IoT-Based Fault Detection and Monitoring System in Solar Power Plant Using Machine Learning and Digital Twin Technology

This project implements a multi-modal AI system for analyzing power grid faults and identifying critical transmission lines. By combining different machine learning paradigms, the system provides robust fault detection capabilities and strategic insights for power system operators.

Three-Phase Line Fault Detection and Protection System, utilizing various machine learning algorithms. The system detect faults in three-phase lines and trigger protective measures to ensure uninterrupted power supply and safeguard electrical systems.

Fault Detection System using Machine Learning (IBM Cloud) A cloud-based system that uses supervised machine learning to detect and classify power system faults using voltage and current phasors. Developed using IBM Watson Studio and Cloud Object Storage, it identifies faults like line-to-ground, line-to-line, and three-phase.

A repository to showcase projects integrating Power Systems and Machine Learning, with a focus on predicting energy demand, fault detection, and optimization of smart grids.

Modern power systems face frequent transmission line faults from short circuits, equipment issues, or natural events. To maintain stability and prevent outages, fast and accurate fault detection is needed. Machine learning helps classify and detect these faults efficiently.