Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods

TL;DR

This paper introduces a machine learning-based integrated fault detection, diagnosis, and control strategy for DER inverters, aiming to enhance fault tolerance and grid stability by predicting and correcting input voltage issues.

Contribution

It presents a novel supervised ML approach that combines fault diagnosis and control in a unified framework for DER inverters, improving fault mitigation capabilities.

Findings

Effective fault detection and classification demonstrated in simulations

Improved inverter resilience to grid faults shown through performance evaluation

Proposed method successfully predicts and corrects input voltage disturbances

Abstract

This paper employs a supervised machine learning (ML) algorithm to propose an integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC) strategy to detect, diagnose, and classify the grid faults and correct the input voltage before affecting the grid-connected distributed energy resources (DER) inverters. This controller can mitigate the impact of grid faults on inverters by predicting and modifying the time series of their input voltage. Simulation results show the effectiveness of the proposed controller and evaluate its operating performance.