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

arXiv: https://arxiv.org/abs/2202.09996 · HTML: https://mlsanity.com/paper/2202.09996/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2022.

## Built on by (most cited first, 2 of 2)

- [Fault Diagnosis in Power Grids with Large Language Model](https://mlsanity.com/paper/2407.08836/) (2024)
- [A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management](https://mlsanity.com/paper/2203.14159/) (2022)

## Abstract (arXiv)

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.