Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's r...

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Published in:ENERGIES
Main Authors: Satpathy, Anshuman; Bin Baharom, Rahimi; Hannon, Naeem M. S.; Nayak, Niranjan; Dhar, Snehamoy
Format: Article
Language:English
Published: MDPI 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001341535400001
author Satpathy
Anshuman; Bin Baharom
Rahimi; Hannon
Naeem M. S.; Nayak
Niranjan; Dhar
Snehamoy
spellingShingle Satpathy
Anshuman; Bin Baharom
Rahimi; Hannon
Naeem M. S.; Nayak
Niranjan; Dhar
Snehamoy
Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
Energy & Fuels
author_facet Satpathy
Anshuman; Bin Baharom
Rahimi; Hannon
Naeem M. S.; Nayak
Niranjan; Dhar
Snehamoy
author_sort Satpathy
spelling Satpathy, Anshuman; Bin Baharom, Rahimi; Hannon, Naeem M. S.; Nayak, Niranjan; Dhar, Snehamoy
Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
ENERGIES
English
Article
This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore-Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform.
MDPI

1996-1073
2024
17
20
10.3390/en17205024
Energy & Fuels
gold
WOS:001341535400001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001341535400001
title Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
title_short Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
title_full Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
title_fullStr Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
title_full_unstemmed Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
title_sort Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation
container_title ENERGIES
language English
format Article
description This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore-Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform.
publisher MDPI
issn
1996-1073
publishDate 2024
container_volume 17
container_issue 20
doi_str_mv 10.3390/en17205024
topic Energy & Fuels
topic_facet Energy & Fuels
accesstype gold
id WOS:001341535400001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001341535400001
record_format wos
collection Web of Science (WoS)
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