Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis
This paper presents advanced frameworks for microgrid predictive maintenance by performing a comprehensive correlative analysis of advanced recurrent neural network (RNN) architectures, i.e., RNNs, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) for photovoltaic (PV) based DC microgr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/6/1535 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper presents advanced frameworks for microgrid predictive maintenance by performing a comprehensive correlative analysis of advanced recurrent neural network (RNN) architectures, i.e., RNNs, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) for photovoltaic (PV) based DC microgrids (MGs). Key contributions of this analysis are development of advanced architectures based on RNN, GRU and LSTM, their correlative performance analysis, and integrating adaptive threshold technique with the algorithms to detect faulty operations of inverters which is indispensable for ensuring the reliability and sustainability of distributed energy resources (DERs) in modern MG systems. The proposed models are trained and evaluated with a dataset of diverse real-world operational scenarios and environmental conditions. Moreover, the performances of those advanced models have been compared with the conventional RNN-based techniques. The achieved decremental MAE scores from 12.102 (advanced RNN) to 10.182 (advanced GRU) to 8.263 (advanced LSTM) and incremental R<sup>2</sup> scores from 0.941 (advanced RNN) to 0.958 (advanced GRU), and finally to 0.971 (advanced LSTM) demonstrate strong predictive capabilities of all, while the proposed advanced LSTM method outperforming other counterparts. This study can contribute to the emerging technology for predictive maintenance of MGs and provide significant insights into the modeling and performance of RNN architectures for improving fault detection in MG systems. The findings can have noteworthy implications to enhance the efficiency and resilience in MG systems, thereby evolving the renewable energy technologies in power sector and contributing to the sustainable and greener energy landscape. |
|---|---|
| ISSN: | 1996-1073 |