Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

Sairam, Seshapalli; Seshadhri, Subathra; Marafioti, Giancarlo; Srinivasan, Seshadhri; Mathisen, Geir; Bekiroğlu, Korkut

doi:10.1016/j.renene.2021.10.063

Cited by 19 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, the study emphasizes the importance of XAI approaches in explaining complex models such as MLPs and highlights their potential to improve user trust in AI systems. Wand et al 143 187 developed an XAI-based fault detection system for incipient failures in PV panels. To detect defects from the data streams for the PV panels, the fault classifier leveraged an XGBoost classifier.…”

Section: Application Of Explainable Ai Models Inmentioning

confidence: 99%

“…It outperformed traditional methods, with a high training efficiency (R 2 = 0.8659), a small model size, and a short training time. Sairam et al developed an XAI-based fault detection system for incipient failures in PV panels. To detect defects from the data streams for the PV panels, the fault classifier leveraged an XGBoost classifier.…”

Section: Explainable Ai In Renewable Energymentioning

confidence: 99%

See 1 more Smart Citation

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.

show abstract

Section: Application Of Explainable Ai Models Inmentioning

confidence: 99%

Section: Explainable Ai In Renewable Energymentioning

confidence: 99%

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

show abstract

“…Researchers proposed a lot of rPV’s structures, including Honey Comb, Series Parallel, Total Cross Tied (TCT), etc. [ 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 ]. According to the articles [ 140 , 141 , 142 ], the last one generates more power in case of PS as compared to other structures.…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…The long-term contribution, including increased capacity of solar energy, depends on solving the remaining tasks of grids integration, high costs, and low efficiency, mainly through the research and development of a smart solar plant system based on integration of cutting-edge technologies, including DNN [ 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 ]. To attain the smart optimization and high efficiency of solar energy, the cloud, big data, ML, EC, IoT, quantum, and sensor technologies need to be adaptively combined and implemented as smart grid, home, and city applications.…”

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

“…Future research can comprise the elaboration of a pipeline for implementing a real time solar plant diagnostic system based on IoT, EC, and/or TinyML technologies [ 138 ]. In [ 139 ], researchers developed based on EC a lightweight ML real-time solar plant failure detection model. Recent cloud-based monitoring solutions were developed in [ 141 , 142 ].…”

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

See 1 more Smart Citation

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

View full text Add to dashboard Cite

A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

show abstract

A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

Thiyagarajan,

Nagabhooshanam,

Prasad

et al. 2024

Int J Communication

View full text Add to dashboard Cite

SummaryEnsuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.

show abstract

Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

Cited by 19 publications

References 29 publications

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

A Review on Machine Learning Applications for Solar Plants

A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

Contact Info

Product

Resources

About