Artificial neural network for fault diagnosis of solar photovoltaic systems: a survey

Z Yuan, G Xiong, X Fu - Energies, 2022 - mdpi.com
Solar energy is one of the most important renewable energy sources. Photovoltaic (PV)
systems, as the most crucial conversion medium for solar energy, have been widely used in …

Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

Y Yuan, J Wei, H Huang, W Jiao, J Wang… - … Applications of Artificial …, 2023 - Elsevier
In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Fault diagnosis of transformer using artificial intelligence: A review

Y Zhang, Y Tang, Y Liu, Z Liang - Frontiers in Energy Research, 2022 - frontiersin.org
Transformer is one of the important components of the power system, capable of transmitting
and distributing the electricity generated by renewable energy sources. Dissolved Gas …

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy …

O Laayati, H El Hadraoui, A El Magharaoui, N El-Bazi… - Energies, 2022 - mdpi.com
After the massive integration of distributed energy resources, energy storage systems and
the charging stations of electric vehicles, it has become very difficult to implement an efficient …

A feature selection and ensemble learning based methodology for transformer fault diagnosis

S Rao, G Zou, S Yang, S Barmada - Applied Soft Computing, 2024 - Elsevier
Dissolved gas analysis (DGA) data are generally used to diagnose a transformer fault.
However, the measurement errors in DGA data are inevitable and will affect the accuracy …

Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment

Q Zhang, LIU Hongshun, GUO Jian, W Yifan… - Electric Power Systems …, 2023 - Elsevier
The continuous and reliable operation of the transformer is the basis to ensure the normal
operation of the power system. Relevant departments collect multi-dimensional and multi …

Power transformer fault diagnosis using neural network optimization techniques

V Rokani, SD Kaminaris, P Karaisas, D Kaminaris - Mathematics, 2023 - mdpi.com
Artificial Intelligence (AI) techniques are considered the most advanced approaches for
diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional …

An attention‐based Logistic‐CNN‐BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises

X Zhang, Y Ma, M Wang - Expert Systems, 2024 - Wiley Online Library
Enterprise credit risk prediction is to predict whether enterprises will default in the future,
according to a variety of historical data by establishing a corresponding relationship …

Predictive maintenance in the military domain: a systematic review of the literature

J Dalzochio, R Kunst, JLV Barbosa, PCS Neto… - ACM Computing …, 2023 - dl.acm.org
Military troops rely on maintenance management projects and operations to preserve the
materials' ordinary conditions or restore them to combat or military training. Maintenance …

Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

A Hechifa, A Lakehal, A Nanfak, L Saidi, C Labiod… - Electrical …, 2024 - Springer
This paper discusses the impact of the feature input vector on the performance of dissolved
gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose …