An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks DOI Open Access

Kaitian Deng,

Xianglian Xu,

Fang Yuan

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(20), P. 4002 - 4002

Published: Oct. 11, 2024

The precise estimation of the operational lifespan insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring efficient and uncompromised safety industrial equipment. However, numerous methodologies models currently employed this purpose often fall short delivering highly accurate predictions. analytical approach that combines Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD as an unsupervised feature learning method to partition data into intrinsic modal functions (IMFs), which are used eliminate noise preserve essential signal. Secondly, BiLSTM integrated supervised purposes, enabling prediction decomposed sequence. Additionally, hyperparameters penalty coefficients optimized utilizing POA technique. Subsequently, various predicted trained model, individual mode predictions subsequently aggregated yield model’s definitive final life prediction. Through case studies involving IGBT aging datasets, optimal model was formulated its capability validated. superiority proposed demonstrated by comparing it benchmark other state-of-the-art methods.

Language: Английский

A simple and high-accuracy method for minute-level water demand forecasting in district metering areas DOI
Haidong Huang,

Guangqi Que,

Meiqiong Wu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 652, P. 132698 - 132698

Published: Jan. 13, 2025

Language: Английский

Citations

0
Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model DOI Creative Commons
Shengli Wang, Xiaolong Guo, Tian-Le Sun

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 403 - 403

Published: Jan. 17, 2025

A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to (PV) generation are filtered using Spearman correlation coefficient. Historical data then clustered into three categories—sunny, cloudy, rainy days—using K-means algorithm. Next, original PV decomposed through VMD. DHKELM-based combined prediction model developed for each component of decomposition, tailored different types. The model’s hyperparameters optimized IDBO. final forecast determined by combining outcomes individual component. Validation performed actual from plant in Australia station Kashgar, China demonstrates. Numerical evaluation results show that proposed improves Mean Absolute Error (MAE) 3.84% Root-Mean-Squared (RMSE) 3.38%, confirming its accuracy.

Language: Английский

Citations

0
HWDQT: A hybrid quantum machine learning method for ultra-short-term distributed photovoltaic power prediction DOI
Wenhui Zhu, Houjun Li,

Xiande Bu

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110122 - 110122

Published: Feb. 12, 2025

Language: Английский

Citations

0
An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks DOI Open Access

Kaitian Deng,

Xianglian Xu,

Fang Yuan

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(20), P. 4002 - 4002

Published: Oct. 11, 2024

The precise estimation of the operational lifespan insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring efficient and uncompromised safety industrial equipment. However, numerous methodologies models currently employed this purpose often fall short delivering highly accurate predictions. analytical approach that combines Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD as an unsupervised feature learning method to partition data into intrinsic modal functions (IMFs), which are used eliminate noise preserve essential signal. Secondly, BiLSTM integrated supervised purposes, enabling prediction decomposed sequence. Additionally, hyperparameters penalty coefficients optimized utilizing POA technique. Subsequently, various predicted trained model, individual mode predictions subsequently aggregated yield model’s definitive final life prediction. Through case studies involving IGBT aging datasets, optimal model was formulated its capability validated. superiority proposed demonstrated by comparing it benchmark other state-of-the-art methods.

Language: Английский

Citations

1