Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network DOI Creative Commons
Minan Tang, Hongjie Wang,

Jiandong Qiu

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0314720 - e0314720

Published: Dec. 5, 2024

The large-scale integration of offshore wind power into the grid has brought serious challenges to system quality. Aiming at problem quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform crested porcupine optimizer (CPO) optimized CNN. Firstly, intrinsic mechanism waveform characteristics grid-connected disturbances are analyzed, simulated signals feature extracted time-frequency diagrams obtained by S-transform. Secondly, CPO is used optimize convolutional neural network determine best hyperparameters so that classifier achieves optimal classification performance. Then, CPO-CNN model for extraction selection multiple disturbances. Finally, simulation experimental platform established MATLAB perform verification comparative analysis classification. results show in effective, accuracy improved 3.47% compared with CNN method, which can accurately identify signals, then help assess control problems.

Language: Английский

Combined Ultra-Short-Term Photovoltaic Power Prediction Based on CEEMDAN Decomposition and RIME Optimized AM-TCN-BiLSTM DOI

Daixuan Zhou,

Yujin Liu,

Xu Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134847 - 134847

Published: Feb. 1, 2025

Language: Английский

Citations

2
A review of PV power forecasting using machine learning techniques DOI

Manvi Gupta,

Archie Arya,

U. Varshney

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100058 - 100058

Published: Jan. 1, 2025

Language: Английский

Citations

1
Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAE DOI Creative Commons
Tareq Salameh, Mena Maurice Farag, Abdul-Kadir Hamid

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100958 - 100958

Published: March 1, 2025

Language: Английский

Citations

1
EMD-CPO-GRU-based Transformer Oil Temperature Prediction DOI

Pengbo Han

Published: Feb. 27, 2025

To improve the accuracy of transformer oil temperature prediction and ensure stability safety transformers during operation, this paper proposes an innovative method—an EMD-CPO-GRU hybrid model based on Empirical Mode Decomposition (EMD), Crested Porcupine Optimization (CPO) algorithm, Gated Recurrent Unit (GRU). The method first decomposes data using EMD, effectively extracting nonlinear non-stationary characteristics signal, thereby providing more representative effective features for subsequent predictions. Next, CPO algorithm is applied to optimize key hyperparameters GRU model, establishing efficient CPO-GRU sub-models each modal component robustness model. Finally, results sub-model are weighted integrated obtain final value. Experimental show that outperforms traditional models other in tasks. In terms accuracy, achieves significant improvement, fully verifying its effectiveness as precise method. This approach not only provides a reliable basis real-time monitoring fault warning power but also offers new ideas solutions similar time-series problems.

Language: Английский

Citations

0
Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm DOI Creative Commons
Faris Kateb, Mahmoud Ragab, Felwa Abukhodair

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 17, 2025

Language: Английский

Citations

0
Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm DOI Creative Commons
Yuhan Wu,

Chun Xiang,

H.X. Qian

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4434 - 4434

Published: Sept. 4, 2024

To enhance the stability of photovoltaic power grid integration and improve prediction accuracy, a method based on an improved snow ablation optimization algorithm (Good Point Vibration Snow Ablation Optimizer, GVSAO) Bi-directional Long Short-Term Memory (Bi-LSTM) network is proposed. Weather data divided into three typical categories using K-means clustering, normalization performed minmax method. The key structural parameters Bi-LSTM, such as feature dimension at each time step number hidden units in LSTM layer, are optimized Good strategy. A model constructed GVSAO-Bi-LSTM, test functions selected to analyze evaluate model. research results show that average absolute percentage error GVSAO-Bi-LSTM under sunny, cloudy, rainy weather conditions 4.75%, 5.41%, 14.37%, respectively. Compared with other methods, this more accurate, verifying its effectiveness.

Language: Английский

Citations

3
Power Supply Risk Identification Method of Active Distribution Network Based on Transfer Learning and CBAM-CNN DOI Creative Commons

Hengyu Liu,

Jiazheng Sun,

Yongchao Pan

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4438 - 4438

Published: Sept. 4, 2024

With the development of power system, users begin to use their own supply in order improve economy, but this also leads occurrence risk self-provided supply. The actual distribution network has few samples and it is difficult identify by using conventional deep learning methods. In achieve high accuracy identification with small samples, paper proposes a combination transfer learning, convolutional block attention module (CBAM), neural (CNN) an active network. Firstly, be able further whether or not will caused based on completing faulty line, we propose that necessary captive line operation. Second, high-precision high-efficiency feature extraction, embed CBAM into CNN form CBAM-CNN model, so as extraction identification. Finally, proposed solve problem low due number fault samples. Simulation experiments show compared other methods, method highest recognition best effect, backup case fewer

Language: Английский

Citations

0
Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network DOI Creative Commons
Minan Tang, Hongjie Wang,

Jiandong Qiu

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0314720 - e0314720

Published: Dec. 5, 2024

The large-scale integration of offshore wind power into the grid has brought serious challenges to system quality. Aiming at problem quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform crested porcupine optimizer (CPO) optimized CNN. Firstly, intrinsic mechanism waveform characteristics grid-connected disturbances are analyzed, simulated signals feature extracted time-frequency diagrams obtained by S-transform. Secondly, CPO is used optimize convolutional neural network determine best hyperparameters so that classifier achieves optimal classification performance. Then, CPO-CNN model for extraction selection multiple disturbances. Finally, simulation experimental platform established MATLAB perform verification comparative analysis classification. results show in effective, accuracy improved 3.47% compared with CNN method, which can accurately identify signals, then help assess control problems.

Language: Английский

Citations

0