Complex Fault Source Identification Method for High-Voltage Trip-Offs of Wind Farms Based on SU-MRMR and PSO-SVM

Wang, Yurong; Zhu, Yingjun; Wang, Qi; Tang, Yi; Duan, Fangwei; Yang, Yingxuan

doi:10.1109/access.2020.3008211

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…Authors in [40] suggested a hybrid fault diagnostic strategy that uses SVM and enhanced particle swarm optimization (PSO) to perform additional diagnostics based on qualitative reasoning. A technique for fault source detection and identification of wind farm high-voltage trip-offs is proposed in [41] based on multi-dimensional attribute indices and the PSO-SVM method. The PSO method is utilized in the hyperparameters SVM optimization for better classification performance in the unbalance and balance mode, single and/or compound fault source identification.…”

Section: A Related Workmentioning

confidence: 99%

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

et al. 2021

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This paper proposes a protection scheme for multi-terminal high voltage direct current (MT-HVDC) transmission grids. The proposed scheme is based on three protection levels; di/dt overcurrent protection as the main protection, dv/dt undervoltage protection as a backup one protection. If the main and backup one fails to detect and clear the fault current, then the fault tripping and clearing is accomplished using the overcurrent protection by AC circuit breakers (ACCBs) in the AC grid side after a coordinated time. The tripping in the main and backup one protection levels are based on using fault isolation devices (FIDs) tripping. Support vector machine (SVM) is proposed for the fault detection and discrimination in the MT-HVDC using the backup two protection system. The Internet of things (IoT) has been used for monitoring and coordination between the AC grid and MT-HVDC grid. The proposed protection scheme is applied and verified on the MT-HVDC network under different transmission system faults using the MATLAB/Simulink ® environment. The results show that the proposed scheme's effectiveness as a fast and reliable scheme in fault detection and discrimination for the MT-HVDC grids.

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Section: A Related Workmentioning

confidence: 99%

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

et al. 2021

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“…e algorithm was effective in high-precision wind speed predictions. Reference [12] proposed a particle swarm optimization-based support vector machine for power forecasting, but there were problems with excessive errors.…”

Section: Introductionmentioning

confidence: 99%

New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model

You

Bai

Wang

et al. 2022

Journal of Electrical and Computer Engineering

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Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms.

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Application of symmetric uncertainty and emperor penguin–grey wolf optimisation for feature selection in motor fault classification

Lee,

Le,

Chien

et al. 2024

IET Electric Power Appl

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The authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three primary phases: feature extraction, feature selection and classification. During the feature extraction phase, crucial features are identified using empirical mode decomposition, fast Fourier transform and multiresolution analysis, resulting in a total of 144 features. The feature selection stage employs a new strategy that combines symmetrical uncertainty in the filter approach with the binary grey wolf optimiser and emperor penguin optimiser in the wrapper approach. Finally, a support vector machine is used for classification to generate fitness values. To validate the model's effectiveness and accuracy, motor fault current signal datasets, case Western Reserve University (CWRU) benchmark datasets and mechanical failure prevention technology benchmark datasets are utilised. In the motor fault current signal dataset, the highest average accuracy achieved is 99.95%, with a minimum average running time of 88.02 s obtained under ∞dB conditions. Regarding benchmark datasets and mechanical failures at CWRU, using the prevention technology benchmark dataset resulted in classification accuracies of 99.54% and 99.52%, respectively. Comparative analysis with traditional algorithms reveals that symmetric uncertainty and emperor penguin–grey wolf optimisation model outperforms traditional models in terms of performance.

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Complex Fault Source Identification Method for High-Voltage Trip-Offs of Wind Farms Based on SU-MRMR and PSO-SVM

Cited by 7 publications

References 21 publications

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

IoT Technology-Based Protection Scheme for MT-HVDC Transmission Grids With Restoration Algorithm Using Support Vector Machine

New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model

Application of symmetric uncertainty and emperor penguin–grey wolf optimisation for feature selection in motor fault classification

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