Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Bai, Xiaoze; Li, Mingduo; Di, Zhigang; Dong, Weichao; Liang, Jing; Zhang, Jingxuan; Sun, Hexu

doi:10.1002/ese3.1637

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…In [17], the Renyi entropy is adopted in the feature selection process to deal with the hard fault and soft fault diagnosis in a superbuck converter circuit (SCC). The "signal decomposition+information entropy" based methods were also adopted in [39][40][41], demonstrating the simplicity and effectiveness of information entropy in fault diagnosis within multi-phase symmetric systems. • Fuzzy Entropy.…”

Section: Entropy-based Diagnostic Methodsmentioning

confidence: 99%

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

Zhou,

Zhao,

2024

Symmetry

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Power converters can convert the electrical energy output by power source into specific forms required by target device. They are widely used in various fields such as electrification, transportation, and power systems, and are the core components of the systems. Power converters are composed of power semiconductor devices (such as IGBTs), which are prone to failure due to abnormal conditions and aging degradation, leading to power converter faults. Therefore, over the past twenty years, extensive fault diagnosis methods for power converters have been studied and proposed. Among them, symmetry-based methods have the characteristics of rapid diagnosis speed and strong robustness, but so far there’s no systemic summary of such methods. Therefore, this paper reviews the relevant research on fault diagnosis of power converters based on symmetry in recent years, analyzes the mathematical principles of these methods, and compares their diagnostic performance. In addition, two factors that may have an influence on the symmetry are pointed out. This paper provides references and suggestions for the future development and research of power converter fault diagnosis.

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Section: Entropy-based Diagnostic Methodsmentioning

confidence: 99%

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

Zhou,

Zhao,

2024

Symmetry

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“…After denoising, variance extraction follows to provide insights into the spread of signal values within each window and to capture the variability of signals over time [27]. In this implementation, a window of size 300 samples is moved along the signal, and at each position, the variance of the signal within that window is computed.…”

Section: Variance Extractionmentioning

confidence: 99%

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

Berghout,

Benbouzid

2024

Electronics

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Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment.

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A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

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Short‐term load forecasting is critical for power system planning and operations, and ensemble forecasting methods for electricity loads have been shown to be effective in obtaining accurate forecasts. However, the weights in ensemble prediction models are usually preset based on the overall performance after training, which prevents the model from adapting in the face of different scenarios, limiting the improvement of prediction performance. In order to improve the accurateness and validity of the ensemble prediction method further, this paper proposes an ensemble deep reinforcement learning approach using Q‐learning dynamic weight assignment to consider local behaviours caused by changes in the external environment. Firstly, the variational mode decomposition is used to reduce the non‐stationarity of the original data by decomposing the load sequence. Then, the recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU) are selected as the basic power load predictors. Finally, the optimal weights are ensembled for the three sub‐predictors by the optimal weights generated using the Q‐learning algorithm, and the final results are obtained by combining their respective predictions. The results show that the forecasting capability of the proposed method outperforms all sub‐models and several baseline ensemble forecasting methods.

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Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Cited by 5 publications

References 24 publications

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

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