Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States

Fu, Lei; Wei, Yanding; Fang, Sheng; Zhou, Xiaojun; Lou, Junqiang

doi:10.3390/en10101564

Cited by 16 publications

(10 citation statements)

References 48 publications

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“…As entropy increases with the degree of randomness for an observed system, it can be utilized in a quantitative analysis for feature extraction in the field of PQD detection. Permutation entropy is one of most popular entropies in signal processing [22]. The temporal information of the monitored object is recognized according to permutation entropy by counting the ordinal patterns.…”

Section: Permutation Entropymentioning

confidence: 99%

“…In highlighting VMD, many studies have tried to enhance its application in signal feature extraction. Fu et al [22] proposed a VMD-based method to assess the health status of wind turbines in condition monitoring fields. Samantaray et al [23] investigated the detection and classification method for single and mixed power quality disturbances by utilizing VMD and a decision tree.…”

Section: Introductionmentioning

confidence: 99%

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Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Zhu

Pan

et al. 2019

Applied Sciences

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Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

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Section: Permutation Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Zhu

Pan

et al. 2019

Applied Sciences

Self Cite

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“…The accurate prediction of single power plants can no longer meet the scheduling and safe operation of large-scale power systems [1]. Regional PV power output is large and relatively stable, and the accurate prediction of regional power generation can help dispatchers develop reasonable power dispatching plans, ensure stable operation of the power grid, reduce standby power generation, decrease operating costs, and lessen the impact of the intermittent characteristics on the power system [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Yang

Yao

et al. 2019

Energies

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Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

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“…In the energy industry, CM is mainly applied to rotating and reciprocating machineries, such as steam turbines, gas turbines that run at large firing temperatures [4][5][6][7], rotating electrical machineries [8,9], the use of new components and system architectures, and the modifications of the operational and environmental conditions. This evolution reflects in modifications of the system behavior, which are typically referred to as concept drifts or operations in an evolving environment (EE) [11,34,35]. To account for these, it is necessary to periodically update the models for signal reconstruction in normal conditions for anomaly detection and classification.…”

Section: Introductionmentioning

confidence: 99%

A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments

et al. 2019

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Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synthetic case study and a real case study concerning the monitoring of the tank pressure of an aero derivative gas turbine lube oil system. The CM model provides satisfactory performances in terms of classification accuracy, while remarkably reducing the expert efforts for data labeling and model (periodic) updating.

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Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States

Cited by 16 publications

References 48 publications

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments

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