Wind farm monitoring using Mahalanobis distance and fuzzy clustering

González-Carrato, Raúl Ruiz de la Hermosa

doi:10.1016/j.renene.2018.02.097

Cited by 37 publications

(18 citation statements)

References 73 publications

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“…The Mahalanobis Distance Method is also a calculation method for distance between vectors. Its main advantage is that it is not impacted by the choice of dimensions and can eliminate the correlation between variables [ 52 , 53 , 54 ]. A Positive Optimal Solution vector with the optimal solutions of various indicators has been composed, and a Negative Optimal Solution vector with the worst solutions has also been constructed.…”

Section: Methodsmentioning

confidence: 99%

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

Jiang

Yang

2020

IJERPH

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At present, China’s air pollution and its treatment effect are issues of general concern in the academic circles. Based on the analysis of the development stages of air pollution in China and the development history of China’s air quality standards, we selected 17 cities of Shandong Province, China as the research objects. By expanding China’s existing Air Quality Index System, the air quality of six major pollutants including PM2.5 and PM10 in 17 cities from February 2017 to January 2020 is comprehensively evaluated. Then, with a forecast model, the air quality of the above cities in the absence of air pollution control policies since June 2018 was simulated. The results of the error test show that the model has a maximum error of 4.67% when simulating monthly assessment scores, and the maximum mean error of the four months is 3.17%. Through the comparison between the simulation results and the real evaluation results of air quality, we found that since June 2018, the air pollution control policies of six cities have achieved more than 10% improvement, while the air quality of the other 11 cities declined. The different characteristics of pollutants and the implementation of governance policies are perhaps the main reasons for the above differences. Finally, policy recommendations for the future air pollution control in Shandong and China were provided.

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Section: Methodsmentioning

confidence: 99%

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

Jiang

Yang

2020

IJERPH

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“…In [195], the authors develop a fuzzy clustering proposal and the Mahalanobis distance for the failure detection of a WT. Based on the component failure and weather variables (humidity, temperature, wind speed and direction), a model of the power curve is obtained by fuzzy clustering and parametric fitting techniques; then, the location and distance to the warnings and alarm curve recorded by the SCADA are analyzed.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

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“…CMS provides direct information reflecting a mechanical fault or failure, whereas SCADA system provides all other auxiliary information which assists in improving the efficiency and accuracy of the fault diagnosis and detection. SCADA system, for example, is an important subsystem in an energy management system which helps reduce the system operation and maintenance costs [5].…”

Section: Introductionmentioning

confidence: 99%

On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach

et al. 2019

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In order to reduce operation and maintenance cost and improve fault diagnosis and detection accuracy for wind turbines, a study on advanced methods has been carried out. The purpose of this paper is to present a new method developed using radar chart and support vector machine (SVM) approach for fault diagnosis and prediction of wind turbine pitch system as it usually has a higher failure rate. In the study, the supervisory control and data acquisition (SCADA) system data are utilized as source data for SVM prediction. First of all, the characteristics of the indicator variable data collected by the SCADA system are analyzed, and the radar charts corresponding to the normal and faulty operation of the wind turbine pitch system are constructed using the indicator variable data. Secondly, the SVM method is used to extract the gray-level co-occurrence matrix (GLCM) features and histogram of oriented gradients (HOG) features of the radar charts, and the SVM classifier is trained. Then, the operational status is predicted, the classification effect is evaluated by the confusion matrix, and the prediction evaluation index is calculated. Thirdly, the support vector regression method is used to analyze the SCADA indicator variable data, the input and output of the regression model are determined, and the training prediction model is established, and the prediction accuracy of the test model is analyzed using the test sample data. Finally, the forecasting evaluation indexes obtained by the above two methods are compared. It proves that the proposed method using SVM to analyze the system radar charts has a higher prediction accuracy of 91.24% than the support vector regression method. The prediction accuracy is improved by 8.6%. Hence, it is verified that the new method using a radar chart and SVM approach has superiority over the support vector regression method.

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Wind farm monitoring using Mahalanobis distance and fuzzy clustering

Cited by 37 publications

References 73 publications

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach

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