Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine

Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.

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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽

10.3390/e23080975 ◽

2021 ◽

Vol 23 (8) ◽

pp. 975

Author(s):

Yancai Xiao ◽

Jinyu Xue ◽

Mengdi Li ◽

Wei Yang

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Gear Tooth ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Empirical Wavelet Transform ◽

Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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Binary Spectrum Feature for Improved Classiﬁer Performance

10.36227/techrxiv.12993122 ◽

2020 ◽

Author(s):

Nalika Ulapane ◽

Karthick Thiyagarajan ◽

sarath kodagoda

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Feature Reduction ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Monitoring Task ◽

Classifier Performance ◽

Spectrum Feature

<div>Classiﬁcation has become a vital task in modern machine learning and Artiﬁcial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classiﬁcation. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classiﬁer performance. In this paper, we consider the case of a given supervised learning classiﬁcation task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classiﬁcation performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classiﬁcation accuracy of a Support Vector Machine (SVM) classiﬁer increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽

10.3390/en14041105 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1105 ◽

Cited By ~ 2

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Andrea Lombardi ◽

Ludovico Terzi

Keyword(s):

Standard Deviation ◽

Wind Turbine ◽

Wind Turbines ◽

Curve Analysis ◽

Gaussian Kernel ◽

Support Vector ◽

Features Selection ◽

Power Curve ◽

Working Parameters ◽

Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

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Applying Machine Learning for Improving Performance Classification on Driving Behavior

IJITEE (International Journal of Information Technology and Electrical Engineering) ◽

10.22146/ijitee.56919 ◽

2021 ◽

Vol 4 (1) ◽

pp. 8

Author(s):

Ahmad Iwan Fadli ◽

Selo Sulistyo ◽

Sigit Wibowo

Keyword(s):

Machine Learning ◽

Traffic Accident ◽

Large Scale ◽

Detection System ◽

Difficult Problem ◽

Sensor Data ◽

Driving Safety ◽

Support Vector ◽

Classification Methods ◽

Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation. It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

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Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽

10.3390/en12173396 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3396 ◽

Cited By ~ 2

Author(s):

Mingzhu Tang ◽

Wei Chen ◽

Qi Zhao ◽

Huawei Wu ◽

Wen Long ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Support Vector Regression ◽

Confidence Intervals ◽

Wind Turbines ◽

Large Scale ◽

Support Vector ◽

Effective Response ◽

Support Vector Regression Model ◽

Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

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Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms

Volume 8: 28th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2016-59686 ◽

2016 ◽

Cited By ~ 4

Author(s):

Taylor Regan ◽

Rukiye Canturk ◽

Elizabeth Slavkovsky ◽

Christopher Niezrecki ◽

Murat Inalpolat

Keyword(s):

Machine Learning ◽

Damage Detection ◽

Wind Turbine ◽

Health Monitoring ◽

Turbine Blades ◽

Machine Learning Algorithms ◽

Support Vector ◽

Test Rig ◽

Wind Turbine Blades ◽

Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.

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Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Applied Sciences ◽

10.3390/app8081280 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1280 ◽

Cited By ~ 14

Author(s):

Yong Kim ◽

Youngdoo Son ◽

Wonjoon Kim ◽

Byungki Jin ◽

Myung Yun

Keyword(s):

Neural Network ◽

Machine Learning ◽

Monitoring System ◽

Multinomial Logistic Regression ◽

Learning Algorithms ◽

Feedback System ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Future Research ◽

Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Investigation of the dissipation in the wake of a wind turbine array

10.5194/wes-2021-13 ◽

2021 ◽

Author(s):

Ingrid Neunaber ◽

Joachim Peinke ◽

Martin Obligado

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Bluff Body ◽

Wind Farms ◽

Energy Transition ◽

Mean Velocity ◽

Maintenance Time ◽

Bluff Body Wake ◽

Turbine Wake ◽

Wind Turbine Wake

Abstract. Within the energy transition, more and more wind turbines are clustered in big wind farms, often offshore. Therefore, an optimal positioning of the wind turbines is crucial to optimize both the annual power production and the maintenance time. Good knowledge of the wind turbine wake and the turbulence within is thus important. However, although wind turbine wakes have been subject to various studies, they are still not fully understood. One possibility to improve the comprehension is to look into the modeling of bluff body wakes. These wakes have been the subject of intensive study for decades, and by means of the scaling behavior of the centerline mean velocity deficit, the nature of the turbulence inside a wake can be classified. In this paper, we introduce the models for equilibrium and non-equilibrium turbulence from classical wake theory as introduced by A. Townsend and W. George, and we test whether the requirements are fulfilled in the wake of a wind turbine. Finally, we apply the theory to characterize the wind turbine wake, and we compare the results to the Jensen and the Bastankhah-Porté-Agel model. We find that the insight into the classical bluff body wake can be used to further improve the wind turbine wake models. Particularly, the classical bluff body wake models perform better than the wind turbine wake models due to the presence of a virtual origin in the scalings, and we demonstrate the possibility of improving the wind turbine wake models by implementing this parameter. We also see how the dissipation changes across the wake which is important to model wakes within wind farms correctly.

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Comparison of common machine learning algorithms trained with multi-zone models for identifying the location and strength of indoor pollutant sources

Indoor and Built Environment ◽

10.1177/1420326x20931576 ◽

2020 ◽

pp. 1420326X2093157

Author(s):

Yu Huang ◽

Zhi Gao ◽

Hongguang Zhang

Keyword(s):

Machine Learning ◽

Meteorological Parameters ◽

Human Life ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Identification Accuracy ◽

Sensor Data ◽

Support Vector ◽

Accurate Identification ◽

Pollutant Sources

The accurate identification of the characteristics of pollutant sources can effectively prevent the loss of human life and property damage caused by the sudden release of harmful chemicals in emergency situations. Machine learning algorithms, artificial neural network (ANN), support vector machine (SVM), k-nearest neighbour (KNN) and naive Bayesian (NB) classification can be used to identify the location of pollutant sources with limited sensor data inputs. In this study, the identification accuracy of the four above-mentioned machine learning algorithms was investigated and compared, considering the different sensor layouts, eigenvector inputs, meteorological parameters and number of samples. The results show that the collection of pollutant concentrations over an extended period of time could improve identification accuracy. Additional sensors were required to reach the same identification accuracy after the introduction of distributed meteorological parameters. Increasing the number of trained samples by a factor of five improved the identification accuracy of KNN by 22% and that of SVM by 1.7%; however, ANN and NB classification remained basically unchanged. When identifying the release mass of the pollutant source, multiple linear, ANN and SVM regression models were adopted. Results show that ANN performs best, whereas SVM provides the least optimal performance.

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Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Energies ◽

10.3390/en11123318 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3318 ◽

Cited By ~ 4

Author(s):

Lixiao Cao ◽

Zheng Qian ◽

Hamid Zareipour ◽

David Wood ◽

Ehsan Mollasalehi ◽

...

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wavelet Packet ◽

Health Indicators ◽

Degradation Process ◽

Remaining Useful Life ◽

Operating Conditions ◽

Support Vector ◽

Useful Life ◽

Artificial Neural Network Ann

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

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