scholarly journals Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Lu Wei ◽  
Zheng Qian ◽  
Yan Pei ◽  
Jingyue Wang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Euclidean Distance ◽  
Wind Farm ◽  
Hamming Distance ◽  
Fault Identification ◽  
Farm Operators ◽  
One Year ◽  
The Right ◽  
Better Than

Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.

scholarly journals Value-Centric Approaches to Design, Operations, and Maintenance of Wind Turbines

2013 ◽  
Author(s):  
Madhur A. Khadabadi ◽  
Karen B. Marais
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Net Present Value ◽  
Operating Cost ◽  
Optimal Level ◽  
Two Dimensions ◽  
Alternative View ◽  
Operations And Maintenance ◽  
Farm Operators

Wind turbine maintenance is emerging as an unexpectedly high component of turbine operating cost and there is an increasing interest in managing this cost. Here, we present an alternative view of maintenance as a value-driver, and develop an optimization algorithm to maximize the value delivered by maintenance. We model the stochastic deterioration of the turbine in two dimensions: the deterioration rate, and the extent of deterioration, and view maintenance as an operator that moves the turbine to an improved state in which it can generate more power and so earn more revenue. We then use a standard net present value (NPV) approach to calculate the value of the turbine by deducting the costs incurred in the installation, operations and maintenance from the revenue due to the power generation. The application of our model is demonstrated using several scenarios with a focus on blade deterioration. We evaluate the value delivered by implementing blade condition monitoring systems (CMS). A higher fidelity CMS allows the blade state to be determined with higher precision. With this improved state information, an optimal maintenance strategy can be derived. The difference between the value of the turbine with and without CMS can be interpreted as the value of the CMS. The results indicate that a higher fidelity (and more expensive) condition monitoring system (CMS) does not necessarily yield the highest value, and, that there is an optimal level of fidelity that results in maximum value. The contributions of this work are twofold. First, it is a practical approach to wind turbine valuation and operation that takes operating and market conditions into account. This work should therefore be useful to wind farm operators and investors. Second, it shows how the value of a CMS can be explicitly assessed. This work should therefore be useful to CMS manufacturers and wind farm operators.

scholarly journals Wind Turbine Anomaly Identification Based on Improved Deep Belief Network with SCADA Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiafei Long ◽  
Shengqing Li ◽  
Xiwen Wu ◽  
Zhao Jin
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Domain Knowledge ◽  
Wind Farm ◽  
Prediction Models ◽  
Moving Average ◽  
Pearson Correlation ◽  
Whale Optimization

This article presents a novel fault diagnosis algorithm based on the whale optimization algorithm (WOA)-deep belief networks (DBN) for wind turbines (WTs) using the data collected from the supervisory control and data acquisition (SCADA) system. Through the domain knowledge and Pearson correlation, the input parameters of the prediction models are selected. Three different types of prediction models, namely, the wind turbine, the wind power gearbox, and the wind power generator, are used to predict the health condition of the WT equipment. In this article, the prediction accuracy of the models built with these SCADA sample data is discussed. In order to implement fault monitoring and abnormal state determination of the wind power equipment, the exponential weighted moving average (EWMA) threshold is used to monitor the trend of reconstruction errors. The proposed method is used for 2 MW wind turbines with doubly fed induction generators in a real-world wind farm, and experimental results show that the proposed method is effective in the fault diagnosis of wind turbines.

Research of Single Component Shock Methods in Wind Turbine Fault Diagnosis

2013 ◽  
Vol 805-806 ◽  
pp. 303-311
Author(s):  
Ning Jia ◽  
Tian Xia Zhang ◽  
Yuan Sheng Li ◽  
Tao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Farm ◽  
Vibration Signal ◽  
Single Component ◽  
Vibration Monitoring ◽  
Generator System ◽  
Diagnosis Method ◽  
Demodulation Method ◽  
Fault Characteristic

The structure of the wind turbine generator system is complex and it is difficult to identify the fault signals because of fault frequency aliasing on the vibration characteristics. The wind turbine fault diagnosis method is raised on single component shock to solve the vibration signal feature extraction during the wind turbines operating. Based on the principle of Hilbert envelope demodulation, this envelope demodulation method is presented for the single IMF component which contains shock fault characteristic frequency to solve the possible problem which fault Frequency is difficult to identify when the original signal is directly asked to envelope. This method has been applied and verified when a wind farm CSC-855W wind turbine vibration monitoring device was presented. The results show that compared with the traditional envelope demodulation method, by this method wind turbine fault characteristic can be more effectively and directly extracted and the accuracy of fault diagnosis can be improved. It is of great practical value.

scholarly journals Radar-derived precipitation climatology for wind turbine blade leading edge erosion

2020 ◽  
Vol 5 (1) ◽  
pp. 331-347 ◽  
Author(s):  
Frederick Letson ◽  
Rebecca J. Barthelmie ◽  
Sara C. Pryor
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Farm ◽  
Leading Edge ◽  
Wind Turbine Blade ◽  
Novel Approach ◽  
The Usa ◽  
Farm Operators ◽  
Deployment Planning ◽  
Blade Leading Edge

Abstract. Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this effect and extend blade lifetimes. This study focuses on the second issue and presents a novel approach to characterizing the erosion potential across the contiguous USA based solely on publicly available data products from the National Weather Service dual-polarization radar. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments. Results from these locations demonstrate the high spatial variability in precipitation-induced erosion potential, illustrate the importance of low-probability high-impact events to cumulative annual total kinetic energy transfer and emphasize the importance of hail as a damage vector.

scholarly journals Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

2021 ◽  
Vol 11 (17) ◽  
pp. 8065
Author(s):  
Mattia Beretta ◽  
Karoline Pelka ◽  
Jordi Cusidó ◽  
Timo Lichtenstein
Keyword(s):  
Wind Farm ◽  
Global Scale ◽  
Information Loss ◽  
Operating Data ◽  
Model Complex ◽  
Energy Domain ◽  
Industry Practice ◽  
Farm Operators ◽  
One Year ◽  
The Uk

 SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK. 

scholarly journals Komparasi Distance Measure Pada K-Medoids Clustering untuk Pengelompokkan Penyakit ISPA

2021 ◽  
Vol 5 (1) ◽  
pp. 99-107
Author(s):  
Mia Nuranti Putri Pamulang ◽  
◽  
Mia Nuur Aini ◽  
Ultach Enri3 ◽  
◽  
...  
Keyword(s):  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Model Based ◽  
The Government ◽  
The Right ◽  
Better Than

K-Medoids is an unsupervised algorithm that uses a distance measure to classify data. The distance measure is a method that can help an algorithm classify data based on the similarity of the variables. Several studies have shown that using the right distance measure can improve the performance of the algorithm in clustering. Euclidean and Chebyshev is two of some distance measures that can be used. In 2016, Karawang Health Office stated that 175.891 Karawang citizens were suffering from ISPA. This figure continued to increase in the following year until 2019. The total of Karawang citizens who suffering from ISPA reached 181.945 people. To assist the government in overcoming this problem, a clustering process will be carried out to group the areas where the ISPA is spreading in Karawang District. The area will be divided into three clusters, namely low, medium and high. Comparison of distance measures is carried out to find the best model based on the evaluation of the Davies Bouldin Index (DBI). The use of Euclidean-distance produces a DBI score of 0,088 meanwhile the use of Chebyshev distance resulted in a DBI score of 0,116. The performance of the K-Medoids algorithm with Euclidean-distance is considered to be better than Chebyshev distance because it produces a DBI score that is near to 0.

On Probabilistic Assessment of Exergy Analysis of a Wind Turbine for Optimum Performance

2021 ◽  
Author(s):  
Mubashir Ali Siddiqui ◽  
Muhammad Uzair Yousuf ◽  
Muhammad Kashan Rashid ◽  
Ahsan Ahmed
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Probability Density ◽  
Wind Farm ◽  
Exergy Efficiency ◽  
Second Law ◽  
Optimum Performance ◽  
Wind Speeds ◽  
One Year ◽  
Performance Conditions

Judgment on the performance of a wind turbine depends upon its first law efficiency as well as its second law efficiency. This paper focuses on the second law efficiency, i.e., the exergy efficiency of a wind turbine. The work introduces a novel technique to determine the optimum performance conditions of a wind turbine. Jhimpir city, Pakistan, has been selected as a case study. The wind speed distribution of the selected area is analyzed using different probability density functions. Three-parameter Weibull Distribution turns out to be the best probability density function fitting the wind speed variation. Probability distribution of total wind exergy is performed, and a one-year variation of wind exergy is plotted, showing maximum exergy around the middle of the year. The exergy efficiency of the turbine using a power curve and wind exergy is determined at different wind speeds. Probabilities of various exergy efficiencies are also determined. Results show that higher exergy efficiency has a high probability but so does low exergy efficiency due to seasonal variations. The proposed method can be extended to any wind farm provided the geographical and meteorological parameters of the site.

scholarly journals Prediction and Validation of the Annual Energy Production of a Wind Turbine Using WindSim and a Dynamic Wind Turbine Model

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6604
Author(s):  
Yuan Song ◽  
Insu Paek
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Dynamic Simulation ◽  
Energy Production ◽  
Wind Farm ◽  
Dynamic Performance ◽  
Dynamic Simulations ◽  
Wind Farm Design ◽  
One Year ◽  
Turbine Model

In this study, dynamic simulations of a wind turbine were performed to predict its dynamic performance, and the results were experimentally validated. The dynamic simulation received time-domain wind speed and direction data and predicted the power output by applying control algorithms. The target wind turbine for the simulation was a 2 MW wind turbine installed in an onshore wind farm. The wind speed and direction data for the simulation were obtained from WindSim, which is a commercial computational fluid dynamics (CFD) code for wind farm design, and measured wind speed and direction data with a mast were used for WindSim. For the simulation, the wind turbine controller was tuned to match the power curve of the target wind turbine. The dynamic simulation was performed for a period of one year, and the results were compared with the results from WindSim and the measurement. It was found from the comparison that the annual energy production (AEP) of a wind turbine can be accurately predicted using a dynamic wind turbine model with a controller that takes into account both power regulations and yaw actions with wind speed and direction data obtained from WindSim.

scholarly journals Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6905
Author(s):  
Ling Zhou ◽  
Qiancheng Zhao ◽  
Xian Wang ◽  
Anfeng Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Search Algorithm ◽  
Principal Component ◽  
Comparison Results ◽  
Auto Associative Neural Network

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

Finding the Right Problems: Some HREM Applications to Interfaces

1984 ◽  
Vol 42 ◽  
pp. 376-379
Author(s):  
D. Cherns
Keyword(s):  
Grain Boundaries ◽  
High Resolution Electron Microscopy ◽  
Boundary Structure ◽  
Atomic Structures ◽  
Grain Boundary Structure ◽  
Resolution Electron ◽  
Point To Point ◽  
The Right ◽  
New Generation ◽  
Better Than

The use of high resolution electron microscopy (HREM) to determine the atomic structure of grain boundaries and interfaces is a topic of great current interest. Grain boundary structure has been considered for many years as central to an understanding of the mechanical and transport properties of materials. Some more recent attention has focussed on the atomic structures of metalsemiconductor interfaces which are believed to control electrical properties of contacts. The atomic structures of interfaces in semiconductor or metal multilayers is an area of growing interest for understanding the unusual electrical or mechanical properties which these new materials possess. However, although the point-to-point resolutions of currently available HREMs, ∼2-3Å, appear sufficient to solve many of these problems, few atomic models of grain boundaries and interfaces have been derived. Moreover, with a new generation of 300-400kV instruments promising resolutions in the 1.6-2.0 Å range, and resolutions better than 1.5Å expected from specialist instruments, it is an appropriate time to consider the usefulness of HREM for interface studies.

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