Search for Author, Title, Keyword
RESEARCH PAPER
Alarms management by supervisory control and data acquisition system for wind turbines
 
More details
Hide details
1
Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain
 
2
Faculty of Electrical and Computer Engineering, University of Tabriz, 5166616471 Tabriz, Iran
 
3
Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark
 
 
Publication date: 2021-03-31
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(1):110-116
 
HIGHLIGHTS
  • We propose a new approach for signal processing, fault detection and diagnosis.
  • A New approach is based on principal component analysis and artificial neural networks
  • We analyse the signals and detect the alarm activation pattern.
  • The dataset has been reduced by 93%
  • The performance of the neural network is incremented by 1000%.
KEYWORDS
ABSTRACT
Wind energy is one of the most relevant renewable energy. A proper wind turbine maintenance management is required to ensure continuous operation and optimized maintenance costs. Larger wind turbines are being installed and they require new monitoring systems to ensure optimization, reliability and availability. Advanced analytics are employed to analyze the data and reduce false alarms, avoiding unplanned downtimes and increasing costs. Supervisory control and data acquisition system determines the condition of the wind turbine providing large dataset with different signals and alarms. This paper presents a new approach combining statistical analysis and advanced algorithm for signal processing, fault detection and diagnosis. Principal component analysis and artificial neural networks are employed to evaluate the signals and detect the alarm activation pattern. The dataset has been reduced by 93% and the performance of the neural network is incremented by 1000% in comparison with the performance of original dataset without filtering process.
 
REFERENCES (36)
1.
Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018; 4: e00938, https://doi.org/10.1016/j.heli....
 
2.
Adouni A, Chariag D, Diallo D, Ben Hamed M, Sbita L. FDI based on artificial neural network for low-voltage-ride-through in dfig-based wind turbine. ISA Transactions 2016; 64: 353-364, https://doi.org/10.1016/j.isat....
 
3.
Alcaide-Moreno BA, Fuerte-Esquivel CR, Glavic M, Van Cutsem T. Electric power network state tracking from multirate measurements. IEEE Transactions on Instrumentation and Measurement 2017; 67: 33-44, https://doi.org/10.1109/TIM.20....
 
4.
Bangalore P, Letzgus S, Karlsson D, Patriksson M. An artificial neural network‐based condition monitoring method for wind turbines with application to the monitoring of the gearbox. Wind Energy 2017; 20: 1421-1438, https://doi.org/10.1002/we.210....
 
5.
Catelani M, Ciani L, Galar D, Patrizi G. Optimizing maintenance policies for a yaw system using reliability centered maintenance and datadriven condition monitoring. IEEE Transactions on Instrumentation and Measurement 2020, https://doi.org/10.1109/TIM.20....
 
6.
Chacón AMP, Márquez FPG. False alarms management by data science. Data Science and Digital Businesse 2019: 301-316, https://doi.org/10.1007/978-3-....
 
7.
Chacón AMP, Ramírez IS, Márquez FPG. False alarms analysis of wind turbine bearing system. Sustainability 2020; 12: 7867, https://doi.org/10.3390/su1219....
 
8.
Feise R.J. Do multiple outcome measures require p-value adjustment? BMC Medical Research Methodology 2002; 2: 8, https://doi.org/10.1186/1471-2....
 
9.
Galar D, Gustafson A, Tormos Martínez BV, Berges L. Maintenance decision making based on different types of data fusion. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2012; 14: 135-144.
 
10.
Gao J-X, An Z-W, Ma Q, Bai X-Z. Residual strength assessment of wind turbine rotor blade composites under combined effects of natural aging and fatigue loads. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22: 601-609, https://doi.org/10.17531/ein.2....
 
11.
García Márquez F, Pliego Marugán A, Pinar Pérez J, Hillmansen S, Papaelias M. Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies 2017; 10: 1111, https://doi.org/10.3390/en1008....
 
12.
García Márquez FP, García‐Pardo IP. Principal component analysis applied to filtered signals for maintenance management. Quality and Reliability Engineering International 2010; 26: 523-527, https://doi.org/10.1002/qre.10....
 
13.
García Márquez FP, Segovia Ramírez I, Mohammadi-Ivatloo B, Marugán AP. Reliability dynamic analysis by fault trees and binary decision diagrams. Information MDPI 2020; 11: 324, https://doi.org/10.3390/info11....
 
14.
Glowacz A. Diagnostics of direct current machine based on analysis of acoustic signals with the use of symlet wavelet transform and modified classifier based on words. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16: 554.
 
15.
Gomes P, Castro R. Wind speed and wind power forecasting using statistical models: Autoregressive moving average (ARMA) and artificial neural networks (ANN). International Journal of Sustainable Energy Development 2012; 1, https://doi.org/10.20533/ijsed....
 
16.
Gómez Muñoz CQ, García Márquez FP. A new fault location approach for acoustic emission techniques in wind turbines. Energies 2016; 9: 40, https://doi.org/10.3390/en9010....
 
17.
Holland S.M. Principal components analysis (pca). Department of Geology, University of Georgia, Athens, GA 2008: 30602-32501.
 
18.
Iqbal R, Maniak T, Doctor F, Karyotis C. Fault detection and isolation in industrial processes using deep learning approaches. IEEE Transactions on Industrial Informatics 2019; 15: 3077-3084, https://doi.org/10.1109/TII.20....
 
19.
Joyce Lee F.Z. Global wind report 2019 (global wind energy council); https://gwec.net/global-wind-r..., 2020.
 
20.
Kabiri M, Amjady N. A new hybrid state estimation considering different accuracy levels of pmu and scada measurements. IEEE Transactions on Instrumentation and Measurement 2018; 68: 3078-3089, https://doi.org/10.1109/TIM.20....
 
21.
Kalogirou SA. Artificial neural networks in renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews 2001; 5: 373-401, https://doi.org/10.1016/S1364-....
 
22.
Li G, Shi J, Zhou J. Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy 2011; 36: 352-359, https://doi.org/10.1016/j.rene....
 
23.
Li Y-F, Huang H-Z, Liu Y, Li H. A new fault tree analysis method: Fuzzy dynamic fault tree analysis. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2012; 14.
 
24.
Ludbrook J. Statistical techniques for comparing measurers and methods of measurement: A critical review. Clinical and Experimental Pharmacology and Physiology 2002; 29: 527-536, https://doi.org/10.1046/j.1440....
 
25.
Marquez FG. An approach to remote condition monitoring systems management. IET International Conference on Railway Condition Monitoring 2006: 156 - 160, https://doi.org/10.1049/ic:200....
 
26.
Márquez FPG. A new method for maintenance management employing principal component analysis. Structural Durability & Health Monitoring 2010; 6: 89-99.
 
27.
Márquez FPG, Karyotakis A, Papaelias M. Renewable energies: Business Outlook 2050. Springer: 2018.
 
28.
Marugán AP, Márquez FPG, Papaelias M. In Multivariable analysis for advanced analytics of wind turbine management, Proceedings of the Tenth International Conference on Management Science and Engineering Management, 2017; Springer: 319-328, https://doi.org/10.1007/978-98....
 
29.
Marugán AP, Márquez FPG, Perez JMP, Ruiz-Hernández D. A survey of artificial neural network in wind energy systems. Applied Energy 2018; 228: 1822-1836, https://doi.org/10.1016/j.apen....
 
30.
Pérez JMP, Márquez FPG, Tobias A, Papaelias M. Wind turbine reliability analysis. Renewable and Sustainable Energy Reviews 2013; 23:463-472, https://doi.org/10.1016/j.rser....
 
31.
Pliego Marugán A, Garcia Marquez FP, Lev B. Optimal decision-making via binary decision diagrams for investments under a risky environment. International Journal of Production Research 2017; 55: 5271-5286, https://doi.org/10.1080/002075....
 
32.
Rademakers L, Braam H, Zaaijer M, Van Bussel G. In Assessment and optimisation of operation and maintenance of offshore wind turbines, Proc. EWEC, 2003.
 
33.
Saufi SR, Ahmad ZAB, Leong MS, Lim MH. Gearbox fault diagnosis using a deep learning model with limited data sample. IEEE Transactions on Industrial Informatics 2020; 16: 6263-6271, https://doi.org/10.1109/TII.20....
 
34.
Schlechtingen M, Ferreira Santos I. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection. Mechanical Systems and Signal Processing 2011; 25: 1849-1875, https://doi.org/10.1016/j.ymss....
 
35.
Walford CA. Wind turbine reliability: Understanding and minimizing wind turbine operation and maintenance costs; Sandia National Laboratories 2006, https://doi.org/10.2172/882048.
 
36.
Zuber N, Bajrić R. Gearbox faults feature selection and severity classification using machine learning. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2020; 22: 748-756, https://doi.org/10.17531/ein.2....
 
 
CITATIONS (17):
1.
Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1
Rio del, Isaac Ramirez, Fausto Marquez
 
2.
International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing
Ramírez Segovia, Sánchez Bernalte, Márquez García
 
3.
Proceedings of the Fifteenth International Conference on Management Science and Engineering Management
Chacón Peco, Fausto García Márquez
 
4.
Ubiquitous Intelligent Systems
del Muñoz, Ramirez Segovia, Márquez García
 
5.
Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1
Alberto Marugan, Fausto Marquez, Jesus Pinar-Perez
 
6.
Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1
Ana Chacon, Fausto Márquez
 
7.
Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1
Pedro Sanchez, Isaac Ramirez, Fausto Marquez
 
8.
Inventive Systems and Control
Isaac Segovia, Pedro Bernalte, Fausto Márquez
 
9.
Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring
Márquez García, Sánchez Bernalte, Ramírez Segovia
Structural Health Monitoring
 
10.
International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing
Isaac Ramirez, Márquez García
 
11.
International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing
Ana Chacón, Isaac Ramirez, Márquez García
 
12.
Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1
Ashutosh Dubey, Abhishek Kumar, Isaac Ramirez, Fausto Marquez
 
13.
Artificial intelligence-based hybrid forecasting models for manufacturing systems
Maria Rosienkiewicz
Eksploatacja i Niezawodnosc - Maintenance and Reliability
 
14.
False alarm detection in wind turbine by classification models
Chacón Peco, Ramirez Segovia, Márquez García
Advances in Engineering Software
 
15.
Pattern recognition based on statistical methods combined with machine learning in railway switches
del Muñoz, Ramirez Segovia, Mayorkinos Papaelias, García Pedro
Expert Systems with Applications
 
16.
Research on false alarm detection algorithm of nuclear power system based on BERT-SAE-iForest combined algorithm
Xiangyu Li, Kun Cheng, Tao Huang, Sichao Tan
Annals of Nuclear Energy
 
17.
Reliability of Wind Turbines In Renewable Rich Microgrid
Mehdi Valiyev
2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT)
 
eISSN:2956-3860
ISSN:1507-2711
Journals System - logo
Scroll to top