RESEARCH PAPER
Alarms management by supervisory control and data acquisition system for wind turbines
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.
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