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

Isaac Ramirez,Fausto Márqueza,Behnam Mohammadi-Ivatloob

doi:10.17531/ein.2021.1.12

Isaac Ramirez, Fausto Márqueza + Show 1 more

Open Access

https://doi.org/10.17531/ein.2021.1.12

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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.

Highlights

Introduction and ObjectivesThe wind energy capacity in the world energy production is increasing every year, being one of the fastest growing renewable energy
This growth in recent years is due to the increasing size, increment of complexity of wind turbines (WTs) and favourable policies adopted by governments
WTs are complex electromechanical systems formed by a rotor that transforms the wind energy into mechanical energy and it is converted into electrical power

Summary

Introduction

The wind energy capacity in the world energy production is increasing every year, being one of the fastest growing renewable energy. This growth in recent years is due to the increasing size, increment of complexity of wind turbines (WTs) and favourable policies adopted by governments. E.g., meteorological units, refrigeration, brakes, security levels, etc., increase the complexity of the WTs [30]. Different researches reported that the gearbox, yaw and hydraulic system, electrical control and blades, concentrate the 60% of the total failures [11], being necessary the application of novel techniques or methodologies [10]

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