Abstract
Condition monitoring for wind turbines is tailored to predict failure and aid in making better operation and maintenance (O&M) decisions. Typically the condition monitoring approaches are concerned with predicting the remaining useful lifetime (RUL) of assets or a component. As the time-based measures can be rendered absolute when changing the operational set-point of a wind turbine, we propose an alternative in a power-based condition monitoring framework for wind turbines, i.e., the remaining power generation (RPG) before a main bearing failure. The proposed model utilizes historic wind turbine data, from both run-to-failure and non run-to-failure turbines. Comprised of a recurrent neural network with gated recurrent units, the model is constructed around a censored and uncensored data-based cost function. We infer a Weibull distribution over the RPG, which gives an operator a measure of how certain any given prediction is. As part of the model evaluation, we present the hyper-parameter selection, as well as modeling error in detail, including an analysis of the driving features. During the application on wind turbine main bearing failures, we achieve prediction in the magnitude of 1 to 2 GWh before the failure. When converting to RUL this corresponds to predicting the failure, on average, 81 days beforehand, which is comparable to the state-of-the-art’s 94 days predictive horizon in a similar feature space.
Highlights
Wind energy remains the leader in renewable energy sources with expected continued growth [1].In order to maintain and monitor the ever-increasing fleet of wind turbines, health prognostics and condition monitoring (CM) techniques are employed, covering abnormality detection, failure mode tracking, and prediction [2,3,4,5,6,7] of failures in rotating parts, in particular, the main bearing
The operator would lose all revenue generated by that particular turbine. To circumvent this dilemma and provide a physical measure of the remaining operation of a failing wind turbine, we propose an approach with a focus on the remaining power generation (RPG)
Remaining Power Generation for Main Bearing Failures. This project is concerned with the RPG of main bearing failures and limited to turbines of the same class, i.e., three balded units composed of the same parts and rated at the same power
Summary
Wind energy remains the leader in renewable energy sources with expected continued growth [1]. Recent works have shown predictive capabilities beyond 90 days with reasonable accuracy [3] These approaches have one thing in common: all perform predictions with respect to the remaining useful lifetime (RUL) and life cycles of an asset or component. These aid in operation and maintenance (O&M) efforts, they are not directly related to the physical nature of a wind turbine, i.e., power production. Energies 2020, 13, 3406 the best course of action would be to shut down the turbine and thereby extend the turbine’s RUL to infinity, or until the socket’s or tower’s expected lifetime In this scenario, the operator would lose all revenue generated by that particular turbine.
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