Abstract
Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.
Highlights
Nowadays, the fastest growing renewable source of energy in the world is wind energy [1].This technology makes an important contribution to meeting the ambitious climate and energy objectives for 2020 established by the European Commission [2]
Operation and maintenance (O&M) improvement tasks are crucial for all the actors involved, taking into account the current economic situation of the sector, without generation bonuses and with the sales price policy of generation restricted by the new regulations
A logistic regression method was used as it generated low mean squared error (MSE) and mean absolute error (MAE) results compared to other methods [22]
Summary
The fastest growing renewable source of energy in the world is wind energy [1]. This technology makes an important contribution to meeting the ambitious climate and energy objectives for 2020 established by the European Commission [2]. It is calculated that the operation and maintenance costs of wind farms represent a range between 10–35% of total. Operation and maintenance (O&M) improvement tasks are crucial for all the actors involved, taking into account the current economic situation of the sector, without generation bonuses and with the sales price policy of generation restricted by the new regulations (see [6,7])
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