Abstract
With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.
Highlights
The number and size of wind turbines (WTs) are increasing, and operation and maintenance costs constitute up to 30% of the total energy cost of WTs [1]
This section is divided into three sub-sections; i.e., (1) modeling of Normal Condition, which is based on training an appropriate artificial neural network; (2) modeling of risk Indicator, which includes the mathematical formulation for modeling the condition of the system and, ; (3) modeling of safe band optimization, which address the an optimization problem
As it is shown in this figure, the inside temperature of the nacelle is about 15–20 °C higher than the ambient temperature, which is rooted in the operation of the WT and the cooling system
Summary
The number and size of wind turbines (WTs) are increasing, and operation and maintenance costs constitute up to 30% of the total energy cost of WTs [1]. The most common faults for WT transformers are combustion, and an abnormal increase in electrical resistance was frequently detected in a large number of windings and lead bars To solve these problems, the authors at [21] propose a series of characterization methods to investigate assembly structure, matrix materials, and macro/microscopic morphologies of failed transformers. Introducing an optimal risked-based methodology for WT condition monitoring; Proposing an artificial neural network-based model for estimating the normal condition of WT key components; Presenting a real-time risk indicator, which is used in the health monitoring and anomaly detection of WT. An optimal temperature-based condition monitoring is proposed for WTs. In the first stage, the normal condition of the WT’s key components, i.e., gearbox, converter, generator, and transformer, has been estimated through an artificial neural network model.
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