Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

Abstract

-This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

“…It was determined that the metrics most frequently found in the consulted articles were RMSE and MAE. In some articles, more than one metric was used to evaluate the accuracy of the proposed models, as can be found in [16,92].…”

“…In [10], it was mentioned that O&M costs represented approximately 20% to 25% of the total power generation costs for offshore WFs and 10% to 15% of the total generation costs for onshore ones. The authors in [11][12][13][14][15][16] broadly agreed that WF O&M costs could account for approximately 25% to 35% of the total cost of power generation. These O&M cost percentages could be increased by recurring failures in the different components of a WT.…”

A health condition assessment method of wind turbine generator system based on multiple turbines cooperation

“…Compared with onshore WTGSs, the offshore WTGSs face a more serious working environment subjecting them to higher failure rates and longer downtime than traditional power generation systems [5]. The operation and maintenance (O&M) costs of WTGSs account for approximately 25–30% of the overall energy generation cost [6]. Consequently, the real‐time health condition assessments have become increasingly important to avoid high electric power lose and save maintenance costs.…”

Applying design knowledge and machine learning to scada data for classification of wind turbine operating regimes

“…design knowledge) and algorithms for dimensionality reduction and clustering. Several works exist that use data from the SCADA system for CM and SHM [1][2][3][4][5][6][7], fault-detection [8][9][10][11][12], and performance assessment [13,14]. Some of these applied signal processing techniques and learning algorithms such as Artificial Neural Networks (ANN).…”