An Automatic Data Cleaning and Operating Conditions Classification Method for Wind Turbines Scada System

The need for renewable energy has led to a fast increase of number of the wind turbines constructed each year. To monitor wind turbines farms, operating and maintenance managers need new effective and automatic tools compatible with a large number of wind turbines. This monitoring task is usually completed by Condition Monitoring System, but researches have been conducted on the utilization of SCADA (Supervisory Control And Data Acquisition) data for condition and predictive maintenance. This paper explains the difficulty of using this new source of information, and introduces the different techniques presented in the literature form 2001 up to 2014. Two classes of approaches can be identified: “internal” approaches using only data from one turbine, and “external” approaches relying on the comparison of one turbine to the other within the same farm. Both approaches have different pros and cons: internal approaches make use of the link between the components in the same turbine and so reduce the influence of operating conditions on fault indicators; external approaches make use of the correlation between SCADA variables measured on different turbines and can thus reduce the influence of wind conditions on the fault indicators. This paper sums up the latest available techniques and it shows that new areas of research can be explored with SCADA data. The obtained fault indicators still remain sensitive to the operating conditions and stochastic variations of the wind load. Combining advantages of the two approaches could reduce both influences.

The high cost of operation and maintenance (O&M) management has become an important factor hindering the sustainable development of the wind power industry. Performing accurate condition assessment of wind turbine components to optimize the structural design and O&M strategy has become a research trend. However, the random and varying operating conditions of wind turbines make this problem difficult and challenging. A Supervisory Control and Data Acquisition (SCADA) system collects signals that contain a large amount of raw and useful information from critical wind turbine sub-assemblies. Extracting key information from the SCADA data is an economical and effective way for condition assessment. A real-time reliability assessment method of wind turbine components using a Back-Propagation Neural Network (BPNN) and SCADA data is presented in this paper. The normal behavior models are established with the processed SCADA data, and the real-time reliability of wind turbine components are assessed based on the prediction result. For verification, the BPNN-based reliability assessment method is applied to a gearbox with real SCADA data of a 1.5MW onshore wind turbine located along the southeast coast of China. The results show the capability of the proposed model in assessing the reliability of wind turbine components continuously and in real time.

The use of failure recognition technology can detect unusualness and deal with it properly to ensure the safe and stable operation of wind turbines (WT). An effective troubleshooting method can quickly distinguish the type of WT fault and reduce wind farm operation and maintenance costs. At present, the relevant data required for fault diagnosis methods comes from the supervisory control and data acquisition (SCADA) system, because the SCADA data contains information associated with the operating characteristics of WT, which can provide a rich source of data for WT fault diagnosis. A deep belief network (DBN) is commonly used as a deep learning method. In the present study, an optimized DBN based on the modified tuna swarm optimization (MTSO) algorithm was established to construct an MTSO-DBN WT fault diagnostic model so as to address the problem that the selection of DBN hyperparameters may affect the classification results. After preprocessing the WT fault data acquired by SCADA, the MTSO-DBN model was used to classify the WT faults. The experimental results reveal that, compared with the support vector machine, extreme learning machine, DBN, particle swarm optimization-DBN, and TSO-DBN classification models, the MTSO-DBN model could effectively improve the accuracy of WT faults for wind farms.

As wind farms scale to include more and more turbines, questions about turbine wake interactions become increasingly important. Turbine wakes reduce wind speed and downwind turbines suffer decreased performance. The cumulative effect of the wakes throughout a wind farm will therefore decrease the performance of the entire farm. These interactions are dynamic and complicated, and it is difficult to quantify the overall effect of the wakes. This problem has attracted some attention in terms of computational modelling for siting turbines on new farms, but less attention in terms of empirical studies and performance validation of existing farms. In this report, Supervisory Control and Data Acquisition (SCADA) data from an existing wind farm is analyzed in order to explore methods for documenting wake interactions. Visualization techniques are proposed and used to analyze wakes in a 67 turbine farm. The visualizations are based on directional analysis using power measurements, and can be considered to be normalized capacity factors below rated power. Wind speed measurements are not used in the analysis except for data pre-processing. Four wake effects are observed; including wake deficit, channel speed up, and two potentially new effects, single and multiple shear point speed up. In addition, an attempt is made to quantify wake losses using the same SCADA data. Power losses for the specific wind farm investigated are relatively low, estimated to be in the range of 3-5%. Finally, a simple model based on the wind farm geometrical layout is proposed. Key parameters for the model have been estimated by comparing wake profiles at different ranges and making some ad hoc assumptions. A preliminary comparison of six selected profiles shows excellent agreement with the model. Where discrepancies are observed, reasonable explanations can be found in multi-turbine speedup effects and landscape features, which are yet to be modelled.

PPFSCADA: Privacy preserving framework for SCADA data publishing

An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition (SCADA) normal data drive. Firstly, the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted. Then the spindle speed is employed as the output parameter, and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the pre-processed data, with the evaluation indexes selected as the optimal model. Finally, calculating the spindle operation status index according to the sliding window principle, ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event, analyzing the 2.5 MW wind turbine SCADA data from a domestic wind field as a sample, The results show that the fault warning time of the early warning model is 5.7 h ahead of the actual fault occurrence time, as well as the identification and early warning of abnormal wind turbine spindle operation without abnormal data or a priori knowledge of related faults.

Effective early warning of wind turbine failures is of great significance to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. At present, most wind farms are installed with supervisory control and data acquisition (SCADA) system, and SCADA data contains a lot of hidden information, which can be used for fault early warning. This paper uses the generator temperature and gearbox oil temperature in the SCADA data as the entry point for fault warning. Firstly, the eXtreme gradient boosting (XGBoost) algorithm is used to establish the normal temperature regression prediction model of wind turbine components. Then, the residual between the predicted value and the actual value is calculated, and the change trend of the residual is monitored by the principle of exponentially weighted moving-average (EWMA) control chart. Finally, by setting an appropriate threshold, the variation trend of the residual is judged to determine the occurrence and development of the fault. This paper uses two fault detection methods: fixed threshold and dynamic threshold based on adaptive algorithm, and compares the advantages and disadvantages of the two methods. Based on the SCADA data of a wind farm in Inner Mongolia (China), this paper designs the fault early warning test of the wind turbine generator and gearbox. The experimental results show that for the generator, the fixed fault threshold method can give the fault alarm 3 hours in advance, while the dynamic fault threshold determination method can give fault alarm 4.25 hours in advance. For gearbox, the fixed fault threshold method can give the fault alarm 2 hours in advance, while the dynamic threshold fault diagnosis method can send out the fault alarm 2.75 hours in advance.

The data fusion with synchro-phasor measurement unit (PMU) and supervisory control and data acquisition (SCADA) is the basis of improving the level of measurement and operational control of distribution network. In order to improve the accuracy of data fusion, a multi-source data fusion method based on multi-time scale is proposed to fuse PMU data and SCADA data. First of all, the high-precision PMU provides a large amount of accurate, high-frequency and multi-type data for the node state, and the topology network structure of open network is reconstructed by using the Depth-First-Search (DFS) algorithm to form an open network with PMU nodes. Secondly, the PMU measurement data is combined with the latest SCADA sampling data based on the sampling time, and the voltage and phase angle without PMU node are derived by power flow calculation. The state estimation based on least square method is used to obtain the state of the whole system. Finally, regularized residual is introduced to identify SCADA data for abnormal data. Numerical results show that the proposed model and method are effective to improve the accuracy of data fusion.

In the context of the rapid development of wind energy as a renewable resource, ensuring effective monitoring and maintenance of wind turbines is increasingly important. Despite the large volume of data collected by Supervisory Control and Data Acquisition (SCADA) systems, extracting key feature information to construct successful condition monitoring models remains a complex and challenging task. This study introduces a deep learning approach, called Convolutional Neural Network-based Support Vector Data Description (CNN-SVDD), aiming at effectively monitoring the long-term health status of wind turbines and providing timely warning for potential faults. The method employs Convolutional Neural Network (CNN) to deeply analyze time-series features and the correlations between sensors from SCADA data, capable of overcoming the challenges posed by environmental noise and changes in operational conditions. Subsequently, within the deep feature space learned by CNN, the SVDD classifier constructs a soft-boundary hypersphere. It allows for some samples to reside outside the hypersphere, thereby enhancing robustness against potential outliers in the SCADA data. Notably, the optimization processes of CNN and SVDD are linked, forming together an end-to-end process. The anomaly score generated by SVDD may act as an intelligent health indicator for assessing long-term performance degradation in wind turbines. Additionally, the radius of the hypersphere serves as a threshold for potential fault warnings. Extensive experiments on SCADA data acquired from multiple wind turbines over an 11-year period demonstrate the effectiveness of this method in early fault warning and long-term wind turbine performance degradation monitoring.

Predicting the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation business. Machine learning methods have been studied using condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data, to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques have attracted significant interest in the detection or prediction of anomalies through unsupervised learning, in which the anomaly pattern is unknown. Although autoencoder-based techniques have been proven to detect anomalies effectively using relatively stable SCADA data, they perform poorly in the case of deteriorated SCADA data. In this letter, we propose a power-curve filtering method, which is a preprocessing technique used before the application of an autoencoder-based technique, to mitigate the dirtiness of SCADA data and improve the prediction performance of wind turbine degradation. We have evaluated its performance using SCADA data obtained from a real wind-farm.

Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation

A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks

Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance.

Transmission line (TL) parameters are the basis of power system calculation. In recent years, artificial intelligence has been widely applied and plays a great role in the power system. However, artificial intelligence is rarely applied to transmission line parameter identification. Therefore, from the perspective of line model and artificial intelligence, combined with median estimation and modified Supervisory Control And Data Acquisition (SCADA) data based on TL model, this paper proposes a robust method for parameter identification of TL based on Radial Basis Function (RBF) neural network and modified SCADA data. Firstly, the line parameter identification method based on RBF neural network is proposed. Then, the training set is established through the π-equivalent model in consideration of different operating conditions and different circuit parameters. Furthermore, the input data of RBF neural network is construed by modifying the SCADA data based on TL model. In addition, the median estimation robust method, which can estimate the final result and reduce the noise interference, is introduced. Finally, the validity and practicability of the proposed method are verified by simulation data and measured SCADA data respectively.

Blade icing is one of the main problems of wind turbines installed in cold climate regions, resulting in increasing power generation loss and maintenance costs. Traditional blade icing detection methods greatly rely on dedicated sensors, such as vibration and acoustic emission sensors, which require additional installation costs and even reduce reliability due to the degradation and failures of these sensors. To deal with this challenge, this paper aims to develop a cost-effective detection system based on the existing operation data collected from the supervisory control and data acquisition (SCADA) systems which are already equipped in large-scale wind turbines. Considering that SCADA data is essentially a multivariate time series with inherent non-stationary and multiscale temporal characteristics, a new wavelet-based multiscale long short-term memory network (WaveletLSTM) approach is proposed for wind turbine blade icing detection. The proposed method incorporates wavelet-based multiscale learning into the traditional LSTM architecture and can simultaneously learn global and local temporal features of multivariate SCADA signals, which improves fault detection ability. A real case study has shown that our proposed WaveletLSTM method achieved better detection performance than the existing methods.