Supervisory Control And Data Acquisition Data Research Articles

On the use of Artificial Intelligence for Condition Monitoring in Horizontal-Axis Wind Turbines

Wind power is one of the fastest-growing renewable energy sectors and is considered instrumental in the ongoing decarbonization process. However, wind turbines (WTs) present high operation and maintenance costs caused by inefficiencies and failures, leading to everincreasing attention to effective Condition Monitoring (CM) strategies. Nowadays, modern WTs are integrated with sensor networks as part of the Supervisory Control and Data Acquisition (SCADA) system for supervision purposes. CM of wind farms through predictive models based on routinely collected SCADA data is envisaged as a viable mean of improving producibility by spotting operational inefficiencies. In this paper, we introduce an unsupervised anomaly detection framework for wind turbine using SCADA data. It involves the use of a multivariate feature selection algorithm based on a novel Combined Power Predictive Score (CPPS), where the information content of combinations of variables is considered for the prediction of one or more key parameters. The framework has been tested on SCADA data collected from an off-shore wind farm, and the results showed that it successfully detects anomalies and anticipates major bearing failures by outperforming a recent deep neural approach.

A dynamic threshold method for wind turbine fault detection based on spatial-temporal neural network

Strong coupling of wind turbines (WTs) makes the supervisory control and data acquisition (SCADA) data spatially relevant and time-dependent. Aiming at identifying the fault state of wind turbines accurately by comprehensively using the change information in the spatial and temporal scale of SCADA data, a novel fault detection method of WTs based on spatial-temporal features' fusion of SCADA data by a deep autoencoder (DAE) and a gated recurrent unit (GRU) is proposed in this paper. The spatial feature extraction capability of the encoder layer and the temporal feature extraction capability of GRU are combined with the symmetric framework of DAE to achieve the spatial-temporal feature extraction of the whole model. Mahalanobis distance (MD) is adopted to convert the multidimensional output into a one-dimensional performance index. To avoid the influence on the fault detection results due to the changeable operational state of WTs, a dynamic threshold regression method based on the gray wolf optimization algorithm and support vector regression model is developed to identify fault data instances. Experiments with SCADA data from real wind farms verify the effectiveness of the proposed method.

Research on Anomaly Detection of Wind Farm SCADA Wind Speed Data

Supervisory control and data acquisition (SCADA) systems are critical for wind power grid integration and wind farm operation and maintenance. However, wind turbines are affected by regulation, severe weather factors, and mechanical failures, resulting in abnormal SCADA data that seriously affect the usage of SCADA systems. Thus, strict and effective data quality control of the SCADA data are crucial. The traditional anomaly detection methods based on either “power curve” or statistical evaluation cannot comprehensively detect abnormal data. In this study, a multi-approach based abnormal data detection method for SCADA wind speed data quality control is developed. It is mainly composed of the EEMD (Ensemble Empirical Mode Decomposition)-BiLSTM network model, wind speed correlation between adjacent wind turbines, and the deviation detection model based on dynamic power curve fitting. The proposed abnormal data detection method is tested on SCADA data from a real wind farm, and statistical analysis of the results verifies that this method can effectively detect abnormal SCADA wind data. The proposed method can be readily applied for real-time operation to support an effective use of SCADA data for wind turbine control and wind power prediction.

Early Fault Diagnosis Strategy for WT Main Bearings Based on SCADA Data and One-Class SVM

To reduce the levelized cost of wind energy, through the reduction in operation and maintenance costs, it is imperative that the wind turbine downtime is reduced through maintenance strategies based on condition monitoring. The standard approach toward this challenge is based on vibration monitoring, which requires the installation of specific tailored sensors that incur associated added costs. On the other hand, the life expectancy of wind parks built during the 1990s wind power boom is dwindling, and data-driven maintenance strategies issued from already accessible supervisory control and data acquisition (SCADA) data is an auspicious competitive solution because no additional sensors are required. Note that it is a major issue to provide fault diagnosis approaches built only on SCADA data, as these data were not established with the objective of being used for condition monitoring but rather for control capacities. The present study posits an early fault diagnosis strategy based exclusively on SCADA data and supports it with results on a real wind park with 18 wind turbines. The contributed methodology is an anomaly detection model based on a one-class support vector machine classifier; that is, it is a semi-supervised approach that trains a decision function that categorizes fresh data as similar or dissimilar to the training set. Therefore, only healthy (normal operation) data is required to train the model, which greatly expands the possibility of employing this methodology (because there is no need for faulty data from the past, and only normal operation SCADA data is needed). The results obtained from the real wind park show that this is a promising strategy.

A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification

Effective condition monitoring is critical to improve the reliability and reduce unplanned downtime of wind turbines (WTs). Supervisory control and data acquisition (SCADA) data with the advantages of easy access and low cost, has been widely used in wind turbine condition monitoring (WTCM). While the existing literature based on SCADA data for WTCM provides whether the condition is normal or abnormal, few have attempted to identify the underlying causes of the anomaly. In this paper, a two-stage anomaly decomposition scheme based on the multi-variable correlation extraction is proposed for WT fault detection and anomaly causes identification. Firstly, a normal behavior model (NBM) based on the multi-variable correlation extraction (Co-NBM) is proposed for WT fault detection using only SCADA data. A network based on the multi-head self-attention mechanism is designed to extract correlation among the variables. It can effectively provide early warning and reduce false alarms rate by exploring the multi-variable correlations. Furthermore, a two-stage anomaly decomposition scheme based on the Hotelling’s T2 method is proposed for WT fault identification. It is capable of effectively identifying the fault locations and anomaly causes based on the defined two-stage abnormal factors. Compared with auto-encoder (AE) and LSTM-attention methods, the proposed method could achieve better performance in fault detection and reduce the false alarm rate. Moreover, the proposed method could also provide the underlying cause of the anomaly, which is useful for the decision making of WT maintenance.

Wind turbine gearbox fault prognosis using high-frequency SCADA data

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

Fault classification in wind turbine based on deep belief network optimized by modified tuna swarm optimization algorithm

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.

Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network

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.

Short-term wind speed forecast for Urla wind power plant: A hybrid approach that couples weather research and forecasting model, weather patterns and SCADA data with comprehensive data preprocessing

Short-term wind speed forecast model that uses both supervisory control and data acquisition (SCADA) based data and weather research and forecasting (WRF) model outputs for Urla wind power plant (WPP) has been proposed in this study. Two different WRF models were run to gather atmospheric variables from four surrounding grids of Urla WPP and calculate weather patterns affecting Urla WPP. After detecting outliers in the SCADA data by coupling of k-mean and isolation forest (IF) methods, statistical methods were used for data treatment and the outputs of WRF models were used for missing data imputation. The effect of each data type and data preprocessing techniques on the model was evaluated separately. The best model performance was achieved with 0.9085 [Formula: see text], and 0.81 MAE in the dataset which includes each data type and each data preprocessing was applied on. Otherwise, the dominant weather pattern affecting Urla WPP was found to be purely advective and the best result was achieved in this pattern.

An Improved Mixture Density Network Via Wasserstein Distance Based Adversarial Learning for Probabilistic Wind Speed Predictions

This paper develops a novel improved mixture density network via Wasserstein distance-based adversarial learning (WA-IMDN) for achieving more accurate probabilistic wind speed predictions (PWSP). The proposed method utilizes the historical supervisory control and data acquisition (SCADA) system data collected from multiple wind turbines (WTs) in different wind farms to predict the wind speed probability density function (PDF) of a targeted WT at the next timestamp. To better capture the fluctuation pattern of historical wind speed sequences and estimate parameters of the probability mixture model for approximating the wind speed PDF, an improved mixture density network (IMDN) is proposed. To address drawbacks of the traditional maximum likelihood estimation (MLE) on training the mixture density network, a Wasserstein distance (WD)-based adversarial learning is developed and the reparameterization trick is employed for the gradient delivery. The effectiveness of the proposed WA-IMDN is validated based on SCADA data (One dataset is publicly accessible) by benchmarking against a set of the commonly considered and recently reported PWSP methods, such as the mixture density network (MDN), maximum likelihood estimation (MLE) based mixture density attention network (MLE-IMDN), recent DMDNN and Improved Deep Mixture Density Network (IDMDN). Results demonstrate the superior performance of the proposed WA-IMDN on the PWSP. To demonstrate the repeatability of the presented research, we release our code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IkeYang/WA-IMDN-</uri> .

A Wind Power Forecasting Model Using LSTM Optimized by the Modified Bald Eagle Search Algorithm

High-precision forecasting of short-term wind power (WP) is integral for wind farms, the safe dispatch of power systems, and the stable operation of the power grid. Currently, the data related to the operation and maintenance of wind farms mainly comes from the Supervisory Control and Data Acquisition (SCADA) systems, with certain information about the operating characteristics of wind turbines being readable in the SCADA data. In short-term WP forecasting, Long Short-Term Memory (LSTM) is a commonly used in-depth learning method. In the present study, an optimized LSTM based on the modified bald eagle search (MBES) algorithm was established to construct an MBES-LSTM model, a short-term WP forecasting model to make predictions, so as to address the problem that the selection of LSTM hyperparameters may affect the forecasting results. After preprocessing the WP data acquired by SCADA, the MBES-LSTM model was used to forecast the WP. The experimental results reveal that, compared with the PSO-RBF, PSO-SVM, LSTM, PSO-LSTM, and BES-LSTM forecasting models, the MBES-LSTM model could effectively improve the accuracy of WP forecasting for wind farms.

A new four‐step method to identify the parameters of transmission line based on SCADA data

Accurate parameters of transmission line (ParTL) are the basis of power system analysis. For high-voltage transmission line (TL), remote terminal units (RTUs) are widely installed to provide supervisory control and data acquisition (SCADA) measurements. Based on multiple snapshots of SCADA data at both ends of the TL, a four-step method to identify ParTL is proposed in this paper. In detail, based on the π-model of TL, the relationship between voltage phase angle difference (VPADs) and parameter identification is presented. And then, the general estimation formulation for VPADs is established. Furthermore, four steps to simplify and solve the formulation are detailed, that is, firstly after the approximated relationship between VPADs and reactance are derived, the estimation of VPADs at multiple snapshots is converted into the optimal problem with reactance of TL, just one variable, and the VPADs are estimated roughly; secondly based on the median estimation, the ParTL are identified roughly; thirdly, combined with Taylor series expansions and the alternating current power flow model, the VPADs are estimated accurately; fourthly, the ParTL are identified accurately. Finally, the results with simulated and field data verify the effectiveness and practicality of the proposed method.

An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection

Wind power curve (WPC) is established through data collected from the Supervisory Control and Data Acquisition (SCADA) system of each wind turbine, which can be used to analyze the operation status. However, numerous outliers are contained in SCADA data caused by wind turbine failures, shutdown maintenance or other extreme conditions to deform the wind power curve. This paper proposes a data cleaning algorithm for wind turbine abnormal data based on wind power curve image by color space conversion and image feature detection. Considering wind speed, wind power and data frequency, a three-dimensional (3D) WPC image is constructed. The scattered outliers are cleared by their statistical characteristics. The Canny edge detection and Hough transform are introduced to extract image features of stacked outliers and locate them accurately. The proposed algorithm is compared with three common outlier detection algorithms, including two data-based algorithms and an image-based algorithm. Extensive experiments conducted on the data of 22 wind turbines from two different wind farms in China indicate the efficiency, stability and reliability of the proposed algorithm.

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges, and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great numbers of data, such as supervisory control and data acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), are already being captured, these data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation offshore wind turbines (OWTs). In the current study the availability of high-frequency SCADA (1 Hz) and acceleration data (&gt;1 Hz) as well as regular 10 min SCADA is taken as the starting point. Along these measured values, the current work also investigates the inclusion of an estimate of the quasi-static thrust load using the 1 s SCADA using an artificial neural network (ANN). After data collection all data are transformed to features on a 10 min interval (feature generation). When considering all possible variations a total of 430 features was obtained. To reduce the dimensionality of the problem this work performs a comparative analysis of feature selection algorithms. The features selected by each method are compared and related to the sensors to decide on the most cost-effective instrumentation of the OWT. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN, which estimates the tower fore–aft (FA) bending moment damage equivalent loads (DELs), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 min features, which will serve as an input for the second tier that estimates the DELs. It is this two-tier methodology that is used to assess the performance of eight realistic instrumentation setups (ranging from 10 min SCADA to 1 s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best-performing instrumentation setup is looked at in greater depth, with validation results of the tower FA DEL ANN model showing an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on two intermediate instrumentation setups (combinations of 1 s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour are investigated.

A Condition Monitoring and Fault Isolation System for Wind Turbine Based on SCADA Data

Condition monitoring of the wind turbine based on supervisory control and data acquisition (SCADA) data has attracted much attention in recent years. Nevertheless, there are some inherent challenges in SCADA data analysis, including the low sampling rate, time-varying working conditions of the wind turbine, and a lack of historical fault data. To solve these problems, this article develops a novel condition monitoring and fault isolation system. First, a covariate-adjusted preprocessing procedure is proposed to account for the various working conditions of the wind turbine. Next, we construct a global monitoring statistic based on all temperature variables contained in the SCADA data, with a view to monitoring the overall health status of the wind turbine. If an alarm is raised, we isolate the fault through a variable selection method without relying on expert knowledge or historical fault data. Simulation and real cases are provided to demonstrate the effectiveness of this system.

Design of a Condition Monitoring System for Wind Turbines

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.

Robust Control Performance Monitoring for Varying-Dimensional Time-Series Data Based on SCADA Systems

The supervisory control and data acquisition (SCADA) system provides information that can be used to free humans from laborious monitoring tasks, such as control performance monitoring (CPM). However, the existing CPM methods rely heavily on the quality of SCADA data. In practice, the missing of measurement and computed signals due to some random and man-induced factors will lead to failures of traditional CPM methods. This paper develops a robust CPM model for varying-dimensional time-series data resulting from the missing variables in SCADA systems. Two attractive advantages of the proposed model are noticed. First, SCADA data with various variable dimensions and missing patterns can be handled through a structural feature extraction module, which constructs specific graphs for input data and explicitly explores the inherent interaction mechanism among variables. A structural vector is then generated to characterize the interaction pattern of multiple variables. Second, the proposed model is designed with the generalization ability by developing parameters-shared node-effect and edge-effect graph neural networks. In this way, the method shows good robustness to the previously unseen missing patterns. Experiments on the simulated and real datasets demonstrate the feasibility of this method.

Wind Turbine Condition Monitoring Based on Bagging Ensemble Strategy and KNN Algorithm

The gearbox is an important component of a wind turbine (WT). Once the gearbox is damaged, problems such as long-term maintenance and high maintenance costs will occur. Therefore, it is necessary to carry out on-line condition monitoring (CM) of WTs. Because a large amount of data is accumulated by the supervisory control and data acquisition (SCADA) system, CMs based on data-driven methods have been widely investigated. In this paper, a CM method that is based on the KNN regression method and bagging ensemble strategy is proposed. The proposed method is validated by SCADA data collected from a field WT. The results show that the ensemble model can achieve the desired estimation accuracy and improve the operation efficiency by approximately 30%.

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

Wind turbine condition monitoring (WTCM) plays an important role in reducing operation & maintenance (O&M) cost and improving the reliability of wind farms. Supervisory control and data acquisition (SCADA) data have advantages such as easy access and strong timeliness and are used widely for WTCM. However, it is difficult to distinguish and label historical SCADA data as healthy or faulty accurately during the model training process. Therefore, a De-ambiguous Condition Monitoring scheme with Transfer layer (DCMT) based on SCADA data is proposed to overcome this problem. This scheme provides a fault early warning for wind turbines. In this scheme, an improved auto-encoder (AE) network with a transfer layer is designed to eliminate the effect of SCADA data in the ambiguous status (ambiguous data) and enhance the reliability of a training dataset. Meanwhile, a structure of Siamese encoder is designed to calculate the residuals between latent features, i.e., the outputs of the Siamese encoders. These residuals can be utilised to identify wind turbine operational conditions. Further, least squares generative adversarial networks (LSGAN) is introduced to learn the distribution of health data while restricting the discriminator and realising the augmentation of health data for model training. The proposed method is applied to two cases of generator winding and gearbox bearing over-temperature faults of wind turbines from northwest China. Compared with other methods, the proposed method effectively detects potential abnormal conditions in advance without raising false alarms.

Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system

The performance of wind turbines can be improved by processing supervisory control and data acquisition (SCADA) data. SCADA data can be processed in a reasonable time to enhance decisions made about maintenance schedules. The pitch system is critical in improving wind turbine operation by analysing data of the most relevant SCADA features. This study gathers the most significant pitch faults, and by implementing the adaptive neuro fuzzy inference system (ANFIS) technique it demonstrates the fault detection potential of this technique. The proposed approach includes the detailed pre-processing of SCADA data, emphasising the labelling process, in which a modified power curve monitoring method is used. During the implementation of the ANFIS, different combinations of the selected parameters were tested for their effects on the performance of fault detection. This methodology was implemented at a windfarm, commissioned in 2004, in five 2.3 MW fixed-speed onshore wind turbines equipped with a traditional servo-valve controlled hydraulic pitch system. Overall, data on 10 years of the operation of each wind turbine were utilised, and a total of nine pitch events were considered. Individual measurement for each blade angle was available for detecting pitch faults. Results demonstrated above 86% achievement of F1-score for pitch fault detection.