Bidirectional wakes over complex terrain using SCADA data and wake models

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.

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.

Abstract. With the number of commissioned and planned wind farms increasing rapidly, analysing wind farm cluster wakes becomes essential for resource assessment and lifetime considerations. Cluster wakes influence wind turbine power in downstream wind farms in certain meteorological situations. Our objective is to ascertain whether far-reaching cluster wakes (15 to 21 km) impact individual turbine loading in a downstream wind farm, considering the influence of atmospheric stratification. We utilized supervisory control and data acquisition (SCADA) data from an offshore wind farm and accelerometer measurements as the load proxy in the absence of load measurements to check short-term fatigue loading effects. We compared the absolute values of relevant SCADA variables of turbines in and out of the cluster wake. We found that while cluster wakes increase fluctuations of rotor speed and power, the load effects were lower than from turbines in the free wind, primarily due to lower wind speeds. We developed a new methodology to quantify loads of turbines affected by the cluster wake while separating the dependency of loads on the inflow wind speed. The turbines within the cluster wake showed a small increase in the load effects (≈2.5 %) when compared to turbines in free wind, but lower than loads of turbines within the wind farm affected by inner-farm wakes (both at the same local inflow wind speeds). We also found atmospheric stratification and the inflow wind speed to have no impact on the magnitude of loads within the cluster wake. Additionally, we found no additional blade mode excitations due to the presence of the cluster wake from the analysis of load spectra. We conclude that wind turbines affected by cluster wakes have a marginal increase in loads when compared to reference conditions in undisturbed inflow. The absolute load effects in the cluster wake are lower due to the lower wind speeds. We propose the use of additional data from load sensors to further determine possible lifetime fatigue effects of cluster wakes on offshore wind turbines. These new insights can potentially add to the design standards of future wind farm clusters.

PPFSCADA: Privacy preserving framework for SCADA data publishing

With the increase in the penetration rate of renewable energy sources, a machine-learning-based forecasting system has been introduced to the grid sector to improve the participation rate in the electricity market and reduce energy losses. In these studies, correlation analysis of mechanical and environmental variables, including geographical figures, is considered a crucial point to increase the prediction’s accuracy. Various models have been applied in terms of accuracy, speed calculation, and amount of data based on a mathematical model that can calculate the wake; however, it can be difficult to derive variables such as air density, roughness length, and the effect of turbulence on the structural characteristics of wind turbines. Furthermore, wake accuracy could decrease due to the excessive variables that come from the wake effect parameters. In this paper, we intend to conduct research to improve prediction accuracy by considering the wake effect of wind turbines using supervisory control and data acquisition (SCADA) data from the Dongbok wind farm. The wake divides the wind direction into four parts and then recognizes and predicts the affected wind turbine. The predicted result is the wake wind speed and its conversion to power generation by applying a power curve. We try to show the efficiency of machine learning by comparing the wake wind speed and the power generation in the wake model. This result shows the error rate using evaluation metrics of regression, such as mean squared error (MSE), root mean squared error (RMSE), and weighted absolute percentage error (WAPE), and attempts to verify power system impact and efficiency through a real-time digital simulator (RTDS).

Field Evaluation of Windbreak Protection for Orchards

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.

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.

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

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.

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.

The Supervisory Control and Data Acquisition (SCADA) system can provide significant information about the wind turbine health monitoring. However, the SCADA data contains enormous abnormal state data due to wind curtailment, equipment or sensor malfunction. In addition, complicated and constant changing operating conditions pose great challenges to effective and reliable fault detection. In this work, an automatic data cleaning and operating conditions classification approach is proposed. First, fundamental characteristics of different operating condition are analyzed according to control strategy, and data cleaning rules are constructed to remove abnormal state data. Then, change-point method is adopted to further clean residual abnormal state data that is stacked below the normal generation state data in the generator rotor speed-power curve. Second, the operating conditions classification criteria are constructed based on fundamental characteristics of different operating stages, and the classification parameter values are obtained adaptively according to data characteristics of different operating stages. Finally, the data cleaning and operating conditions classification approach is evaluated on the real-world SCADA dataset that was collected from a wind farm in East China. The results demonstrate that the proposed method can effectively distinguish normal power generation state data from abnormal power state data and realize operating conditions classification for different wind turbines automatically.

Complex terrain can influence wind turbine wakes and wind speed profiles in a wind farm. Consequently, predicting the performance of wind turbines and energy production over complex terrain is more difficult than it is over flat terrain. In this preliminary study, an engineering wake model, that considers acceleration on a two-dimensional hill, was developed based on the momentum theory. The model consists of the wake width and wake wind speed. The equation to calculate the rotor thrust, which is calculated by the wake wind speed profiles, was also formulated. Then, a wind-tunnel test was performed in simple flow conditions in order to investigate wake development over a two-dimensional hill. After this the wake model was compared with the wind-tunnel test, and the results obtained by using the new wake model were close to the wind-tunnel test results. Using the new wake model, it was possible to estimate the wake shrinkage in an accelerating two-dimensional wind field.

In this study, wind characteristics in the Belen-Hatay province situated in southern Turkey were investigated by using the Wind Atlas Analysis and Application Program (WAsP) for future wind power generation projects. Hourly wind speeds and directions between the years 2004 and 2005 were collected by the General Directorate of Electrical Power Resources Survey Administration (EIEI). Before the construction of the wind turbine generator in Belen-Hatay province, several fundamental properties of the site such as wind behavior, availability, continuity, and probability were carried out in order to provide the necessary information to the potential investors about cost and economical aspects of the planning wind energy project. The dominant wind directions, probability distributions, Weibull parameters, mean wind speeds, and power potentials were determined according to the wind directions, years, seasons, months, and hours of day, separately. Finally, at a 10 m height above ground level, mean wind speed and power potential of the site were found to be 7.0 m/s and 378 W/m2, respectively.

In this study, wind power forecasting is performed for a Wind Power Plant (WPP) with an installed capacity of 135 MW in Turkey. The ruggedness index (RIX) of the terrain where WPP was installed is analyzed with Wind Atlas Analysis and Application Program (WAsP). According to the obtained RIX value, the terrain of WPP is found to be complex. Due to the complexity of the terrain, wind power forecasting becomes difficult. To deal with this problem, a forecasting method with fast, accurate, and high performance is needed. Therefore, Extreme Learning Machine (ELM) based method is proposed for wind power forecasting in this study. Electrical and meteorological measurements are obtained from WPP for the application of the proposed method. These measurements are provided with high quality measuring devices. Also, Global Positioning System (GPS) time synchronization is used to prevent lags between measurements. The wind speed, wind direction, and wind power data of 1-year period are obtained from WPP. These data are used to compare the proposed method with a classical Artificial Neural Network (ANN) based method in terms of two, three and four hours-ahead wind power forecast performances. In the forecast studies performed for all data related to 2, 3, and 4-hours ahead, Normalized Root Mean Square Error (NRMSE) values of ELM are obtained as 7.01, 10.12, and 12.06, respectively, while these values are found as 8.19, 12.18, and 13.09 for ANN. In addition, the values of Correlation Coefficients (R) of the proposed forecast method results regarding 2, 3, and 4-hours ahead are 0.96588, 0.93528, and 0.88984, respectively. The R values related to ANN are observed as 0.95421, 0.91373, and 0.87576, respectively. According to the obtained results, it is observed that ELM has better performance features than classic method under all forecast conditions and it is clearly seen that ELM has by far short training time than other one.