Supervisory Control And Data Acquisition Measurements Research Articles

Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data

Assessing the structural health of operational wind turbines is crucial, given their exposure to harsh environments and the resultant impact on longevity and performance. However, this is hindered by the lack of data in commercial machines and accurate models based on manufacturers’ proprietary design data. To overcome these challenges, this study focuses on using Gaussian Process Regression (GPR) to evaluate the loads in wind turbines using a hybrid approach. The methodology involves constructing a hybrid database of aero-servo-elastic simulations, integrating publicly available wind turbine models, tools and Supervisory Control and Data Acquisition (SCADA) measurement data. Then, constructing GPR models with hybrid data, the prediction is validated against the hybrid and SCADA measurements. The results, derived from a year of SCADA data, demonstrate the GPR model’s effectiveness in interpreting and predicting turbine performance metrics. The findings of this study underscore the potential of GPR for the health and reliability assessment and management of wind turbine systems.

A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data

The data imbalance problem extensively exists in wind turbine fault diagnosis, resulting in the compromise between learning attention to majority and minority classes. In this paper, a deep neural network method is proposed to resolve the mentioned problem. Specifically, convolutional and recurrent neural networks are designed to extract spatial and temporal features within supervisory control and data acquisition (SCADA) measurements. To improve the reliability of fault diagnosis results by collective decision, a coarse learner and multiple fine learners are established. With the consideration of data imbalance and learning diversity, fault-related information can be revealed. Moreover, a learner selection scheme is designed to ensure high computational efficiency. The effectiveness of the proposed method is demonstrated by experiments based on simulated data and real-world SCADA measurements from a wind farm. Experimental results show that the accuracy in identifying health conditions can be improved by the proposed method regardless of the data imbalance. On the two datasets, the proposed method outperforms four benchmark approaches as the learning attention to all classes can be enhanced. Therefore, the proposed method is a promising solution to wind turbine fault diagnosis.

A Real-Time Recursion Correction Hybrid Linear State Estimator Using Stream Processing

This study intends to improve the accuracy, efficiency, and timeliness of state estimation (SE) for large-scale electric power systems by presenting a recursion correction hybrid linear state estimator that utilizes all the field measurements received from supervisory control and data acquisition (SCADA) systems and phasor measurement units (PMUs). We present a novel reformulation of the SE problem, where the SCADA and PMU measurements are processed asynchronously. Here, the PMU measurements along with the previous SE results are utilized during the time gap between two successive SCADA measurement sets to run a recursion correction, and thereby, provide real-time updates of the system state estimates. Moreover, the efficiency of this process is guaranteed through multithreaded stream processing. The effectiveness of the proposed method is demonstrated based on simulations involving IEEE 14-bus, 118-bus, and Polish 2383-bus systems.

A Survey on Hybrid SCADA/WAMS State Estimation Methodologies in Electric Power Transmission Systems

State estimation (SE) is an essential tool of energy management systems (EMS), providing power system operators with an overall grasp of the actual power system operating conditions and aiding them in sustaining reliable and secure operation of the grid. In modern transmission sectors, two main measurement systems are deployed, namely the supervisory control and data acquisition (SCADA) and the wide area monitoring systems (WAMS). The multiple advantages of augmenting conventional SCADA-based SE algorithms with synchrophasor measurements from WAMS are already well-established; thus, an abundance of different methodologies has been reported in the field of hybrid SE (HSE). Under this premise, this paper provides a thorough literature review of novel HSE methods in transmission systems and proposes a classification based on the scope and mathematical modeling of each method. Following a brief introduction to the concept of SE based on WAMS and SCADA measurements, an insight into the main challenges emerging in HSE implementations is provided. Various HSE methods which overcome these challenges are reviewed, for both static and dynamic SE implementations. In conclusion, the research trends in the area of HSE are summarized, and the main findings of this literature review are discussed.

State Estimation Using SCADA and PMU Measurements for Networks Containing Classic HVDC Links

• Hybrid state estimation (HSE) algorithm based on SCADA and PMUs measurements for AC power systems with embedded classic high voltage direct current (HVDC) links. • AC and DC measurements are modeled independently as functions of AC and DC states, respectively. • AC and DC measurements (states) are processed (estimated) simultaneously. • Coupling of AC and DC states is expressed by a set of three nonlinear equality constraints. This paper proposes a hybrid state estimation (HSE) algorithm which employs conventional measurements received from supervisory control and data acquisition (SCADA) system and synchrophasors measured from phasor measurement units (PMUs) for AC power systems with embedded classic high voltage direct current (HVDC) links. DC measurements obtained from the HVDC link are assumed to be sampled at the same rate as AC measurements. Zero injections and AC/DC coupling equations are modeled as equality constraints. By applying the nonlinear weighted least squares (WLS) algorithm with equality constraints, the HSE calculates both AC and DC states simultaneously. The effectiveness of the proposed algorithm is verified by simulations on the IEEE standard 14 and 30 bus test systems. The test results reveal the benefits of the proposed approach in terms of estimation accuracy and convergence speed.

Distribution Joint State Estimation with Multiple Snapshots Based on SCADA and AMI Measurements

To adapt to the distribution network unobservable problem from the scarcity of real-time measurements, the distribution system state estimation (DSSE) method based on hybrid measurements of the supervisory control and data acquisition (SCADA) and the advanced metering infrastructure (AMI) is proposed. Firstly, the ratio of energy data from AMI is used to construct pseudo-measurements, which satisfies the observability of DSSE. Secondly, multiple SCADA acquisition snapshots joint state estimation model is constructed by the SCADA measurements and the energy data of AMI with multiple snapshots. In a rectangular coordinate system, the joint state estimation model and the weighted least squares (WLS) method are described in quadratic polynomial form, leading to a quadratic constraint quadratic estimation (QCQE) model. The QCQE model can decouple the modeling and algorithm implementation of state estimation and enhance the efficiency of algorithm implementation significantly. Otherwise, more state estimation mature algorithms can be adopted for the joint state estimation model in quadratic polynomial form. Simulations on the IEEE 33-bus system verify the accuracy and effectiveness of the proposed method.

Identification of wind turbine main-shaft torsional loads from high-frequency SCADA (supervisory control and data acquisition) measurements using an inverse-problem approach

Abstract. To assess the structural health and remaining useful life of wind turbines within wind farms, the site-specific structural response and modal parameters of the primary structures are required. In this regard, a novel inverse-problem-based methodology is proposed here to identify the dynamic quantities of the drivetrain main shaft, i.e. torsional displacement and coupled stiffness. As a model-based approach, an inverse problem of a mathematical model concerning the coupled-shaft torsional dynamics with high-frequency SCADA (supervisory control and data acquisition) measurements as input is solved. It involves Tikhonov regularisation to minimise the measurement noise and irregularities on the shaft torsional displacement obtained from measured rotor and generator speed. Subsequently, the regularised torsional displacement along with necessary SCADA measurements is used as an input to the mathematical model, and a model-based system identification method called the collage method is employed to estimate the coupled torsional stiffness. It is also demonstrated that the estimated shaft torsional displacement and coupled stiffness can be used to identify the site-specific main-shaft torsional loads. It is shown that the torsional loads estimated by the proposed methodology is in good agreement with the aeroelastic simulations of the Vestas V52 wind turbine. Upon successful verification, the proposed methodology is applied to the V52 turbine to identify the site-specific main-shaft torsional loads and damage-equivalent load. Since the proposed methodology does not require a design basis or additional measurement sensors, it can be directly applied to wind turbines within a wind farm that possess high-frequency SCADA measurements.

Wind turbine power output prediction using a new hybrid neuro-evolutionary method

Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we deployed a novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, which is developed in three stages. At the beginning stage (pre-processing), the k-means clustering method and an autoencoder are employed to detect and filter noise in the SCADA measurements. In the Next step (decomposition), in order to decompose the SCADA time-series data, we proposed a new hybrid variational mode decomposition (HVMD) method, that consists of VMD and two heuristics: greedy Nelder-Mead search algorithm (GNM) and adaptive randomised local search (ARLS). Both heuristics are applied to tune the hyper-parameters of VMD that results in improving the performance of the forecasting model. In the third phase, based on prior knowledge that the underlying wind patterns are highly non-linear and diverse, we proposed a novel alternating optimisation algorithm that consists of self-adaptive differential evolution (SaDE) algorithm and sine cosine optimisation method as a hyper-parameter optimizer and then combine with a recurrent neural network (RNN) called Long Short-term memory (LSTM). This framework allows us to model the power curve of a wind turbine on a farm. A historical dataset from supervisory control and data acquisition (SCADA) systems were applied as input to estimate the power output from an onshore wind farm in Sweden. Two short time forecasting horizons, including 10 min ahead and 1 h ahead, are considered in our experiments. The achieved prediction results supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models applied in this paper.

Online Non-Iterative Estimation of Transmission Line and Transformer Parameters by SCADA Data

Utilization of abundant measurements provided by supervisory control and data acquisition (SCADA) system has attracted increasing attention. Real-time estimation of transmission line parameters, utilizing voltage and power flow measurements provided by remote terminal units (RTUs) located at two substations across the line, has been investigated, recently. This paper improves this approach by introducing a novel exact linear reformulation of the problem, which can be solved in closed form. The distributed-parameter model of long transmission lines is considered and its parameters are estimated in a non-iterative manner using RTU measurements. The method is also extended to estimate transformer series impedance and tap position by SCADA measurements, linearly. As such, the disadvantages associated with the previous iterative approach, e.g. concern over convergence, for transmission line parameters are avoided. Moreover, the novel technique for estimating transformer parameters allows to determine the tap position as well as updated transformer series impedance. Furthermore, a thorough analysis is presented to take the measurement accuracy into account. Simulation results for different transmission lines and transformers in the IEEE 118-bus test system are reported, where the result indicate successful performance of the proposed algorithms.

Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks

Wind power plays a key role in reducing global carbon emission. The power curve provided by wind turbine manufacturers offers an effective way of presenting the global performance of wind turbines. However, due to the complicated dynamics nature of offshore wind turbines, and the harsh environment in which they are operating, wind power forecasting is challenging, but at the same time vital to enable condition monitoring (CM). Wind turbine power prediction, using supervisory control and data acquisition (SCADA) data, may not lead to the optimum control strategy as sensors may generate non-calibrated data due to degradation. To mitigate the adverse effects of outliers from SCADA data on wind power forecasting, this paper proposed a novel approach to perform power prediction using high-frequency SCADA data, based on isolate forest (IF) and deep learning neural networks. In the predictive model, wind speed, nacelle orientation, yaw error, blade pitch angle, and ambient temperature were considered as input features, while wind power is evaluated as the output feature. The deep learning model has been trained, tested, and validated against SCADA measurements. Compared against the conventional predictive model used for outlier detection, i.e. based on Gaussian Process (GP), the proposed integrated approach, which coupled IF and deep learning, is expected to be a more efficient tool for anomaly detection in wind power prediction.

A New Hybrid State Estimation Considering Different Accuracy Levels of PMU and SCADA Measurements

This paper proposes a new hybrid state estimation (SE) model where the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements are decoupled. The proposed hybrid SE can effectively handle different accuracy levels of PMU and SCADA measurements which is a problem for conventional hybrid SEs due to increasing the condition number of SE gain matrix. In addition, the proposed hybrid SE model is fully compatible with PMU measurements, particularly current measurements, which increase the complexity of conventional hybrid SE formulation. The proposed hybrid SE model is tested on the IEEE 118-bus test system as well as on the real-world Iran’s power system. Moreover, the proposed hybrid SE is compared with conventional hybrid SE using a large number of scenarios modeling uncertain telemetry noises. The comparative results illustrate better performance of the proposed hybrid SE than conventional hybrid SE in terms of the gain matrix condition number, SE run time, and SE accuracy.

SCADA and PMU Measurement Based Methods for Robust Hybrid State Estimation

Phasor measurement units (PMUs) are being deployed in incremental fashion in power systems. While PMUs may improve the quality of the state estimator (SE), the limited number of PMUs installed may not ensure complete observability of the system. Hybrid state estimator (HSE), considering PMU as well as conventional measurements, is therefore still needed for monitoring the power system. This article proposes a new algorithm belonging to the group of HSEs. Even in case of incomplete observability with PMUs, a two-step state estimation algorithm is presented to optimally utilize the PMU measurements, along with the conventional measurements from supervisory control and data acquisition (SCADA) system. A projection statistic based robust SE is used, when both SCADA and PMU measurements are available. During the interval between two successive arrivals of SCADA measurements, a linear SE is proposed, to exploit the available PMU measurements. The particular advantage of the proposed algorithm is its robustness and insensitivity to bad data. The proposed methods are successfully tested on IEEE-14 bus, 118 bus, and NRPG Indian 246 bus systems.

Stepwise robust distribution system state estimation considering PMU measurement

In order to deal with the problem of robustness and computing speed of distribution system state estimation with the development of phasor technology, a stepwise distribution system state estimation with robust performance and considering real-time phasor measurement unit (PMU) measurements is proposed. The proposed method utilizes the absolute value of residuals to construct the weight function and uses the unit weight variance to standardize the residuals so that the residual precision can be unified on the same scale to improve robustness. Besides, the state estimation is divided into two steps. The first step makes use of supervisory control and data acquisition (SCADA) measurements and branch current phasors measured by PMU to perform the robust estimation, and due to the high sparsity of the Jacobian matrix, the computational efficiency can be ensured. The second step combines the estimation results of the first step with the voltage phasors measured by PMU to perform linear state estimation, which can update the result of the first step and realize the combination of SCADA and PMU measurements in the state estimation. Finally, the proposed method is verified by the Institute of Electrical and Electronics Engineers (IEEE) standard systems.

Hybrid State Estimation for Distribution Systems With AMI and SCADA Measurements

The number of real-time supervisory control and data acquisition (SCADA) measurements in power distribution systems is scarce. This limits the reliability of state estimation (SE) results for distribution systems. Therefore, some studies seek to enhance the observability and SE accuracy of distribution systems by incorporating advanced metering infrastructure (AMI) data with the SCADA measurements. However, the hourly updated AMI data may be too coarse to capture system changes, especially in the presence of intermittent renewable energy sources. This issue is addressed by proposing a hybrid SE framework integrating a data-driven estimator and a model-based estimator. To be specific, the data-driven estimator combined with a topology identification method is presented to solve the DSSE problem between AMI scans, and the model-based estimator is employed to ensure robust estimation results against gross errors at a lower time scale. The proposed hybrid SE switches from the data-driven estimator to the model-based estimator once the AMI data is updated. Such a solution allows for capturing system changes at different time scales and improving the real-time and reliability of distribution system state estimation. Simulations are conducted on a sample distribution system to illustrate the characteristics of the proposed hybrid SE.

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

Power system dynamic state estimation considering correlation of measurement error from PMU and SCADA

SummaryIt is well known that measurements from phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) are not generally independent. Since the correlation of measurement error is a very representative feature of the actual measurement system, traditional assumptions on error independency are not adequate. In this paper, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, a novel correlated extended Kalman filter (CEKF) is proposed. The actual measurement configurations are analyzed with the consideration of measurement error transfer characteristics. Then, the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix. At last, IEEE 14‐bus system and 57‐bus system are provided to illustrate the effectiveness and superiority of the method, respectively.

A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System

With the increasing importance of renewable energy and flexible loads, the operation of the distribution system is becoming more stochastic and complex, and it is necessary to monitor the power system in real-time. Considering the gradual applications of intelligent electronic devices in the distribution systems, a hybrid state estimator based on supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements is proposed in this paper, which consists of the improved robust estimation and linear state estimation. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA measurements with PMU measurements is performed. To eliminate the effect of bad data, the internal student residual method is introduced, and the robust thresholds are adjusted adaptively. Then the linear state estimation is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly correct the robust estimation results and track the changes of the distribution system. Finally, the effectiveness and performance of the proposed method are verified in a modified IEEE 33-bus distribution system and a real distribution system in China.

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

This paper develops a robust power system state estimation framework that accounts for correlations and imperfect time synchronization of the measurements. In this framework, correlations of the measurements obtained from the supervisory control and data acquisition (SCADA) system and the phasor measurement units (PMUs) are separately calculated through the unscented transformation and a vector auto-regression (VAR) model. Specifically, the PMU measurements during the waiting period of two successive SCADA measurement scans are buffered via a VAR model whose parameters are robustly estimated using the projection statistics. The latter take into account their temporal and spatial correlations and provide the needed measurement redundancy to suppress bad data and mitigate imperfect time synchronization. In the case where the SCADA and the PMU measurements do not arrive simultaneously at the control center, yielding imperfect measurement time synchronization, either the forecasted PMU measurements or the prior SCADA measurements from the latest state estimation run are leveraged to restore system observability. Finally, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate the measurement error correlations and to handle the outliers, also known as bad data. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.

Adaptive state estimator with intersection of confidence intervals based preprocessing

The paper presents an effective solution for improving power system state estimation performance by applying the intersection of confidence intervals (ICI) algorithm, a state-of-the-art adaptive signal processing technique used in signal denoising. Since many power utilities worldwide still run state estimators based on the weighed least squares (WLS) algorithm using supervisory control and data acquisition (SCADA) measurements, the ICI algorithm is added to pre-process SCADA measurements without changing the structure of the WLS algorithm. Due to its adaptive window size and high sensitivity to noise in the input measurement series, the proposed ICI-based solution results in an enhancement of the state estimator output and overall performance when compared to the original algorithm. As test beds, the IEEE systems with 30 and 118 buses were used, while as an example of the real power system, the complete mathematical model of the Croatian transmission power system was simulated. Several case studies indicate that the ICI-based state estimator reduces the input measurements mean squared error by up to 30.8%, the mean absolute error by up to 20.8%, and the maximum estimation error by up to 22.6%. Furthermore, this also led to an enhanced final output of the state estimator for all tested systems in view of the state estimation accuracy and convergence.

A Framework for Robust Hybrid State Estimation With Unknown Measurement Noise Statistics

In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.