Supervisory Control And Data Acquisition Research Articles

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Wind turbine gearbox condition monitoring using AI-enabled virtual indicators*

Abstract At present, the condition monitoring of wind turbine gearbox mostly involves selecting monitoring indicators in advance and establishing normal behavior models based on deep learning. However, single physical monitoring indicator cannot fully characterize the operating status of the wind turbine gearbox. To address the above issues, a wind turbine gearbox condition monitoring method using convolutional neural network (CNN)-long and short term memory network (LSTM)-autoencoder (AE) enabled virtual indicators is proposed in this paper. This method first establishes a cleaning principle to preprocess the data of supervisory control and data acquisition (SCADA) system and screen out effective SCADA data further. A CNN-LSTM-AE monitoring model was trained using SCADA data during normal operating process, various characteristic parameters of the gearbox are integrated to construct the AI-enabled virtual indicators, and meanwhile warning thresholds was determined based on the probability density distribution of virtual indicators to analyze the operating status of the gearbox. Finally, the proposed method was validated using real SCADA data from two wind turbines in a cooperated wind farm with my group. Compared with a single physical indicator, the virtual indicator enables to detect gearbox early faults 5 d in advance, indicating that the proposed method can effectively alert wind turbine gearbox failures.

Profit-optimal data-driven operation of a hybrid power plant participating in energy markets

An energy management system (EMS) is formulated for a hybrid power plant (HPP), consisting of a wind power plant and battery storage plant, participating in bidding stages in the German energy market. The EMS utilizes supervisory control and data acquisition (SCADA) measurements from the site to improve power forecast from the wind power plant. First, the measurement data are used together with numerical weather prediction data to accurately forecast local wind conditions. Second, the measurement data are used to adapt a baseline engineering wake model that gives the total wind power generation for a given input wind condition. The EMS also uses an online cyclic damage minimization approach to accurately balance the battery damage cost against the revenue obtained by market bidding. An HPP controller is formulated to ensure proper tracking of optimal set-points. When compared with standard formulations, the proposed approach shows an accurate estimation and balancing of revenue and costs and a significant reduction in the power deviation penalty, which leads to significantly higher overall profit.

Wind power forecasting based on a novel gated recurrent neural network model

Accurate wind power forecasting is crucial for upgrading the operation and maintenance of wind turbines and the effectiveness in wind power dispatching. In this work, density-based spatial clustering of applications with noise (DBSCAN) is adopted to exclude outliers, and is examined to yield top-quality data filtering for neural networks when implemented on supervisory control and data acquisition (SCADA) data from a 7-MW wind turbine. Apart from features, such as wind and rotor speeds, time is a vital feature involved with synchronous or asynchronous events. Thus, a model-agnostic vector representation for time, Time2Vec (T2V), is used. This can be embedded with neural network architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The novel neural network model, T2V-GRU, concatenates T2V into vanilla GRU, replacing the notion of time vector by T2V. Forecasting results are evaluated by mean absolute error (MAE), root mean squared error (RMSE), and accuracy. In comparison to T2V-LSTM, LSTM and GRU, the proposed T2V-GRU model in combination with DBSCAN refinement has superior forecasting results for the 7-MW turbine, yielding maximum forecasting accuracy (93.12 %) and minimal prediction errors in terms of MAE (286.10 kW) and RMSE (489.54 kW), while simultaneously reducing the computational cost and time.

IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service

Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.

Power System State Estimation Based on Fusion of PMU and SCADA Data

This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy.

A Developed Tunnel Ventilation System Modeling for an Intelligent Transportation System

This paper presents a Laplace transform model for an urban tunnel ventilation system. This model allows one to witness higher performance for supervisory control and data acquisition (SCADA) in terms of monitoring and control of an urban area tunnel based on measurement systems. This proposed model illustrates the ventilation control system framework as well as the emergency response system for urban area tunnels such that smoother controllability and higher security in the operation of tunnels can be envisioned. The salient contributions of this work can be stated as a novel method for modeling tunnel ventilation systems and the implementation of an emergency response plan for a futuristic intelligent transportation system. The simulation results exhibit that the proposed model outperforms the ventilation system in the high-density traffic jams and further the efficient operation of the tunnel. Likewise, comparison results and experimental results are addressed to emphasize the validation of this method and to be helpful in proving the reliability of the results obtained in this study. These results show that the ventilation control system reaches the desired CO value either in high-traffic volume conditions or in normal traffic conditions.

Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis

This work explores the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behavior of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behavior. This study is centered on a small WF of five WTs and uses the observation that the same signals from different WTs in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, averaged signals from the SCADA system are used and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations are explored. The signals are encoded using the Discrete Cosine Transform, which reduces the problem’s dimensions. A hierarchical tree is built in each time window. Clusters are formed by pruning the tree using a threshold interpretable in terms of distance. It is unnecessary to work with an a priori known number of clusters. A protocol for enumerating the clusters based on the tree’s shape is then established, making it easier to follow the evolution of the clusters over time. The capability to automatically identify WTs whose signals differ from the group’s behavior can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

Research on condition operation monitoring of power system based on supervisory control and data acquisition model

In order to better detect and identify the running state of the wind turbine equipment and its generator in the power system wind farm, and then improve the operation reliability of the wind turbine in the power system wind farm, the monitoring method of power system state operation based on Supervisory Control and Data Acquisition (SCADA) model was studied. The SCADA model pre installs different types of sensors on the units of the power system to collect, record and store the power data of the power system. The empirical wavelet transform method is selected to extract the signal characteristics from the state operation signals collected by the SCADA model. ReliefF algorithm is used to select features related to equipment status monitoring from the extracted status operation signal features. Fuzzy C-means clustering algorithm is used to analyze the correlation of feature selection results and identify power system operating conditions. According to the condition identification results of power system, support vector regression prediction algorithm is selected to output the monitoring results of power system state operation. The experimental results show that the monitoring time of the generator and other equipment is less than 100 ms, which provides a strong guarantee for the reliable operation of the power system. This result not only validates the validity of the monitoring and data acquisition model proposed in this paper, but also provides a new idea and method for the intelligent operation and safety management of power system.

An Open-Source Supervisory Control and Data Acquisition Architecture for Photovoltaic System Monitoring Using ESP32, Banana Pi M4, and Node-RED

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper.

Digital twin‐driven online intelligent assessment of wind turbine gearbox

AbstractRemaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.

Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h.

Wind turbine fault detection based on the transformer model using SCADA data

The growth of installed wind power worldwide and its significant contribution to the energy market is mainly due to the evolution of wind turbines (WTs) and their ability to withstand a wide range of dynamic loads. WT failures can be costly and lead to extended downtime. Early detection of such failures is critical in reducing the costs associated with operation and maintenance (O&M) tasks and unscheduled shutdowns of WTs. This paper applies two Deep Learning (DL) models based on the Transformer model to predict failures in the IGBT module of WTs at an onshore wind farm in Ecuador. To this end, SCADA (Supervisory Control and Data Acquisition) operational and alarm data are used, together with the maintenance record (MR). These data are analyzed and processed, applying different feature selection methods. The results show that the two proposed models perform well, with high accuracy and an approximate prediction of 4.25 months before failure occurrence. The results are promising due to the possibility of using SCADA data for early and accurate identification of faults in different components of WTs.

Wind turbine fault detection and identification using a two-tier machine learning framework

A proactive approach is essential to optimize wind turbine maintenance and minimize downtime. By utilizing advanced data analysis techniques on the existing Supervisory Control and Data Acquisition (SCADA) system data, valuable insights can be gained into wind turbine performance without incurring high costs. This allows for early fault detection and predictive maintenance, ensuring that unscheduled or reactive maintenance is minimized and revenue loss is mitigated. In this study, data from a wind turbine SCADA system in the southeast of Ireland were collected, preprocessed, and analyzed using statistical and visualization techniques to uncover hidden patterns related to five fault types within the system. The paper introduces a conditional function designed to test two given scenarios. The first scenario employs a two-tier approach involving fault detection followed by fault identification. Initially, faulty samples are detected in the first tier and then passed to the second tier, which is trained to diagnose the specific fault type for each sample. In contrast, the second scenario involves a simpler solution referred to as naive, which treats fault types and normal cases together in the same dataset and trains a model to distinguish between normal samples and those related to specific fault types. Machine learning models, particularly robust classifiers, were tested in both scenarios. Thirteen classifiers were included, ranging from tree-based to traditional classifiers, neural networks, and ensemble learners. Additionally, an averaging feature importance technique was employed to select the most impactful features on the model decisions as a starting point. A comparison of the results reveals that the proposed two-tier approach is more accurate and less time-consuming, achieving 95% accuracy in separating faulty from normal samples and approximately 91% in diagnosing each fault type. Furthermore, ensemble learners, particularly bagging and stacking, demonstrated superior fault detection and identification performance. The performance of the classifiers was validated using t-SNE and explainable AI techniques, confirming that the impactful features align with the findings and that the proposed two-tier solution outperforms the naive solution. These results strongly indicate that the proposed solution is accurate, independent, and less complex compared to existing solutions.

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the Graph Attention Estimation Network (GAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

A Survey of Security Challenges in Cloud-Based SCADA Systems

Supervisory control and data acquisition (SCADA) systems enable industrial organizations to control and monitor real-time data and industrial processes. Migrating SCADA systems to cloud environments can enhance the performance of traditional systems by improving storage capacity, reliability, and availability while reducing technical and industrial costs. However, the increasing frequency of cloud cyberattacks poses a significant challenge to such systems. In addition, current research on cloud-based SCADA systems often focuses on a limited range of attack types, with findings scattered across various studies. This research comprehensively surveys the most common cybersecurity vulnerabilities and attacks facing cloud-based SCADA systems. It identifies four primary vulnerability factors: connectivity with cloud services, shared infrastructure, malicious insiders, and the security of SCADA protocols. This study categorizes cyberattacks targeting these systems into five main groups: hardware, software, communication and protocol-specific, control process, and insider attacks. In addition, this study proposes security solutions to mitigate the impact of cyberattacks on these control systems.

Question 65 What on‐farm techniques can increase water productivity? General report

AbstractTechnological interventions in tackling water scarcity in agriculture delve into the water productivity issue through available alternative water resources and on‐farm techniques including both structural measures and agronomic practices accompanied by a sound water management approach. On‐farm water management has been addressed under Congress Question 65 through deliberations on irrigation efficiency, soil moisture forecasts and hydrological predictions, efficient and timely distribution of water, controlling non‐beneficial evaporation and reducing non‐returnable losses of irrigation water at existing and state‐of‐the‐art levels using technologies such as SCADA (Supervisory Control and Data Acquisition) and sensor technology. A wide range of land, water and crop‐related important parameters such as soil degradation, soil moisture, zero tillage, conservation agriculture, water demand and supply, future irrigation demand, biologically degradable mulching materials, cost‐effective design of drip irrigation, water quality and cultivation practices through simulation for evaluating environmental impact and adaptation capacity of farmers were addressed. The major outcomes inferred increasing water productivity through optimizing agricultural water use that led to higher crop yields. IoT (Internet of Things) technologies can provide accurate and dynamic information of water consumption and rainfall in real time and have the potential of adapting to climate change scenarios.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data

Offshore wind turbines (OWTs) can be equipped with two types of monitoring systems: (1) a Supervisory Control and Data Acquisition (SCADA) system that monitors operational data such as wind speed and power generation, and (2) vibration sensors like accelerometers and strain gauges to track structural dynamics. While strain gauges enable fatigue damage calculations, not all OWTs in a wind farm have these sensors installed. This paper proposes a Gaussian process regression (GPR) strategy to predict the bending moment time-histories of an OWT. The model takes into account various SCADA data such as wind speed, power, and nacelle acceleration as input and learns to predict the high and low-frequency dynamic response of the system. The strategy is implemented and tested to predict the bending moment response of a 6 MW offshore wind turbine in the fore-aft and side-side directions of the turbine. The accuracy and reliability of the proposed strategy are evaluated and demonstrated considering different operational conditions and multiple hotspot locations along the height of the turbine. The proposed strategy proves to be an efficient virtual sensing strategy and can be easily transferred to other turbines in the same wind farm without the need for widespread installation of strain gauges.