Digital Twin as a Virtual Sensor for Wind Turbine Applications

Abstract

Digital twins (DTs) have been implemented in various applications, including wind turbine generators (WTGs). They are used to create virtual replicas of physical turbines, which can be used to monitor and optimize their performance. By simulating the behavior of physical turbines in real time, DTs enable operators to predict potential failures and optimize maintenance schedules, resulting in increased reliability, safety, and efficiency. WTGs rely on accurate wind speed measurements for safe and efficient operation. However, physical wind speed sensors are prone to inaccuracies and failures due to environmental factors or inherent issues, resulting in partial or missing measurements that can affect the turbine's performance. This paper proposes a DT-based sensing methodology to overcome these limitations by augmenting the physical sensor platform with virtual sensor arrays. A test bench of a direct drive WTG based on a permanent magnet synchronous generator (PMSG) was prepared, and its mathematical model was derived. MATLAB/Simulink was used to develop the WTG virtual model based on its mathematical model. A data acquisition system (DAS) equipped with an ActiveX server was used to facilitate real-time data exchange between the virtual and physical models. The virtual sensor was then validated and tuned using real sensory data from the physical turbine model. The results from the developed DT model showed the power of the DT as a virtual sensor in estimating wind speed according to the generated power.

1. Introduction

The advent of the fourth industrial revolution introduced DT technology to improve the development process. A digital twin (DT) is a virtual replica of a physical object that facilitates real-time simulation and analysis of its performance [1]. DTs are valuable in applications involving wind turbines (WTs) where they can be utilized to monitor the real-time performance of the turbine. This enables engineers to promptly detect and resolve any issues that may arise, thereby enhancing the efficiency and reliability of the WTG while lowering the cost of maintenance and repair [2]. DTs can also be used to optimize the operation of wind turbines by predicting the energy output of the turbine based on the wind conditions. By using the DT to model the turbine's behavior in different wind conditions, engineers can optimize the operation of the turbine to maximize its energy output [3].

Wind speed sensors are a critical component of wind farms and are extensively utilized to enable wind energy monitoring, control, and decision support for wind turbines [4]. These sensors measure wind speed, thereby playing a pivotal role in determining the operational performance of wind turbines. The accuracy of these measurements significantly impacts the wind energy capture rate, fatigue load, and service life. Wind speed sensors are known to be susceptible to errors, which can have adverse effects on the basic as well as advanced functionalities of wind turbines. This can lead to a decrease in the overall performance of wind farms and an increased risk level for the system [5]. Therefore, it is imperative to conduct research on online monitoring, identification, and accommodation methods for the sensors to ensure their reliability and accuracy.

A virtual sensor is a software-based mechanism that processes data that would otherwise be obtained from physical sensors based on the available information [6]. By observing the readings from different instruments and learning to interpret the relationships between various variables, the virtual sensor functions in a manner that is like a physical sensor. The simulation is designed to imitate the behavior of real-world products and can be used to capture measurements at various locations. The readings obtained from the virtual sensor can supplement the data collected from physical sensors. The literature showed increased interest in virtual sensors for wind turbine applications [4], [8]–[11]. This paper highlights the usage of DT as a virtual sensor for WTG. The primary objective is to present a pioneering approach that leverages DT to enhance sensor capabilities and address the inherent limitations of conventional physical sensors.

2. Wind Turbine Generator Basic Principles

The WTG comprises two distinct models: the mechanical turbine model and the electric PMSG model. The wind turbine mathematical model aims to model the aerodynamic aspects, focusing on the wind profile and turbine models. Wind power (Pwind) can be expressed as Pwind = ½ · ρ · A · V³ · Xc, where ρ is air density, A is area perpendicular to wind direction, V is wind speed, and Xc is the turbine's performance coefficient. The d–q model is used to analyze the PMSG separately in two axes, where the d-axis is aligned with the rotor magnetic field and the q-axis is perpendicular to it. The combined equation relates wind speed to electromagnetic torque and generated power: Pe = ½ · ρ · A · V³ · Xc · ηg · (λ − λmin)/(λrated − λmin), demonstrating that generated power is proportional to the cube of wind speed.

3. WTG—Digital Twin Development Procedures

DT consists of three primary elements: physical model, virtual model, and interconnecting interface. The physical model represents the physical entity, such as a test bench. The virtual model is a digital portrayal of the physical object. The interconnecting interface establishes a connection between both models, enabling data exchange. The physical model of the direct drive WTG test bench is a comprehensive setup centered on a 600 W, eight-pole surface-mounted PMSG connected to a servo motor acting as a load emulator. The virtual model was created using MATLAB Simulink to simulate the combined turbine-generator behavior. The system uses an ActiveX server to fetch measured data from DAS channels and transfer it to the MATLAB workspace for real-time Simulink feeding.

4. WTG-DT Model Validation

Validation and tuning of the simulation model with the physical model are crucial steps in the DT development process, ensuring it accurately represents the real-world WTG behavior. Real data from the test bench were used as inputs to the simulation model. The servo motor drove the WTG under different speed conditions to emulate various wind speeds. Real-time wind speed was precisely measured using an anemometer and compared with the model's estimates. It was observed that the estimated wind speeds from the simulation model closely approximated around 83% of the corresponding values from the wind speed sensor. The relatively small MAE values across data points (ranging from approximately 0.26 to 0.57) suggest that the wind speed estimation model is performing with good accuracy.

5. Discussion

The application of DT technology as a virtual sensor for WTG has shown promising results in this study. The DT model, based on the provided wind turbine specifications and an estimation method, was used to estimate wind speeds based on generated power. The comparison between the estimated wind speeds and the real wind speeds demonstrated close agreement within 80% for the given generated power values. However, it is important to acknowledge the limitations and inherent uncertainties associated with the estimation method used. The accuracy of the estimated wind speeds depends on various factors, including the wind turbine's design, efficiency, and environmental conditions, which may not be fully captured in the DT model.

6. Conclusions

This research underscores the significance of digital twins as a valuable tool for wind turbine applications. The proposed DT-based sensing methodology offers a promising solution to the limitations of physical wind speed sensors, contributing to the overall efficiency and performance of wind turbine generators. The test bench of a direct drive WTG based on a PMSG served as a physical platform for developing and validating the DT model. The results from the developed DT model demonstrated its capability to estimate wind speed based on the generated power, showcasing its potential as a reliable alternative to traditional physical wind speed sensors. Future directions include conducting tests under varied loading conditions, incorporating additional factors, and leveraging AI and machine learning techniques for advanced analysis.

References

Full reference list (14 sources) available in the original PDF.