Wind power is becoming increasingly popular across the world as it plays a vital role in both sustainable and emission-free energy production, making it a perfect energy resource for reducing carbon footprint and global warming. Wind turbines with modern technologies are complex machines combining aerodynamics, mechanics, and electrical with advanced control systems. They continue to grow in size, and they are increasingly being deployed offshore in hostile and operationally demanding environments. To ensure the systems are safe, profitable, and cost-effective, it is imperative to implement a well-organized operation and maintenance strategy based on a digital solution (Garlick et al. 2009). The ongoing global digital revolution, sparked by the Industry 4.0 initiative, has brought new concepts and emerging technologies to the fore that can help these missions to be accomplished. One of the core concepts of Industry 4.0 is digital twin, which can be defined as a digital representation of a physical asset. A digital twin is intended to accurately represent a physical object, based on data and simulation, that can be used for forecasting, monitoring, controlling, and optimizing through the entire lifespan of the asset. Many applications of digital twins have already been developed, including for power generation, manufacturing and processes, building structures, meteorology, healthcare systems, education systems, automotive industries, and urban planning (Rasheed et al. 2020). This paper introduces and demonstrates the concept of predictive digital twin for wind farm operation and predictive maintenance. To this end, we develop a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication. Specifically, our proposed digital platform is used to provide predictive information regarding the potential failures of wind turbine components.
Motivation and scope
The wind industry is looking for a way to increase its energy production as the demand for renewable energy develops. One way to boost the energy output is by increasing the size of the rotor blades. The rising size of the blades can put more strain on the turbine’s construction and other components. Lightning strikes, blade icing, material or power regulator failure, damage from external objects, and poor design are all contributing to blade failure, which can result in costly repairs and income loss if the turbine is standstill (Tavner et al. 2013). Furthermore, the generator, gearbox, and bearing are also prone to failure. The main causes for the generator failure can be attributed to wind loads, weather conditions, manufacturing or design flaws, incorrect installation, lubricant contamination, and insufficient electrical insulation. Based on historical data and research, bearings and gears account for the majority of the gearbox failures (Elasha et al. 2019). Unclean lubricant, inaccurate bearing settings, temperature and vibration variations, and inappropriate maintenance are just a few of the variables that might cause failure. In general, wind turbine failures can be divided into two categories: external and internal, as shown in Fig. 1. Electrical failures mostly are caused by moisture and temperature inside the converter enclosure. This environmental condition creates a seasonal conversion climate. Short circuits caused by condensation is also one of the most common electrical failures. This usually happens after a scheduled or unplanned shutdown resulting in damage to the components, necessitating replacement, and reducing the wind turbine’s lifetime. Mechanical failures inside the nacelle largely occur due to temperature problems, moisture reaction with metal parts that weaken and degrade mechanical elements, problems with the hydraulic and cooling system, blade icing, and erosion.
Due to the nature of the offshore environment, operation and maintenance of wind farms can be difficult and expensive. Thus, there is an incentive to plan operation and maintenance in safer and smarter ways. Digital twins can be viewed as an enabling technology for intelligent wind farm operation. A digital twin can be defined as a virtual model designed to accurately reflect a physical asset (Jones et al. 2020; Liu et al. 2021). Unlike the current practice, which is based on the Supervisory Control and Data Acquisition (SCADA) system, digital twins can be used for prediction and forecasting (Dai et al. 2018). Remark that digital twins are implemented in a software, for which algorithms that can be used for prediction and forecasting are written based on Machine Learning (ML) or Artificial Intelligence (AI). In our case, we use the Prophet algorithm, which is a type of ML algorithm. There are many types of digital twin, e.g., monitoring digital twin, imaginary digital twin, prescriptive digital twin, and predictive digital twin (Verdouw et al. 2021). The objective of this paper is to design and demonstrate a predictive digital twin platform for offshore wind farms based on Unity3D and OPC-UA, which can be used to predict abnormalities and possible failures in each individual wind turbine. Due to the vastness of the subject and the considerable variety of subtopics, we narrow the subject down to mechanical component failures. Having said that, the predictive digital twin platform can also be implemented for electrical or control system failure prediction. One of the most crucial components of rotary equipment is the bearing. The key point to monitor the bearing effectively is the accurate degradation process prediction, which helps to prevent total failures and reduce maintenance costs. Therefore, the case example selected in this paper is about bearing failures since they are a major source of unscheduled maintenance, repairs, and replacements, resulting in energy production downtime (Dong et al. 2014).
Literature review
Digital twin has various aspects and comes with different definitions. Boschert and Rosen (2016) define a digital twin as a description of the physical and functional characteristics of a component, a product, or a system that includes more or less all information that can be useful throughout its entire life-cycle. Fuller et al. (2020) describe a digital twin as an integration of data between physical and virtual machines in either direction with ease. Glaessgen and Stargel (2012) express that in order to accurately reflect the life of a physical asset, a digital twin utilizes the best available physical models, sensor updates, fleet history, etc., to create an integrated multi-physics, multiscale, and probabilistic simulation. Finally, according to Verdouw et al. (2015), a digital twin is a digital representation of an object with a unique identification that can be trusted, is of integrity, is immediately available, and can serve its intended purpose. According to all mentioned definitions, it can be said that a digital twin provides predictability, control, monitoring, and optimization of physical assets by utilizing data and simulations during the entire life-cycle of the assets. The aforementioned definitions implicitly underline the importance of communication between the physical asset and its digital twin. The OPC-UA is an industrial machine-to-machine communication developed by the OPC Foundation (Mühlbauer et al. 2020). The OPC-UA is based on commonly used communication standards like the Hypertext Transfer Protocol (HTTP). Thus, it can be used in different operating systems. Because of its flexibility, the OPC-UA has been considered as a pillar of representing semantic digital twins (Perzylo et al. 2019). For this reason, we use OPC-UA as the communication protocol in our digital twin platform.
Digital twin of wind farms is beneficial for monitoring and operating individual wind turbines remotely (Pimenta et al. 2020). They enable cost-effective maintenance and ensure greater reliability of the components used to convert wind energy into electricity (Moghadam et al. 2021). Oñederra et al. (2019) outlined development of a digital twin for a medium voltage cable prototype in a wind farm which can be used to simulate its behavior and increase its lifespan in order to accomplish preventative maintenance. In their work, a hybrid model of a dynamic medium voltage cable model and an interpolation technique was created in OpenModelica. Furthermore, as part of a predictive maintenance plan, Sivalingam et al. (2018) provided a unique approach for predicting the Remaining Useful Life (RUL) of an offshore wind turbine in a digital twin shell by monitoring the turbine conditions. Wang et al. (2021) summarized recent work regarding reliability of offshore wind turbine structures and reviewed some possible damages/failure. Moreover, they proposed a digital twin concept to monitor offshore wind turbine support structures as a solution to some problematic challenges. Botz et al. (2019) conducted research to apply digital twin framework by gathering vital data from attentively chosen spots of hybrid wind turbine structure for updating material models of the wind turbine in order to improve maintenance and operating parameters and extend the turbine’s useful life. In another publication, Kooning et al. (2021) presented a summary of recent research on modelling methodologies to build a digital twin for a wind turbine by considering the components, aerodynamics, structural and mechanics, power electronic converters, pitch and yaw systems. Furthermore, Pimenta et al. (2020) created a digital twin by using SCADA to create a feasible trustworthy numerical model of a floating wind turbine.
Applying predictive methodologies into a digital twin platform to estimate failure probability provides the ability to schedule on-time maintenance for reducing repair time and unplanned maintenance, as well as organize proper spare parts to mitigate inventory costs. The prediction of wind turbine failures is mostly conducted based on the time scales of short-, medium-, and long-term (Foley et al. 2012). Long-term prediction focuses on estimating the variables over time periods of days, whereas medium-term prediction examines the variables at hourly intervals, and short-term forecasting seeks to estimate the values at 10-s or 10-min intervals. Different prediction methods have been used to forecast wind turbine components, which can be divided into four categories: statistical models, physics-based methods, data mining algorithms, and hybrid models (Kusiak et al. 2013). Statistical forecasting approaches are popular because of their objective in analyzing data and identifying patterns that can be used for future forecasts. An example of statistical approaches is the Auto Regressive Integrated Moving Average (ARIMA). The ARIMA is a statistical method that uses previous values to describe the values of a time series. This method is built on two basic characteristics of past values and errors. Furthermore, the method utilizes historical data to determine the performance of the model by using estimated errors (Menculini et al. 2021). Physics-based methodologies are based on the physical models that define and represent the behavior of the variables providing more reliable predictions for the future trend of the model. In general, the nonlinear part of the data is considered as numerical methods concentrate on capturing the linear data. Data mining methodology is used to model both linearity and non-linearity of the data. Data mining models are created through heuristics and calculations. It first searches for patterns or trends among the provided data to create a model. The Neural Network (NN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and tree-based regression algorithms are examples of data mining methods used for prediction. The SVM, in particular, is a prominent supervised learning technique that is utilized to solve both classification and regression issues. However, it is mostly applied in machine learning for classification problems. This algorithm aims to construct the best line or decision boundary in n-dimensional space that can be used to categorize the data easily in the future, which is known as a hyperplane, to easily place the new points in the correct category. The SVM creates the hyperplane by choosing extreme points. As a result, extremes are called support vectors, and the algorithm that employs them is known as a Support Vector Machine (Jose et al. 2013). The Auto-Encoder Neural Network (AENN) is a type of unsupervised Artificial Neural Networks (ANN) to rebuild and decode the data from the compact encoded model into a representation close to the original input. It is basically designed to minimize data dimensionality by learning how to disregard data noise (Ren et al. 2018).
The aforementioned methods above are, however, prone to large trend errors when there is a change in trend near the cutoff period and they fail to capture any seasonality, which underlies the idea of the Prophet prediction algorithm (Taylor and Letham 2017). The Prophet fits non-linear trends with annual, weekly, and daily seasonality, as well as holiday impacts, to forecast time series data (Jana et al. 2022). It is an open-source software developed by Facebook’s Core Data Science team. It is used for time series forecasting with substantial seasonal consequences and chronological data from several seasons. Generally, Prophet can handle outliers and missing data and is robust to changes in the trend.
Contribution of this paper
The novelty of this paper is the development of a predictive digital twin architecture and software for wind farm applications based on OPC-UA and Unity3D. Thus, the contribution is within the area of system engineering and software development (please refer to Fig. 5). All codes are available in this link: https://github.com/hamirashkan/Predictive_DigitalTwin_WindFarm. The OPC-UA is a secure and reliable mechanism for information exchange between systems (Stojanovic et al. 2021), while the Unity3D is one of the most popular game engines in the gaming industry. The proposed digital twin platform is further combined with the Prophet algorithm to predict the component failures in individual wind turbines.
The contributions of this paper can be summarized as follow:
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Development of a bi-directional digital twin platform architecture for monitoring and controlling of wind farms based on Unity3D and OPC-UA. To the best of our knowledge, this is the first time such architecture is proposed, developed, and demonstrated for wind farm applications.
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Development of a failure prediction method based on the Prophet algorithm for component failure prediction in individual wind turbines. To the best of our knowledge, this is the first time the algorithm is used for prediction in wind farm applications.
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Development of augmented reality for interactive visualization in the predictive digital twin platform.
Outline of this paper
This paper is divided into eight sections. Section 1 provides the introduction. Section 2–5 describe the platform development and its related algorithms for prediction and visualization tools. Section 6–7 discuss the experimental setup and results. Finally Section 8 is the conclusion. The complete outline is given below:
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Introduction, which includes motivation & scope, literature review, contribution of this paper, and outline of this paper.
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Failure prediction in wind turbines, which includes failure prediction procedure, data processing & cleaning, and modelling & forecasting.
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Digital twin platform development, which includes communication architecture, data source, and visualization interface.
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Predictive twin algorithms, which includes algorithms for temperature and vibration failure prediction.
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Augmented reality for interactive visualization.
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Experimental setup, which includes data availability and simulation setup.
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Result and discussion.
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Conclusion.