## 1 Introduction
(4) Cloud computing standards, given that data transfer and processing, especially in large-scale and geographically distributed energy systems, relies heavily on cloud infrastructure, such as ISO/IEC TR 22678:2019 [32], dedicated to cloud resource management [27]. (5) Security and data protection standards, which are indispensable in cloud-based environments. The industry uses network security protocols like IEC 62443 and implements cryptographic algorithms following standards such as RFC 3447 [33]. (6) Communication protocol standards, including TCP, UDP, and MQTT, which facilitate reliable data transmission. (7) Connectivity standards for sensors and actuators, encompassing both wireless technologies (Wi-Fi, Bluetooth, LoRa) and wired options (CAN, SPI), as defined by IEEE 1451 and IEEE 2888 [34]. In its broadest sense, the digital twin (DT) concept involves creating a digital model that accurately replicates a physical object, operation, or system as it functions in the real world [35]. This versatile technology has been successfully applied across various domains, including aerospace, healthcare, smart cities, agriculture, transportation, construction, and energy [36]. These applications showcase the potential of DTs in enabling real-time monitoring, optimization, and decision-making across complex environments. In the specific context of machine monitoring, DT technology is used to develop a digital representation of a machine that faithfully reproduces its behavior under different operating conditions, including both healthy and faulty states. Achieving this requires the integration of both model-based and data-driven approaches [37]. The application of DT technology in wind energy generation has been demonstrated through real-world examples. The first example involves a collaboration between Siemens Games and NVIDIA, where the Omniverse platform is used to optimize wind turbine layout and enhance overall energy production [38]. The second example is General Electric's Predix Platform, which enables the collection, visualization, and analysis of data based on DT principles, aiming to optimize the operation of wind turbines and boost energy output [39]. Real-world phenomena are manifested through various parameters, such as vibration, acoustics, current, voltage, and temperature. These parameters must align with the outputs of the physical model for each operational state. This alignment allows for the anticipation of machine states and enables timely and real-time responses to potential issues [40]. The development of a DT involves several key stages. First, the systems to be monitored must be clearly defined. Next, appropriate monitoring tools must be installed, using techniques such as vibration analysis, infrared thermography, or oil analysis. Following this, a highly accurate model of the system must be constructed, incorporating kinematic and dynamic properties to replicate the system's behavior effectively. The model is then used to calculate outputs based on the monitored variables and sensor placements [41]. Finally, the healthy state of the machine is used to calibrate and fine-tune the model to ensure its accuracy. This final step is particularly critical because it addresses unavoidable real-world phenomena. For example, on the mechanical side, even with precise manufacturing, there is always a tolerance margin. Similarly, on the data acquisition side, the noise introduced by the acquisition chain is inevitable. By accounting for these factors, the DT becomes a reliable tool for anticipating machine behavior, optimizing performance, and minimizing downtime [42]. The analysis presented highlights the critical need to deploy effective monitoring systems as a top priority in the wind energy sector. In this regard, the DT framework emerges as a promising solution, particularly through the integration of artificial intelligence and signal processing techniques.