# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 1 Introduction  
### Background  
Recent years have witnessed a rapid expansion in wind turbine numbers and capacities, notably impacting the global energy landscape. In 2022, global wind power integration reached 77.6 GW, culminating in a total installed capacity of 906 GW [1]. Given the advancement of the wind power industry and the extended lifespan of turbines, the operation and maintenance of wind turbines have received significant attention. According to statistics, operational and maintenance costs are projected at 10-15% for onshore wind farm projects, while offshore wind farms might incur higher costs, ranging from 14% to 30% of total project expenses [2]. Hence, investigating condition monitoring and fault diagnosis technologies becomes essential for advancing preventive maintenance strategies and subsequently lowering operational and maintenance costs.  
Fault diagnosis methods for wind turbines can be categorized into analyzing sensor signals, model-based methods, and data-driven methods. Analyzing sensor signals primarily includes vibration signal analysis [3] and acoustic emission signal analysis [4]. Nevertheless, these methods necessitate the installation of additional sensors to capture vibration and other data, thereby increasing the cost of fault diagnosis. Model-based methods entail the creation of precise physical models of wind turbine subcomponents, relying on advanced expertise [5]. However, the complexity and diverse interactions among subcomponents present difficulties in achieving universal applicability for these methods. Conversely, data-driven fault diagnosis methods have garnered increasing attention due to their independence from strict mathematics. Furthermore, as information technologyadvances, modern wind turbines commonly incorporate Supervisory Control and Data Acquisition (SCADA) systems. These systems collect and store vital operational signals from nearly all wind turbine subcomponents. The cost-effective acquisition and real-time monitoring potential offered by SCADA data have generated substantial research interest in data-driven methods for wind turbine fault diagnosis. Employing SCADA data has demonstrated promising results for wind turbine fault diagnosis [6; 7; 8].  
Compared to SCADA data, employing alarm data to diagnose wind turbine faults is relatively overlooked. Alarm data are generated by the alarm system of a wind turbine, which is based on SCADA data. The alarm system monitors real-time values of SCADA parameters and triggers relevant alarms when one or more parameters exceed predefined thresholds [9]. These triggered alarms are stored chronologically as code and text. Alarm data generation intends to promptly alert wind farm operators about abnormal conditions in wind turbines and guide them in maintenance tasks [10]. However, diagnosing wind turbine faults through alarms remains a challenge for on-site operators. As a result, alarm data are often disregarded by operators in wind farms.  
In recent years, with the increasing demand for online condition monitoring in large-scale wind turbines, there has been a rise in alarm configurations. Researchers have started to pay more attention to alarm data. The research directions encompass quantifying the reliability of wind turbines using alarm data [11; 12], labeling SCADA data with alarm data to enhance the wind turbine fault diagnosis models' accuracy [13], and uncovering the propagation mechanisms of wind turbine faults using alarm data [14]. Among these, the most prominent area of interest lies in wind turbine fault diagnosis using alarm data.