## 1 Introduction  
As wind power installations continue worldwide, wind power is in a rapid transition toward becoming a fully commercialized, unsubsidized technology. It is thus vital to reduce the levelized wind power energy cost for enhancing the competitiveness of wind farms during the transition to fully commercial, market-based operations. Due to the remote and harsh operational environment, the operation and maintenance (O&M) costs of wind farms are high. Statistics show that the O&M costs account for 10-15% of total wind farm project costs [1]. For an offshore wind farm, the O&M costs account for up to 14-30% [2]. To reduce O&M costs, it is necessary to improve the reliability of wind turbines. Therefore, condition monitoring and fault diagnosis methods are commonly developed and employed [3]. Supervisory control and data acquisition (SCADA) systems are a standard installation for large wind turbines, and provide a wide range of operational information for almost all the subcomponents. As a potentially low-cost and wide-coverage solution, plentiful studies using SCADA data for condition monitoring and fault diagnosis were developed [4, 5]. Moreover, the SCADA system also provides alarms to operators when a process key variable crosses a pre-fixed threshold, or a fault of a subcomponent occurs. These alarms can be used as emergency event indicators that assist operators in mitigating risk. However, these SCADA alarms are often overlooked in industrial applications for the following reasons: (1) The occurrence of a fault usually raises alarm floods. An alarm flood refers to a situation during which tens or hundreds of alarms appear in a short time [6]. The operator is overwhelmed by these alarm floods because it exceeds his response capability. (2) Because of the bad alarm configuration and the causal relationships among the measured variables,a large proportion of alarms are nuisance alarms, such as chattering alarms [7] and other related alarms [8]. It is difficult for the operator to respond to the critical alarms promptly due to the presence of these nuisance alarms. (3) Alarms typically contain descriptive information about an abnormal situation. The operator cannot find the root cause of a fault directly through SCADA alarms. When overwhelmed by alarms, the operator needs to rely on extra expert consultation. This paper aims to solve the above problems existing in wind turbine SCADA alarms and assist the operator in wind-turbine fault diagnosis. Some researchers focused on the use of SCADA alarms for the fault diagnosis of wind turbines. A time sequence method and probability-based method were proposed in [9] for analyzing SCADA alarms. The fault cases on the wind turbine converter and pitch system were used to verify the proposed methods. The results showed that both methods had the potential to rationalize alarm data and identify fault locations. However, the issue of time consumption must be solved when the methods are applied to larger data. A study of wind turbine alarm processing and diagnosis using an artificial neural network was proposed in [10]. The alarm behaviors were transformed into an alarm matrix, which was obtained by inputting the data for each fault into an alarm vector. Each vector represented the alarm pattern of one pitch system fault. The repetitive occurrence of alarm patterns was not studied in depth. Reference [11] used an improved Apriori algorithm to analyze the alarms, which occurred during blade angle asymmetry faulta. The results showed that the related alarms could be integrated into one critical alarm to reduce the number of alarms. The accuracy of the method is limited due to its dependence on sufficient sample data. A method for identifying relevant alarms and grouping similar alarm sequences was presented in [12]. Similar alarm sequences were grouped using clustering techniques. However, the used cluster algorithms did not perform as well as hoped.