Towards an Integrated PHM framework of the Mutriku Wave Power Plant. Air Turbine Case Study

The Mutriku Wave Power Plant (WPP) is a wave energy conversion plant based on the oscillating water column technology that was commissioned by the Basque Energy Agency in 2011 (Torre-Enciso, Ortubia, De Aguileta, & Marqués, 2009). The Basque Energy Agency is currently the responsible for the operation and maintenance tasks of the plant. From the beginning of the operation of the plant, different degradation and failure events have been reported for WPP components including air-turbines and electric generators (Lekube, Ajuria, Ibeas, Igareta,& Gonzalez,2018), which required unplanned maintenance actions. Despite the thorough monitoring system included in the WPP, a-posteriori root cause failure diagnostics has been challenging due to the lack of experience in similar systems. In this context, without a direct cause-effect correlation between failures and events, the implementation of maintenance actions has been implemented through trial-and-error events, i.e. replacement of components based on intuition and expert knowledge, until the system recovered its optimal operation mode. The Mutriku WPP is designed as a test facility and it is located onshore into the breakwater. Therefore, the operational consequence of unplanned maintenance actions are not as critical as in future commercial open ocean WPPs. However, all the monitored information collected over the years of operation can be used to develop diagnostics models that integrate statistical learning strategies with expert knowledge and accordingly assist engineers in the maintenance decision-making processes of future WPPs. This paper presents an integrated prognostics & health management (PHM) framework for the Mutriku WPP. Figure 1 shows the conceptual block diagram. FIGURE 1 (see attached PDF) The expert knowledge of plant engineers will be combined with collected data and signal-processing methods to detect anomalies, diagnose the failure cause, and predict the remaining useful life (RUL) of plant components and the overall plant (Aizpurua & Catterson, 2015). The development of this approach will permit the prompt detection of anomalies for future operation events and avoid unplanned maintenance actions. The main components evaluated in this paper will be the air-turbines, including different information of the WPP, such as rotational speed, bearing vibration, generated power of the turbine, and pressure loss through the turbine impeller. Firstly, the paper will provide a detailed view of the developed PHM framework for Mutriku WPP (cf. Figure 1). Secondly, after the identification of abnormal patterns, a conditional anomaly detection model will be designed (Catterson, McArthur, & Moss, 2010) from the characteristic operation curve of the turbine and operation condition of the plant as shown in Figure 2. FIGURE 2 (see attached PDF) The proposed approach will be validated with real on-site monitored data. Figure 3 shows the empirical characteristic curve of an air turbine. FIGURE 3 (see attached PDF) Based on the normal operation of the turbines, including contextual information, such as sea-state and plant operational state, probabilistic multivariate models will be developed for the turbines and the operation environment and then their probabilistic correlations will be defined so as to estimate the probability of a turbine being healthy, given the operational information (see Figure 2). Figure 4 shows early results of the anomaly detection model, where it is possible to observe anomalies with very low likelihood. FIGURE 4 (see attached PDF) Vertical dashed line in Figure 4 indicates end of training data, and gray dashed area indicates confidence intervals for improved decision-making under uncertainty.