# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 1 Introduction  
In an era marked by mounting concerns about the environmental impact of traditional energy sources, the quest for sustainable and renewable alternatives has taken center stage in the global discourse. The imperative to reduce greenhouse gas emissions, secure energy independence, and mitigate the effects of climate change has driven a fundamental shift in our approach to power generation [1; 2]. At the forefront of this transformative journey stands wind energy, a clean and abundant resource harnessed to provide a solution to our growing energy needs while reducing our carbon footprint [3; 4]. Wind turbines gracefully punctuate landscapes symbolizing our commitment to a cleaner and more sustainable future. However, the pursuit of harnessing the power of the wind comes with its own set of challenges [5; 6; 7]. The intermittent nature of wind, the need for effective grid integration, and the demands of operating and maintaining these complex machines have sparked a revolution in how we manage and optimize wind energy facilities. Sustainable maintenance practices ensure equipment availability, reliability, and safety while minimizing environmental impact [8; 9]. These practices focus on preventive maintenance, efficient resource use, and eco-friendly technologies. Integrating sustainability into maintenance involves aligning activities with broader goals like reducing energy consumption and waste generation. By adopting these practices, organizations enhance operational efficiency, cut costs, and promote environmental conservation. At the heart of Wind farm operation and maintenance revolution lies the Supervisory Control and Data Acquisition (SCADA) system, a vital component that allows us to remotely monitor, control, and collect crucial data from wind turbines [10; 11]. The insights offered by data obtainedfrom SCADA systems ensure the efficient operation of wind turbines but also play a pivotal role in their longevity [12; 13]. Refs. [14; 15] discusses the use of SCADA data for Wind Turbine Performance and explainable artificial intelligence. They also mention the potential of multivariate time series analysis, which we have exploited in this work. In this study, we leverage SCADA data from 13 wind turbines located in a wind farm in India to predict the power output of the wind turbines, employing advanced time series methods, specifically Functional Neural Networks (FNN) [16; 17] and Long Short-Term Memory (LSTM) networks [18]. A key innovation lies in an ensemble of FNN and LSTM model to capitalize on their collective learning to make a more effective prediction. This approach outperforms the individual models, ensuring high accuracy of the power output predictions when wind turbine is in a good state (good timeline) and significantly lower accuracy when the wind turbine is in a bad state (bad timeline). This difference is crucial in making sure we are learning the correct mapping between the features and power output of a wind turbine. Then, statistical techniques are applied to the prediction performance errors to detect wind turbine deterioration, enabling proactive maintenance strategies and health assessment. Our analysis leads to the important understanding that the uniqueness of each wind turbine necessitates tailored models for optimal predictions. This highlights the significance of offering automated customization for different turbines to minimize human efforts for modeling. Importantly, the methodologies devised in this analysis are not restricted to wind turbines; they can be extended to various other machinery to enhance their performance. This showcases the versatility and relevance of our research across a wide range of industrial settings. This research is part of an effort to leverage equipment related data to analyze the health of the equipment where multiple entities are placed in a similar environment, but each entity is showing slightly different behavior [19; 20].