The article from ESS Open Archive, titled 'Construction of Wind Farm Asset Health Index Based on Multi-Dimensional Indicators and Analytic Hierarchy Process and Its Correlation with Operational Performance,' presents a significant research contribution to the field of renewable energy. It introduces a methodology for creating an Asset Health Index (AHI) for wind farms by integrating multi-dimensional indicators with the Analytic Hierarchy Process (AHP). The primary goal of this research is to establish a clear correlation between the health of wind farm assets and their overall operational performance. The ESS Open Archive serves as a community server for the open discovery and dissemination of early research outputs, including preprints, in earth, environmental, and space sciences. While content on ESS Open Archive is not peer-reviewed by the archive itself, it allows for faster dissemination of scientific findings and often hosts manuscripts undergoing peer review for other journals. The concepts of a Wind Farm Asset Health Index and the Analytic Hierarchy Process are well-established and widely recognized in academic and industrial contexts. AHIs are crucial for enabling proactive maintenance, detecting anomalies early, reducing unplanned downtime, and extending the lifespan of wind turbines. Similarly, AHP is a robust multi-criteria decision-making method frequently applied in energy sector research for site selection, barrier assessment, and performance evaluation, demonstrating its utility in complex decision environments. This research is particularly relevant for an audience in India, where wind energy plays a vital role in the country's clean energy transition. Studies have highlighted India's increasing wind capacity and the challenges of maintaining high uptime and performance, making advanced asset management solutions crucial. The proposed methodology can help optimize wind farm operations, improve efficiency, and support India's ambitious renewable energy targets by providing a data-driven approach to asset management. There is no apparent misinformation or exaggeration in the article, as it adheres to the descriptive and methodological language typical of scientific research.