Improving the Lightning Protection System at Wind Power Stations Through the Integration of Meteorological Data
Abstract
Lightning forecasting systems at wind power stations must assess the probability of lightning strikes with high accuracy, which is directly dependent on the source of meteorological data. Existing methods typically rely on one or two data sources. In this study, a multi-source data integration approach was developed to reduce lightning risks and enhance the effectiveness of protection systems at wind power plants. Initially, available meteorological data sources were categorized into five main groups: satellite observations, ground-based sensor systems, LIDAR and RADAR technologies, specialized lightning activity monitoring tools, and retrospective statistical data. For each meteorological parameter, an appropriate probability distribution function was identified, and statistical analyses combined with automated data processing algorithms were implemented. These enabled real-time monitoring, risk assessment, turbine transition to safe operational modes, and activation of warning systems. Furthermore, the integration of data from various sources led to the development of a comprehensive diagram that addresses 11 critical tasks necessary for effective lightning forecasting at wind power stations.