Forecasting and Early Warning of Wind‐Farm‐Tripping Event under Strong‐Wind and Cold‐Wave Conditions Based on Combined Nonparametric Estimation Model and K‐Value Method

Abstract

Wind power, as a pivotal renewable energy source, is increasingly vital in the global energy structure transformation. However, extreme weather, such as strong winds and cold waves, can result in rapid fluctuations in wind power generation and even lead to large‐scale wind‐farm‐tripping events. Consequently, forecasting and early warning of these events under extreme weather conditions are crucial for ensuring the safe operation of the power system. In this article, a forecasting and early warning method for wind‐farm‐tripping events is proposed, which is based on a multi‐nonparametric estimation approach. In this article, the error distribution of forecasting wind speed and temperature in numerical weather prediction (NWP) is modeled, and interval forecasting for these parameters is conducted. When forecasting intervals of NWP reach the thresholds of wind‐turbine‐tripping protection strategy, it will lead to the wind‐farm‐power‐tripping events. Furthermore, in this article, a method for calculating the wind‐power‐tripping capacity based on the K‐value method is introduced. The effectiveness of the proposed method is verified through actual wind‐power‐tripping events in the wind farm. The methodology proposed in the article provides a solution of early warning and forecasting for strong–wind‐ and cold‐wave wind‐farm‐tripping events.