考虑气象参数预测误差条件分布的架空输电线路载流量概率预测方法

Current-carrying Capacity Probability Prediction of Overhead Transmission Line Considering Conditional Distribution Prediction Errors of Meteorological Parameters

准确考虑气象参数预测误差是实现架空输电线路动态载流量精准预测的基本前提。通过统计与计算分析,首次发现在不同气象参数预测值与气象环境下,气象参数预测误差具有不同分布特征。然而,现有架空输电线路载流量概率预测方法并未考虑以上2种因素的影响,难以实现线路载流量的准确预测。为此,首先将气象参数预测误差分析问题构建为气象参数预测值与气象环境2种影响因素下的气象参数预测误差条件分布求解问题;其次引入Sklar定理及其Copula函数和非参数核密度估计法,构建了一种气象参数预测误差条件分布的求解方法;然后结合蒙特卡洛采样法,提出了一种考虑气象参数预测误差条件分布的架空输电线路载流量概率预测新方法。最后通过计算分析发现:相比于2种传统方法,所提方法在考虑气象参数预测值与气象环境2种因素对气象参数预测误差概率分布的影响后,预测区间覆盖率分别提高了5.51、1.99个百分点,预测区间标准化平均宽度分别降低了7.86、3.62个百分点,验证了该方法的准确性与实用性。

Accurately accounting for the errors in meteorological parameter predictions is essential for the precise forecasting of dynamic current-carrying capacity in overhead power transmission lines.Statistical and computational analyses have revealed for the first time the distinct distribution characteristics of meteorological parameter prediction errors under varying forecasted weather conditions and environmental contexts.Existing methods for probabilistic load capacity forecasting of overhead lines fail to consider the impact of these two critical factors,leading to challenges in achieving accurate predictions.Addressing this gap,the issue of meteorological parameter prediction error analysis is formulated as a problem of solving for the conditional distribution of errors,influenced by both forecasted meteorological conditions and the environment.Incorporating Sklar's theorem,its associated Copula function,and non-parametric kernel density estimation,a novel approach to determining the conditional distribution of prediction errors is established.Further,a new methodology for probabilistic forecasting of transmission line load capacity that integrates the conditional distribution of meteorological parameter prediction errors is proposed,using Monte Carlo sampling techniques.Comparative computational analysis has demonstrated that,relative to two conventional approaches,the proposed method significantly enhances the coverage of prediction intervals by 5.51 and 1.99 percentage points,and concurrently reduces the normalized average width of these intervals by 7.86 and 3.62 percentage points.These improvements confirm the method's heightened accuracy and practicality.