基于LSTM及分位数回归理论的配电变压器重过载概率预测

配电变压器的重过载是导致变压器故障和损坏的主要原因之一。因此,准确地预测配电变压器的运行情况对于电力系统的安全和可靠运行至关重要。由于配电台区负荷受到诸多复杂变量的影响,同时这些复杂变量的影响往往无法可靠建模估计,故最终预测结果表现出一定的不确定性。传统单点预测为预测单一最优值,无法充分量化预测的不确定性。本文融合多维特征与变压器历史运行数据,采用分位数回归方法对台区负荷情况进行建模,通过将条件分位数与一般线性或非线性模型结合来构建概率预测模型,分位数回归能够对整个条件分布建模,相对于标准回归方法其可以提供更多信息。传统的点预测由于其无法估计预测结果的不确定性,故对业务部门的决策具有一定的风险,而概率预测不仅可以像点预测一样提供未来最优预测点,也可以提供未来预测值的分布情况,概率预测以预测区间或分位数的形式可以更好地估计重过载情况。

Probabilistic prediction of distribution transformer heavy overload based on LSTM and quantile regression theory.

The heavy overload of distribution transformers is one of the main causes of transformer failures and damage. Therefore, accurately predicting the operating conditions of distribution transformers is crucial for the safe and reliable operation of the power system. Due to the influence of various complex variables, the distribution load in distribution substations is often difficult to reliably model and estimate, resulting in certain uncertainties in the final prediction results. Traditional point predictions aim to predict a single optimal value, which cannot fully quantify the uncertainty of the predictions.This paper integrates multidimensional features and historical operating data of transformers, and models the load conditions in distribution substations using quantile regression methods. By combining conditional quantiles with general linear or nonlinear models, a probabilistic prediction model is constructed. Quantile regression can model the entire conditional distribution and provide more information compared to standard regression methods. Traditional point predictions, due to their inability to estimate the uncertainty of the prediction results, carry a certain risk for decision-making in business departments. In contrast, probabilistic predictions can not only provide the future optimal prediction point, similar to point predictions, but also provide information about the distribution of future predicted values. Probabilistic predictions, in the form of probability density, prediction intervals, or quantiles, can better estimate the uncertainty of the distribution load.Therefore, probabilistic predictions play a crucial role in accurately assessing the uncertainty of distribution load, ensuring the safety and reliable operation of the power system, and making informed decisions.