基于SDS-SSA-LSTM的变压器油中溶解气体浓度预测

油中溶解气体浓度预测对变压器早期故障检测至关重要。为了提高预测精度，本文提出了奇异谱分析(SSA)结合长短期记忆网络(LSTM)的预测模型。首先针对传统序列分解做法中的数据泄露问题，提出一种基于SSA逐步分解的采样策略，然后基于该策略将特征复杂的原始油中溶解气体浓度序列分解为特征相对单一的趋势分量与波动分量，最后利用LSTM网络对各个分量分别进行单步和多步预测。累加各分量的预测值，得到原气体浓度的预测结果。算例表明，相较于单一LSTM，本文所提模型在实验天数内整体的预测精度更高。

Prediction of dissolved gas concentration in transformer oil based on SDS-SSA-LSTM

Prediction of dissolved gas concentration in oil is very important for transformer early fault detection. a prediction model based on singular spectrum analysis (SSA) combined with long and short-term memory network (LSTM) is proposed to improve the prediction accuracy. First, To solve the problem of data leakage in traditional sequence decomposition, a sampling strategy based on SSA stepwise decomposition is proposed. Then based on this strategy, the concentration sequence of dissolved gas in original oil with complex characteristics is decomposed into relatively single trend component and fluctuation component. Finally using LSTM network for each component for single step and multi-step prediction respectively. The predicted values of each component are accumulated to obtain the prediction result of original gas concentration. The example shows that compared with the single LSTM model, the overall prediction accuracy of the proposed method is higher in the experimental days.