This research proposes a novel predictive model to improve the gas prediction accuracy in transformer oil and provide guarantees for accident prevention. First, this study constructs a cross-entropy loss function with variable thresholds and dynamic weights to reduce error transmission in the deep residual shrinkage network, enhancing the sensitivity of the normal and abnormal transformer states by the network. Second, the multiobjective particle swarm algorithm and random walk strategy are adopted to optimize the long short-term memory (LSTM) network to ensure the prediction model's objectivity. Finally, the improved subchannel threshold depth residual shrinkage network is integrated with the optimized LSTM network. The new model can identify potential abnormal conditions in advance and preproduce an approximate fault model using a feature gas vector (similar to image recognition). Experiments verify the effectiveness of the method. Compared with the existing prediction methods, the new method can avoid blind prediction and significantly improve the prediction accuracy and efficiency, which provides essential value for transformer fault prevention.

中文翻译：

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基于多特征增强和细化长短期记忆的变压器故障预测新方法


本研究提出了一种新颖的预测模型，以提高变压器油中气体的预测精度，为事故预防提供保障。首先，本研究构建了具有可变阈值和动态权重的交叉熵损失函数，以减少深度残差收缩网络中的误差传递，增强网络对正常和异常变压器状态的敏感性。其次，采用多目标粒子群算法和随机游走策略来优化长短期记忆（LSTM）网络，保证预测模型的客观性。最后，将改进的子通道阈值深度残差收缩网络与优化的LSTM网络集成。新模型可以提前识别潜在的异常情况，并利用特征气体向量（类似于图像识别）预先生成近似的故障模型。实验验证了该方法的有效性。与现有预测方法相比，新方法可以避免盲目预测，显着提高预测精度和效率，对变压器故障预防具有重要价值。