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作 者:曹正江 付文龙[1,2] 文斌 花雅文 CAO Zhengjiang;FU Wenlong;WEN Bin;HUA Yawen(Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station,China Three Gorges University,Hubei Yichang 443000,China;School of Electrical Engineering and New Energy,China Three Gorges University,Hubei Yichang 443000,China)
机构地区:[1]三峡大学梯级水电站运行与控制湖北省重点实验室,湖北宜昌443000 [2]三峡大学电气与新能源学院,湖北宜昌443000
出 处:《高压电器》2024年第8期156-166,共11页High Voltage Apparatus
基 金:梯级水电站运行与控制湖北省重点实验室开放基金(2017KJX06)。
摘 要:油中溶解气体分析可以反映变压器的运行状态,对其体积分数精准预测可以为变压器早期故障判别和预警提供理论支撑。为此提出了一种基于时变滤波经验模态分解和多模型融合的变压器油中溶解气体体积分数预测方法。首先,通过时变滤波经验模态分解将气体体积分数序列分解为多个子序列,降低其非平稳性;其次,利用多模型融合策略,将4种不同单模型的预测结果进行融合重构,因单模型权重系数对预测结果有显著影响,利用改进黏菌算法对权重系数进行优化,以提高预测精度;最后,通过算例验证表明,相比于传统的预测模型,所提方法具有更高的预测精度,可以更好地预测油中气体体积分数的变化趋势。Dissolved gas analysis in oil can reflect the operation status of transformer.The accurate prediction of volume fraction of the dissolved gas can provide theoretical support for discrimination and warning of early fault of the transformer.Therefore,a prediction method of dissolved gas volume fraction in transformer oil based on time varying filter empirical mode decomposition and multi-model fusion is proposed.Firstly,the gas volume traction sequence is decomposed into multiple sub-sequences by time varying filter empirical mode decomposition to reduce its non-stationarity.Then,the multi-model fusion strategy is used to fuse and reconstruct the prediction results of four different single models.Since the weight coefficient of single model has a significant impact on the prediction results,the improved slime mold algorithm is used to optimize the weight coefficient to improve the prediction accuracy.Finally,it is shown through calculation variation that the proposed method,compared to traditional prediction model,has higher prediction accuracy,and can better predict the variation trend of gas volume fraction in the oil.
关 键 词:油中溶解气体体积分数预测 时变滤波经验模态分解 改进黏菌算法 多模型融合
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