基于多维度分析的换流变预警技术研究与应用
Multi-dimensional Analysis and Early Warning Model of Converter Transformer
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摘要: 由于换流变设备运行环境和工况的差异性,相关运维规范设定的阈值在设备异常诊断方面具有一定的局限性。从工程实际应用出发,提出一种换流变多维度分析和预警方法,针对换流变重点监盘的关键参数建立温度、油位和冷却能力的多维度分析算法,对换流变当前状态进行评价。在多维度分析的基础上,提出基于长短期记忆网络的油温预测算法,实现换流变运行状态的趋势辨识。在±800 kV穗东站进行部署和应用算法模型,结果表明算法模型可有效识别出换流变运行状态异常。Abstract: Due to the difference of operation environment and working conditions of converter equipment, the threshold set by relevant operation and maintenance specifications has certain limitations in abnormal diagnosis. Based on the engineering application, a multi-dimensional analysis and early warning method is proposed. The multi-dimensional analysis algorithm of temperature, oil level and cooling capacity are established to evaluate the current state of converterfor the key parameters of converter transformer. On the basis of multi-dimensional analysis, the oil temperature prediction algorithm based on LSTM is proposed to realize the trend identification of the operation state of the converter. The algorithm model is deployed and applied in ±800 kV Suidong substation, and the results show that the algorithm model can effectively identify the abnormal operation state of converter transformer.