摘要
为满足电力系统对变压器资产管理和风险评估的需求,提出了一种基于人工神经网络和信息融合技术的变压器状态评估方法。以预防性试验数据和在线监测数据为例,选择具有代表意义的信息量作为开展评估的静态状态量,除此之外还选取部分静态状态量的变化趋势作为开展评估的渐变状态量,采用非线性指标评价函数对状态量进行归一化处理,综合应用人工神经网络(ANN)和Dempster-Shafer(D-S)证据理论构建多信息融合的变压器状态评估模型。通过对某台500 kV变压器数据的实例分析,验证了该评估模型应用于变压器状态评估中的有效性。该方法将在线监测数据与部分参数的变化趋势紧密结合,有助于提高变压器状态评估的时效性和准确性。
To meet the needs of assets management and risk assessment for power transformers in power systems, we proposed a condition assessment method of power transformer based on artificial neural network and information fusion technology. Taking preventative test parameters and on-line monitoring parameters as the example, we chose some repre- sentative part of them as static condition parameters, and chose the variation trends of parts of the static condition parameters as trend condition parameters. We normalized these condition parameters using a nonlinear index evaluation function, and established a model of multi-information fusion transformer condition assessment based on the artificial neuron network （ANN） and Dempster-Shafer （D-S） evidence theory. Moreover, we analyzed data of an example from a 500 kV power transformer, and the results verified the effectiveness of the proposed model. It is concluded that combining on-line monitoring parameters and their variation trends, the proposed method is helpful to improving the accuracy and timeliness of transformer condition assessment.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2014年第3期822-828,共7页
High Voltage Engineering
基金
国家电网公司科技项目(SG10028)
国网湖北省电力公司科技项目(201110101)~~
关键词
变压器状态评估
多信息融合
D-S证据理论
人工神经网络
趋势分析
非线性指标评价函数
transformer condition assessment
multi-information fusion
D-S evidence theory
artificial neural network
trend analysis
nonlinear index evaluate function
