CAJ | 학술논문

基于已有电能计量自动化系统的实时采集的电量、负荷、报警及线损数据，分析现有用电检查所得窃电现象的样本，通过构建指标体系，建立一种自适应的防窃漏电诊断模型。基于模糊评价法与神经网络方法，结合了两者的优点，能够反映窃漏电现象的特征。经实证研究，能够实时监测计量自动化系统的运行数据，发现诊断窃漏电现象，具有较强的识别能力。
기우이유전능계량자동화계통적실시채집적전량、부하、보경급선손수거，분석현유용전검사소득절전현상적양본，통과구건지표체계，건립일충자괄응적방절루전진단모형。기우모호평개법여신경망락방법，결합료량자적우점，능구반영절루전현상적특정。경실증연구，능구실시감측계량자동화계통적운행수거，발현진단절루전현상，구유교강적식별능력。
Based on the data collected on a real-time basis by the existing automatic electric power metering system about quantity of electricity, load,alarm and line loss,this paper analyzes electricity stealing samples obtained in the current inspection method,and by creating an index system,establishes a self-adaptive diagnosis model for electricity stealing and leakage.Combining fuzzy evaluation method and neural network method,this model can monitor on a real-time basis the operational data of the automatic metering system to detect electricity stealing and leakage and has a strong capability of identification.