CAJ | 학술논문

为了提高故障电缆测距模型的预测精度，对已提出的基于零序直流原理的模型进行理论分析，得出消除电网电压波动和过渡电阻对检测电流的影响的测距方法。利用神经网络的联想记忆功能，对模型中不易测得且会发生变化的量进行动态网络辨识；同时根据电缆分支的多样性，引入了并行神经网络和基于减聚类的模糊C均值聚类算法，避免聚类中心陷入局部最优。建立多分支故障电缆距离预测动态模型，通过仿真表明该模型具有良好的预测效果。
위료제고고장전람측거모형적예측정도，대이제출적기우령서직류원리적모형진행이론분석，득출소제전망전압파동화과도전조대검측전류적영향적측거방법。이용신경망락적련상기억공능，대모형중불역측득차회발생변화적량진행동태망락변식；동시근거전람분지적다양성，인입료병행신경망락화기우감취류적모호C균치취류산법，피면취류중심함입국부최우。건립다분지고장전람거리예측동태모형，통과방진표명해모형구유량호적예측효과。
To improve the prediction accuracy of fault cable location model based on zero sequence DC, a way which can eliminate the effect of the power grid voltage fluctuation and transition resistance on the detective currents is proposed. Then, the neural network associative memory function is used to dynamically identify parameters that are difficult to measure and prone to changes. Moreover, due to the diversity of power grid, parallel neural network and fuzzy c-means algorithm based on subtractive clustering are introduced to avoid clustering centers to be trapped into local optima. Finally, the location prediction dynamic model is established for multi-branch fault cables. Simulation shows the above-mentioned model has good predictive performance.