CAJ | 학술논문

在线预测输电线路的动态热容量,合理安排负荷高峰时期运行方式和调度管理,对输电线路的安全和经济运行具有重要意义,同时也对确定风电等间歇式可再生能源的接入容量提供技术支持.为此,提出了利用径向基神经网络实现线路动态容量的在线预测方法.该方法首先利用径向基神经网络进行风速和日照辐射温度的在线学习和预测,基于IEEE 738标准进行输电线路动态容量的在线短期预测.利用典型的夏季和冬季实测数据进行动态容量预测后发现,预测未来1、2、4 h的动态容量的最大相对误差分别在10%、20%、40%以内.将短期的负荷预测与该方法结合起来,可为电力紧张地区和负荷高峰时期的智能调度提供决策支持.
재선예측수전선로적동태열용량,합리안배부하고봉시기운행방식화조도관리,대수전선로적안전화경제운행구유중요의의,동시야대학정풍전등간헐식가재생능원적접입용량제공기술지지.위차,제출료이용경향기신경망락실현선로동태용량적재선예측방법.해방법수선이용경향기신경망락진행풍속화일조복사온도적재선학습화예측,기우IEEE 738표준진행수전선로동태용량적재선단기예측.이용전형적하계화동계실측수거진행동태용량예측후발현,예측미래1、2、4 h적동태용량적최대상대오차분별재10%、20%、40%이내.장단기적부하예측여해방법결합기래,가위전력긴장지구화부하고봉시기적지능조도제공결책지지.
It is significant for secure and economic operation of transmission lines to predict dynamic line rating (DLR) in realtime mode and reasonably arrange the operation mode during peak load period and scheduling management, meanwhile it can provide technical support for determining grid-connected capacity of intermittent renewable energy resources such as wind power and so on. For this reason, using redial base function neural network (RBFNN) an online prediction method to implement the prediction of DLR is proposed. Firstly, the online learning and prediction of wind and sunshine radiation temperature is performed by RBFNN, then based on IEEE 738 standard the online short-term DLR is predicted. In the prediction of DLR by use of typical measured data in summer season and winter season, it is found that the maximum relative error in the prediction of dynamic capacity for next one, two and four hours are less than 10%, 20% and 40% respectively. Combining the short-term load forecasting method with the proposed method, the decision-making can be provided to intelligent dispatching during the peak load period in the region lacking of power supply.