CAJ | 학술논문

根据有功功率与五项电能质量稳态指标的相关性以及有功功率的数据特点，提出了一种对电能质量稳态指标的预测方法。该方法利用ARIMA时间序列算法对有功功率进行了预测，并根据有功功率与五项电能质量稳态指标的相关性建立神经网络预测模型对五项常规指标进行预测。通过分析预测结果与真实值的误差可得平均误差均在20%以内，该方法可以有效预测出电能质量指标序列的变化趋势，从而对电力系统的稳定性、安全性和经济性起到很好的作用。
근거유공공솔여오항전능질량은태지표적상관성이급유공공솔적수거특점，제출료일충대전능질량은태지표적예측방법。해방법이용ARIMA시간서렬산법대유공공솔진행료예측，병근거유공공솔여오항전능질량은태지표적상관성건립신경망락예측모형대오항상규지표진행예측。통과분석예측결과여진실치적오차가득평균오차균재20%이내，해방법가이유효예측출전능질량지표서렬적변화추세，종이대전력계통적은정성、안전성화경제성기도흔호적작용。
Based on the active power with five power quality indicators as well as the relevance of active power data characteristics,pro-pose a steady-state power quality indicators forecasting method. This method uses the ARIMA time series algorithm to predict the active power,and in accordance with the relevance of the active power with five steady-state power quality indicators,establish neural network model to predict the five conventional indicators. By analyzing the predicted and actual values of the error can be an average error of less than 20%,so the method can predict the sequence change trends of power quality,and thus playing a very good role for the power system stability,security and economy.