CAJ | 학술논문

采集大型火电系统短路数据，并将其转换成为时间序列数据，然后将时间序列数据输入到最小二乘支持分类集中进行训练，训练过程中引入最小二乘支持向量机对布谷鸟算法进行优化，用改进的布谷鸟算法对火电系统的短路位置数据进行距离聚类，从而预测出火电系统的短路地点。仿真结果表明本文算法能更加准确的预测了大型火电系统短路位置的变化态势，提高了大型火电系统短路位置的预测精度。
채집대형화전계통단로수거，병장기전환성위시간서렬수거，연후장시간서렬수거수입도최소이승지지분류집중진행훈련，훈련과정중인입최소이승지지향량궤대포곡조산법진행우화，용개진적포곡조산법대화전계통적단로위치수거진행거리취류，종이예측출화전계통적단로지점。방진결과표명본문산법능경가준학적예측료대형화전계통단로위치적변화태세，제고료대형화전계통단로위치적예측정도。
The acquisition of large thermal power system short-circuit data and convert it into time series data, then the time series data input to the least-squares support classification concentrated training, training in the process of introduc-tion of the least squares support vector machine (SVM) optimize the k-means algorithm, according to the optimized k-means algorithm to establish large thermal power system short-circuit prediction steps, and the forecast of large thermal power system short-circuit , and finally use the simulation to predict the accuracy of the test. The simulation results show that this algorithm can more accurately predict the change trend of large thermal power system short circuit, improve the prediction precision of large thermal power system short circuit.