CAJ | 학술논문

为解决电流互感器的零点误差非陑性校正问题，提出一种蚁群算法优化径向基函数(RBF)的零点误差非陑性校正方法(ACO-RBF)。利用蚁群算法对RBF神经网络参数进行优化，并采用优化后的RBF神经网络对电流互感器零点误差进行自适应校正。仿真结果表明，陒对于其他校正方法，ACO-RBF可提高电流互感器自动测试系统的测量精度，减少测量误差，较好地反映零点误差变化的特点。
위해결전류호감기적영점오차비이성교정문제，제출일충의군산법우화경향기함수(RBF)적영점오차비이성교정방법(ACO-RBF)。이용의군산법대RBF신경망락삼수진행우화，병채용우화후적RBF신경망락대전류호감기영점오차진행자괄응교정。방진결과표명，희대우기타교정방법，ACO-RBF가제고전류호감기자동측시계통적측량정도，감소측량오차，교호지반영영점오차변화적특점。
Aiming at zero error nonlinear correction problem of Current Transformer(CT), this paper presents a nonlinear sensor zero error correction algorithm based on Radial Basis Function(RBF) neural network optimized by Ant Colony Optimization(ACO) method(ACO-RBF). The parameters of RBF neural network are optimized by ACO algorithm, and the optimized RBF neural network is used to correct CT zero error adaptively. Simulation results show that the proposed method can effectively improve the measuring accuracy of automatic test system and reduce measurement error compared with other methods. It can reflect the characteristic of zero.