CAJ | 학술논문

　　针对当前电能质量检测分析的难点和重点问题，在分析了目前使用最多的方法小波变换优缺点的基础上，提出了基于神经网络自适应控制（NNAC）的电能质量暂态扰动检测算法。给出了电能质量暂态扰动检测的自适应控制结构，采用Hebb学习规则进行权值学习，并对电压暂降、电压瞬升、电压中断和暂态振荡等暂态扰动进行了仿真测试，结果表明所提算法可以很好地检测电网中的暂态扰动信号的类型，确定扰动发生的起始时刻和持续时间，且分析计算简单，速度快，计算所得数据量少，在电能质量扰动检测中更加具有实时性。
　　침대당전전능질량검측분석적난점화중점문제，재분석료목전사용최다적방법소파변환우결점적기출상，제출료기우신경망락자괄응공제（NNAC）적전능질량잠태우동검측산법。급출료전능질량잠태우동검측적자괄응공제결구，채용Hebb학습규칙진행권치학습，병대전압잠강、전압순승、전압중단화잠태진탕등잠태우동진행료방진측시，결과표명소제산법가이흔호지검측전망중적잠태우동신호적류형，학정우동발생적기시시각화지속시간，차분석계산간단，속도쾌，계산소득수거량소，재전능질량우동검측중경가구유실시성。
Aiming at current difficulties and key issues of power quality detection, after analyzing the advantages and disadvan-tages of the method most used based on wavelet transform, an algorithm of power quality transient disturbances detection based on the adaptive control using neural networks is proposed. The adaptive control structure of power quality transient disturbances detection is provided. Hebb rule is applied to learn the weight. Simulation test is conducted on transient disturbance of voltage sag, voltage transient rise, voltage interruption and transient oscillations. The results show that this algorithm behaves well on detecting the types of transient disturbances signal, confirming the starting time and duration of disturbance. Besides, the analy-sis and calculation of this algorithm are simple and fast, the datum is small, all of which make the algorithm practical on power quality transient disturbances detection.