Protection scheme for power transmission lines based on SVM and ANN considering the presence of non-linear loads
Protection scheme for power transmission lines based on SVM and ANN considering the presence of non-linear loads
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DOI：
10.1049/iet-gtd.2016.1802
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发表时间：
2017-06-22
影响因子：
2.5
通讯作者：
Mohanta, Dusmanta K.
中科院分区：
文献类型：
--
作者：
Koley, Ebha;Shukla, Sunil K.;Mohanta, Dusmanta K.
近年来，由于电力系统中谐波的存在，非线性负载的激增对电能质量有很大的影响。谐波会导致继电保护的误测和误动作，从而降低输电线路保护的效率。为了提高保护方案在变非线性负载条件下的可靠性，提出了一种基于支持向量机（SVM）、人工神经网络（ANN）和卡尔曼滤波器的三相输电线路电压谐波保护算法。故障后的电压信号由卡尔曼滤波器处理，以估计谐波分量，作为特征向量执行故障检测和分类的支持向量机和区域识别，以及由人工神经网络的位置。谐波信息区分故障的基础上的基波分量的变化的干扰，以提高选择性和准确性。考虑到电力系统故障发生的随机性，所提出的方案的有效性进行了验证，使用Monte Carlo仿真。实验结果证实了利用电压谐波提高输电线路保护可靠性的优越性。
The proliferation of non-linear loads in recent times has a strong bearing on power quality due to the presence of harmonics in the power system. Harmonics lead to erroneous measurement and malfunctioning of protective relays, thus reducing the efficacy of transmission line protection. With the aim of improving the dependability of the protection scheme under varying non-linear loading condition, the study presents a hybrid support vector machine (SVM), artificial neural network (ANN) and Kalman filter based algorithm using voltage harmonics for the protection of three-phase transmission line. The post-fault voltage signals are processed by a Kalman filter to estimate the harmonic components, which serve as feature vectors for performing the fault detection and classification by SVM and zone identification as well as the location by ANN. The harmonic information discriminates faults from disturbances based on variations in the fundamental component, to improve the selectivity and accuracy. Considering the stochastic nature of fault occurrence in power systems, the efficacy of the proposed scheme is validated using Monte Carlo simulation. The experimental results confirm the superiority of using voltage harmonics for improving the dependability of transmission line protection.