An Approach for Assessing the Effectiveness of Multiple-Feature-Based SVM Method for Islanding Detection of Distributed Generation

Abstract

Islanding detection is a critical protection issue, as conventional protection schemes such as vector surge (VS) and rate of change of frequency relays do not guarantee islanding detection for all network conditions. Integration of multiple distributed generation (DG) units of different sizes and technologies into distribution grids makes this issue even more critical. This paper presents a comprehensive analysis of the effectiveness of a new method for islanding detection in DG networks. The proposed method, which is based on multiple features and support vector machine (SVM) classification, has the potential to overcome the limitations of conventional protection schemes. The multifeature-based SVM technique utilizes a set of features generated from numerous set of offline dynamic events simulated under different network contingencies, operating conditions, and power imbalance levels. Parameters (such as voltage, frequency, and rotor angle) showing distinguishable variation during the formation of islanding are selected as features for classification of the events. Features associated with different islanding and nonislanding events are used to train the SVM. The trained SVM is tested on a typical distribution network containing multiple DG units. Simulation results indicate that the proposed method can work effectively with high degree of accuracy under different network contingencies and critical levels of power imbalance that may exist during islanding.

An islanding detection strategy for distribution network connected with hybrid DG resources

“…The extracted features are then used as inputs to a support vector machine to classify the event as islanding or non-islanding. The simulation results indicated that the technique possessed accuracy of 98% and above [35]. Similarly, application of support vector machine for enhancement in the performance of passive islanding detection technqiue has been proposed in [36][37][38].…”

“…However, efforts have been done to reduce the NDZ of the passive islanding detection techniques by combining it with artificial intelligence techniques. Alam et al [35] have combined the support vector machine (SVM) with conventional vector surge (VS) and rate of change of frequency relay to reduce their NDZ. The technique obtained features of voltage, frequency, and rotor angle by generating numerous sets of offline dynamic simulations under different operating conditions.…”

Apparent Power-Based Anti-Islanding Protection for Distributed Cogeneration Systems

“…Such utility-grade protective devices are mostly based on over/under voltage and frequency relays, which are specified for co-generation systems in accordance with the utility voltage and frequency tripping requirements. Utility voltage and frequency tripping requirements are defined by the upper and lower limits of the voltage V GU and frequency f GU for any generating unit as [11]- [24]:…”

A Granular Classifier By Means of Context-based Similarity Clustering

“…Especially, one can extend SVM to obtain nonlinear decision hyperplanes by exploiting kernelization techniques [9][10][11][12]. Pioneering studies by Xue et al [13], Sebald et al [14], Alam et al [15], and Morsier et al [16] led to different improved SVM models. By now, the research has focused on the efficient of linear or nonlinear classification.…”