Name
Title | ||
|---|---|---|
A Universal Islanding Detection Technique for Distributed Generation Using Pattern Recognition |
Abstract | ||
|---|---|---|
Anti-islanding protection methods, proposed in the literature, are distributed generation (DG) type dependent or in other words work efficiently for a specific DG type (synchronous or inverter based). In this paper, we investigate the possibility of developing an efficient universal islanding detection method that can be applied to both inverter and synchronous-based DG. The proposed method relies on extracting a group of features, from measured data simulated for both types of DGs, from which the best features are selected for islanding detection. A random forest (RF) classification technique is used to detect islanding and non-islanding situations with an objective of minimizing the non-detection zone as well as avoiding nuisance DG tripping during non-islanding conditions. Islanding and non-islanding cases were generated for the IEEE 34-bus system and used to train and test the proposed technique. In the paper, k-fold cross-validation was used in order to test the accuracy of the proposed algorithm for detecting islanding. The results show that the proposed methodology has zero non-detection zone, high accuracy, and fast response when applied to both types of DGs independently of the size of the island. Among the various classification approaches investigated, the RF technique proved to be the most efficient approach for the proposed islanding detection method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSG.2014.2302439 | Smart Grid, IEEE Transactions |
Keywords | Field | DocType |
distributed power generation,pattern classification,power generation protection,random processes,DG type dependent,IEEE 34-bus system,RF technique,anti-islanding protection methods,distributed generation,inverter,k-fold cross-validation,nuisance DG tripping avoidance,pattern recognition,random forest classification technique,synchronous-based DG,universal islanding detection technique,zero nondetection zone,Decision tree,inverter-based distributed generator,islanding detection,naïve Bayes,neural network,random forest (RF),support vector machine (SVM),synchronous-based distributed generator | Decision tree,Inverter,Tripping,Naive Bayes classifier,Electronic engineering,Control engineering,Distributed generation,Engineering,Random forest,Artificial neural network,Islanding | Journal |
Volume | Issue | ISSN |
5 | 4 | 1949-3053 |
Citations | PageRank | References |
4 | 0.49 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar N. Faqhruldin | 1 | 4 | 0.49 |
| El-Saadany, E.F. | 2 | 54 | 10.87 |
| Hatem H. Zeineldin | 3 | 109 | 22.18 |