###### Abstract  
Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.  
artificial neural network; distribution grid; islanding detection; local methods; distributed generation +
Footnote †: journal: _Sciences_  
methods based on computer intelligence (CI). One of the first review papers that deals with new signal processing procedures and methods based on computational intelligence is [3]. In [4], authors made a more detailed presentation of modern trends in the development of IDM. Most authors [1; 2; 3; 4] agree that artificial neural networks (ANNs) are a promising tool for islanding detection applications and certainly one of the most investigated with a large body of scientific literature published in the last decade. In particular, in [5], which deals with review of different computational intelligence methods for ID, authors noted that neural networks and decision trees are the most used methods. The accuracy of the ANN-based method is high (according to [5], the accuracy can be as high as 98.19%). Also, they are capable of islanding detection with high accuracy in multi-inverter systems [6], they can deal with nonlinear systems, and they can also overcome the requirement of threshold settings [7].  
Since the authors of the previously published review papers [1; 2; 3; 4; 5; 6; 7; 8] make a general comparison of different islanding detection methods and do not present a detailed analysis of the neural network application to islanding detection, the authors of this paper decided to conduct a comprehensive review of ANN applications for the islanding detection problem. This paper aims to make a literature review to answer the following questions regarding the use of ANN for islanding detection:  
1. How are ANNs integrated into islanding detection?
2. What types of input data are used for ANN?
3. How are the \"raw\" input data transformed and processed to make it suitable for ANN?
4. What types of ANN architectures are used in islanding detection applications?
5. Which ANN learning algorithms are used for ID applications?  
The structure of the paper is as follows. In Section 2, the islanding detection principles are explained, and a general IDM description and classification are presented. Section 3 describes the general architecture of artificial neural networks and associated learning algorithms. Analysis of the used input data and their transformation is described in Section 4, while in Section 5, ANN architectures and learning algorithms are reviewed. In the last section, types of the analyzed electrical grids and types of DGs involved in the islanding operation are reviewed.