Electric Power Systems Research 110 (2014) 180–187 Contents lists available at ScienceDirect Electric Power Systems Research j o ur nal ho me page: www.elsevier.com/lo cate/epsr A new passive islanding detection method and its performance evaluation for multi-DG systems A.H. Mohammadzadeh Niaki ∗ , S. Afsharnia School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14395-515, Iran a r t i c l e i n f o Article history: Received 12 September 2013 Received in revised form 14 January 2014 Accepted 20 January 2014 Keywords: Distributed generation Islanding detection Empirical mode decomposition Intrinsic mode function Multi-DG system a b s t r a c t This paper presents a passive islanding detection method for inverter-based distributed generation based on empirical mode decomposition (EMD) technique. The voltage of point of common coupling (PCC) is measured and its intrinsic mode functions (IMFs) are obtained using EMD. The first IMF component of PCC per unit voltage is the parameter used for islanding detection. Performance of the proposed method is evaluated for single-DG and multi-DG cases. Simulation results performed in MATLAB/SIMULINK envi- ronment show that the islanding can be detected in less than two cycles, even for zero power mismatch. Moreover, the proposed method functions properly for various configurations of multi-DG systems, DGs switching events, various loadings of DGs and different DG interface controls. © 2014 Elsevier B.V. All rights reserved. 1. Introduction Environmental pollution of fossil fuels caused an increased application and penetration of distributed generation (DG) systems using renewable energy sources. Integration of these DGs to dis- tribution network has remarkable advantages, including increased reliability and reduced line losses. On the other hand, some prob- lems and concerns may be generated. One of the most critical concerns is islanding detection. Islanding is a condition in which a portion of the distribution network comprising local loads and one or more DGs remains energized while isolated from the rest of the system. Islanding detection is one of the mandatory requirements for DGs, specified in the IEEE Std. 929-2000 and IEEE Std. 1547- 2003 [1,2]. Based on these standards, an unintentional island shall be detected within 2 s and the related DGs shall be isolated from the distribution system. Therefore, a fast and accurate islanding detection method is essential. Islanding detection techniques are classified in two categories: remote and local techniques. Remote methods are based on the communication between utilities and DGs. In contrast, local meth- ods use measured data at the DG site. Remote techniques are more reliable than local ones, but their implementation is more expen- sive. So, local methods are widely used for islanding detection. They can be categorized into passive, active and hybrid methods. ∗ Corresponding author. Tel.: +98 9111009150. E-mail addresses: a.mohammadzadeh@ece.ut.ac.ir (A.H. Mohammadzadeh Niaki), safshar@ut.ac.ir (S. Afsharnia). In passive techniques, system parameters like voltage, fre- quency, etc. are continuously monitored and compared with a predetermined threshold. Active methods intentionally inject dis- turbances into the system. Hybrid methods are a combination of passive and active methods. A comprehensive survey on islanding detection methods is presented in [3–6]. Active methods have relatively smaller non-detection zone (NDZ) than passive methods. But they degrade the power quality due to the perturbations introduced to the system. Since the pas- sive methods are usually simple and easy to implement and do not introduce any disturbance, applying a passive method with small NDZ is preferred to an active method. Time-frequency transform-based passive anti-islanding tech- niques have been recently proposed. Wavelet transform and S-transform have been presented for islanding detection in [7–12]. These transforms are applied on PCC voltage and current signals to get useful information and calculate suitable parameters, e.g. high frequency components and spectral energy of the signal. Wavelet transform is basically a time-scale analysis, not a real time-frequency analysis. One of the problems of the wavelet analy- sis is its non-adaptive nature. Once the mother wavelet is selected, it cannot be changed during the analysis and have to be used to analyze all the data. Moreover, spectral wavelet analysis under- lies an uncertainty principle, indicating that a time or frequency dependent information cannot be classified by the same accuracy, simultaneously. The S-transform is a combination of the short time Fourier trans- form (STFT) and the wavelet transform by changing the shape of the S-transform wavelet. Although the S-transform can perform 0378-7796/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.epsr.2014.01.016