Research on a novel passive islanding detection method


  • Tongling University, Department of Electrical Engineering, Tongling, 244000, China


In distributed generation systems, islanding detection is a necessary function of grid-connected inverters. In view of the performance disadvantages of traditional passive and active islanding detection methods, this paper proposes a novel passive islanding detection method. The proposed method first extracts characteristic parameters from the inverter output voltage signal and inverter output current signal through lifting wavelet transform, and then conducts the pattern recognition of these extracted characteristic parameters via BP neural network, so as to judge if there is an islanding phenomenon. As verified by the simulation and experiment results, the islanding detection method proposed in this paper is effective, and is featured by high detection speed and small non-detection zone, without affecting electric energy quality; its detection performance has been remarkably improved in comparison with that of traditional islanding detection methods.


Inverter, islanding detection, lifting wavelet, neural network

Full Text:


Serban E., Pondiche C., Ordonez M.,(2015): “Islanding detection search sequence for distributed power generators under AC grid faults”, IEEE Transactions on Power Electronics, vol. 30, pp.3106-3121, June.

Dong Dong, Bo Wen, Mattavelli P., Boroyevich D., Yaosuo Xue, (2014): “Modeling and design of islanding detection using Phase-Locked Loops in three-phase grid-interface power converters”, IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, pp. 1032-1040, April.

Guo Y., Li K., Laverty D.M., Xue Y. (2015): “Synchrophasor-based islanding detection for distributed generation systems using systematic principal component analysis approaches”, IEEE Transactions on Power Delivery, vol. 30, pp.2544-2552, June.

Al Hosani M., Zhihua Qu, Zeineldin H.H., (2015): “Development of dynamic estimators for islanding detectionof inverter-based DG”, IEEE Transactions on Power Delivery, vol. 30, pp. 428-436, January.

Xiaolong Chen, Yongli Li. (2014): “An islanding detection algorithm for inverter-based distributed generation based on reactive power control”, IEEE Transactions on Power Electronics, vol. 29, pp. 4672- 4683, September.

Samet H., Hashemi F., Ghanbari T. (2015):“Islanding detection method for inverter-based distributed generation with negligible non-detection zone using energy of rate of change of voltage phase angle”, IET Generation, Transmission & Distribution, vol.9, pp. 2337-2350, November.

Alam M.R., Muttaqi K.M., Bouzerdoum A. (2014): “An approach for assessing the effectiveness of multiplefeature- based SVM method for islanding detection of distributed generation”, IEEE Transactions on Industry Applications, vol. 50, pp. 2844-2852, April.

Jun Zhang, Dehong Xu, Guoqiao Shen, Ye Zhu, Ning He, Jie Ma(2013):“An improved islanding detection method for a grid-connected inverter with intermittent bilateral reactive power variation”, IEEE Transactions on Power Electronics, vol. 28, pp. 268- 278, January.

Lidula, A. D. Rajapakse (209): “Fast and reliable detection of power islands using transient signals” Fourth International Conference on Industrial and Information Systems, pp.1-6.

Samui A., Samantaray S.R. (2013): “Wavelet singular entropy-based islanding detection in distributed generation”, IEEE Transactions on Power Delivery, Vol.28, pp.411-418, January.

Lidula N.W.A., Rajapakse A.D. (2012): “A patternrecognition approach for detecting power islands using transient signals—part II: performance evaluation”, IEEE Transactions on Power Delivery, vol.27, pp.1071-1080, March.

Yang Tao, Feng Yongxin, Ren Yong, Tang Lei, Li Yanghai(2012): “Parameter Identification of Steam Turbine Speed Governor System”, 2012 Asia-Pacific Power and Energy Engineering Conference, pp. 1-8.

Kuei-Hsiang Chao, Min-Sen Yang, Chin-Pao Hung, (2013): “Applying a CMAC neural network to a photovoltaic system islanding detection”, International Conference on Machine Learning and Cybernetics, Tianjin, pp. 259-264.

Wenhao Huang, Haikun Hong, Guojie Song, Kunqing Xie, (2014): “Deep process neural network for temporal deep learning”, [14] 2014 International Joint Conference on Neural Networks, pp. 465-472.

S. Wang, H. Yang, L. Wang. (2005): GB/T 19939-2005 technical requirements of grid-connected photovoltaic system, national standard of the people’s Republic of China, Nov.


  • There are currently no refbacks.