An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the *d<\/em>-axis current which leads to a frequency deviation at the terminal of the RLC<\/em> load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.<\/p>\r\n","references":"[1]\tM. Ciobotaru, V. Agelidis, and R. Teodorescu, \u201cAccurate and less-disturbing active anti-islanding method based on PLL for grid-connected PV Inverters,\u201d in 2008 Proc. IEEE Power Electronics Specialists Conf., pp. 4569-4576.\r\n[2]\tIEEE, Std. 1547, \u201cIEEE standard for interconnecting distributed resources with electric power systems,\u201d 2003.\r\n[3]\tUL 1741, \u201cInverters, converters, and controllers for use in independent power systems,\u201d 2002.\r\n[4]\tIEEE, Std. 929-2000, \u201cIEEE recommended practice for utility interface of photovoltaic (PV) systems,\u201d 2000.\r\n[5]\tM. E. Ropp, M. Begovic, A. Rohatgi, \u201cAnalysis and performance assessment of the active frequency drift method of islanding prevention,\u201d IEEE Trans. Energy Conversion, vol. 14, no.3, pp. 810\u2013816, Sep. 1999.\r\n[6]\tH. Karimi, A. Yazdani, and R. Iravani, \u201cNegative-sequence current injection for fast islanding detection of a distributed resource unit,\u201d IEEE Trans. Power Electronics, vol. 23, no. 1, pp. 298\u2013307, Jan. 2008.\r\n[7]\tB. Y. Bae, J. K. Jeong, J. H. Lee, and B. M. Han, \u201cIslanding detection method for inverter-based distributed generation systems using a signal cross-correlation scheme,\u201d J. Power Electronics, vol. 10, no. 6, pp. 762\u2013768, 2010.\r\n[8]\tT. T. Ma, \u201cNovel voltage stability constrained positive feedback anti-islanding algorithms for the inverter-based distributed generator systems,\u201d IET Renewable Power Generation, vol. 4, no. 2, pp. 176-185, March, 2010.\r\n[9]\tF. Wang, and Z. Mi, \u201cPassive islanding detection method for grid connected PV system,\u201d in 2009 Proc. Int. Conf. on Industrial and Information Systems, pp. 409-412\r\n[10]\tY. Gao, and M. J. Er, \u201cAn intelligent adaptive control scheme for postsurgical blood pressure regulation,\u201d IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 475-483, March, 2005.\r\n[11]\t\tF. J. Lin, H. J. Shieh, P. K. Huang, and L. T. Teng, \u201cAdaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator,\u201d IEEE Trans. Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, no. 9, pp. 1649-1661, Sept. 2006.\r\n[12]\tM. Tripathy, R. P. Maheshwari, and H. K. Verma, \u201cApplication of probabilistic neural network for differential relaying of power ransformer,\u201d IET Generation, Transmission and Distribution, vol. 1, no. 2, pp. 218\u2013222, March, 2007.\r\n[13]\tN. Perera, and A. D. Rajapakse, \u201cRecognition of fault transients using a probabilistic neural-network classifier,\u201d IEEE Trans. Power Delivery, vol. 1, no. 26, pp. 410-419, Jan., 2011.\r\n[14]\tM. Tripathy, R. P.Maheshwari, and H. K. Verma, \u201cPower transformer differential protection based on optimal probabilistic neural network,\u201d IEEE Trans. Power Delivery, vol. 25, no. 1, pp. 102-112, Jan., 2010.\r\n[15]\tH. X. Li, and Z. Liu, \u201cA probabilistic neural-fuzzy learning system for stochastic modeling,\u201d IEEE Trans. Fuzzy Systems, vol. 16, no. 4, pp. 898-908, Aug., 2008.\r\n[16]\tF. J. Lin, Y. S. Huang, K. H. Tan, Z. H. Lu, and Y. R. Chang, \u201cIntelligent-controlled doubly fed induction generator system using PFNN,\u201d Neural Computing and Applications, vol. 22, no. 7-8, pp. 1695-1712, 2013.\r\n[17]\tA. Yafaoui, B. Wu, and S. Kouro, \u201cImproved active frequency drift anti-islanding detection method for grid connected photovoltaic systems,\u201d IEEE Transactions on Power Electronics, vol. 27, no. 5. pp. 2367-2375, May, 2012.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 113, 2016"}*