Active Islanding Detection Method Using Intelligent Controller
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-axis current which leads to a frequency deviation at the terminal of the RLC 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.
[1] M. Ciobotaru, V. Agelidis, and R. Teodorescu, “Accurate and less-disturbing active anti-islanding method based on PLL for grid-connected PV Inverters,” in 2008 Proc. IEEE Power Electronics Specialists Conf., pp. 4569-4576.
[2] IEEE, Std. 1547, “IEEE standard for interconnecting distributed resources with electric power systems,” 2003.
[3] UL 1741, “Inverters, converters, and controllers for use in independent power systems,” 2002.
[4] IEEE, Std. 929-2000, “IEEE recommended practice for utility interface of photovoltaic (PV) systems,” 2000.
[5] M. E. Ropp, M. Begovic, A. Rohatgi, “Analysis and performance assessment of the active frequency drift method of islanding prevention,” IEEE Trans. Energy Conversion, vol. 14, no.3, pp. 810–816, Sep. 1999.
[6] H. Karimi, A. Yazdani, and R. Iravani, “Negative-sequence current injection for fast islanding detection of a distributed resource unit,” IEEE Trans. Power Electronics, vol. 23, no. 1, pp. 298–307, Jan. 2008.
[7] B. Y. Bae, J. K. Jeong, J. H. Lee, and B. M. Han, “Islanding detection method for inverter-based distributed generation systems using a signal cross-correlation scheme,” J. Power Electronics, vol. 10, no. 6, pp. 762–768, 2010.
[8] T. T. Ma, “Novel voltage stability constrained positive feedback anti-islanding algorithms for the inverter-based distributed generator systems,” IET Renewable Power Generation, vol. 4, no. 2, pp. 176-185, March, 2010.
[9] F. Wang, and Z. Mi, “Passive islanding detection method for grid connected PV system,” in 2009 Proc. Int. Conf. on Industrial and Information Systems, pp. 409-412
[10] Y. Gao, and M. J. Er, “An intelligent adaptive control scheme for postsurgical blood pressure regulation,” IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 475-483, March, 2005.
[11] F. J. Lin, H. J. Shieh, P. K. Huang, and L. T. Teng, “Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator,” IEEE Trans. Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, no. 9, pp. 1649-1661, Sept. 2006.
[12] M. Tripathy, R. P. Maheshwari, and H. K. Verma, “Application of probabilistic neural network for differential relaying of power ransformer,” IET Generation, Transmission and Distribution, vol. 1, no. 2, pp. 218–222, March, 2007.
[13] N. Perera, and A. D. Rajapakse, “Recognition of fault transients using a probabilistic neural-network classifier,” IEEE Trans. Power Delivery, vol. 1, no. 26, pp. 410-419, Jan., 2011.
[14] M. Tripathy, R. P.Maheshwari, and H. K. Verma, “Power transformer differential protection based on optimal probabilistic neural network,” IEEE Trans. Power Delivery, vol. 25, no. 1, pp. 102-112, Jan., 2010.
[15] H. X. Li, and Z. Liu, “A probabilistic neural-fuzzy learning system for stochastic modeling,” IEEE Trans. Fuzzy Systems, vol. 16, no. 4, pp. 898-908, Aug., 2008.
[16] F. J. Lin, Y. S. Huang, K. H. Tan, Z. H. Lu, and Y. R. Chang, “Intelligent-controlled doubly fed induction generator system using PFNN,” Neural Computing and Applications, vol. 22, no. 7-8, pp. 1695-1712, 2013.
[17] A. Yafaoui, B. Wu, and S. Kouro, “Improved active frequency drift anti-islanding detection method for grid connected photovoltaic systems,” IEEE Transactions on Power Electronics, vol. 27, no. 5. pp. 2367-2375, May, 2012.
[1] M. Ciobotaru, V. Agelidis, and R. Teodorescu, “Accurate and less-disturbing active anti-islanding method based on PLL for grid-connected PV Inverters,” in 2008 Proc. IEEE Power Electronics Specialists Conf., pp. 4569-4576.
[2] IEEE, Std. 1547, “IEEE standard for interconnecting distributed resources with electric power systems,” 2003.
[3] UL 1741, “Inverters, converters, and controllers for use in independent power systems,” 2002.
[4] IEEE, Std. 929-2000, “IEEE recommended practice for utility interface of photovoltaic (PV) systems,” 2000.
[5] M. E. Ropp, M. Begovic, A. Rohatgi, “Analysis and performance assessment of the active frequency drift method of islanding prevention,” IEEE Trans. Energy Conversion, vol. 14, no.3, pp. 810–816, Sep. 1999.
[6] H. Karimi, A. Yazdani, and R. Iravani, “Negative-sequence current injection for fast islanding detection of a distributed resource unit,” IEEE Trans. Power Electronics, vol. 23, no. 1, pp. 298–307, Jan. 2008.
[7] B. Y. Bae, J. K. Jeong, J. H. Lee, and B. M. Han, “Islanding detection method for inverter-based distributed generation systems using a signal cross-correlation scheme,” J. Power Electronics, vol. 10, no. 6, pp. 762–768, 2010.
[8] T. T. Ma, “Novel voltage stability constrained positive feedback anti-islanding algorithms for the inverter-based distributed generator systems,” IET Renewable Power Generation, vol. 4, no. 2, pp. 176-185, March, 2010.
[9] F. Wang, and Z. Mi, “Passive islanding detection method for grid connected PV system,” in 2009 Proc. Int. Conf. on Industrial and Information Systems, pp. 409-412
[10] Y. Gao, and M. J. Er, “An intelligent adaptive control scheme for postsurgical blood pressure regulation,” IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 475-483, March, 2005.
[11] F. J. Lin, H. J. Shieh, P. K. Huang, and L. T. Teng, “Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator,” IEEE Trans. Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, no. 9, pp. 1649-1661, Sept. 2006.
[12] M. Tripathy, R. P. Maheshwari, and H. K. Verma, “Application of probabilistic neural network for differential relaying of power ransformer,” IET Generation, Transmission and Distribution, vol. 1, no. 2, pp. 218–222, March, 2007.
[13] N. Perera, and A. D. Rajapakse, “Recognition of fault transients using a probabilistic neural-network classifier,” IEEE Trans. Power Delivery, vol. 1, no. 26, pp. 410-419, Jan., 2011.
[14] M. Tripathy, R. P.Maheshwari, and H. K. Verma, “Power transformer differential protection based on optimal probabilistic neural network,” IEEE Trans. Power Delivery, vol. 25, no. 1, pp. 102-112, Jan., 2010.
[15] H. X. Li, and Z. Liu, “A probabilistic neural-fuzzy learning system for stochastic modeling,” IEEE Trans. Fuzzy Systems, vol. 16, no. 4, pp. 898-908, Aug., 2008.
[16] F. J. Lin, Y. S. Huang, K. H. Tan, Z. H. Lu, and Y. R. Chang, “Intelligent-controlled doubly fed induction generator system using PFNN,” Neural Computing and Applications, vol. 22, no. 7-8, pp. 1695-1712, 2013.
[17] A. Yafaoui, B. Wu, and S. Kouro, “Improved active frequency drift anti-islanding detection method for grid connected photovoltaic systems,” IEEE Transactions on Power Electronics, vol. 27, no. 5. pp. 2367-2375, May, 2012.
@article{"International Journal of Electrical, Electronic and Communication Sciences:72976", author = "Kuang-Hsiung Tan and Chih-Chan Hu and Chien-Wu Lan and Shih-Sung Lin and Te-Jen Chang", title = "Active Islanding Detection Method Using Intelligent Controller", abstract = "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-axis current which leads to a frequency deviation at the terminal of the RLC 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.
", keywords = "Distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone. ", volume = "10", number = "5", pages = "661-7", }
