Research on islanding detection of solar distributed generation based on best wavelet packet and neural network

• Autorzy: Xi Zhongmei , Zhao Faqi , Zhao Xiangyang , Peng Hong , Xi Chuanxin

Identyfikatory

DOI

10.24425/aee.2019.130678

• Języki publikacji: EN

Abstrakty

EN

The active distribution network (ADN) represents the future development of distribution networks, whether the islanding phenomenon occurs or not determines the control strategy adopted by the ADN. The best wavelet packet has a better time-frequency characteristic than traditional wavelet analysis in the different signal processing, because it can extract better and more information from the signal effectively. Based on wavelet packet energy and the neural network, the islanding phenomenon of the ADN can be detected. Firstly, the wavelet packet is used to decompose current and voltage signals of the public coupling point between the distributed photovoltaic (PV) system and power grid, and calculate the energy value of each decomposed frequency band. Secondly, the network is trained using the constructed energy characteristic matrix as a neural network learning sample. At last, in order to achieve the function of identification for islanding detection, lots of samples are trained in the neural network. Based on the actual circumstance of PV operation in the ADN, the MATLAB/SIMULINK simulation model of the ADN is established. After the simulation, there are good output results, which show that the method has the characteristics of high identification accuracy and strong generalization ability.

Słowa kluczowe

EN

active distribution network; islanding detection; neural network; solar distributed generation; wavelet pocket transform

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2019
• Tom: Vol. 68, nr 4
• Strony: 703--717
• Opis fizyczny: Bibliogr. 22 poz., rys., wz.

Twórcy

autor

Xi Zhongmei

• Shandong Agricultural University Tai’an, China
• Shandong Provincial Key Laboratory of Horticultural Mechineries and Equipments Tai’an, China

autor

Zhao Faqi

• Shandong Agricultural University Tai’an, China
• Shandong Provincial Key Laboratory of Horticultural Mechineries and Equipments Tai’an, China

autor

Zhao Xiangyang

• Dezhou Power Supply Company of State Grid Shandong Electric Power Company Dezhou, China

autor

Peng Hong

• Chengdong Branch of Tianjin Electric Power Company Tianjin, China

autor

Xi Chuanxin

• Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Company Hangzhou, China

Bibliografia

• [1] D’ Adamo C., Jupe S., Abbey C., Global survey on planning and operation of active distribution networks-update of CIGRE C6.11 working group activities [C], Proceedings of the 20th International Conference and Exhibition on Electricity Distribution: Part I, Prague, Czech: CIGRE C6.11 working group, pp. 1–4 (2009).
• [2] Zhong Qing, Yu Nanhua et al., Distribution generation programming and economical analysis of active distribution network, Proceedings of the CSU-EPSA, vol. 26, no. 11, pp. 82–86 (2014).
• [3] Zhong Qing, Zhang Wenfeng et al., Hierarchical and distribution control strategy for active distribution network & its implementation, Power System Technology, vol. 39, no. 6, pp. 1511−1517 (2015).
• [4] Ahmadipour Masoud et al., Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system, Applied Energy, vol. 231, no. 1, pp. 645–659 (2018).
• [5] Ahmadipour Masoud et al., Islanding detection method using ridgelet probabilistic neural network in distributed generation, Neurocomputing, vol. 329, no. 15, pp. 188–209 (2019).
• [6] Pouryekta Aref, Islanding Detection and Enhancement of Microgrid Performance, IEEE Systems Journal, vol. 12, no. 4, pp. 3131–3141 (2018).
• [7] National legislation (Germany), DIN VDE 0126-1-1 Automatic disconnection device between a generator and the public low-voltage grid (1999).
• [8] Fan Mingtian, Zhang Zuping et al., Enabling technologies for active distribution systems, Proceedings of the CSEE, vol. 33, no. 22, pp. 12–18 (2013).
• [9] Cheng Qiming, Wang Yingfei et al., Overview study on islanding detecting methods for distributed generation grid-connected system, Power System Protection and control, vol. 39, no. 6, pp. 147−154 (2011).
• [10] Shrivastava Smita et al., Two level islanding detection method for distributed generators in distribution networks, International Journal of Electrical Power & Energy Systems, vol. 87, pp. 222−231 (2017).
• [11] Li Xiang, Research on measurement signal processing technology based on wavelet analysis [D], Harbin: Harbin Institute of Technology (2009).
• [12] Liang Xuefei, Chen Xinji, Islanding detection and disturbance based on wavelet entropy theory and BP neural network, Power System and Clean energy, vol. 28, no. 6, pp. 61–65 (2012).
• [13] Jiang Yingchun, Basic principles of wavelet analysis [M], Tianjin: Tianjin University Press, pp. 13−21 (2012).
• [14] Ding Ming, Wang Lei, Bi Rui, A short-term prediction model to forecast output power of photovoltaic system based on improved BP neural network, Power System Protection and Control, vol. 40, no. 11, pp. 93–99 (2012).
• [15] Lei Chenghua, Liu Gang, Li Qinhao, Dynamic calculation of conductor temperature of single-cable using BP neural network, High Voltage Engineering, vol. 37, no. 1, pp. 184–189 (2011).
• [16] Shi Yan, Han Liqun, Lian Xiaoqin, Neural network design method and case analysis [M], Beijing: Beijing University of Posts and Telecommunications Press, pp. 152-161 (2009).
• [17] Zhang Yanxia, Zhao Jie, Application of recurrent neural networks to generated power forecasting for photovoltaic system, Power System Protection and control, vol. 39, no. 15, pp. 96–101 (2011).
• [18] Xu Yan, Huang Xinyi, Research on virtual inertial control technology for improving transient stability of DC distribution network, IEEE Conference on Energy Internet, pp. 1–5 (2017).
• [19] Haider Raza et al., Harmonic-signature-based islanding detection in grid-connected distributed generation systems using Kalman filter, IET Renewable Power Generation, vol. 12, no. 15, pp. 1813–1822 (2018).
• [20] IEEE Std 929-2000, IEEE recommended Practice for utility interface of Photovoltaic (PV) system [S] (2000)
• [21] Kumar Dhruba et al., Artificial neural network and phasor data-based islanding detection in smart grid, IET Generation Transmission & Distribution, vol. 12, no. 21, pp. 5843–5850 (2018).
• [22] Pacurar Razvan, Balc Nicolae, Berce Petru et al., Research on Improving the Mechanical Properties of the SLS Metal Parts, 19th International Symposium of the Danube-Adria-Association-for-Automationand-Manufacturing Location: Trnava, SLOVAKIA (2008).

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).