AN IMPROVED CWT-BASED ISLANDING DETECTION METHOD FOR A DEVELOPED MICROGRID IN REAL-TIME

Year 2020, Special Issue 2020, 10 - 17, 20.07.2020

Alper Yılmaz , Gökay Bayrak

https://doi.org/10.22531/muglajsci.654432

Cited By: 5

Abstract

The island mode operation problem is a significant event of deterioration in a power system, and this fault must be detected in the fastest and most accurate way for the reliable operation of the microgrid structure. Recently, numerous islanding detection methods based on signal processing have been proposed in the literature. In this study, an improved, continuous wavelet transform (CWT)-based islanding detection method is proposed for microgrids. Island mode conditions are investigated in the developed PV-based microgrid connected to a low voltage grid. The proposed method uses only the voltage signal on the point of common coupling (PCC). A series of discrete values are selected for scales and shifts of continuous wavelets and then CWT is applied for PCC voltage. In this way, the computational load is minimized. This method has many advantages comparing to conventional methods and has been tested in real-time for a PV-based microgrid prototype. The results show that the developed CWT-based islanding detection method can detect different types of island modes in the developed microgrid. Besides, the islanding detection time of the proposed method varies between 105-110 ms in any island mode operations, and it is faster than the conventional detection methods. None detection zone (NDZ) is also almost zero in the proposed method. Thus, the CWT-based islanding detection method provides both a reliable NDZ and a short detection time for microgrid applications.

Keywords

Island mode detection, Continuous wavelet transform, Microgrids, Automatic fault detection

References

• [1] Hassaine L, Olías E, Quintero J, Barrado A., “Power control for grid-connected applications based on the phase shifting of the inverter output voltage with respect to the grid voltage,” Electrical Power and Energy Systems, 57., 250-260, 2014
• [2] Eltawil ME, Zhao Z. “Grid-connected photovoltaic power systems: a technical and potential problems-a review.” Renewable and Sustainable Energy Reviews, Vol. 14, 112-129, 2010.
• [3] Lin, W. M., Wu, C. H., Lin, C. H., & Cheng, F. S. “Detection and classification of multiple power-quality disturbances with wavelet multiclass SVM,” IEEE Transactions on Power Delivery, Vol. 23(4), 2575-2582, 2008.
• [4] Masoum, M. A. S., Jamali, S., & Ghaffarzadeh, N. “Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks” IET Science, Measurement & Technology, Vol. 4(4), 193-205, 2010.
• [5] Bayrak, G. and Cebeci, M., “A communication-based islanding detection method for photovoltaic distributed generation systems” International Journal of Photoenergy, 2014.
• [6] Kamel RM, Chaouachi A, Nagasaka K., “Enhancement of micro-grid performance during islanding mode using storage batteries and new fuzzy logic pitch angle controller,” Energy Conversion and Management, Vol. 52: 2204–2216, 2011
• [7] Shayeghi H, Sobhani B., “Zero NDZ assessment for anti-islanding protection using wavelet analysis and neuro-fuzzy system in an inverter-based distributed generation” Energy Conversion and Management Vol. 79, 616–625, 2014.
• [8] Raza, S., Mokhlis, H., Arof, H., Laghari, J. A., and Wang, L., “Application of signal processing techniques for islanding detection of distributed generation in the distribution network,” A review. Energy Conversion and Management, Vol. 96, 613-624, 2015.
• [9] Jurado, F., & Saenz, J. R., “Comparison between discrete STFT and wavelets for the analysis of power quality events.” Electric Power Systems Research, Vol. 62(3), 183-190, 2002.
• [10] Karegar, H. K., & Sobhani, B., “Wavelet transform method for islanding detection of wind turbines.” Renewable Energy, Vol. 38(1), 94-106, 2012.
• [11] Latran M. B., Teke A., “A novel wavelet transform based voltage sag/swell detection algorithm,” International Journal of Electrical Power & Energy Systems , Vol.71, 131-139, 2015.
• [12] Bayrak, G., “Wavelet transform-based fault detection method for hydrogen energy-based distributed generators.” International Journal of Hydrogen Energy, Vol. 43(44), 20293-20308, 2018.
• [13] Yılmaz, A., and Bayrak, G., “A real-time UWT-based intelligent fault detection method for PV-based microgrids.” Electric Power Systems Research, 177, 105984. 2019.
• [14] Zhu, Y., Yang, Q., Wu, J., Zheng, D., and Tian, Y. “A novel islanding detection method of distributed generator based on wavelet transform,” International Conference on Electrical Machines and Systems, 2686-2688, 2008.
• [15] Cohen, L., 1995, Time-Frequency Analysis, Prentice-Hall, Englewood Cliffs.
• [16] National Instruments, LabVIEW 2014 advanced signal processing toolkit manual (2014).
• [17] Bayrak, G., & Yılmaz, A., “Assesment of Power Quality Disturbances For Grid Integration of PV Power Plants,” Sakarya University Journal of Science, Vol. 23(1), 1-1, 2019.
• [18] Mishra, P. P., and Bhende, C. N., “Islanding detection using sparse S-transform in distributed generation systems,” Electrical Engineering, Vol. 100(4), 2397-2406, 2018.
• [19] Pigazo, A., Liserre, M., Mastromauro, R. A., Moreno, V. M., and Dell'Aquila, A., “Wavelet-based islanding detection in grid-connected PV systems,” IEEE Transactions on Industrial Electronics, Vol. 56(11), 4445-4455, 2008.