Recognition of disturbances in hybrid power system interfaced with battery energy storage system using combined features of Stockwell transform and Hilbert transform

Virendra Sharma; Lata Gidwani; Virendra Sharma; Lata Gidwani

doi:10.3934/energy.2019.5.671

AIMS Energy

2019, Volume 7, Issue 5: 671-687. doi: 10.3934/energy.2019.5.671

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Recognition of disturbances in hybrid power system interfaced with battery energy storage system using combined features of Stockwell transform and Hilbert transform

Virendra Sharma ^,,
Lata Gidwani

Department of Electrical Engineering, Rajasthan Technical University, Kota, India

Received: 01 September 2019 Accepted: 23 October 2019 Published: 25 October 2019

This paper presents an algorithm using combined features of Stockwell transform and Hilbert transform for analysis of disturbances in the hybrid power system interfaced with battery energy storage system (BESS). Hybrid power system is realized using five nodes test network to which BESS supported by distribution static compensator (DSTATCOM), wind and solar photovoltaic (PV) generators are integrated. A disturbance detection index (DDI) based on combined features of Stockwell transform and Hilbert transform is proposed for detection of various types of disturbances. Results are obtained in the absence and presence of the proposed BESS supported by DSTATCOM to investigate the effect of BESS on performance of the hybrid power system. Investigated events include the switching ON/OFF the resistive load, outage of wind generator and simultaneous outage of both wind and solar PV generators. It is anticipated that performance of proposed method will be high in all investigated cases of study. This could be established in MATLAB/Simulink environment. Proposed BESS will be effective to reduce the disturbance level up to 91%.
- battery energy storage system,
- distribution static compensator,
- Hilbert transforms,
- hybrid power system,
- Stockwell transform
Citation: Virendra Sharma, Lata Gidwani. Recognition of disturbances in hybrid power system interfaced with battery energy storage system using combined features of Stockwell transform and Hilbert transform[J]. AIMS Energy, 2019, 7(5): 671-687. doi: 10.3934/energy.2019.5.671

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Abstract

This paper presents an algorithm using combined features of Stockwell transform and Hilbert transform for analysis of disturbances in the hybrid power system interfaced with battery energy storage system (BESS). Hybrid power system is realized using five nodes test network to which BESS supported by distribution static compensator (DSTATCOM), wind and solar photovoltaic (PV) generators are integrated. A disturbance detection index (DDI) based on combined features of Stockwell transform and Hilbert transform is proposed for detection of various types of disturbances. Results are obtained in the absence and presence of the proposed BESS supported by DSTATCOM to investigate the effect of BESS on performance of the hybrid power system. Investigated events include the switching ON/OFF the resistive load, outage of wind generator and simultaneous outage of both wind and solar PV generators. It is anticipated that performance of proposed method will be high in all investigated cases of study. This could be established in MATLAB/Simulink environment. Proposed BESS will be effective to reduce the disturbance level up to 91%.

References

[1]	Shaik AG, Mahela OP (2018) Power quality assessment and event detection in hybrid power system. Electr Pow Syst Res 161: 26-44. doi: 10.1016/j.epsr.2018.03.026
[2]	Mahela OP, Shaik AG (2016) Power quality improvement in distribution network using DSTATCOM with battery energy storage system. Int J Elec Pow 83: 229-240. doi: 10.1016/j.ijepes.2016.04.011
[3]	Mahmud MA, Pota HR, Hossain MJ (2013) Nonlinear DSTATCOM controller design for distribution network with distributed generation to enhance voltage stability. Int J Elec Pow 53: 974-979. doi: 10.1016/j.ijepes.2013.06.017
[4]	Rezvani A, Gandomkar M (2016) Modeling and control of grid connected intelligent hybrid photovoltaic system using new hybrid fuzzy-neural method. Sol Energy 127: 1-18. doi: 10.1016/j.solener.2016.01.006
[5]	Rezvani A, Gandomkar M (2017) Simulation and control of intelligent photovoltaic system using new hybrid fuzzy-neural method. Neural Comput Appl 28: 2501-2518. doi: 10.1007/s00521-016-2210-2
[6]	Dadfar S, Wakil K, Khaksar M, et al. (2019) Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode. Int J Hydrogen Energ 44: 14642-14660. doi: 10.1016/j.ijhydene.2019.04.174
[7]	Shi X, Dini A, Shao Z, et al. (2019) Impacts of photovoltaic/wind turbine/microgrid turbine and energy storage system for bidding model in power system. J Clean Prod 226: 845-857. doi: 10.1016/j.jclepro.2019.04.042
[8]	Rezvani A, Esmaeily A, Etaati H, et al. (2019) Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode. Front Energ 13: 131-148. doi: 10.1007/s11708-017-0446-x
[9]	Rikos E, Tselepis S, Hoyer-Klick C, et al. (2008) Stability and power quality issues in microgrids under weather disturbances. IEEE J-STARS 1: 170-179.
[10]	Mahela OP, Shaik AG, Gupta N. (2015) A critical review of detection and classification of power quality events. Renew Sust Energ Rev 41: 495-505. doi: 10.1016/j.rser.2014.08.070
[11]	Dash PK, Padhee M, Panigrahi TK (2012) A hybrid time-frequency approach based fuzzy logic system for power island detection in grid connected distributed generation. Int J Elec Pow 42: 453-464. doi: 10.1016/j.ijepes.2012.04.003
[12]	Shao Z, Wakil K, Usak M, et al (2018) Kriging Empirical Mode Decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid. Appl Therm Eng 145: 58-70. doi: 10.1016/j.applthermaleng.2018.09.028
[13]	Shi X, Gholamalizadeh E, Moheimani R (2019) Applying a micromechanics approach for predicting thermal conducting properties of carbon nanotube-metal nanocomposites. J Alloy Compd 789: 528-536. doi: 10.1016/j.jallcom.2019.03.064
[14]	Mishra M, Sahani M, Rout PK (2017) An islanding detection algorithm for distributed generation based on Hilbert-Huang transform and extreme learning machine. Sustain Energ Grids 9: 13-26. doi: 10.1016/j.segan.2016.11.002
[15]	Mahela OP, Shaik AG (2015) Power quality detection in distribution system with wind energy penetration using discrete wavelet transform. 2nd International Conference on Advances in Computing and Communication Engineering. IEEE, 328-333.
[16]	Sharma A, Ola SR, Mahela OP (2016) Impact of grid disturbances on the output of grid connected wind power generation. 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE, 1-6.
[17]	Mitra P, Venayagamoorthy GK (2009) An adaptive control strategy for DSTATCOM applications in an electric ship power system. IEEE T Power Electr 25: 95-104.
[18]	Labeeb M, Lathika BS (2011) Design and analysis of DSTATCOM using SRFT and ANN-fuzzy based control for power quality improvement. IEEE Recent Adv Intell Comput Syst 274-279.
[19]	Singh B, Jayaprakash P, Kothari DP (2008) A T-connected transformer and three-leg VSC based DSTATCOM for power quality improvement. IEEE T Power Electr 23: 2710-2718. doi: 10.1109/TPEL.2008.2004273
[20]	Singh B, Jayaprakash P, Kothari DP (2008) Isolated H-bridge VSC Based 3-phase 4-wire DSTATCOM for power quality improvement. IEEE Int Conf Sustain Energ Technol IEEE, 366-371.
[21]	Singh B, Niwas R, Dube SK (2014) Load leveling and voltage control of permanent magnet synchronous generator-based DG set for standalone supply system. IEEE T Ind Inform 10: 2034-2043. doi: 10.1109/TII.2014.2341952

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