An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

Foday Conteh; Shota Tobaru; Mohamed E. Lotfy; Atsushi Yona; Tomonobu Senjyu; Foday Conteh; Shota Tobaru; Mohamed E. Lotfy; Atsushi Yona; Tomonobu Senjyu

doi:10.3934/energy.2017.5.814

AIMS Energy

2017, Volume 5, Issue 5: 814-837. doi: 10.3934/energy.2017.5.814

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An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

1.
Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru,Nishihara-cho, Nakagami, Okinawa 903-0123 Japan
2.
Department of Electrical Power and Machines, Zagazig University, Zagazig 44519, Egypt

Received: 12 July 2017 Accepted: 06 September 2017 Published: 14 September 2017

In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.
- micro-grids,
- energy management,
- distributed generation,
- back propagation artificial neural network,
- stochastic gradient descent,
- adaptive neuro-fuzzy inference system
Citation: Foday Conteh, Shota Tobaru, Mohamed E. Lotfy, Atsushi Yona, Tomonobu Senjyu. An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system[J]. AIMS Energy, 2017, 5(5): 814-837. doi: 10.3934/energy.2017.5.814

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Abstract

In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

References

[1]	Bakar NNA, Hassan MY, Sulaima MF, et al. (2017) Microgrid and load shedding scheme during islanded mode: A review. Renew S ust Energ Rev 71: 161-169. doi: 10.1016/j.rser.2016.12.049
[2]	Laghari JA, Mokhlis H, Bakar AHA, et al. (2013) Application of computational intelligence techniques for load shedding in power systems. Energy Convers Manage 75: 130-140. doi: 10.1016/j.enconman.2013.06.010
[3]	Pushpanjali M, Sujatha MS (2015) A novel multi objective under frequency load shedding in a micro-grid using genetic algorithm. Int J Advan Res Electri Electro Instru Eng 4: 5037-5038.
[4]	Kaffashan I, Mortezaee SMTM, Amraee T (2016) A robust under voltage load shedding scheme against voltage. Turk J Electr Eng Comp S ci 24: 3310-3311.
[5]	Hooshmand R, Moazzami M (2012) Optimal design of adaptive under frequency load shedding using artificial neural networks in isolated power system. Int J Elec Power 42: 220-228. doi: 10.1016/j.ijepes.2012.04.021
[6]	Boland J, Burdett R, Edwards C (2015) Optimization techniques for planning automatic under frequency load shedding in New Zealand's power system. Anziam J 57: M3-M4.
[7]	Suwanasri C, Suwanasri T, Prachuab N (2013) Load shedding control strategy in an electric distribution system. GMS RAN Int J 7: 47-52.
[8]	Moazzami M, Morshed MJ, Fekih A, et al. (2016) A new optimal unified power flow controller placement and load shedding coordination approach using the hybrid imperialist competitive algorithm-pattern search method for voltage collapse prevention in power system Int J Elec Power 79: 263-274.
[9]	Aponte EE, Nelson JK (2006) Time optimal shedding for distributed power systems. IEEE T Power S yst 21: 269-277. doi: 10.1109/TPWRS.2005.857826
[10]	Das K, Nitsas A, Altin M, et al. (2016) Improved load shedding scheme considering distributed generation. IEEE T Power Delivery, 1-4.
[11]	Malekpour AR, Seifi AR, Hesamzadeh MR, et al. (2008) An optimal load shedding approach for distribution networks with DGs considering capacity deficiency modelling of bulked power supply. World Academic S ci Eng Tech 22: 825-826.
[12]	Marzband M, Moghaddm MM, Akorede MF, et al. (2016) Adaptive load shedding scheme for frequency stability enhancement in micro-grids. Electr Pow S yst Res 140: 78-86. doi: 10.1016/j.epsr.2016.06.037
[13]	Tahir MF, Tehzeeb-ul-Hassan, Saqib MA (2016) Optimal scheduling of electrical power in energy-deficiency scenarios using artificial neural network and bootstrap aggregation. Int J Elec Power 83: 49-53. doi: 10.1016/j.ijepes.2016.03.046
[14]	Kuriakose E, Francis F (2013) Enhancement of power system stability by optimal adaptive under frequency load shedding using artificial neural networks. Int J Advan Res Electri Electro Instru Eng 2: 12-20.
[15]	Amooshahi H, Rahmat-Allah H, Khodabakhshian A, et al. (2016) A new load-shedding approach for micro-grids in the presence of wind turbines. Electr Pow Compo S ys 44: 1-11. doi: 10.1080/15325008.2015.1091861
[16]	Koohi-Kamali S, Rahim NA (2016) Coordinated control of smart micro-grid during and after is landing operation to prevent under frequency load shedding using energy storage system. Energ Convers Manage 127: 623-646. doi: 10.1016/j.enconman.2016.09.052
[17]	Laghari JA, Mokhlis H, Karimi M, et al. (2015) A new under-frequency load shedding technique based on combination of fixed and random priority of loads for smart grid applications. IEEE T Power S yst 30: 2507-2515. doi: 10.1109/TPWRS.2014.2360520
[18]	Sapari NM, Mokhlis H, Laghari JA, et al. (2017) Load shedding scheme based on frequency and voltage stability for islanding operation of distribution network connected to mini-hydro generation. Turk J Electr Eng Co 25: 1852-1863. doi: 10.3906/elk-1601-111
[19]	Karimi M, Mohamad H, Mokhlis H, et al. (2012) Under-Frequency load Shedding scheme for islanded distribution network connected to mini-hydro generation. Electrical Power Energy S ystems 42: 127-138. doi: 10.1016/j.ijepes.2012.03.017
[20]	Khezri R, Golshannavaz S, Vakili R, et al. (2017) Multi-layer fuzzy-based under-frequency load shedding in back-pressure smart industrial microgrids. Energy 132: 96-105. doi: 10.1016/j.energy.2017.05.059
[21]	Dragomir OE, Dragomir F, Stefan V, et al. (2014) Adaptive neuro-fuzzy inference systems as a strategy for predicting and controlling the energy produced from renewable sources. Energies 8: 13047-13061.
[22]	Bhatnagar S, Chopra S, Bhati S, et al. (2015) Under frequency load shedding using adaptive neuro-fuzzy inference system. IJEEE 7: 99.
[23]	Lakra P, Kirar M (2015) Load shedding techniques for system with cogeneration: A review. ELELIJ 4: 83-96. doi: 10.14810/elelij.2015.4307
[24]	Khamis A, Shareef H, Mohamed A, et al. (2015) A load shedding scheme for DG integrated islanded power system utilizing backtracking search algorithm. Ain S hams Eng J, 2-3.
[25]	Swetha GC, Sudarshana RHR (2017) Voltage stability assessment in power network using artificial neural network. Int J Advan Res Electri Electro and Instru Eng, 7993-8002.
[26]	Kuriakose E, Francis F (2017) Enhancement of power system stability by optimal adaptive under frequency load shedding using artificial neural networks. Int J Advan Res Electri Electro Instru Eng, 12-20.

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