Harmonic Amplification Damping Using a DSTATCOM-based Artificial Intelligence Controller

Author(s): Raghad Ali Mejeed, Ahmed K. Jameil*, Husham Idan Hussein.

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 9 , Issue 4 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background & Objective: Harmonic amplification is one of the primary issues in power system networks. The objective of this study is to manage the harmonic event and its significant effects on power quality. A new control approach that uses Artificial Intelligence (AI) is proposed and applied to a Distribution Static Synchronous Compensator (DSTATCOM). DSTATCOM is a FACTS device that can achieve highly effective reactive power compensation to reduce and/or damp the harmonic amplification in power system networks.

Results & Conclusion: Simulation results are obtained using the MATLAB/Simulink package. The validity and effectiveness of using the AI approach are proven based on the DSTATCOM FACTs device with linear and nonlinear loads. Analysis results are discussed.

Keywords: Neural network, power disturbances, power harmonic filters, power quality, voltage sag, harmonics damping.

[1]
Akagi H. Control strategy and site selection of a shunt active filter for damping of harmonic propagation in power distribution systems. IEEE Trans Power Deliv 1997; 12(1): 354-63.
[http://dx.doi.org/10.1109/61.568259]
[2]
Singh RS, Singh DK. Simulation of D-STATCOM for voltage fluctuation. 2nd Int Conf Adv Comput Commun Tech. 2012; pp. 226-31.
[3]
Vazquez PS. Active power filter control using neural network technologies IEE Proc-Electric Power Appl 54(1): 61-76.
[4]
Abdeslam DO, Wira P, Flieller D. A unified artificial neural network architecture for active power filters. IEEE Trans Ind Electron 2012; 54(1): 61-76.
[http://dx.doi.org/10.1109/TIE.2006.888758]
[5]
Lai LL. Intelligent system applications in power engineering: evolutionary programming and neural networks. John Wiley & Sons, Inc. New York, NY, USA, 1998; pp. 264.
[6]
IEEE recommended practice for evaluating electric power system compatibility with electronic process equipment. IeeeXplore 1998.
[7]
Salman GA, Hussein HI, Hasan MS. Enhancement the dynamic stability of The Iraq’s power station using PID controller optimized by FA and PSO based on different objective functions. Elektroteh Vestn 2018; 85(2): 42-8.
[8]
Dugan RC, Mc Granaghan MF, Santoso S. Electrical power systems quality. 2nd ed. In: Mcgraw-Hill 2004.
[9]
Hong AY, Chen YY. Placement of power quality monitors using enhanced genetic algorithm and wavelet transform. IET Gener Transm Distrib 2011; 5(4): 461-6.
[http://dx.doi.org/10.1049/iet-gtd.2010.0397]
[10]
Guo W, Liu F, Si J, He D, Harley R, Mei S. Online supplementary ADP learning controller design and application to power system frequency control with large-scale wind energy integration. IEEE Trans Neural Netw Learn Syst 2016; 27(8): 1748-61.
[http://dx.doi.org/10.1109/TNNLS.2015.2431734] [PMID: 26087500]
[11]
Guo SMW, Liu F, Si J, He D, Harley R. Approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability. Neurocomputing 2004; 170: 417-27.
[http://dx.doi.org/10.1016/j.neucom.2015.03.089]
[12]
Tang Y, Yang J, Yan J, He H. Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources. Neurocomputing 2015; 170: 406-16.
[http://dx.doi.org/10.1016/j.neucom.2015.04.092]
[13]
Li S, Fairbank M, Alonso E. Vector control of a grid-connected rectifier/inverter using an artificial neural network. The 2012 Int Joint Conf. Neural Netw 2012; 10-5.
[http://dx.doi.org/10.1109/IJCNN.2012.6252614]
[14]
Li S, Fairbank M, Fu X, Wunsch DC, Alonso E. Nested-loop neural network vector control of permanent magnet synchronous motors. The 2013 Int Joint Conf. Neural Netw 2013.
[http://dx.doi.org/10.1109/IJCNN.2013.6707124]
[15]
Bhavsar S, Shah PVA, Gupta V. Voltage dips and short interruption immunity test generator
[16]
Investigations into the performance of photovoltaics-based active filter configurations and their control schemes under uniform and non-uniform radiation conditions. IET Renew Power Gener 2010; 4(1): 12-22.
[http://dx.doi.org/10.1049/iet-rpg.2008.0081]
[17]
Tan RHG, Ramachandaramurthy VK. Simulation of power quality events using simulink model power eng optim conf (PEOCO2013). Langkawi, Malaysia. 2013; pp. vol. 3-4: 277-81.
[http://dx.doi.org/10.1109/PEOCO.2013.6564557]
[18]
Mohaghegi S, Valle Y, Venayagamoorthy GK, Harley RG. A comparison of PSO and backpropagation for training RBF neural. Proc IEEE Swarm Intell Symp. 2005; pp. 381-4.
[19]
Hussein HI. Neural network controller based dstatcom for voltage sag mitigation and power quality issue. Int J Eng Technol 2016; 8(1): 405-20.
[20]
Guerrero JM, Mehdi S, Alireza J, Juan CV. Secondary control for voltage quality enhancement in microgrids. IEEE Trans Smart Grid 2012; 3(4): 1893-902.
[http://dx.doi.org/10.1109/TSG.2012.2205281]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 9
ISSUE: 4
Year: 2019
Page: [521 - 530]
Pages: 10
DOI: 10.2174/2210327909666190611142348
Price: $58

Article Metrics

PDF: 12
HTML: 2