Open Access Open Access  Restricted Access Subscription or Fee Access

Advanced Wide-Area Monitoring System Design for Electrical Power System

Muhammad Abdillah(1*), Herlambang Setiadi(2)

(1) Department of Electrical Engineering, Universitas Pertamina, Jalan Teuku Nyak Arief, Simprug, Kebayoran Lama, Kota Jakarta Selatan, Indonesia 12220, Indonesia
(2) School of Advanced Technology and Multidiciplinary, Universitas Airlangga, Surabaya, Indonesia, Indonesia
(*) Corresponding author


DOI: https://doi.org/10.15866/iremos.v13i6.17734

Abstract


A monitoring system is an essential part of power system operation and planning. This system model is used to observe the system stability behaviour, to detect the unforeseen power system instabilities and to prevent the system from being collapsed. Since the power system scheme is a complex and non-linear system, employing the conventional method based on a mathematical model for designing a monitoring system is quite complicated. This paper proposes an advanced wide-area monitor design using a machine learning method called a multi-output least-square support vector machine to overcome the problem above. The application of the kernel approach in a multi-output least-square support vector machine provides higher accuracy in predicting result and flexibility. The kernel technique will transform the input data into a higher dimensional feature subspace as an input data of a multi-output least-square support vector machine. Then this non-linear pattern will be transformed into a linear pattern and intensive computing time can also be reduced. The major problem of multi-output least-square support vector machine parameters is on finding the best parameter. If the trial and error method is used to find the parameter of multi-output least-square support vector machine parameters, the machine is ineffective in obtaining good performance for a wide range of operating conditions and various load change scenarios in a multi-area power system. A quantum-inspired evolutionary algorithm is employed to achieve the machine’s appropriate parameters automatically and accurate output by minimizing the objective function. In order to measure the rigorousness of multi-output least-square support vector machine output, mean square error is used as an objective function in this study. The simulation results show that the amalgamation of the multi-output least-square support vector machine and quantum-inspired evolutionary algorithm provides a satisfactory result when compared to standard multi-output least-square support vector machine output. In addition, this proposed method can be considered as a promising technique in the future as a wide-area monitor model.
Copyright © 2020 Praise Worthy Prize - All rights reserved.

Keywords


Monitoring System; Wide-Area Monitor; Multi-Output Least-Square Support Vector Machine; Quantum-Inspired Evolutionary Algorithm

Full Text:

PDF


References


N. Yorino, M. Abdillah, Y. Sasaki, and Y. Zoka, Robust Power System Security Assessment Under Uncertainties Using Bi-Level Optimization, IEEE Trans. Power Syst., Vol. 33 (No.1): 352–362, January 2018.
https://doi.org/10.1109/tpwrs.2017.2689808

S Alizadeh, S., Mathematical Modelling of the Effect of X/R and Short Circuit Ratio on Voltage in a Distribution System Connected Wind Farm, (2020) International Review on Modelling and Simulations (IREMOS), 13 (2), pp. 132-140.
https://doi.org/10.15866/iremos.v13i2.18601

H. Setiadi, N. Mithulananthan, R. Shah, T. Raghunathan, and T. Jayabarathi, Enabling Resilient Wide-Area POD at BESS in Java, Indonesia 500 kV Power Grid, IET Gener. Transm. Distrib., Vol. 13 (No.16): 3734–3744, August 2019.
https://doi.org/10.1049/iet-gtd.2018.6670

Amirullah, A., Penangsang, O., Soeprijanto, A., High Performance of Unified Power Quality Conditioner and Battery Energy Storage Supplied by Photovoltaic Using Artificial Intelligent Controller, (2018) International Review on Modelling and Simulations (IREMOS), 11 (4), pp. 221-234.
https://doi.org/10.15866/iremos.v11i4.14742

Y. Guo, K. Li, Z. Yang, J. Deng, and D. M. Laverty, A Novel Radial Basis Function Neural Network Principal Component Analysis Scheme for PMU-Based Wide-Area Power System Monitoring, Electr. Power Syst. Res., Vol. 127: 197–205, October 2015.
https://doi.org/10.1016/j.epsr.2015.06.002

G. K. Venayagamoorthy and R. G. Harley, A continually online trained artificial neural network identifier for a turbogenerator, IEEE International Electric Machines and Drives Conference. IEMDC’99. Proceedings (Cat. No. 99EX272), pp. 404–406, Seattle, USA, May 1999.
https://doi.org/10.1109/iemdc.1999.769128

B. Luitel, G. K. Venayagamoorthy, and C. E. Johnson, Enhanced wide area monitoring system, 2010 Innovative Smart Grid Technologies (ISGT), pp. 1–7, Gothenburg, Sweden, January 2010.
https://doi.org/10.1109/isgt.2010.5434727

W.Zou, D. Froning, Y. Shi, W. Lehnert, A Least-Squares Support Vector Machine Method for Modeling Transient Voltage in Polymer Electrolyte Fuel Cells, Applied Energy, Vol. 271: 1-14, August 2020.
https://doi.org/10.1016/j.apenergy.2020.115092

X. Yuan, Q. Tan, X. Lei, Y. Yuan, and X. Wu, Wind Power Prediction using Hybrid Autoregressive Fractionally Integrated Moving Average and Least Square Support Vector Machine, Energy, Vol. 129: 122–137, June 2017.
https://doi.org/10.1016/j.energy.2017.04.094

L. Liu, W. Huang, and C. Wang, Hyperspectral Image Classification with Kernel-Based Least-Squares Support Vector Machines in Sum Space, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(Issue 4): 1144-1157, April 2018.
https://doi.org/10.1109/jstars.2017.2768541

F. Kaytez, A Hybrid Approach Based on Autoregressive Integrated Moving Average and Least-Square Support Vector Machine for Long-Term Forecasting of Net Electricity Consumption, Energy, Vol. 197: 1-12, April 2020.
https://doi.org/10.1016/j.energy.2020.117200

K. Li, R. Zhang, F. Li, L. Su, H. Wang, and P. Chen, A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine, IEEE Access, Vol. 7: 26571 - 26580, February 2020.
https://doi.org/10.1109/access.2019.2901363

J. Huang, Y. Liang, H. Bian, and X. Wang, Using Cluster Analysis and Least Square Support Vector Machine to Predicting Power Demand for the Next-Day, IEEE Access, Vol. 7: 82681 - 82692, .June 2019.
https://doi.org/10.1109/access.2019.2922777

K. H. Han and J. H. Kim, Quantum-Inspired Evolutionary Algorithm for A Class of Combinatorial Optimization, IEEE Trans. Evol. Comput., Vol. 6 (No. 6): 580–593, December 2002.
https://doi.org/10.1109/tevc.2002.804320

R. S. Pavithr, Quantum Inspired Social Evolution (QSE) Algorithm for 0-1 Knapsack Problem, Swarm Evol. Comput., Vol. 29: 33–46, August 2016.
https://doi.org/10.1016/j.swevo.2016.02.006

S. Gupta, S. Mittal, T. Gupta, I. Singhal, B. Khatri, and A. K.Gupta, Parallel Quantum-Inspired Evolutionary Algorithms for Community Detection in Social Networks, Appl. Soft Comput., Vol. 61: 331–353, December 2017.
https://doi.org/10.1016/j.asoc.2017.07.035

M. Yuanyuan and L. Xiyu, Quantum Inspired Evolutionary Algorithm for Community Detection in Complex Networks, Phys. Lett. A, Vol. 382(No. 34): 2305–2312, August 2018.
https://doi.org/10.1016/j.physleta.2018.05.044

Y. Pan, F. Liu, L. Chen, J. Wang, F. Qiu, C. Shen, and S. Mei, Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation, IEEE Trans. Power Syst., Vol. 33 (No. 2): 1790–1799, March 2018.
https://doi.org/10.1109/tpwrs.2017.2714759

M. T. K. Niazi, Arshad, M. A. Mughal, and A. Hussain, Influence of DDSG wind turbine and fault on stability of two-area power system, 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), pp. 1–5, Manama, Bahrain, April 2019.
https://doi.org/10.1109/icmsao.2019.8880440

J. A. K. Suykens, J. Vandewalle, and B. De Moor, Optimal Control by Least Squares Support Vector Machines, Neural networks, Vol. 14(No. 1): 23–35, February 2001.
https://doi.org/10.1016/s0893-6080(00)00077-0

L. Stefanini and M. Arana-Jiménez, Karush–Kuhn–Tucker Conditions for Interval and Fuzzy Optimization in Several Variables Under Total and Directional Generalized Differentiability, Fuzzy Sets Syst., Vol. 362: 1–34, May 2019.
https://doi.org/10.1016/j.fss.2018.04.009

J. Preskill, Quantum Computing in the NISQ Era and Beyond, Quantum, Vol. 2: 1 - 20, August 2018.

J. Chen, X. Qi. L. Chen, F. Chen, G. Cheng, Quantum-Inspired Ant Lion Optimized Hybrid K-Means for Cluster Analysis and Intrusion Detection, Knowledge-Based Systems, Vol. 203: 1 - 10, September 2020.
https://doi.org/10.1016/j.knosys.2020.106167

H. Setiadi and M. Abdillah, Simultaneous Parameter Tuning of PSS and Wide-Area POD in PV Plant using FPA, Eng. J., Vol. 23(No. 6): 55–66, November 2019.
https://doi.org/10.4186/ej.2019.23.6.55

L. Wang, C. Chang, B. Kuan, and A. V Prokhorov, Stability Improvement of a Two-Area Power System Connected With an Integrated Onshore and Offshore Wind Farm Using a STATCOM, IEEE Trans. Ind. Appl., Vol. 53(No. 2): 867 – 877, March 2017.
https://doi.org/10.1109/tia.2016.2628361

Ben Safar, S., A New Approach for Faults Detection and Classification in Overhead Line Systems Using Multiple Methods, (2020) International Review of Electrical Engineering (IREE), 15 (5), pp. 412-420.
https://doi.org/10.15866/iree.v15i5.16800

Bani Yassein, M., Alomari, O., Detecting the Online Shopping Factors Using the Arab Tweets on Media Technology, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (3), pp. 206-211.
https://doi.org/10.15866/irecap.v10i3.19230

Moloi, K., Jordaan, J., Hamam, Y., The Development of a High Impedance Fault Diagnostic Scheme on Power Distribution Network, (2020) International Review of Electrical Engineering (IREE), 15 (1), pp. 69-79.
https://doi.org/10.15866/iree.v15i1.17074

Marrugo Cardenas, N., Amaya Hurtado, D., Ramos Sandoval, O., Comparison of Multi-Class Methods of Features Extraction and Classification to Recognize EEGs Related with the Imagination of Two Vowels, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (5), pp. 398-405.
https://doi.org/10.15866/irecap.v8i5.12709


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2021 Praise Worthy Prize