Detection and Identification of Voltage Variation Events Based on Artificial Neural Network

Dimas Okky Anggriawan; Rauf Hanrif Mubarok; Eka Prasetyono; Endro Wahjono; M. Iqbal Fitrianto; Aji Akbar Firdaus; Anang Budikarso; Anang Tjahjono

doi:10.15866/ireaco.v13i5.18141

Detection and Identification of Voltage Variation Events Based on Artificial Neural Network

Dimas Okky Anggriawan^(1*), Rauf Hanrif Mubarok⁽²⁾, Eka Prasetyono⁽³⁾, Endro Wahjono⁽⁴⁾, M. Iqbal Fitrianto⁽⁵⁾, Aji Akbar Firdaus⁽⁶⁾, Anang Budikarso⁽⁷⁾, Anang Tjahjono⁽⁸⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/ireaco.v13i5.18141

Abstract

Identifying voltage variations has an important role to mitigate degradation of power quality disturbances due to their negative impact on the equipment. Therefore, fast and accurate detection and identification are required in order to evaluate and activate the protection. In this paper, Root Mean Square (RMS) algorithm combined with artificial neural network is presented in order to detect and identify voltage variation such as sag, swell, undervoltage and overvoltage. Artificial neural network to identification of voltage variation using feed forward neural network because of its simplicity and accurate. In order to model voltage variations required, a generator is required to emulate of voltage variation waveform. It consists of transformer single input multi output, SSR Switch and microcontroller. Moreover, Identification is implemented with microcontroller STM32F47. Varying neuron under hidden layer is tested to validate the proposed algorithm. The results show that the proposed algorithm is effective to identify each variation with the average percent of error 2.3749% for 8 neurons in the first layer and 0.0658% for 10 neurons in the first layer.
Copyright © 2020 Praise Worthy Prize - All rights reserved.

Keywords

Neural Network; Overvoltage; Sag; Swell; Undervoltage; Microcontroller

Full Text:

PDF

References

EM Committee. IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE Std (2009): c1-81.

Priyadarshini, M. S., & Sushama, M. (2016, March). Classification of short-duration voltage variations using wavelet decomposition based entropy criteria. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 2192-2196). IEEE.
https://doi.org/10.1109/wispnet.2016.7566531

Mejia-Barron, A., Valtierra-Rodriguez, M., Granados-Lieberman, D., Amezquita-Sanchez, J. P., Perez-Ramirez, C. A., & Camarena-Martinez, D. (2016, November). Tracking of voltage variations by means of an adaptive filter and fuzzy logic. In 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (pp. 1-6). IEEE.
https://doi.org/10.1109/ropec.2016.7830610

Valtierra-Rodriguez, M., Granados-Lieberman, D., Torres-Fernandez, J. E., Rodríguez-Rodríguez, J. R., & Gómez-Aguilar, J. F. (2016). A new methodology for tracking and instantaneous characterization of voltage variations. IEEE Transactions on Instrumentation and Measurement, 65(7), pp. 1596-1604.
https://doi.org/10.1109/tim.2016.2540866

Sun, H., Yi, H., Yang, G., Zhuo, F., & Hu, A. (2019). Voltage sag source identification based on few-shot learning. IEEE Access, 7, pp. 164398-164406.
https://doi.org/10.1109/access.2019.2953226

Zaro, F. R., & Abido, M. A. (2019, April). Real-Time Detection and Classification for Voltage Events based on Wavelet Transform. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 872-877). IEEE.
https://doi.org/10.1109/jeeit.2019.8717476

Hu, W. X., Xiao, X. Y., & Zheng, Z. X. (2019). Voltage sag/swell waveform analysis method based on multi-dimension characterisation. IET Generation, Transmission & Distribution, 14(3), 486-493.
https://doi.org/10.1049/iet-gtd.2019.1038

Bastos, A. F., Freitas, W., Todeschini, G., & Santoso, S. (2019). Detection of inconspicuous power quality disturbances through step changes in rms voltage profile. IET Generation, Transmission & Distribution, 13(11), 2226-2235.
https://doi.org/10.1049/iet-gtd.2018.6511

Han, Y., Feng, Y., Yang, P., Xu, L., Xu, Y., & Blaabjerg, F. (2019). Cause, Classification of Voltage Sag, and Voltage Sag Emulators and Applications: A Comprehensive Overview. IEEE Access, 8, 1922-1934.
https://doi.org/10.1109/access.2019.2958965

Macii, D., & Petri, D. (2019). Rapid voltage change detection: limits of the IEC standard approach and possible solutions. IEEE Transactions on Instrumentation and Measurement, 69(2), pp. 382-392.
https://doi.org/10.1109/tim.2019.2903617

Tee, W., Yusoff, M. R., Yaakub, M. F., & Abdullah, A. R. (2020). Voltage variations identification using gabor transform and rule-based classification method. International Journal of Electrical and Computer Engineering, 10(1), pp. 681-689.
https://doi.org/10.11591/ijece.v10i1.pp681-689

Daud, K., Abidin, A. F., Ismail, A. P., Hasan, M. D. A., Shafie, M. A., & Ismail, A. (2019). Evaluating windowing-based continuous S-transform with neural network classifier for detecting and classifying power quality disturbances. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), pp. 1136-1142.
https://doi.org/10.11591/ijeecs.v13.i3.pp1136-1142

Patnaik, B., Panigrahi, R. R., Mishra, M., Jena, R. K., & kumar Swain, M. (2020). Detection and Classification of Voltage Sag Causes Based on S-Transform and Extreme Learning Machine. In Innovation in Electrical Power Engineering, Communication, and Computing Technology (pp. 277-289). Springer, Singapore.
https://doi.org/10.1007/978-981-15-2305-2_22

Pujiantara, M., Anggriawan, D., Tjahjono, A., Permadi, D., Priyadi, A., Purnomo, M., A Real-Time Current Harmonic Monitoring System Based on Stockwell Transform Method, (2016) International Review of Electrical Engineering (IREE), 11 (2), pp. 193-199.
https://doi.org/10.15866/iree.v11i2.8227

Mahela, O. P., Khan, B., Alhelou, H. H., & Siano, P. (2020). Power quality assessment and event detection in distribution network with wind energy penetration using stockwell transform and fuzzy clustering. IEEE Transactions on Industrial Informatics, 16(11), pp. 6922-6932.
https://doi.org/10.1109/tii.2020.2971709

Pujiantara, A. E. M., Priyadi, A., Pujiantara, M., Penangsang, O., Anggriawan, D. O., & Tjahjono, A. (2016, July). Improvement of power quality monitoring based on modified S-transform. In 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA) (pp. 539-544). IEEE.
https://doi.org/10.1109/isitia.2016.7828717

Pujiantara, M., Anggriawan, D., Tjahjono, A., Priyadi, A., Purnomo, M., Real Time Power Quality Analysis for Industrial Power Systems Based on Fast S-Transform, (2017) International Review of Electrical Engineering (IREE), 12 (3), pp. 277-286.
https://doi.org/10.15866/iree.v12i3.11947

Zaid, M. M., Malik, M. U., Bhatti, M. S., Razzaq, H., & Aslam, M. U. (2017). Detection and classification of short and long duration disturbances in power system. Journal of Electrical Systems, 13(4), pp. 779-789.

Macii, D., & Petri, D. (2019). Rapid voltage change detection: limits of the IEC standard approach and possible solutions. IEEE Transactions on Instrumentation and Measurement, 69(2), pp. 382-392.
https://doi.org/10.1109/tim.2019.2903617

Bastos, A. F., Freitas, W., Todeschini, G., & Santoso, S. (2019). Detection of inconspicuous power quality disturbances through step changes in rms voltage profile. IET Generation, Transmission & Distribution, 13(11), pp. 2226-2235.
https://doi.org/10.1049/iet-gtd.2018.6511

Katruksa, S., Jiriwibhakorn, S., Evaluation of Mid-Term Load Forecasting Case Study Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs), (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 283-293.
https://doi.org/10.15866/iree.v15i4.17766

Matar, M., Mohamed, O., Fault Classification on a Power Transmission Line Using Discrete Wavelet Transform and Artificial Neural Networks, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 349-357.
https://doi.org/10.15866/iree.v14i5.17017

Ebhota, V., Isabona, J., Srivastava, V., Effect of Learning Rate on GRNN and MLP for the Prediction of Signal Power Loss in Microcell Sub-Urban Environment, (2019) International Journal on Communications Antenna and Propagation (IRECAP), 9 (1), pp. 36-45.
https://doi.org/10.15866/irecap.v9i1.15329

Kumar, A., Ghosh, A., A GPR Based Novel Approach for Aerodynamic Parameter Estimation from Flight Data, (2018) International Review of Aerospace Engineering (IREASE), 11 (6), pp. 252-259.
https://doi.org/10.15866/irease.v11i6.14521

Refbacks

There are currently no refbacks.

Username
Password
Remember me