A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Technical Data Schedule Uninterruptible Power Supply System for Ghana Gas Limited
Article Sidebar
Main Article Content
Abstract
Power supply systems can have problems, and Ghana Gas Limited is not an exception. Ghana Gas Limited uses an intricate Uninterruptible Power Supply (UPS) system called the Technical Data Schedule (TDS), which is made up of several parts such as electromechanical components, PCB boards, and electrolytic capacitors.The majority of components have technical lifespans that are governed by usage, operational environment, and working conditions, such as electrical stress, working hours, and working cycles. Most of the time, these errors affect the integrity and power supply after manufacture. The issue is that it takes longer for the professionals who operate on this machine to recognize these flaws, which makes it difficult for them to predict errors quickly or anticipate the likelihood of faults happening in the system components at an early stage for effective corrective action to be performed. Support vector machines (SVM) and decision trees were used in this study to anticipate faults for technical data scheduling of uninterruptible power supply systems for Ghana Gas Limited in an efficient manner. Based on a comparative analysis using these two techniques, faults in Ghana Gas Limited's power supply system were predicted using a four-hour daily interval dataset on TDS UPS recordings, including input voltage, battery voltage, battery current, and alarm, spanning from August 2017 to October 2023. The findings depicted that the support vector machine was more efficient in detecting the fault locations in the power supply system with an accuracy of 96.80%, recall of 99.80%, precision of 100 %, F1-score of 93.15%.The results from the error metrics also validate the measures in assessing the predictive ability of the model with MAE of 0.42%, MSE of 1.18%, RMSE of 4.45%, R2 of 99.97%, RMSLE of 0.036%, and MAPE of 0.21%.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Nan, B., Chen, L., Rodrigo, N.D., Borodin, O., Piao, N., Xia, J., Pollard, T., Hou, S., Zhang, J., Ji, X. and Xu, J., 2022. Enhancing Li+ transport in NMC811|| graphite lithium‐ion batteries at low temperatures by using low‐polarity‐solvent electrolytes. Angewandte Chemie International Edition, 61(35), p.e202205967.
Lukovic, M., Lukovic, V., Belca, I., Kasalica, B., Stanimirovic, I. and Vicic, M., 2016. LED-based Vis-NIR spectrally tunable light source-the optimization algorithm. Journal of the European Optical Society-Rapid Publications, 12, pp. 1-12.
Guerrero, J.M., Matas, J., de Vicuna, L.G., Castilla, M. and Miret, J., 2007. Decentralized control for parallel operation of distributed generation inverters using resistive output impedance. IEEE Transactions on industrial electronics, 54(2), pp. 994-1004.
Low, K.S. and Cao, R., 2008. Model predictive control of parallel-connected inverters for uninterruptible power supplies. IEEE Transactions on Industrial Electronics, 55(8), pp. 2884-2893.
Martin, F. and Aguado, J.A., 2003. Wavelet-based ANN approach for transmission line protection. IEEE Transactions on power delivery, 18(4), pp.1572-1574.
Sharafi, A., Jafarian, P. and Sanaye-Pasand, M., 2010, May. A combined algorithm for high speed transmission-line protection based on traveling-wave. In 2010 9th International Conference on Environment and Electrical Engineering, pp. 132-135).
Park T, Efros AA, Zhang R, Zhu JY. Contrastive learning for unpaired image-to-image translation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Springer International Publishing, Part IX 16 2020, pp. 319-345.
Katipamula, S. and Brambley, M.R., 2005. Methods for fault detection, diagnostics, and prognostics for building systems—A review, part II. Hvac&R Research, 11(2), pp.169-187.
Awadallah, M.A. and Morcos, M.M., 2003. Application of AI tools in fault diagnosis of electrical machines and drives-an overview. IEEE Transactions on energy conversion, 18(2), pp. 245-251.
Giuntini, L. and Brioschi, M., 2015, March. Markov chain analysis for failure prediction of power converters. In 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 1336-1341). IEEE.
Eusgeld I, Fraikin F, Rohr M, Salfner F, Wappler U. Software reliability. Dependability Metrics: Advanced Lectures. 2008:104-25.
Salfner, F. and Tschirpke, S., 2008. Error Log Processing for Accurate Failure Prediction. WASL, 8, pp.4.
Zhang, X., Zhou, X., Lin, M. and Sun, J., 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856).
Tao L, Cinquanta E, Chiappe D, Grazianetti C, Fanciulli M, Dubey M, Molle A, Akinwande D. Silicene field-effect transistors operating at room temperature. Nature nanotechnology. 2015 Mar;10(3): pp. 227-31.
Guardado, J.L., Naredo, J.L., Moreno, P. and Fuerte, C.R., 2001. A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis. IEEE Transactions on Power delivery, 16(4), pp. 643-647.
Eon, C., Breadsell, J., Morrison, G. and Byrne, J., 2019. Shifting home energy consumption through a holistic understanding of the home system of practice. Decarbonising the built environment: Charting the transition, pp. 431-447.
Giuntini, L. and Brioschi, M., 2015, March. Markov chain analysis for failure prediction of power converters. In 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 1336-1341, IEEE.
Jan, S.U., Lee, Y.D., Shin, J. and Koo, I., 2017. Sensor fault classification based on support vector machine and statistical time-domain features. IEEE Access, 5, pp. 8682-8690.
Bigdeli, M., Vakilian, M. and Rahimpour, E., 2012. Transformer winding faults classification based on transfer function analysis by support vector machine. IET electric power applications, 6(5), pp. 268-276.
Linzen, D., Buller, S., Karden, E. and De Doncker, R.W., 2005. Analysis and evaluation of charge-balancing circuits on performance, reliability, and lifetime of supercapacitor systems. IEEE transactions on industry applications, 41(5), pp. 1135-1141.
Litchfield, N.J., Villamor, P., Dissen, R.J.V., Nicol, A., Barnes, P.M., A. Barrell, D.J., Pettinga, J.R., Langridge, R.M., Little, T.A., Mountjoy, J.J. and Ries, W.F., 2018. Surface rupture of multiple crustal faults in the 2016 M w 7.8 Kaikōura, New Zealand, Earthquake. Bulletin of the Seismological Society of America, 108(3B), pp. 1496-1520.
Lind, H. and Muyingo, H., 2012. Building maintenance strategies: planning under uncertainty, Property Management, 30(1), pp. 14-28.
Aamir, M., Kalwar, K.A. and Mekhilef, S., 2016. Uninterruptible power supply (UPS) system. Renewable and sustainable energy reviews, 58, pp. 1395-1410.
Cherkassky V, Mulier FM. Learning from data: concepts, theory, and methods. John Wiley & Sons; 2007 Sep 10.
Suykens JA. Nonlinear modelling and support vector machines. InIMTC 2001. proceedings of the 18th IEEE instrumentation and measurement technology conference. Rediscovering measurement in the age of informatics (Cat. No. 01CH 37188) 2001 May 21 (Vol. 1, pp. 287-294). IEEE.
Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres F. Review of photovoltaic power forecasting. Sol Energy. 2016;136: pp. 78-111.
Sharma G, Tripathi V, Mahajan M, Srivastava AK. Comparative analysis of supervised models for diamond price prediction. In: Proceedings of the Conflu 2021 11th Int Conf Cloud Comput Data Sci Eng. Published online 2021: pp. 1019-1022.
Fontana F A, Mäntylä MV, Zanoni M, Marino A. Comparing and experimenting machine learning techniques for code smell detection. Empir Softw Eng Vol. 2016;21: pp. 1143–1191.
Muhammad IA , Fabio P, Lin S, Qing W. Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Inf Softw Technol. 2019;108 (1), pp. 15-138.
Ofosu A. R, Odoi B, Asamoah M, “Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system”, Serbian Electr Eng. 2021;18(1): pp. 75-94.
Twumasi-Ankrah, S., Odoi, B., Adoma Pels, W. and Gyamfi, E.H., 2019. Efficiency of imputation techniques in univariate time series.
Ofosu R. A, Zhu H, Odoi B. A Hybrid Prediction Fault Location Model for Copper Wire Manufacturing Process. Acta Polytechnica Hungarica. 2024; 21(6).
Odoi B., Boahen A. D. and Brew L. (2024), Predicting Nitrous Oxide Emissions in Ghana using Long Short-term Memory and Gated Recurrent Neural Network, eNergetics 2023, 9 th Virtual International Conference on Science, Technology and Management in Energy, Conference, 81
Rathakrishnan V, Bt. Beddu S, Ahmed AN. Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using a boosting machine, learning algorithms. Sci Rep. 2022;12(1): pp. 1-16.
Liu Q, Wang X, Huang X, Yin X. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunn Undergr Sp Technol. 2020; 106: pp. 1-15.
Nguyen H, Vu T, Vo TP, Thai HT. Efficient machine learning models for prediction of concrete strengths. Constr Build Mater. 2021; 266: pp.1-17.
Saucedo-Dorantes J.J, Arellano-Espitia F, Delgado-Prieto M, Osornio-Rios RA. Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings. Sensors. 2021 Aug 30;21(17):5832.