I. J. of Electrical & Electronics Research
Review Article | 
Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems
Author(s): Dr. K. Sasikala1, Dr. J. Jayakumar2, Dr. A. Senthil Kumar3, Dr. Shanty Chacko4 and Dr. Hephzibah Jose Queen5
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 3
Publisher : FOREX Publication
Published : 07 September 2022
e-ISSN : 2347-470X
Page(s) : 550-556
Abstract
The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained. Correlation between Input and output variable using spearman’s correlation analysis using Heat maps. Followed by the Multiple Linear Regression based Training and testing of the Modified IEEE 14 and IEEE30 Bus systems for base load case, 10% and 20% load increment with the 5-fold cross validation is also presented. Comparative analysis is performed to find the best fit ML Model for our research.
Keywords: Regression-based Machine Learning, Correlation Analysis, Power system Analysis, Voltage Stability, Cost analysis
Dr. K. Sasikala, Vels Institute of Science and Technology, Chennai, India
Dr. J. Jayakumar, Karunya Institute of Technology and Sciences, Coimbatore, India; Email: jayakumar@karunya.edu
Dr. A. Senthil Kumar, Dilla University, Ethiopia
Dr. Shanty Chacko, Karunya Institute of Technology and Sciences, Coimbatore, India
Dr. Hephzibah Jose Queen, Karunya Institute of Technology and Sciences, Coimbatore, India
-
[1] Kessel, P., &Glavitsch, H. (1986). Estimating the Voltage Stability of a Power System. IEEE Transactions on Power Delivery, 1(3). [Cross Ref]
-
[2] Kumar, S., Kumar, A., & Sharma, N. K. (2020). A novel method to investigate voltage stability of IEEE-14 bus wind integrated system using PSAT. Frontiers in Energy, 14(2).[Cross Ref]
-
[3] Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., & Yan, Z. (2009). A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews, 13(4).[Cross Ref]
-
[4] Leonardi, B., Ajjarapu, V., Djukanovic, M., & Zhang, P. (2010, August).Application of multi-linear regression models and machine learning techniques for online voltage stability margin estimation. 2010 IREP Symposium Bulk Power System Dynamics and Control - VIII (IREP).[Cross Ref]
-
[5] Musirin, I., & Abdul Rahman, T. K. (n.d.). Novel fast voltage stability index(FVSI) for voltage stability analysis in power transmission system. Student Conference on Research and Development.[Cross Ref]
-
[6] Parwaiz, A., Kumar Jain, V., Ansari, B., M., Jaipur, J., Professor, A.,(2008). Comparative Analysis of Load Flow Methods on Standard Bus System. International Research Journal of Engineering and Technology, 775[Cross Ref]
-
[7] Rahi, O. P., Yadav, A. K., Malik, H., Azeem, A., & Kr, B. (2012). Power System Voltage Stability Assessment through Artificial Neural Network. Procedia Engineering.[Cross Ref]
-
[8] Ren, C., Xu, Y., Zhang, Y., & Zhang, R. (2020). A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems. IEEE Transactions on Industrial Informatics, 16(6)[Cross Ref]
-
[9] Ruisheng Diao, Kai Sun, Vittal, V., O’Keefe, R. J., Richardson, M. R., Bhatt,N., Stradford, D., &Sarawgi, S. K. (2009a). Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements. IEEE Transactions on Power Systems, 24(2)[Cross Ref]
-
[10] Ruisheng Diao, Kai Sun, Vittal, V., O’Keefe, R. J., Richardson, M. R., Bhatt, N., Stradford, D., & Sarawgi, S. K. (2009b). Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements. IEEE Transactions on Power Systems, 24(2)[Cross Ref]
-
[11] Jayakumar, Chitra, Shanty Chcacko Identification of Power Leakage and Protection of Over Voltage in Residential Buildings, International Journal of Electrical and Electronics Research, Volume 10, Issue 1, Pages 51 - 5630 March 2022[Cross Ref]
-
[12] J. Jayakumar and Honey Baby, Operating Cost Analysis of Microgrid Including Renewable Energy Sources and a Battery Under Dynamic Pricing, Lecture Notes in Electrical Engineering, 2022, 795, pp. 291–302[Cross Ref]
-
[13] J. Jayakumar and Hepsibah jose queen Comparative techno-economic analysis of power system with and without renewable energy sources and statcom Journal of Green Engineering, 2021, 11(2), pp. 1648–1667[Cross Ref]
-
[14] J. Jayakumar and Hepsibah jose queen Machine Learning-Based Predictive Techno-Economic Analysis of Power System IEEE Access, 2021, 9, pp. 123504–123516[Cross Ref]
-
[15] Shivesh Singh, Shaurya Singh, Sudeept Singh Yadav and Avneesh Kumar (2022), Free Hold Price Predictor Using Machine Learning. IJEER 10(2), 138-143. DOI: 10.37391/IJEER.100215.[Cross Ref]
-
[16] Mersha Nigus and H.L Shashirekha (2022), A Comparison of Machine Learning and Deep Learning Models for Predicting Household Food Security Status. IJEER 10(2), 308-311. DOI: 10.37391/IJEER.100241.[Cross Ref]
Dr. K. Sasikala, Dr. J. Jayakumar, Dr. A. Senthil Kumar, Dr. Shanty Chacko, Dr. Hephzibah Jose Queen (2022), Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems. IJEER 10(3), 550-556. DOI: 10.37391/IJEER.100324.