An electric grid consists of transformers, generation centers, communication links, control stations, and distributors. Collectively these components help in moving power from one electricity station to commercial and domestic consumers. Traditional grid stations can’t predict the dynamic need of consumers’ electricity. Furthermore, these traditional grids are not sufficiently strong and adaptable. This is the driving force for the transition towards a smart grid. A modern smart grid is a self-healing, long-lasting electrical system that can adapt to changing client needs. Machine learning has aided in grid stability calculation in the face of dynamically shifting consumer demands. By avoiding a breakdown, the smart grid has been transformed into a reliable smart grid. The authors of this study used a variety of machine learning-based algorithms to estimate grid stability to avoid a breakdown situation. An open-access dataset lying on Kaggle repository has been used for experimental work. Experiments are conducted in a simulation environment generated through Python. Using the Bagging classifier algorithm, the suggested model has attained an accuracy level of 97.9% while predicting the load. A precise prediction of power demand will aid in the avoidance of grid failure, hence improving grid stability and robustness.

[1] Ahmed W., Ansari H., Khan B., Ullah Z., Ali S., Mehmood C., Qureshi M., Hussain I., Jawad M., Khan M., and Ullah A., “Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts,” IEEE Access, vol. 8, pp. 185059-185078, 2020.

[2] Alazab M., Khan S., Krishnan S., Pham Q., Reddy M., and Gadekallu T., “A Multidirectional LSTM Model for Predicting The Stability of A Smart Grid,” IEEE Access, vol. 8, pp. 85454- 85463, 2020.

[3] Arif A., Javaid N., Anwar M., Naeem A., Gul H., and Fareed S., “Electricity Load and Price Forecasting Using Machine Learning Algorithms in Smart Grid: A Survey,” in Proceedings of 34th International Conference on Advanced Information Networking and Applications, Caserta, pp. 471-483, 2020.

[4] Arzamasov V., Böhm K., and Jochem P., “Towards Concise Models of Grid Stability,” in Proceedings of IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, Aalborg, pp. 1-6, 2018.

[5] Bashir A., Khan S., Prabadevi B., Deepa N., Alnumay W., Gadekallu T., and Maddikunta P., “Comparative Analysis of Machine Learning Algorithms for Prediction of Smart Grid Stability,” International Transactions on Electrical Energy Systems, vol. 31, no. 9, pp. 1- 23, 2021.

[6] Devi S. and Radhika Y., “A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction,” International Journal of Machine Learning and Computing, vol. 8, no. 2, pp.133-139, 2018.

[7] Fang X., Misra S., Xue G., and Yang D., “Smart Grid—The New and Improved Power Grid: A Survey,” IEEE Communications Surveys and Tutorials, vol. 14, no. 4, pp. 944-980, 2011.

[8] Ghojogh B. and Crowley M., “Linear and Quadratic Discriminant Analysis: Tutorial,” arXiv preprint arXiv:1906.02590, 2019.

[9] Ghosh A. and Kole A., “A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction,” TechRxiv, 2021.

[10] Gorzałczany M., Piekoszewski J., and Rudziński F., “A Modern Data-Mining Approach Based on Genetically Optimized Fuzzy Systems for Interpretable and Accurate Smart-Grid Stability Prediction,” Energies, vol. 13, no. 10, pp. 25-59, 2020.

[11] Hafeez G., Alimgeer K., Wadud Z., Shafiq Z., Khan M., Khan I., Khan F., and Derhab A., “A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in A Smart Grid,” Energies, vol. 13, no. 9, pp. 22-44, 2020.

[12] Jain A., Tiwari S., and Sapra V., “Two-Phase Heart Disease Diagnosis System Using Deep Learning,” International Journal of Control and Automatio, vol. 12, no. 5, pp. 558-573, 2019.

[13] Krč R., Kratochvílová M., Podroužek J., Apeltauer T., Stupka V., and Pitner T., “Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment,” Sustainability, vol. 13, no. 5, pp. 29-54, 2021.

[14] Lamba R., Gulati T., Dhlan K. and Jain A., “A Systematic Approach to Diagnose Parkinson’s Disease through Kinematic Features Extracted from Handwritten Drawings,” Journal of Reliable Intelligent Environments, vol. 7, no. 3, pp. 253-262, 2021.

[15] Ma J., Feuerborn S., Black C., and Venkatasubramanian V., “A Comprehensive Software Suite for Power Grid Stability Monitoring Based on Synchrophasor Measurements,” in Proceedings of IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, Washington, pp. 1-5, 2017.

[16] Malbasa V., Zheng C., Chen P., Popovic T., and Kezunovic M., “Voltage Stability Prediction Machine Learning-Based Model for Prediction of Power Consumption in Smart Grid 329 Using Active Machine Learning,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 3117-3124, 2017.

[17] Miraftabzadeh S., Foiadelli F., Longo M., and Pasetti M., “A Survey of Machine Learning Applications For Power System Analytics,” in Proceedings of International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe, Genova, pp. 1-5, 2019.

[18] Moldovan D. and Salomie I., “Detection of Sources of Instability in Smart Grids Using Machine Learning Techniques,” in Proceedings of 15th International Conference on Intelligent Computer Communication and Processing, Cluj- Napoca, pp. 175-182, 2019.

[19] Panda D. and Das S., “Regression Analysis of Grid Stability under Decentralized Control,” in Proceedings of International Conference on Engineering, Science, and Industrial Applications, pp. 1-6, Tokyo, 2019.

[20] Parseh M., Rahmanimanesh M., and Keshavarzi P., “Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods,” The International Arab Journal of Information Technology, vol. 17, no. 4, pp. 572-578, 2020.

[21] Qinyu B., Lin Y., Jiayi M., Tianqi L., Xiaotian Z., and Youyin W., “Analysis of Influence with Connected wind Farm Power Changing and Improvement Strategies on Grid Voltage Stability,” in Proceedings of China International Conference on Electricity Distribution, Tianjin, pp. 2029-2033, 2018.

[22] Rai S. and De M., “Analysis of Classical and Machine Learning Based Short-Term and Mid- Term Load Forecasting for Smart Grid,” International Journal of Sustainable Energy, vol. 40, no. 9, pp. 821-839, 2021.

[23] Rani P., Kumar R., Ahmed N., and Jain A., “A Decision Support System for Heart Disease Prediction Based Upon Machine Learning,” Journal of Reliable Intelligent Environments, vol. 7, no. 3, pp. 263-275, 2021.

[24] Rani P., Kumar R., and Jain A., “HIOC: A Hybrid Imputation Method to Predict Missing Values in Medical Datasets,” International Journal of Intelligent Computing and Cybernetics, vol. 14, no. 4, pp. 598-616, 2021.

[25] Sharma S., Challa R., and Kumar R., “An Ensemble-based Supervised Machine Learning Framework for Android Ransomware Detection,” The International Arab Journal of Information Technology, vol. 18, no. 3A, pp. 422-429, 2021.

[26] Tiwari S. and Jain A., “Convolutional Capsule Network for Covid-19 Detection Using Radiography Images,” International Journal of Imaging Systems and Technology, vol. 31, no. 2, pp. 525-539, 2021.

[27] Wang R., Liu Y., Ye X., Tang Q., Gou J., Huang, M., and Wen Y., “Power System Transient Stability Assessment Based on Bayesian Optimized Lightgbm,” in Proceedings of IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, pp. 263- 268, 2019.

[28] Yu W., An D., Griffith D., Yang Q., and Xu G., “Towards Statistical Modeling and Machine Learning Based Energy Usage Forecasting in Smart Grid,” ACM SIGAPP Applied Computing Review, vol. 15, no. 1, pp. 6-16, 2015.