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All Journal International Journal of Applied Power Engineering (IJAPE)
Angadi, Ravi V.
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Electrical & Electronics Engineering

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An ensemble based data mining model for contingency analysis of power system under STLO Angadi, Ravi V.; Mangai, J. Alamelu; Manohar, V. Joshi; Daram, Suresh Babu; Rao, Paritala Venkateswara
International Journal of Applied Power Engineering (IJAPE) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v12.i4.pp349-358

Abstract

In a large, interconnected power system, contingency analysis is a useful tool for pinpointing the potential consequences of post-event scenarios on the system's safety. In this work, the Newton-Raphson technique is applied to every single outage of a transmission line to compute the load flows. For the static security classification of the power system, the line voltage stability performance index (LVSI) is used. There are three levels of static security of power system namely: non-critical (the least severe), semi-critically insecure (the next lowest severe), and critical (the next highest severe). The various data mining techniques such as decision trees, bagging-based ensemble methods, and boosting-based ensemble methods were applied to assess the severity of the line under various loading and contingency conditions. Test systems based on the IEEE 30 bus system were used with the proposed machine learning classifiers. The experimental results proved that bagging based ensemble method provided better accuracy compared to the decision tree and the AdaBoost ensemble method for predicting the power system security assessment. The bagging-based ensemble method has a predictive accuracy of 85% and an AUC of 0.94.
Enhancing power grid reliability: a hybrid blockchain and machine learning approach Angadi, Ravi V.; Kumar, Suresh; Vijayalakshmi, A. K.; Shree, G. N. Vidya
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp421-429

Abstract

As contemporary power grids are becoming more complex with the integration of renewable energy sources, distributed generation, and smart grid technologies. Conventional contingency analysis techniques, based on centralized architectures and static rule-based evaluations, tend to be inadequate in real-time fault detection, automated response, and cybersecurity. This paper suggests a hybrid approach that combines machine learning algorithms with blockchain technology to improve both predictive intelligence and security of contingency analysis. For the IEEE 30-bus test case, different line outage and generator failure cases were simulated. Different machine learning models, such as random forest (RF), support vector machine (SVM), and gradient boosting (GB), were trained to classify and predict these contingencies. In parallel, cryptographic primitives like advanced encryption standard (AES), Rivest–Shamir–Adleman (RSA), and elliptic curve cryptography (ECC) were tested in a blockchain setting to provide security for event data and enable automatic recovery steps through smart contracts. Outcomes illustrate that the GB showed the maximum fault classification rate (93.4%), and ECC ensured light yet robust data protection for blockchain activities. Against the conventional system, the designed model enhanced the response time in case of faults, accuracy, and system fault tolerance. This two-layer mechanism presents a scalable, proactive, and cyber-safe mechanism for the power grid in the future.
Co-Authors Daram, Suresh Babu Kumar, Suresh Mangai, J. Alamelu Manohar, V. Joshi Rao, Paritala Venkateswara Shree, G. N. Vidya Vijayalakshmi, A. K.