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New Machine Learning Study Results from Princess Sumaya University for Technology Described (Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management)

  • Dec 2, 2021
Robotics & Machine Learning Daily News

2021 DEC 01 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- Investigators publish new report on machine learning. According to news reporting from Amman, Jordan, by NewsRx journalists, research stated, “The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts.”

Our news journalists obtained a quote from the research from Princess Sumaya University for Technology: “In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naive Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 mSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features.”

According to the news reporters, the research concluded: “Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.”

For more information on this research see: Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management. Energies, 2021,14(6935):6935. (Energies - The publisher for Energies is MDPI AG.

A free version of this journal article is available at

Our news editors report that additional information may be obtained by contacting Qasem Abu Al-Haija, Department Computer Science, Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman 11941, Jordan. Additional authors for this research include Abdallah A. Smadi, Mohammed F. Allehyani.


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