首页|Mansoura University Researchers Provide New Study Findings on Machine Learning ( Detect, Classify, and Locate Faults in DC Microgrids Based on Support Vector Mac hines and Bagged Trees in the Machine Learning Approach)
Mansoura University Researchers Provide New Study Findings on Machine Learning ( Detect, Classify, and Locate Faults in DC Microgrids Based on Support Vector Mac hines and Bagged Trees in the Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting out of Mansour a, Egypt, by NewsRx editors, research stated, “The DC microgrids possess numerou s pros, including enhanced reliability, increased efficiency, and a less complic ated control system. Further, they provide a simplified system that facilitates the incorporation of renewable energy sources (RES), battery storage systems, an d DC loads.” Our news reporters obtained a quote from the research from Mansoura University: “DC microgrids improve resource coordination and utilization, thus offering a po tential alternative to modern energy systems. DC power systems have unique featu res that make protecting DC microgrids from different types of faults very hard. These include large DC capacitors, low-impedance DC cables, no natural zero-cro ssing points, and significant transient current and voltage changes that happen very quickly. Also, solid-toground faults could result in a rapid increase in D C fault current. Therefore, a cost-effective and reliable system protection mech anism capable of detecting, locating, and isolating faults is crucial to prevent ing DC microgrids from experiencing power outages and failures. This paper prese nts a machine-learningbased protection approach for DC microgrids. The proposed methodology relies solely on measuring the current passing through the positive terminal at bus_1 in the modified IEEE 14-bus configuration. Durin g the measurement, the DC microgrid encountered several fault scenarios. The gat hered data is analyzed to train a supervised machine-learning method that uses m edium-gaussian support vector machines and bagged tree classification algorithms . The effectiveness of this method was evaluated by conducting tests on a partic ular subset of the collected data using the trained model. The proposed protecti on technique was verified using MATLAB/Simulink software under several pole-pole (P-P) and pole-ground (P-G) fault conditions.”
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