Tomin N., Kurbatsky V., Panasetsky D., Sidorov D., Zhukov A. Voltage/VAR Control and Optimization: AI approach // IFAC-PapersOnLine. Vol.51. No.28. 2018. P.103-108. DOI: 10.1016/j.ifacol.2018.11.685 Volt-VAr control systems provide the optimal solution with remote automatic or manual control of the capacitor banks and tap positions on the voltage regulators. However, such control possesses inherent characteristics of complexity, nonlinearity, inaccuracy and high requirement for control speed, parts...
Теги: machine learning , multi-agent system , power system , random forest , security , volt-var control , decision trees , intelligent agents , learning systems , value engineering , voltage regulators , inherent characteristics , optimal solutions , random forests , traditNegnevitsky M., Tomin N., Kurbatsky V., Panasetsky D., Zhukov A., Rehtanz C. A random forest-based approach for voltage security monitoring in a power system // 2015 IEEE Eindhoven PowerTech, PowerTech 2015. 1 p. ISBN (print): 9781479976. DOI: 10.1109/PTC.2015.7232460 Voltage collapse is a critical problem that impacts power system operational security. Timely and accurate assessment of voltage security is necessary to detect alarm states in order to prevent a large-scale blackout. This paper presents...
Теги: artificial intelligence , decision trees , electric power system security , learning systems , blackout , critical problems , large-scale blackout , operational security , random forests , security monitoring... position (ROI) then the defect is described by its features. Finally, each defect is classified these different defect features. In our approach the following algorithms have been using: Markov Random Fields and Graph cuts method for image segmentation, Random Forests algorithm for data classification. нет
Теги: feature extraction , defect pavement , defects detection , markov random fields , graph cut , random forests , computer vision
