Control of TCSC and SVC using Least Square Support Vector Regression (LS-SVR) to improve voltage stability

Wibowo, Rony Seto; Bahrowi, Aw; Anam, Khoirul; Abdillah, Muhammad; Soeprijanto, Adi; Penangsang, Ontoseno; Yorino, Naoto

doi:10.1109/iciteed.2013.6676264

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…SVR can create a nonlinear function with a relatively high tolerance to noise in data by de ining a range to allow differences from real data (i.e., regarding the difference as 0 when it is within a prede ined range); thus, it can realize superior nonlinear regression considering both itting to real data and generalization ability. LS-SVR was proposed in 2005 [38] as an extension to the typical SVR, and has been applied in many research ields [39]- [41]. Though both typical SVR and LS-SVR have almost the same generalization ability, LS-SVR has several advantages over the typical SVR; such as lower computation cost, lexible selection of kernel functions, simpliied maintenance, and lower memory footprint.…”

Section: Ls-svrmentioning

confidence: 99%

Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

Miyazawa¹,

Kafle²,

Yokota³

et al. 2023

ITU J-FET

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The recent rapid advancement of cloud-native networking infrastructure has leveraged the resource virtualization technology of containers to realize diverse microservice-based applications in 5G/6G networks and clouds. Containers drastically enhance the efficiency of computational resource allocation and utilization as compared to the related virtualization technology of Virtual Machines (VMs). The networking environment leveraging both VM and container mixed virtualization technologies makes the most use of them to realize a computational platform whose resources can be dynamically adjusted to a fine granularity. To continuously meet the required levels of quality of services in 5G/6G networks and clouds in that platform, an agile and autonomous data analytics system in the control plane is essential for the accurate prediction of server workloads and dynamic allocation of enough amount of computational resource. In this paper, we introduce a framework, which complies with Recommendation ITU-T Y.3177, for autonomous computational resource control and management. The framework consists of an advanced data analytics system and a resource control system. We propose an architecture for the advanced data analytics system consisting of learning and prediction components. The learning component includes a three-stage intelligent model pipelining with three cascaded machine learning models, nonlinear regression, clustering, and multiple regression. These models determine the fluctuation trends in CPU utilization, classify services with similarities in the trends, and predict the peak CPU utilization of each containerized microservice. We evaluate the proposed models through experiments and numerical analysis. The results prove that the models support agile data analytics, which can complete data processing in the time granularity of seconds and achieve higher prediction accuracy of CPU utilization.

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Section: Ls-svrmentioning

confidence: 99%