Ying Qu¹, Jinhao Wang¹, Xueting Cheng¹, Jie Hao¹, Weiru Wang¹, Zhewen Niu², Yuxiang Wu^2,*

Energy Engineering, Vol.121, No.7, pp. 1847-1863, 2024, DOI:10.32604/ee.2024.048300 - 11 June 2024

Abstract The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of… More >

Kiranbir Kaur^{1, *}, Sandeep Sharma¹, Karanjeet Singh Kahlon²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1625-1647, 2020, DOI:10.32604/cmc.2020.011535 - 20 August 2020

Abstract Vendor lock-in can occur at any layer of the cloud stack-Infrastructure, Platform, and Software-as-a-service. This paper covers the vendor lock-in issue at Platform as a Service (PaaS) level where applications can be created, deployed, and managed without worrying about the underlying infrastructure. These applications and their persisted data on one PaaS provider are not easy to port to another provider. To overcome this issue, we propose a middleware to abstract and make the database services as cloud-agnostic. The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS… More >