Economic Analysis of a Power System Network using Optimal Placement of PMUs and DULRs Through Complete and Incomplete Observability Analysis

Krishna, K. Bala; Rachananjali, K; Rosalina, K. Mercy; Rao, Yi

doi:10.1007/s42835-020-00567-7

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…The SVM method may create a classification model for estimating the likelihood of occurrence of potential hazards in PS by learning the characteristics and classification labels of the sample data in PN security risk evaluation. The SVM algorithm's configurable parameters have a significant impact on its accuracy and robust performance, though, and it has a lengthy training cycle 4 . It is advisable to consider the selection of the parameters using the Lightning Search technique (LSA) technique in order to swiftly choose the best SVM parameters.By modeling the formation and propagation of lightning, the LSA method is able to locate the global best solution in the search space 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid

Qi,

Zhu,

et al. 2024

Sci Rep

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Due to theintricate and interdependent nature of the smart grid, it has encountered an increasing number of security threats in recent years. Currently, conventional security measures such as firewalls, intrusion detection, and malicious detection technologies offer specific protection based on their unique perspectives. However, as the types and concealment of attacksincrease, these measures struggle to detect them promptly and respond accordingly. In order to meet the social demand for the accuracy and computation speed of the power network security risk evaluation model, the study develops a fusion power network security risk evaluation algorithm by fusing the flash search algorithm with the support vector machine. This algorithm is then used as the foundation for building an improved power network security risk evaluation model based on the fusion algorithm.The study's improved algorithm's accuracy is 96.2%, which is higher than the accuracy of the other comparative algorithms; its error rate is 3.8%, which is lower than the error rate of the other comparative algorithms; and its loss function curve convergence is quicker than that of the other algorithms.The risk evaluation model's accuracy is 97.8%, which is higher than the accuracy of other comparative models; the error rate is 1.9%, which is lower than the error rate of other comparative models; the computing time of the improved power network security risk evaluation model is 4.4 s, which is lower than the computing time of other comparative models; and its expert score is high. These findings are supported by empirical analysis of the improved power network security risk evaluation model proposed in the study. According to the study's findings, the fusion algorithm and the upgraded power network security risk evaluation model outperform other approaches in terms of accuracy and processing speed. This allows the study's maintenance staff to better meet the needs of the community by assisting them in identifying potential security hazards early on and taking the necessary preventative and remedial action to ensure the power system's continued safe operation.

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Section: Introductionmentioning

confidence: 99%