The rapid prediction of the full life cycle cost of substation has guiding significance for the construction of substation. In this paper, a substation full life cycle cost prediction model based on advanced particle swarm optimization (advanced PSO, APSO) least squares support vector machine is established. The relevant characteristic index of the substation life cycle is used as the input of the model, and the output is the substation full life cycle cost. The simulation results are compared with the prediction results of APSO optimized LS-SVM, traditional LS-SVM, BP neural network four prediction models and related performance indicators. The simulation results show that the APSO optimized LS-SVM model has better prediction accuracy, and can predict and evaluate the life cycle cost quickly and accurately during substation design and construction, and improve the economics of substation construction.
On the basis of collecting a large number of practical engineering cases, this paper uses the improved neural network prediction model based on multiple linear regression to build an improved substation project cost prediction model based on the division of substation projects. Using the actual project sample data for empirical research, the results show that the prediction model has high accuracy for the prediction of substation engineering. This research work provides a reliable technical scheme for the cost prediction of power transmission project.
Original scientific paper https://doi.org/10.2298/TSCI190829010X At present, the prediction of the life cycle cost of fabricated substation is of great significance for the construction of fabricated substation. An enhanced prediction model based on quantum particle swarm optimization (QPSO) via least squares support vector machine is established. The relevant characteristic index of the life cycle of the fabricated substation is used as the input of the model, and the output is the life cycle cost. The simulation results are compared with the prediction results of QPSO optimized least squares support vector machine (LS-SVM), PSO optimized LS-SVM, traditional LS-SVM, and backpropagation neural network, which shows that the QPSO optimized LS-SVM model has better prediction accuracy, can predict and evaluate the life cycle cost more quickly, and can improve the benefits of fabricated substation construction.
With the construction of “West-to-East Power Transmission” and national networked engineering projects, high-voltage transmission towers, which are important components of transmission lines, are important power engineering facilities. They are inevitably erected on the ridges, edges of steep slopes, and river banks. Therefore, induced by internal factors and external factors, it is easy to cause landslide disasters in these areas, resulting in power grid accidents such as tilting of power poles, disconnections, and trips. This article takes the Yanzi landslide in Badong County and 500kV transmission tower on the landslide as the research object. For the combined foundation excavation form of the tower foundation, the relative positions of different tower foundations and landslides and the loading of spoil are analyzed. The influence of excavation of transmission tower foundation and pile dumping on the stability of the landslide is discussed. The research results show that the effect of the combined foundation excavation on the stability of the landslide is negligible, but it will affect the stability of the local landslide, and the stability of the local landslide is related to the relative position of the tower foundation and the landslide. In addition, the pile loading of spoil will also affect the stability of local landslides, and the degree of impact is related to the size of the spoil pile.
Tasks in real-time systems have strict time requirements and need to be completed before their deadlines. Whenever the real-time task set changes, the real-time system reruns the schedulability analysis method to determine whether the tasks in the new task set can be completed before their deadlines. Since new tasks can not be executed until the execution of the schedulability analysis method is completed, the execution efficiency of the schedulability analysis method directly affects the actual performance of the real-time system. Response time analysis (RTA) is a typical method for schedulability analysis. It calculates the response times of tasks and tests the schedulability of tasks by comparing the calculation results and the relative deadlines of tasks. We found that the execution efficiency of existing RTAs can be improved by reasonably sorting the calculation order of the tasks. A task sequencing strategy is proposed to improve the execution efficiency of existing RTAs.
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