KEYWORDS: Control systems, Data modeling, Data transmission, System integration, Modeling, Switches, Optical fibers, Optical transmission, Analog electronics
Due to the use of turn-off device IGBT in flexible HVDC transmission, the fault current is a short time change process. In the initial stage of flexible DC protection failure, if the converter valve cannot be quickly closed and the AC test switch cannot be turned off, it will bring great harm to the converter equipment. At present, the test of flexible DC control protection mainly depends on RTDS or PSCAD simulation test. This testing method needs to build a very complex test environment, and the workload of on-site configuration and debugging is very huge. Therefore, this paper establishes an integrated test system of flexible DC control and protection based on modelling decoupling to solve the above problems. The integrated test system realizes the field testing of flexible DC protection control systems with different configurations, which greatly reduces the workload of on-site debugging personnel and improves the safe and stable operation ability of the power grid.
KEYWORDS: Data modeling, Deep learning, Education and training, Performance modeling, Renewable energy, Power grids, Neural networks, Neodymium, Power consumption, Windows
With the improvement of renewable energy utilization rate, its proportion in power generation continues to increase. However, the uncertainty of renewable energy power generation has always been an urgent problem. Accurate forecast of short-term power load can assist modern power systems in accurately allocating the proportion of traditional and renewable energy generation. This is crucial for the safety and economic operation of the power grid. Power load forecasting can be seen as a multivariate time series forecasting problem. Most of existing studies have focused on predicting the next moment power load. However, a great number of decisions in power planning scenarios require predictive models that can provide a complete conditional distribution with richer information, namely probability distribution prediction. To address this limitation, we applied a multi-step probability prediction model based on recurrent neural networks on short-term power load forecasting. Experiments conducted on the annual power load of multiple regions in the United States and comparison with some classic models verify the superiority of the applied model. The results indicate that the model applied in the article has a promising predictive performance.
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