A new round of electricity market reform has been carried out in China since 2015. Due to the spatial mismatch of electricity supply and demand in China, inter-provincial electricity market will occupy an important po...
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Accurate short-term wind power forecast (WPF) is essential for power system with high proportion of renewable energy (RE) integration. Teleconnected information of numerical weather predictions (NWPs) can give more ab...
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ISBN:
(纸本)9781665407380
Accurate short-term wind power forecast (WPF) is essential for power system with high proportion of renewable energy (RE) integration. Teleconnected information of numerical weather predictions (NWPs) can give more abundant meteorological information for short-term WPF so as to improve the accuracy. A novel short-term WPF system considering teleconnected NWPs is established in this paper. Firstly, the relationship between teleconnected NWPs and wind power is analyzed quantitatively. Furthermore, a data cleaning (DC) strategy and WPF model structure combining teleconnected NWPs are design. We apply our algorithm based on actual operation data of a Shandong wind farm. Results show that after applying the framework built in this paper, the performance of common used short-term WPF models such as support vector regression (SVR), multi-layer perceptron (MLP) and long short-term memory (LSTM) have been improved effectively.
Continuous-time random disturbances from the renewable generation pose a significant impact on power system dynamic behavior. In evaluating this impact, the disturbances must be considered as continuous-time random pr...
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With the rapid development of ultra-high voltage (UHV) transmission technology in China, insulation diagnosis for power equipment has been applied widely to improve the reliability of the giant power system. Partial d...
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Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-g...
Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.
The ultra-short-term prediction of photovoltaic power system is of great significance to the safe operation of power ***,the daily and annual periodicity of solar radiation brings strong non-stationary to the photovol...
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The ultra-short-term prediction of photovoltaic power system is of great significance to the safe operation of power ***,the daily and annual periodicity of solar radiation brings strong non-stationary to the photovoltaic power series,which makes it difficult to predict the photovoltaic power *** order to overcome the problem that the detailed clear sky model relies on a large number of plant parameters,this paper proposes a prediction algorithm based on a modified clear sky ***,historical data and geographical location of a certain site are utilized to build a modified clear sky model,then the theoretical clear sky power curve fitted by the modified clear sky model are divided by the actual historical power output to get a stabilized time series,finally the method of "online update" is adopted to forecast PV plant power output in the ultrashort-term time horizon（0-4 h）.The test results of a photovoltaic power station in Ningxia show that the proposed model can reduce the prediction error of the 4 th hour to about 3%.
When the large-scale renewable power island is connected to VSC-HVDC transmission system, we should figure out the steady-state operation area of VSC-HVDC converter station. Based on the equivalent model of renewable ...
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In order to solve the problem that the core chips of domestic protective relaying devices generally rely on foreign imported chips and realize the localized replacement of FPGAs, this paper designs the verification pe...
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With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stoch...
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With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stochastic variables obeying specific distributions by neglecting the temporal correlations. Conventional approaches to hedge the negative effects caused by such uncertainties are thus hard to pursue a trade-off between computation efficiency and optimality. As an alternative, the theory of stochastic process can naturally model temporal correlation in closed forms. Attracted by this feature, our research group has been conducting thorough researches in the past decade to introduce stochastic processes within renewable powersystems. This paper summarizes our works from the perspective of both the frequency domain and the time domain, provides the tools for the analysis and control of powersystems under a unified framework of stochastic processes, and discusses the underlying reasons that stochastic process-based approaches can perform better than conventional approaches on both computational efficiency and optimality. These work may shed a new light on the research of analysis, control and operation of renewable power ***, this paper outlooks the theoretic developments of stochastic processes in future’s renewable powersystems.
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