4篇
您的检索式：作者名="Changsen Feng"
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| 1 | Data-driven Probabilistic Static Security Assessment for Power System Operation Using High-order Moments显示文摘In this letter,a new formulation of Lebesgue integration is used to evaluate the probabilistic static security of power system operation with uncertain renewable energy generation.The risk of power flow solutions violating any pre-defined operation security limits is obtained by integrating a semialgebraic set composed of polynomials.With the high-order moments of historical data of renewable energy generation,the integration is reformulated as a generalized moment problem which is then relaxed to a semi-definite program(SDP).Finally,the effectiveness of the proposed method is verified by numerical examples. | Guanzhong Wang Zhiyi Li Feng Zhang Ping Ju Hao Wu Changsen Feng | 2021 | Journal of Modern Power Systems and Clean Energy2021，9，5： | 1 |
| 2 | Theoretical and experimental investigations into crack detection with BOTDA distributed fiber pptic sensors 显示文摘 | Feng Xin Zhou Jing Sun Changsen | 2013 | Journal of Engineering Mechanics2013，139，12： | 1 |
| 3 | Research on Evaluation of Multi-Timescale Flexibility and Energy Storage Deployment for the High-Penetration Renewable Energy of Power Systems显示文摘With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is uncertain,and thus flexible regulation for the power balance is highly demanded.Considering the multi-timescale output characteristics of renewable energy,a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper.Through the multi-timescale decomposition algorithm on the basis of mathematical morphology,the multi-timescale components are separated to determine the flexibility requirements on different timescales.Based on the obtained flexibility requirements,a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation.This optimization model can allocate corresponding flexibility resources according to the economy,flexibility and reliability requirements of the power system,and achieve the trade-off between them.Finally,case studies demonstrate the effectiveness of our model and method. | Hongliang Wang Jiahua Hu Danhuang Dong Cenfeng Wang Feixia Tang Yizheng Wang Changsen Feng | 2023 | Computer Modeling in Engineering & Sciences2023，，2： | 0 |
| 4 | A Fault Risk Warning Method of Integrated Energy Systems Based on RelieF-Softmax Algorithm显示文摘The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multienergy complementary ways.Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network,a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper.The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm,and thereby achieved a hierarchical and non-repeated sampling.Then,the improved RelieF algorithm is used to identify the feature vectors,calculate the feature weights,and select the preferred feature subset according to the initially set threshold.In addition,a correlation coefficient method is applied to reduce the feature subset,and further eliminate the redundant feature vectors to obtain the optimal feature subset.Finally,the softmax classifier is used to obtain the early warnings of the integrated energy system.Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper. | Qidai Lin Ying Gong Yizhi Shi Changsen Feng Youbing Zhang | 2022 | Computer Modeling in Engineering & Sciences2022，，9： | 0 |
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