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System Strength Assessment Based on Multi-task Learning
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Increase in permeability of renewable energy sources(RESs)leads to the prominent problem of voltage stability in power system,so it is urgent to have a system strength eval-uation method with both accuracy and practicability to control its access scale within a reasonable range.Therefore,a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method.First,calculation of critical short circuit ratio(CSCR)is set as the direc-tion of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator.Then,the construction process of CSCR dataset is proposed,and a batch simulation program of samples is developed accordingly,which provides a data basis for subsequent research.Finally,a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point,which significantly reduces evaluation error caused by weak links.Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system,which provides strong technical support for real-time monitoring of system strength.
Critical short circuit ratiohybrid intelligence enhancementmulti-task learningsystem strength
Baoluo Li、Shiyun Xu、Huadong Sun、Zonghan Li、Lin Yu
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Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education,Shandong University,Jinan 250014,China
China Electric Power Research Institute,Beijing 100192,China
维普
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