In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in ord...In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in order to avoid directly solving a large-scale nonlinear optimization problem.We select photovoltaic inverters as agents to adjust system voltage in a distribution network,taking the reactive power output of inverters as action variables.An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment.OPENDSS is used to output system node voltage and network loss.This method realizes the goal of optimal VVC in distribution network.The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm.Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.展开更多
基金supported by the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.under Grant B311JY21000A。
文摘In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in order to avoid directly solving a large-scale nonlinear optimization problem.We select photovoltaic inverters as agents to adjust system voltage in a distribution network,taking the reactive power output of inverters as action variables.An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment.OPENDSS is used to output system node voltage and network loss.This method realizes the goal of optimal VVC in distribution network.The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm.Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.
文摘目的:探讨3D成像技术在乳腺癌保乳术中的术前评估应用价值。方法:回顾性分析2017年4月至2019年1月武汉科技大学附属孝感医院38例在3D成像技术辅助下行乳腺癌保乳术患者的临床资料。所有患者术前均行3.0 T乳腺MRI检查,通过医学数字成像及通信(digital imaging and communication of medicine,DICOM)数据立体建模,构建3D成像技术重建虚拟图像。比较术前预计切除组织体积(predicted resected tissue volume,PRTV)及术中实际切除组织体积(actual resection tissue volume,ARTV)差异性及一致性,并行保乳术后组织标本切缘及乳房美学评价。结果:3D成像技术能准确反映乳腺、肿瘤、腺体及血管等解剖结构及其三维毗邻关系。术中所见与术前3D图像吻合度为97.4%(37/38)。术前PRTV为(61.7±20.1)m L,术中ARTV为(65.1±20.7)m L,两者比较差异无统计学意义(P>0.05),具有较好的一致性(P<0.01)。术后补充二次手术1例,发生率2.6%(1/38)。术后乳房外形满意度100%(38/38)。结论:3D成像技术可清晰地显示乳腺肿瘤与周围组织的解剖关系,准确评估保乳术切除体积,指导手术切除。
