Article(id=1309639384916386576, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733328000000, receivedDateStr=2024-12-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171961287, onlineDateStr=2026-09-23, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171961287, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171961287, creator=13701087609, updateTime=1790171961287, updator=13701087609, issue=Issue{id=1309639371255542472, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='11', pageStart='1421', pageEnd='1562', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171958030, creator='13701087609', updateTime=1790231751141, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890161824063505, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890161824063506, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639371255542472, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1527, endPage=1536, ext={EN=ArticleExt(id=1309639385109324561, articleId=1309639384916386576, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Optimization method for control parameters of new energy low voltage ride through considering the reactive power and voltage interaction, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The reactive power output of new energy and the recovery rate of grid voltage will affect the voltage characteristics of other nodes in the system. Traditional parameter determination methods for a single station are difficult to ensure the minimum overall voltage deviation of important nodes in the system. To solve the issue, an optimization method for new energy low voltage ride through (LVRT) control parameters considering the reactive power and voltage interaction is proposed in this paper. Firstly, the impact of LVRT control parameters and reactive power interaction on the overall voltage support capability is analyzed. Then, an index for the coupling degree of reactive power and voltage between nodes is proposed, based on this index as well as the degree of voltage drop at the new energy grid connection point, the value range of the reactive power current proportion coefficients of LVRT in different stations are determined. Finally, within the value ranges, with the goal of minimizing the overall voltage deviation of important nodes in the system, a deep reinforcement learning algorithm is used to coordinate and optimize the above parameters. The results of case studies show that the proposed method can improve the overall voltage recovery characteristics of the system compared to traditional methods, and the pre determination of the control parameter value range can enhance the convergence speed of the algorithm reward function.

, authors=Wen Hua¹, Wei Dong¹, Ying Yang¹, Yi Lu¹, Chenyi Zheng², Jianxiong Hu², authorsList=Wen Hua, Wei Dong, Ying Yang, Yi Lu, Chenyi Zheng, Jianxiong Hu, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639385331622674, articleId=1309639384916386576, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=考虑无功电压交互影响的新能源低电压穿越控制参数优化方法, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

新能源无功功率输出及并网点电压恢复速率会影响系统其他节点的电压特性，传统针对单一场站的参数整定方法难以保证系统重要节点的整体电压偏差最小。针对这一问题，文章提出了考虑无功电压交互影响的新能源低电压穿越控制参数优化方法。首先，分析了低电压穿越控制参数及无功交互对整体电压支撑能力的影响；然后，提出了节点无功电压耦合程度指标，并基于该指标及新能源并网点电压跌落程度整定各场站低电压穿越无功电流比例系数的取值范围；最后，在该取值范围内，以系统重要节点整体电压偏差最小为目标，采用深度强化学习算法对多新能源场站的无功电流比例系数进行协调优化。算例分析结果表明，与传统方法相比，文章所提方法能够改善系统整体电压恢复特性，对于控制参数取值范围的预先整定能够提升算法奖励函数的收敛速度。

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华文（1986-），男，硕士，高级工程师，研究方向为电力系统稳定分析与控制。E-mail: 2250799089@qq.com

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华文（1986-），男，硕士，高级工程师，研究方向为电力系统稳定分析与控制。E-mail: 2250799089@qq.com

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