Article(id=1268934130952737299, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1268980803322155008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1006-4613.2026.02.007, pmid=null, cstr=null, oa=null, hot=1, price=null, onlineType=2, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1767024000000, revisedDateStr=2025-12-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1780467072474, onlineDateStr=2026-06-03, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=null, onlineIssueDateStr=null, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=1780467072474, onlineFirstDateStr=2026-06-03, sourceXml=null, magXml=null, createTime=1780467072474, creator=13701087609, updateTime=1781587231442, updator=13701087609, issue=Issue{id=1268980803322155008, tenantId=1146029695717560320, journalId=1268266715432370238, year='2026', volume='', issue='2', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=null, articleOrder=1, issueType=1, specialIssue=null, createTime=1780478200020, creator=13701087609, updateTime=1780478456774, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1268981880268427353, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1268980803322155008, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1268981880268427354, tenantId=1146029695717560320, journalId=1268266715432370238, issueId=1268980803322155008, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=60, endPage=67, ext={EN=ArticleExt(id=1268934131347001878, articleId=1268934130952737299, tenantId=1146029695717560320, journalId=1268266715432370238, language=EN, title=Prediction of Primary Frequency Modulation Capability of Thermal Power Units Based on KOA-GRU Network, columnId=1268934131263115797, journalTitle=Angang Technology, columnName=Research and Development, runingTitle=null, highlight=null, articleAbstract=
To improve the prediction accuracy of the primary frequency modulation capability of thermal power units and assist in ensuring grid frequency stability and safe operation of the power system, a primary frequency modulation capability prediction method is proposed that combines the Kepler Optimization Algorithm (KOA) with the Gated Recurrent Unit (GRU) network. Taking the actual operation data of primary frequency modulation of a 350 MW coal-fired thermal power unit as the sample, key characteristic variables were extracted through correlation analysis. The hyperparameters of GRU network model were optimized using KOA to construct a KOA-GRU prediction model, which was further compared with the Long Short-term Memory (LSTM) network model, the Particle Swarm Optimization (PSO) network model, the original GRU network model, and PSO-GRU network model. The results showed that the fitness value of the KOA-GRU network model stabilized at 0.127 after 7 iterations, indicating better convergence performance than the other four models. Meanwhile, the proposed model exhibited superior prediction performance under various evaluation metrics, with the Root Mean Square Error (RMSE) reaching 0.148 MW and the Mean Absolute Error (MAE) dropping to 0.092 MW, which demonstrated high prediction accuracy.
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为提升火电机组一次调频能力的预测精度，辅助保障电网频率稳定及电力系统安全运行，提出一种结合开普勒优化算法（Kepler Optimization Algorithm, KOA）与门控循环单元（Gated Recurrent Unit, GRU）网络的一次调频能力预测方法。以某350 MW燃煤火电机组一次调频的实际运行数据为样本，通过相关性分析挖掘关键特征变量；采用开普勒优化算法对GRU网络模型的超参数进行优化，构建KOA-GRU预测模型，并将该模型与长短期记忆（LSTM）网络模型、粒子群优化的长短期记忆（PSO-GRU）网络模型、GRU网络模型及粒子群优化的GRU网络模型进行对比。结果表明，KOA-GRU网络模型的适应度值在经过7次迭代后稳定在0.127，收敛性优于其他四种模型；同时，该模型在不同评估指标下均表现出更优的预测效果，均方根误差（RMSE）为0.148 MW，平均绝对误差（MAE）为0.092 MW，具有较高的预测精度。
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喻书非，硕士生，上海电力大学清洁能源技术专业。E-mail: 2325306920@qq.com
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喻书非，硕士生，上海电力大学清洁能源技术专业。E-mail: 2325306920@qq.com
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