{"title":"Frequency handling by reinforcement of predictive 2DoF-MPC and state observer LADRC for smart power system","authors":"Muhammad Majid Gulzar","doi":"10.1016/j.isatra.2025.05.046","DOIUrl":null,"url":null,"abstract":"<div><div>The preservation of stability is essential for the efficient and reliable functioning of the electrical transmission<span><span> system. Frequency oscillations are prevalent in interconnected power systems<span> (IPS) and may lead to instability; therefore, it is crucial to monitor and examine them meticulously. Effective frequency management is essential for regulating frequency output in an interconnected smart power system (ISPS) that includes </span></span>renewable energy<span><span> sources (RESs), redox flow batteries (RFBs) and static synchronous series compensators (SSSCs). In view of the challenge presented, this research introduces an efficient control architecture that utilizes a 2 degree of freedom-based model predictive controller (2DoF-MPC) to enhance </span>system performance<span>. Additionally, it integrates a linear active disturbance rejection control<span> (LADRC) to employ a state observer alongside the evolving frequency management. The convergence of the predictive and state observer frameworks results in a robust 2DoF-MPC-LADRC to manage frequency disturbances and uncertainties in the power system. The suggested technique is thoroughly validated across several parameters for ISPS, instilling confidence in its capacity to attain minimal frequency variation in multiple scenarios. The performance of the proposed controller design shows that the frequency performance in area 1 and area 2 settles in 5.085 sec and 3.965 sec, when the load changes by 3 %, and it settles in 4.655 sec and 4.050 sec, respectively, when the load changes by 5 %.</span></span></span></span></div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"165 ","pages":"Pages 128-142"},"PeriodicalIF":6.5000,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019057825002800","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The preservation of stability is essential for the efficient and reliable functioning of the electrical transmission system. Frequency oscillations are prevalent in interconnected power systems (IPS) and may lead to instability; therefore, it is crucial to monitor and examine them meticulously. Effective frequency management is essential for regulating frequency output in an interconnected smart power system (ISPS) that includes renewable energy sources (RESs), redox flow batteries (RFBs) and static synchronous series compensators (SSSCs). In view of the challenge presented, this research introduces an efficient control architecture that utilizes a 2 degree of freedom-based model predictive controller (2DoF-MPC) to enhance system performance. Additionally, it integrates a linear active disturbance rejection control (LADRC) to employ a state observer alongside the evolving frequency management. The convergence of the predictive and state observer frameworks results in a robust 2DoF-MPC-LADRC to manage frequency disturbances and uncertainties in the power system. The suggested technique is thoroughly validated across several parameters for ISPS, instilling confidence in its capacity to attain minimal frequency variation in multiple scenarios. The performance of the proposed controller design shows that the frequency performance in area 1 and area 2 settles in 5.085 sec and 3.965 sec, when the load changes by 3 %, and it settles in 4.655 sec and 4.050 sec, respectively, when the load changes by 5 %.
期刊介绍:
ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.