{"title":"A New Model-Free Adaptive Integral Sliding Mode Control for Interconnected Power Systems Load Frequency Control","authors":"Ghazally Mustafa, Haoping Wang, M. D. Masum","doi":"10.1002/rnc.7756","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The paper proposes a novel model-free adaptive robust controller for load frequency control in multi-area interconnected power systems. The controller combines model-free control based on a nonlinear disturbance observer (NDOB) and adaptive integral sliding mode control. The main aim of the controller is to maintain the power system frequency close to the nominal value and achieve a balanced power exchange between tie-lines, considering the system's nonlinearities and disturbances. The proposed controller comprises four components. First, model-free intelligent PID control is implemented to overcome the complexity of the current controller, introduce the required dynamics, and reduce higher-order output derivatives. Second, a nonlinear disturbance observer is utilized to estimate the system dynamics considering uncertainties and load fluctuations. Third, fast convergence is achieved by employing an integral sliding surface. Finally, an adaptation gain dynamic is used to achieve high accuracy. The advantage of the proposed model-free adaptive robust controller lies in its simple structure and ease of regulation. The closed-loop system's stability and finite-time convergence are examined using Lyapunov stability theory. A comparison with recently published papers is conducted to validate the proposed controller's effectiveness. Additionally, robustness testing of the proposed method is performed in different scenarios.</p>\n </div>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"35 5","pages":"1792-1808"},"PeriodicalIF":3.2000,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7756","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The paper proposes a novel model-free adaptive robust controller for load frequency control in multi-area interconnected power systems. The controller combines model-free control based on a nonlinear disturbance observer (NDOB) and adaptive integral sliding mode control. The main aim of the controller is to maintain the power system frequency close to the nominal value and achieve a balanced power exchange between tie-lines, considering the system's nonlinearities and disturbances. The proposed controller comprises four components. First, model-free intelligent PID control is implemented to overcome the complexity of the current controller, introduce the required dynamics, and reduce higher-order output derivatives. Second, a nonlinear disturbance observer is utilized to estimate the system dynamics considering uncertainties and load fluctuations. Third, fast convergence is achieved by employing an integral sliding surface. Finally, an adaptation gain dynamic is used to achieve high accuracy. The advantage of the proposed model-free adaptive robust controller lies in its simple structure and ease of regulation. The closed-loop system's stability and finite-time convergence are examined using Lyapunov stability theory. A comparison with recently published papers is conducted to validate the proposed controller's effectiveness. Additionally, robustness testing of the proposed method is performed in different scenarios.
期刊介绍:
Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.