Parameter Estimation of Uncertain Fractional-order Chaotic Power System Using an Improved State Transition Algorithm

Parameter Estimation of Uncertain Fractional-order Chaotic Power System Using an Improved State Transition Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Parameter Estimation of Uncertain Fractional-order Chaotic Power System Using an Improved State Transition Algorithm Chunyu Ai, Shan He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1368372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fractional-order chaotic power system parameter estimation is a multidimensional function optimization problem, and it is a prerequisite for controlling such systems. Aiming at the problem that the parameter estimation of a fractional-order chaotic power system is easily affected by external interference and estimation is difficult, a state transition algorithm based on the reverse learning strategy of lens imaging is proposed, taking a fractional six-order chaotic power system as the study model. First, the tent chaotic mapping to initialize the population, thus increasing population diversity. Second, we optimize parameters \(\gamma\) and \(\delta\) of the basic state transition algorithm to improve its global and single-dimensional search abilities. We use the lens imaging learning strategies to avoid the local optima. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition, particle swarm optimization, genetic and gray wolf algorithms. The simulation results show that the parameters of the fractional sixth-order chaotic power system are identified accurately. even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm. Fractional-order chaotic power system Parameter estimation State transition algorithm Reverse learning strategy of lens imaging Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1368372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":86729185,"identity":"ea26a340-74dd-41f8-8da2-152a2fc2a019","order_by":0,"name":"Chunyu Ai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACfvkHCQck/9TI8bM3HyBOi2RDwsMDlg3HjCV7jiUQp8XgQOLjA5UNzIkGN3IMiHTZgcMJB27uYEuQbMj5eOMNg52cbgMBHYyNbQkHZ56RyeNnOLvZcg5DsrHZAQJamJl5Eg5LsLEVSzb2bpPmYTiQuI2QFjY2/g+H/7AxJ244zPOMOC08PAzAQG4DajnGw0acFgkJoBaJM6BAZjO2nGNAhF/sbzAkf5CoAEal/OOHN95U2MkR1IJqJQ+xUYOkhVQdo2AUjIJRMCIAAFrpSA8hEveWAAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Ai","suffix":""},{"id":86729186,"identity":"44d31411-e7de-4179-8f5a-970809175dc4","order_by":1,"name":"Shan He","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2022-02-17 07:55:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1368372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1368372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18701754,"identity":"694ce9ed-1a3f-453c-85af-1b15844b4fca","added_by":"auto","created_at":"2022-02-28 19:58:56","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":560632,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1368372/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eParameter Estimation of Uncertain Fractional-order Chaotic Power System Using an Improved State Transition Algorithm\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1368372/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fractional-order chaotic power system, Parameter estimation, State transition algorithm, Reverse learning strategy of lens imaging","lastPublishedDoi":"10.21203/rs.3.rs-1368372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1368372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFractional-order chaotic power system parameter estimation is a multidimensional function optimization problem, and it is a prerequisite for controlling such systems. Aiming at the problem that the parameter estimation of a fractional-order chaotic power system is easily affected by external interference and estimation is difficult, a state transition algorithm based on the reverse learning strategy of lens imaging is proposed, taking a fractional six-order chaotic power system as the study model. First, the tent chaotic mapping to initialize the population, thus increasing population diversity. Second, we optimize parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\delta\\)\u003c/span\u003e\u003c/span\u003e of the basic state transition algorithm to improve its global and single-dimensional search abilities. We use the lens imaging learning strategies to avoid the local optima. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition, particle swarm optimization, genetic and gray wolf algorithms. The simulation results show that the parameters of the fractional sixth-order chaotic power system are identified accurately. even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm.\u003c/p\u003e","manuscriptTitle":"Parameter Estimation of Uncertain Fractional-order Chaotic Power System Using an Improved State Transition Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-28 19:58:47","doi":"10.21203/rs.3.rs-1368372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"452d85fd-ae0f-44ac-8551-9629c285f237","owner":[],"postedDate":"February 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-12T16:50:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-28 19:58:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1368372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1368372","identity":"rs-1368372","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}