{"id":{"repo_id":"liverpool-jm","oai_identifier":"oai:researchonline.ljmu.ac.uk:8524"},"canonical_url":"https://search.dev.ndltd.org/etd/liverpool-jm/oai:researchonline.ljmu.ac.uk:8524","repository":{"repo_id":"liverpool-jm","name":"Liverpool Jon Moores University","base_url":"https://researchonline.ljmu.ac.uk/cgi/oai2"},"display":{"title":"FAULT DETECTION AND ISOLATION FOR WIND TURBINE DYNAMIC SYSTEMS","abstract":"This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier.","abstract_html":"This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier.","abstract_has_math":false,"creators":["Liu, Y"],"institution":"Liverpool John Moores University","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Yu, D"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-04","date_published":"2018-04","updated_at":"2026-07-24T06:30:22Z","subjects":["TA Engineering (General). Civil engineering (General)","TJ Mechanical engineering and machinery","TK Electrical engineering. Electronics. Nuclear engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.24377/LJMU.t.00008524","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yu, D"]},{"key":"dc:creator","label":"Author","values":["Liu, Y"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-04-19"]},{"key":"dc:date.issued","label":"Date","values":["2018-04"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Electronics and Electrical Engineering (merged with Engineering 10 Aug 20)"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Liverpool John Moores University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://researchonline.ljmu.ac.uk/id/eprint/8524/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["TA Engineering (General). Civil engineering (General)","TJ Mechanical engineering and machinery","TK Electrical engineering. Electronics. Nuclear engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.24377/LJMU.t.00008524"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://researchonline.ljmu.ac.uk/id/eprint/8524/1/2018YushengPhD.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["FAULT DETECTION AND ISOLATION FOR WIND TURBINE DYNAMIC SYSTEMS"]}]}],"canonical_facts":{"dc:contributor":["Yu, D"],"dc:creator":["Liu, Y"],"dc:date":["2018-04-19"],"dc:date.issued":["2018-04"],"dc:description.abstract":["This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier."],"dc:format":["text"],"dc:identifier.doi":["10.24377/LJMU.t.00008524"],"dc:identifier.uri":["https://researchonline.ljmu.ac.uk/id/eprint/8524/1/2018YushengPhD.pdf"],"dc:publisher.department":["Electronics and Electrical Engineering (merged with Engineering 10 Aug 20)"],"dc:publisher.institution":["Liverpool John Moores University"],"dc:relation.isreferencedby":["https://researchonline.ljmu.ac.uk/id/eprint/8524/"],"dc:subject":["TA Engineering (General). Civil engineering (General)","TJ Mechanical engineering and machinery","TK Electrical engineering. Electronics. Nuclear engineering"],"dc:title":["FAULT DETECTION AND ISOLATION FOR WIND TURBINE DYNAMIC SYSTEMS"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T06:30:22Z"}