Power system static and dynamic state estimation methods using heterogeneous measurements

Δαρμής, Ορέστης

dc.contributor.author	Δαρμής, Ορέστης
dc.date.accessioned	2025-09-22T09:46:51Z
dc.date.available	2025-09-22T09:46:51Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62516
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.30212
dc.rights	Default License
dc.subject	Hybrid state estimation	en
dc.subject	Phasor measurement unit	en
dc.subject	Forecasting-aided state estimation	en
dc.subject	Wide area monitoring systems	en
dc.subject	Υβριδική εκτίμηση κατάστασης	el
dc.subject	Μονάδες μέτρησης φασιθετών	el
dc.subject	Εκτίμηση κατάστασης με περιορισμούς ισότητας	el
dc.subject	Equality-constrained state estimation	en
dc.subject	Συστήματα εποπτείας ευρείας περιοχής	el
dc.subject	Εκτίμηση κατάστασης υποστηριζόμενη από πρόβλεψη	el
dc.title	Power system static and dynamic state estimation methods using heterogeneous measurements	en
dc.contributor.department	Τομέας Ηλεκτρικής Ισχύος - Εργαστήριο Συστημάτων Ηλεκτρικής Ενέργειας	el
heal.type	doctoralThesis
heal.secondaryTitle	Μέθοδοι στατικής και δυναμικής εκτίμησης κατάστασης συστημάτων ηλεκτρικής ενέργειας με χρήση ετερογενών μετρητικών δεδομένων	el
heal.classification	Συστήματα Ηλεκτρικής Ενέργειας	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-07-02
heal.abstract	Power system state estimation (SE) constitutes an essential function of energy management systems (EMS), enabling operators to maintain a comprehensive awareness of system operating conditions through available field measurements. In the modern transmission sector, two primary measurement technologies are employed, namely the supervisory control and data acquisition (SCADA) system and the wide area monitoring systems (WAMS). The benefits of augmenting conventional SCADA-based SE algorithms with synchrophasor measurements from phasor measurement units (PMUs) – the measurement devices within WAMS – are widely recognized, resulting in numerous methodologies being proposed for hybrid state estimation (HSE), aimed at optimally integrating SCADA and PMU data. Under this premise, this thesis introduces several contributions to the research domain of HSE. Initially, fundamental concepts of static and dynamic SE are elaborated from both mathematical and practical implementation perspectives, followed by an introduction to the principles of HSE. Subsequently, key challenges associated with HSE implementations are identified, accompanied by a comprehensive literature review focusing on novel static and dynamic HSE methods designed to overcome these challenges. Furthermore, a classification of existing methods is proposed based on their scope and underlying mathematical formulations. Subsequently, the main contributions of the thesis are detailed. Firstly, a weighted least squares (WLS)-based static HSE formulation is developed, separately handling SCADA and WAMS measurements. The principal advantages of the proposed method include its modular design and practical applicability, making it particularly suitable for PMU integration into existing SE software through minimal modifications. Moreover, considering the widespread adoption of high-voltage direct current (HVDC) transmission technology, particularly for renewable energy integration and submarine interconnections, modern state estimators must accommodate relevant component models. Accordingly, a model suitable for current source converter (CSC)-HVDC links in static HSE implementations is proposed and validated via numerical simulations involving both SCADA and PMU measurements on the AC side, along with diverse combinations of DC-side measurements. Additionally, the thesis investigates the inclusion of current injection phasors from PMUs in static HSE algorithms, examining how various current measurement configurations – whether flows or injections – influence HSE performance, a topic inadequately addressed in prior literature. Recognizing the increasing complexity and stochastic behavior of contemporary power systems, transitioning toward advanced SE algorithms capable of providing enhanced system visibility and situational awareness becomes imperative. In response, this thesis proposes a hybrid forecasting-aided state estimation (FASE) approach leveraging an extended Kalman filter (EKF) framework. The method supplements existing static state estimators by incorporating additional information derived from the temporal evolution of system states through multi-sensor data fusion, employing a transition model that combines dense, real-time PMU measurements with forecasted state estimates. To address synchronization discrepancies between SCADA and PMU data, a post-processing correction step based on the modified Bryson-Frazier fixed-interval smoothing algorithm is implemented. In the final two chapters, algorithms dedicated to detecting and suppressing bad data within the context of the proposed HSE approaches are formulated. Additionally, practical aspects of the research are demonstrated using a laboratory-scale experimental setup that integrates both commercial and low-cost PMUs with a digital real-time power system simulator, thereby enabling comprehensive testing and validation of synchrophasor-based monitoring and control algorithms.	en
heal.advisorName	Κορρές, Γεώργιος
heal.committeeMemberName	Κορρές, Γεώργιος
heal.committeeMemberName	Παπαθανασίου, Σταύρος
heal.committeeMemberName	Γεωργιλάκης, Παύλος
heal.committeeMemberName	Δημέας, Άρης
heal.committeeMemberName	Προυσαλίδης, Ιωάννης
heal.committeeMemberName	Κανέλλος, Φώτιος
heal.committeeMemberName	Κόντης, Ελευθέριος
heal.academicPublisher	Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	245
heal.fullTextAvailability	false