PhD thesis defense to be held on July 2, 2025, at 13:00 (Via internet)
Thesis title: Power system static and dynamic state estimation methods using heterogeneous measurements
Abstract: Power system state estimation (SE) is an essential tool of energy management systems (EMS), providing power system operators with an overall grasp of the actual power system operating conditions, and aiding them in sustaining reliable and secure operation of the grid. In modern transmission sectors, two main measurement systems are deployed, namely the supervisory control and data acquisition (SCADA) and wide area monitoring systems (WAMS). The multiple advantages of augmenting conventional SCADA-based SE algorithms with synchrophasor measurements from phasor measurement units (PMUs) – the measurement system of WAMS – are already well-established; thus, an abundance of different methodologies has been reported in the field of hybrid SE (HSE). Under this premise, this thesis aims to introduce several contributions to the field of HSE.
First, the basic concepts of static and dynamic SE, both in terms of mathematical formulation and practical implementation, are elaborated, followed by an introduction to the concept of HSE based on WAMS and SCADA measurements. Insight into the main challenges emerging in HSE implementations is provided, and a thorough literature review of novel HSE methods which overcome these challenges is conducted, for both static and dynamic SE implementations. A classification based on the scope and mathematical foundation of each method is also proposed.
Subsequently, the main contributions of this thesis are elaborated. The first aspect pertains to the formulation of a weighted least squares (WLS) static HSE that handles SCADA and WAMS measurements separately. The main advantages of the proposed method lie in its modularity and applicability, being ideal for enhancing the existing SE software in the EMS with PMU measurements with minor modifications. The inclusion of current injection phasors obtained from PMUs in static HSE algorithms and the impact of different current measurement configurations – in the form of flows or injections – on HSE performance are also investigated, as they have not been adequately addressed in literature. Furthermore, considering the extensive integration of high voltage direct current (HVDC) transmission technologies, especially in connecting renewable energy sources and deploying submarine interconnections, contemporary power system state estimators need to integrate appropriate models for such components. Thus, a model for current source converter (CSC)-HVDC links suitable for static HSE implementations is proposed and is validated using numerical simulations considering SCADA and PMU AC measurements, along with various combinations of DC-side measurements.
Given the increased complexity and stochasticity of the modern power system, a transition to more advanced SE algorithms offering extended system visibility and situational awareness is not only expected, but rather necessary. In this context, a hybrid forecasting-aided state estimation (FASE) method is introduced, based on the extended Kalman filter (EKF) statistical framework, for readily enhancing existing static state estimators. Additional information deriving from the temporal evolution of system states is included in the SE solution, using a multi-sensor data fusion approach, forming a transition model based on a combination of dense real-time PMU measurements and forecasted state estimates. A post-processing correction stage, based on the modified Bryson-Frazier fixed-interval smoothing algorithm, is utilized to address the effects of asynchronization between SCADA and PMU measurements.
In the two final Chapters of this dissertation, bad data detection and suppression algorithms are formulated within the context of the proposed HSE methods, and practical aspects of this research are demonstrated through a laboratory-scale setup, which integrates both commercial and low-cost PMUs with a digital real-time power system simulator, facilitating the comprehensive testing and evaluation of synchrophasor-based monitoring algorithms.
Supervisor: Professor George Korres
PhD Student: Orestis Darmis