Orestis Darmis

This paper presents a multi-stage forecasting-aided state estimation (FASE) framework with equality constraints, utilizing the extended Kalman filter (EKF) to independently process supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) data. The conventional SCADA-based state estimator remains unaltered and is augmented with synchronized PMU data and a priori state information derived from the EKF. An estimation fusion technique leveraging real-time PMU measurements is developed to enhance the accuracy of the FASE forecasting model. To address measurement asynchronization and random delays, the modified Bryson-Frazier (MBF) fixed-interval smoothing algorithm is applied, combining current state information with a series of prior FASE solutions to optimally infer the system states. Extensive numerical simulations on the IEEE 14-, 118-, and 300 -bus benchmark systems demonstrate the efficacy and practical applicability of the proposed method.

This paper proposes an equality-constrained hybrid state estimation algorithm that processes data from the supervisory control and data acquisition (SCADA) and wide area monitoring systems. The devised method is founded upon the widely accepted weighted least squares approach and is viable in single- and multi-stage architectures. The main advantage of this method is found in its non-intrusive implementation, that is, keeping the core functions of conventional state estimation software intact in the energy management system, while improving the quality of the conventional SCADA-based estimators. Following a detailed description of the derivation of the proposed approach and its alternative formulations, numerical simulations on the IEEE 14-, 118-, and 300-bus benchmark systems are conducted to assess its performance. The proposed estimation scheme is found to outperform similar methods found in recent literature with respect to various performance metrics.

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 the wide area monitoring systems (WAMS). The multiple advantages of augmenting conventional SCADA-based SE algorithms with synchrophasor measurements from 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 paper provides a thorough literature review of novel HSE methods in transmission systems and proposes a classification based on the scope and mathematical modeling of each method. Following a brief introduction to the concept of SE based on WAMS and SCADA measurements, an insight into the main challenges emerging in HSE implementations is provided. Various HSE methods which overcome these challenges are reviewed, for both static and dynamic SE implementations. In conclusion, the research trends in the area of HSE are summarized, and the main findings of this literature review are discussed.

This letter proposes an equality-constrained weighted least squares (WLS) state estimation model for networks containing LCC-HVDC links. Voltage and current phasor measurements for the AC network are provided by phasor measurement units (PMUs) and DC voltage and current measurements for the DC transmission lines are acquired by appropriate DC sensors. AC/DC coupling equations and zero injections are treated as equality constraints. Simulation results for a 6-bus hybrid AC/DC network are shown to validate the proposed model and algorithm.