Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

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Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations

In order to reduce the dimension of wind farm model and improve the simulation efficiency under the premise of ensuring the accuracy, this article proposes a dynamic equivalent method for permanent magnet synchronous generator (PMSG) based wind farm cluster utilizing the adaptive extended Kalman filters. Combining the dynamic model of PMSG, the equivalent wind turbine parameters that need to be identified are incorporated into the extended state variables, and an online parameters identification method based on extended Kalman filter (EKF) theory is put forward. To eliminate the influence of random noise on the accuracy of identification, the adaptive technology is introduced into EKF to estimate the process and measurement noise covariance in real time through the Sage–Husa estimator. Thus, a wind farm cluster equivalent model is obtained. Finally, the effectiveness and robustness of the proposed method are verified by time-domain simulation and frequency-domain simulation in IEEE 39-bus system.

Adaptive Extended Kalman Filter Based Dynamic Equivalent Method of PMSG Wind Farm Cluster

This article presents a novel method for identification of synchronous generator parameters that is based on sudden short-circuit test data and a novel metaheuristic algorithm, called the adaptive black widow optimization algorithm. Unlike traditional methods defined by IEEE and International Electrotechnical Commission (IEC) standards, which rely on the armature current oscillogram, the method proposed in this article uses the field current waveform during the short-circuit test. Moreover, the standard graphical method for extraction of the generator parameters is replaced by an effective metaheuristic algorithm. The proposed algorithm tends to minimize the normalized sum of squared errors (NSSE) between simulation and experimental results. The applicability and accuracy of the proposed optimization technique are verified using experimentally obtained results from a 100-MVA synchronous generator at the Bajina Basta hydropower plant.

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Field Current Waveform-Based Method for Estimation of Synchronous Generator Parameters Using Adaptive Black Widow Optimization Algorithm

Parameter estimation of fully regulated synchronous generators using Unscented Kalman Filters

This paper presents a parameter estimation technique for a variable-speed wind turbine with permanent magnet synchronous generator and back-to-back voltage source converter. The proposed technique applies the cubature Kalman filter for the joint estimation of the system dynamic state and a modified set of parameters from which the original model parameters can be algebraically recovered. To the authors knowledge, this work is the first attempt to apply such an innovative technique to a PMSM detailed estimation model including the control parameters of the voltage source converter.

Parameter Estimation of Wind Turbines With PMSM Using Cubature Kalman Filters

This paper presents a procedure for the derivation of an equivalent thermal network-based model applied to three-core armored submarine cables. The heat losses of the different metallic cable parts are represented as a function of the corresponding temperatures and the conductor current, using a curve-fitting technique. The model was applied to two cables with different filler designs, supposed to be equipped with distributed temperature sensing (DTS) and the optical fiber location in the equivalent circuit was adjusted so that the conductor temperature could be accurately estimated using the sensor measurements. The accuracy of the proposed model was tested for both stationary and dynamic loading conditions, with the corresponding simulations carried out using a hybrid 2D-thermal/3D-electromagnetic model and the finite element method for the numerical resolution. Mean relative errors between 1 and 3% were obtained using an actual current profile. The presented procedure can be used by cable manufacturers or by utilities to properly evaluate the cable thermal situation.

A Thermal Model for Three-Core Armored Submarine Cables Based on Distributed Temperature Sensing

This work presents a novel methodology for systematically processing the time series that report the number of positive, recovered and deceased cases from a viral epidemic, such as Covid-19. The main objective is to unveil the evolution of the number of real infected people, and consequently to predict the peak of the epidemic and subsequent evolution. For this purpose, an original nonlinear model relating the raw data with the time-varying geometric ratio of infected people is elaborated, and a Kalman Filter is used to estimate the involved state variables. A hypothetical simulated case is used to show the adequacy and limitations of the proposed method. Then, several countries, including China, South Korea, Italy, Spain, UK and the USA, are tested to illustrate its behavior when real-life data are processed. The results obtained clearly show the beneficial effect of the social distancing measures adopted worldwide, confirming that the Covid-19 epidemic peak is left behind in those countries where the outbreak started earlier, and anticipating when the peak will take place in the remaining countries.

https://medrxiv.org/content/10.1101/2020.05.11.20098087v1.full.pdf

M.A. González-Cagigal

Papers

Parameter estimation of fully regulated synchronous generators using Unscented Kalman Filters

Parameter Estimation of Wind Turbines With PMSM Using Cubature Kalman Filters

A Thermal Model for Three-Core Armored Submarine Cables Based on Distributed Temperature Sensing

Monitoring and Tracking the Evolution of a Viral Epidemic Through Nonlinear Kalman Filtering: Application to the Covid-19 Case

Application of nonlinear Kalman filters to the identification of customer phase connection in distribution grids