State estimation is a key Energy Management System (EMS) function, responsible for estimating the state of the power system. Since state estimation is computationally expensive, it is not easy to execute it repetitively at short intervals, which is a requirement for real time monitoring and control. Hence in order to obtain a computationally inexpensive real time update of the state vector, tracking state estimation algorithms have been proposed and discussed in various research papers available in the literature. Tracking state estimation provides a fast real time update on the state of the power system with out any physical modeling of the time varying nature of the system. Dynamic state estimation models the time varying nature of the system, which allows it to predict the state vector in advance. Hence proves to be a major advantage in security analysis and other control functions. Various techniques for tracking and dynamic state estimation are available in the literature. This paper presents a bird's view on different methodologies and developments in both of these techniques based on our comprehensive survey of the available literature.

http://web2py.iiit.ac.in/publications/default/download/inproceedings.pdf.a3706aa88baaa01a.707772207379732e706466.pdf

Power system tracking and dynamic state estimation

Dynamic State Estimation (DSE) techniques have the ability to foresee potential contingencies and security risks. Any improvement in its ability to estimate would definitely go a long way in reducing the security risks in the modern power system. One important factor affecting the quality of estimation is the measurement accuracy. Phasor Measurement Unit (PMU) has revolutionized the way state estimation is performed. The unique ability to measure the voltage and current phasors (magnitude and phase angle) with very high accuracy makes PMU extremely useful in modern Energy Management Systems (EMS). Due to the high price, technology level and communication capacity, the PMU can't be equipped in all buses in the system nowadays. Therefore, this paper brings forward an improved method on dynamic state estimation that combines some buses measurements from PMU with measurements from SCADA. As Relevance Vector Machine (RVM) has a better performance on the regression, the state estimation algorithm is based on RVM in this article. Since the input data dimension is too large, pre-processing of data is needed. Autoencoder Network (Autoencoder) can be used for data dimensionality reduction. So this paper uses Autoencoder to reduce the data dimensionality, and then uses RVM to estimate the state of power system. Streszczenie. W artykule przedstawiono metode estymacji stanow dynamicznych w sieci elektroenergetycznej, wykorzystujący pomiary fazy i amplitudy napiecia i prądu oraz pomiary SCADA z poszczegolnych punktow sieci. W algorytmie wykorzystano maszyne wektorow RVM. Ze wzgledu na zbyt duzy wymiar danych wejściowych, zastosowano pre-processing z wykorzystaniem sieci neuronowej auto-encoderowej. (Estymacja stanow dynamicznych w sieci elektroenergetycznej).

A Dynamic State Estimation Method Based on Mixed Measurements for Power System

To identify the incidence of power flow transferring and to evaluate the power flow distribution in power system after the transmission line was cut, the concept of relative cluster of the transmission line (RCTL) is proposed in this paper, and a method to calculate relative factor of RCTL based on shortest paths is deduced Taking CEPRI 36-bus system as the case, the power flow transferring is simulated and the simulation results show that the algorithm is accurate and effective

A new RCTL algorithm based on shortest paths

GPS based Phasor Measurement Units (PMUs) are prevailing in power systems. This paper introduces a dynamic state estimation method for power systems. As installation of PMUs at all buses is impractical, this method employs SCADA data that covers the whole system and PMU data that covers part of the system. The PMU samples are interpolated into the SCADA data to increase the measuring density. An iterative state estimation algorithm is established with the coordination of PMU and SCADA measurements. Example shows that the method can increase the estimating precision and provide more information on power system dynamics.

A dynamic state estimation method for power systems

In this paper the tracking of the time varying power system state is analysed. A tracking state estimator is a digital feedback loop (a computer algorithm) which uses real time measurements of voltage magnitudes, real power flows and reactive power flows to track the static state as it varies during the daily load cycle. Two different tracking algorithms were tested: suitable to Snapshot scan and Sequential scan mode of measurement system. Results are presented for AEP-30 bus power system

S. Chohan

Papers

On line tracking state estimation in power system