Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community

Dynamic State Estimation of Microgrid With Imperfect Data Communication

Dynamic state estimation of power systems is essential for wide area control purposes. In this thesis, we present the results of dynamic state estimation for a grid-connected microgrid including two synchronous generators and three loads. The Unscented Kalman filter (UKF) and the Extended Kalman filter (EKF) are implemented using a classical generator model connected to a Thevenin equivalent of the remainder of the microgrid. The model is used to estimate the six states variables of the generator; namely, rotor angle, speed variant, d- and q- axis transient voltages, d-axis damper flux, and q-axis second damper flux. Both real power and reactive power are used as measurements in our state estimation algorithm. The estimation results are compared with the true values to demonstrate the accuracy of the state estimator. In addition to data loss or delay, sensor measurements may include outliers that distort state estimation. We utilized the Generalized Maximum Likelihood-extended Kalman filter (GM-EKF), as a robust estimator, which exhibits good tracking capabilities suppressing the effects of bad data (outliers). We also used two methods of state estimation on UKF to deal with bad data. Simulation results obtained from the UKFs are compared with those of GM-EKF. We present simulation results at a high frequency of 1 kHz of state estimation for different scenarios that include normal operation, fault at Point of Common Coupling (PCC), loss of generator, and loss of load. We also developed a scheme to use delayed data in Kalman filter estimation and used it to simulate the effect of data loss and/or delay in the communication system of the microgrid. For the same scenarios, we also present simulation results at 50 Hz, which is compatible with Phasor Measurement Units (PMU), including bad data as well as data loss or delay. Our results demonstrate that while both filters successfully detect bad data, the UKF methods provide better estimates than those of the GM-EKF

Power system dynamic state estimation: motivations, definitions, methodologies, and future work

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community

A Consensus Based Decentralized State Estimation for Power Distribution Networks

This thesis presents a new Decentralized State Estimation algorithm using agents directed mainly to distribution power systems. This new algorithm solves problems that occur when one tries to estimate the state of the distribution power systems. By various reasons such as high levels of quality of service, automation capabilities and comparatively less size, those problems do not occur so frequently on the transmission systems. A consensus based static state estimation strategy for radial power distribution systems is proposed in this research. This thesis concentrates on the balanced systems.;There are buses acting as agents using which we can evaluate the local estimates of the entire system. Therefore each measurement model reduces to an under determined nonlinear system and in radial distribution systems, the state elements associated with an agent may overlap with neighboring agents. We propose a state estimation strategy, which effectively integrates the principles of local consensus and least squares technique and finally provides a decentralized solution to the radial power distribution grid. At the end of the thesis, we present the results of the application of the developed approach to a network based on a modified IEEE 13 bus test system and IEEE 33 bus Test System. The states of these systems are first estimated through centralized approach using least squares technique to compare with the proposed algorithm

A Novel Technique to Detect False Data Injection Attacks on Phasor Measurement Units

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.Peer reviewe