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.

/pdf/power-system-dynamic-state-estimation-motivations-lul365qej6.pdf

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

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

/pdf/micro-synchrophasors-for-distribution-systems-exec74t7c8.pdf

Micro-synchrophasors for distribution systems

Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods.

Dynamic State Estimation of a Synchronous Machine Using PMU Data: A Comparative Study

Power system model quality is key to safe and reliable electricity delivery. A novel method using disturbance data recorded by phasor measurement units (PMUs) is proposed to improve the integrity of dynamic models, which consists of both model validation and model parameter identification. In the model validation step, event playback is applied to simulate the model's response for a rigorous comparison with measured response captured by PMUs. A large mismatch indicates model deficiency. In the parameter identification step, an automatic calibration method using the extended Kalman filter (EKF) techniques and trajectory sensitivity analysis is formulated to save significant effort and time from manual tuning. Case studies are presented to demonstrate that the proposed method has adequate performance for model validation and parameter calibration in terms of accuracy, good convergence speed and robustness with respect to measurement noises. The proposed method is cost-effective and complements traditional equipment testing for improving dynamic model quality.

Generator dynamic model validation and parameter calibration using phasor measurements at the point of connection

In this paper, the empirical observability Gramian calculated around the operating region of a power system is used to quantify the degree of observability of the system states under specific phasor measurement unit (PMU) placement. An optimal PMU placement method for power system dynamic state estimation is further formulated as an optimization problem which maximizes the determinant of the empirical observability Gramian and is efficiently solved by the NOMAD solver, which implements the Mesh Adaptive Direct Search algorithm. The implementation, validation, and the robustness to load fluctuations and contingencies of the proposed method are carefully discussed. The proposed method is tested on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system by performing dynamic state estimation with square-root unscented Kalman filter. The simulation results show that the determined optimal PMU placements by the proposed method can guarantee good observability of the system states, which further leads to smaller estimation errors and larger number of convergent states for dynamic state estimation compared with random PMU placements. Under optimal PMU placements an obvious observability transition can be observed. The proposed method is also validated to be very robust to both load fluctuations and contingencies.

/pdf/optimal-pmu-placement-for-power-system-dynamic-state-3rfod84pr3.pdf

Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian

Phasor measurement has previously been used for sub-system model validation, which enables rigorous comparison of model simulation and recorded dynamics and facilitates identification of problematic model components. Recent work extends the sub-system model validation approach with a focus on how model parameters may be calibrated to match recorded dynamics. In this paper, a calibration method using Extended Kalman Filter (EKF) technique is proposed. This paper presents the formulation as well as case studies to show the validity of the EKF-based parameter calibration method. The proposed calibration method is expected to be a cost-effective means complementary to traditional equipment testing for improving dynamic model quality.

Application of extended Kalman filter techniques for dynamic model parameter calibration

This paper presents the results of simulating and evaluating an under-voltage protection scheme, which takes stalled air-conditioner (a/c) units offline so that the slow voltage recovery phenomena can be prevented in areas heavily loaded with a/c motors during summer peak periods. First, a three-feeder test-bed was used to quantify the effectiveness of the protection scheme and the sensitivity of the under-voltage relay settings. Then, two real system events from the Western US power grid were simulated to evaluate the area-level impact of the protection scheme proposed by Southern California Edison. The study demonstrates that after 75% or more of the stalled a/c units are disconnected, the feeder voltage recovers in a few seconds, much quicker than the tens of seconds that standard thermal relays need to trip the stalled motor offline. The drawback is that after the voltage recovered, it settles at a higher value than prior to the faults because a large proportion of load is shed. Therefore, the coordination among the capacitor bank operation is recommended to suppress the overvoltage caused by tripping of the a/c motor load.

The system impact of air conditioner under-voltage protection schemes

Power system planning extensively uses model simulation to understand the dynamic behaviors and determine the operating limits of a power system. Model quality is key to the safety and reliability of electricity delivery. Planning model benchmarking, or model validation, has been one of the central topics in power engineering studies for years. As model validation aims at obtaining reasonable models to represent dynamic behavior of power system components, it has been essential to validate models against actual measurements. The development of phasor technology provides such measurements and represents a new opportunity for model validation as phasor measurements can capture power system dynamics with high-speed, time-synchronized data. Previously, methods for rigorous comparison of model simulation and recorded dynamics have been developed and applied to quantify model quality of power plants in the Western Electricity Coordinating Council (WECC). These methods can locate model components which need improvement. Recent work continues this effort and focuses on how model parameters may be calibrated to match recorded dynamics after the problematic model components are identified. A calibration method using Extended Kalman Filter technique is being developed. This paper provides an overview of prior work on model validation and presents new development on the calibration method and initial results of model parameter calibration.

Benchmarking of planning models using recorded dynamics

This paper proposes to validate dynamic model of pacific DC intertie with the concept of hybrid simulation by combining simulation with PMU measurements. The playback function available in GE PSLF is adopted for hybrid simulation. It is demonstrated for the first time the feasibility of using playback function on multi-terminal subsystem. Sensitivity studies are also presented as a result of common PMU measurement quality problem, ie, offset noise and time synchronization. Results indicate a good tolerance of PDCI model generally. It is recommended that requirements should apply to phasor measurements in model validation work to ensure better analysis. Key parameters are identified based on impact of value change to model behavior. Two events are employed for preliminary model validation with PMU measurements. Suggestions are made for PDCI model validation work in the future.

Multi-terminal subsystem model validation for pacific DC intertie

this paper proposes to validate dynamic model of Pacific DC Intertie with the concept of hybrid simulation by combing simulation with PMU measurements. The Playback function available in GE PSLF is adopted for hybrid simulation. It is demonstrated for the first time the feasibility of using Playback function on multi-terminal subsystem. Sensitivity studies are also presented as a result of common PMU measurement quality problem, ie, offset noise and time synchronization. Results indicate a good tolerance of PDCI model generally. It is recommended that requirements should apply to phasor measurements in model validation work to ensure better analysis. Key parameters are identified based on impact of value change to model behavior. Two events are employed for preliminary model validation with PMU measurements. Suggestions are made for PDCI model validation work in the future.

http://ieeexplore.ieee.org/iel5/4584435/4595968/04596870.pdf

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