Shaobu Wang

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal “basis” of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.

Measurement-based coherency identification and aggregation for power systems

Model reduction techniques are often applied to large-scale complex power systems to increase simulation performance. The bottleneck of existing methods to get a high reduction ratio lies in: (1) Coherency identification is static and conservative. Some coherent generators are not detected when system topology or operating point changes. (2) Solitary generators outside any coherency group are not aggregated regardless of their importance. To overcome the first problem, a measurement-based online coherency identification method was used in this paper. By analyzing post-fault trajectories measured by phasor measurement units (PMUs), coherency generators were identified through principal component analysis. The method can track conherency groups with time-varying system topology and operating points. To address the second problem, sensitivity analysis was employed into model reduction in this paper. The sensitivity of tie-line power flows against injected active power of external system generators was derived. Those generators having minimal impacts on tie-line power flows were replaced with negative impedances. Case studies show that the proposed method can handle well these solitary generators and the reduction ratio can be enhanced. Future work will include generalization of the sensitivity method.

Capturing real-time power system dynamics: Opportunities and challenges

The power grid evolves towards a new mix of generation and consumption that introduces new dynamic and stochastic behaviors. These emerging grid behaviors would invalidate the steady-state assumption in todays state estimation - an essential function for real-time power grid operation. This paper examines this steady-state assumption and identifies the need for estimating dynamic states. Supporting technologies are presented as well as a proposed formulation for estimating dynamic states. Metrics for evaluating methods for solving the dynamic state estimation problem are proposed, with example results to illustrate the use of these metrics. The overall objective of this paper is to provide a basis that more research on this topic can follow.

Capturing Dynamics in the Power Grid: Formulation of Dynamic State Estimation through Data Assimilation

With the increasing complexity resulting from uncertainties and stochastic variations introduced by intermittent renewable energy sources, responsive loads, mobile consumption of plug-in vehicles, and new market designs, more and more dynamic behaviors are observed in everyday power system operation. To operate a power system efficiently and reliably, it is critical to adopt a dynamic paradigm so that effective control actions can be taken in time. The dynamic paradigm needs to include three fundamental components: dynamic state estimation; look-ahead dynamic simulation; and dynamic contingency analysis (Figure 1). These three components answer three basic questions: where the system is; where the system is going; and how secure the system is against accidents. The dynamic state estimation provides a solid cornerstone to support the other 2 components and is the focus of this study.

Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, todays power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parameters with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.

Dynamic state estimation and parameter calibration of a DFIG using the ensemble Kalman filter

With the growing interest in the application of wind energy, doubly fed induction generators (DFIG) play an increasingly essential role in the power industry. It has been well recognized that modeling and monitoring the dynamic behavior of DFIGs are important to ensure power system reliability. Real-time estimation of the dynamic states of a DFIG is possible with high-speed measurements. But how to use such measurements to have high-quality estimation remains to be a challenge. Estimating dynamic states relies on a good dynamic model of the DFIG. Building a high-fidelity model is a problem in tandem with the dynamic state estimation problem. In this paper, we propose an ensemble Kalman filter (EnKF)-based method for the state estimation and parameter calibration of a DFIG. The mathematical formulation of state estimation combining with parameter estimation is presented. Simulation cases were studied to demonstrate the accuracy of both dynamic state estimation and parameter estimation. Sensitivity analysis is performed with respect to the measurement noise, initial state errors and parameter errors. The results indicate this EnKF-based method has a robust performance on the state estimation and parameter calibration of a DFIG.

Stochastic Operations and Planning

This document discusses PNNLs efforts to mitigate the changing patterns of electrical system behavior, how it is dispatched, and exchanges of energy.

Dynamic response of large wind power plant affected by diverse conditions at individual turbines

Diverse operating conditions at individual wind turbine generators (WTG) within wind power plants (WPPs) can affect the WPP dynamic response to system faults. For example, individual WTGs can experience diverse terminal voltage and power output caused by different wind direction and speed, affecting the response of protection and control limiters. In this paper, we present a study to investigate the dynamic response of a detailed WPP model under diverse power outputs of its individual WTGs. Wake effect is considered as the reason for diverse power outputs. The diverse WTG power output is evaluated in a test system where a large 168-machine test WPP is connected to the IEEE-39-bus system. The power output from each WTG is derived from a wake effect model that uses realistic statistical data for incoming wind speed and direction. The results show that diverse WTG output due to wake effect can affect the WPP dynamic response activating specialized control in some turbines. In addition, transient stability is affected by exhibiting uncertainty in critical clearing time calculation.

Decoupled Modulation Control

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Non-iterative Voltage Stability

This report demonstrates promising capabilities and performance characteristics of the proposed method using several power systems models. The new method will help to develop a new generation of highly efficient tools suitable for real-time parallel implementation. The ultimate benefit obtained will be early detection of system instability and prevention of system blackouts in real time.