Publications

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The Cramér-Rao Lower Bound (CRLB) is used as a classical benchmark to assess estimators. Online algorithms for estimating modal properties from ambient data, i.e., mode meters, can benefit from accurate estimates of forced oscillations. The CRLB provides insight into how well forced oscillation parameters, e.g., frequency and amplitude, can be estimated. Previous works have solved the lower bound under a single-channel PMU measurement; thus, this paper extends works further to study CRLB under two-channel PMU measurements. The goal is to study how correlated/uncorrelated noise can affect estimation accuracy. Interestingly, these studies shows that correlated noise can decrease the CRLB in some cases. This paper derives the CRLB for the two-channel case and discusses factors that affect the bound.

Primary frequency control in power systems is being challenged by the large-scale integration of inverter-based resources (IBRs) because they do not typically respond to frequency fluctuations. This paper suggests introducing new reserve products into the electricity market that provide incentive for IBRs to contribute to primary frequency control in ways that take advantage of their fast-acting capabilities. In addition to a Primary Frequency Response (PFR) reserve product, which accommodates standard droop control, we suggest introducing a Fast Frequency Response (FFR) reserve product, a reserve product for Virtual Inertia (VI), which is also known as synthetic inertia, and an inertia product. We adopt a reserve requirement that guarantees sufficient primary frequency response reserve to adequately arrest frequency decline in response to a large generator outage within a certain margin. We place this reserve requirement into a real-time co-optimization problem, derive prices for each product and analyze the incentives provided to IBRs.

This paper uses clusters of solar photovoltaic units, wind turbines, and battery energy storage systems as a single controllable plant and proposes a method to enable adaptive plant wide droop control. Each of these clusters is defined as a Flexpower plant. The proposed control is presented with multiple configurations that enable the same overall droop characteristic to the Flexpower plant, but use each of the resource technologies in a different manner. One of the control configurations considers the availability of the resources for wind and solar units, as well as the state of charge of energy storage units, when distributing droop action to each unit that comprise the Flexpower plant. The proposed approaches were tested in a small power system where it is shown that the Flexpower plant can provide frequency regulation to the system in a variety of ways depending on which of the proposed control configurations was selected.

This paper studies the differences in a synthetic inertia controller of using two different feedback measurements: (i) an estimate of the rate of change of frequency from local voltage measurements, and (ii) a remote machine acceleration from a generator nearby to the actuator. The device that provides the synthetic inertia action is a converter interfaced generator (CIG). The paper carries out analysis in the frequency domain, using Bode plots, to show that synthetic inertia control using frequency estimates is more prone to instabilities than for the case where a machine speed is used. The paper then proposes a controller (or a filter) to mitigate these effects. In addition, the paper shows the effects that a delay of the machine speed signal of the nearby generator has on the synthetic inertia control of the system and how a controller is also needed in this case. Finally, the paper shows the difference in performance of a synthetic inertia controller when using these different measurement signals with simulations in time domain a electromagnetic transient program platform.

A novel method for fault classification and location is presented in this paper. This method is divided into an initial signal processing stage that is followed by a machine learning stage. The initial stage analyzes voltages and currents with a window-based approach based on the dynamic mode decomposition (DMD) and then applies signal norms to the resulting DMD data. The outputs for the signal norms are used as features for a random-forests for classifying the type of fault in the system as well as for fault location purposes. The method was tested on a small distribution system where it showed an accuracy of 100% in fault classification and a mean error of ~ 30 m when predicting the fault location.

This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ~13 m in the fault location task.

The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution power system Traveling Wave (TW) - based protection schemes. Simulated faults on the IEEE 34 system are processed by using the Karrenbauer Transform and the Stationary Wavelet Transform (SWT), and the energy of the resulting signals is calculated using the Parseval's Energy Theorem. This data is used to train Graph Convolutional Networks (GCNs) to perform fault zone location. Several levels of measurement noise are considered for comparison. The results show outstanding performance, more than 90% for the most developed models, and outline a fast, reliable, asynchronous and distributed protection scheme for distribution level networks.

Grid technologies connected via power electronic converter (PEC) interfaces increasingly include the grid support functions for voltage and frequency support defined by the IEEE 1547-2018 standard. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. In this paper, a method for assessing photovoltaic (PV) inverter dynamics using a data-driven technique with power hardware-in-the-loop is presented. The data-driven modeling technique uses various probing signals to estimate commercial off-the-shelf (COTS) inverter dynamics. The MATLAB system identification toolbox is used to develop a dynamic COTS inverter model from the perturbed grid voltage (i.e., probing signal) and measured current injected to the grid by the inverter. The goodness-of-fit of COTS inverter dynamics in Volt-VAr support mode under each probing signal is compared. The results show that the logarithmic square-chirp probing signal adequately excites the COTS inverter in Volt-VAr mode to fit a data-driven dynamic model.

This paper uses co-located wind and photovoltaic generation, along with battery energy storage, as a single plant and introduces a method to provide a flexible synthetic inertia (SI) response based on plant-wide settings. The proposed controller accounts for variable resources and correctly adjusts device responses when an inverter-based resource (IBR) may become unavailable to provide a consistent plant level SI response. The flexible SI response is shown to adequately replace the lost synchronous inertial response from equivalent conventional generation when IBR penetration is approximately 25% in a small power system. Furthermore, it is shown that a high gain SI response provided by the combined IBR plant can reduce the rate of change of frequency magnitude over 50% from the equivalently rated conventional generation response.

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This paper presents a preliminary investigation on controlling the existing high voltage dc (HVDC) links connecting the North American western interconnection (WI) to the other interconnections, to provide damping to inter-area oscillations. The control scheme is meant to damp inter-area modes of oscillation in the WI by using wide area synchrophasor feedback. A custom model is developed in General Electric's PSLF software for the wide area damping control scheme, and simulations are analyzed on a validated full 22,000 bus WI model. Results indicate that implementing the proposed control technique to the existing HVDC links in the WI can significantly improve the damping of the inter-area modes of the system.

As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.

This paper presents a new method for detecting power quality disturbances, such as faults. The method is based on the dynamic mode decomposition (DMD)-a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The proposed method uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The paper shows how the proposed method can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed. In addition, a study on the effect that noise has on the proposed approach is presented.

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As renewable energy sources are becoming more dominant in electric grids, particularly in micro grids, new approaches for designing, operating, and controlling these systems are required. The integration of renewable energy devices such as photovoltaics and wind turbines require system design considerations to mitigate potential power quality issues caused by highly variable generation. Power system simulations play an important role in understanding stability and performance of electrical power systems. This paper discusses the modeling of the Global Laboratory for Energy Asset Management and Manufacturing (GLEAMM) micro grid integrated with the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) test site, providing a dynamic simulation model for power flow and transient stability analysis. A description of the system as well as the dynamic models is presented.