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High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for discussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines.

The retirement of conventional synchronous generators and the integration of inverter-based renewable energy sources (RES) are transforming power systems in unprecedented ways. Understanding the impact and consequences of this resource mix transition is crucial to maintaining reliable grid operation. In this regard, this paper examines the impact of increased RESs on the oscillatory behavior of the U.S. Western Interconnection. From our analysis, we found that increasing RES penetration levels can have a significant impact on the system modes. However, no single trend could be identified that can correlate this impact to the RES penetration levels. This impact was, in fact, dependent on the group of synchronous generators that were replaced by the RES machine models; for instance, replacing synchronous generators in Alberta, that have high participation factors and significantly contribute to the damping of the NS-A mode, had the most impact on the NS-A mode while the impact on the NS-A mode by replacing synchronous generators in the rest of the WI was less.

The retirement of conventional synchronous generators and the integration of inverter-based renewable energy sources (RESs) are transforming power systems in unprecedented ways. Understanding the impact and consequences of this resource mix transition is crucial to maintaining reliable grid operations. Aiming at this, the paper examines the impact of increasing RESs on the oscillatory behavior of the U.S. Western Interconnection (WI). Two approaches were used in the paper for performing this assessment: measurement- and model-based. In the measurement-based approach, quantile-regression-based correlation analysis was performed using historical data collected from the WI to analyze the impact of increasing RES penetration levels on system modes. In the model-based approach, the 2018 heavy summer operating WI model was used as a base-case to create several use-cases by replacing synchronous machine models with RES machine models in different proportions to analyze the impact of increasing RES penetration levels on the system modes. In this analysis, four scenarios were considered that included system-wide and area-specific increases in RES penetration levels. In the model-based approach, a detailed eigenvalue analysis was also performed to understand the trends observed in the damping ratio of system modes. Our detailed analysis concludes that the increasing RES penetration levels can have a significant impact on system modes. However, no single trend could be identified to correlate the impact of the increasing RES penetration levels on system modes. According to our model-based analysis, this impact was, in fact, dependent on the group of synchronous generators that were replaced by the RES machine models; for instance, replacing synchronous generators in Alberta, that have high participation factors and significantly contribute to the damping of the NS-A mode, had the most impact on the NS-A mode, while the impact on the NS-A mode was little when synchronous generators were replaced in the rest of the WI. Based on this analysis, the authors conclude that the impact of the increasing RES penetration level on a system mode is dependent on the overall dynamics of the evolving system and the damping contribution to that mode by the generators having high participation factors in that mode.

The retirement of conventional synchronous generators and the integration of inverter-based renewable energy sources (RES) are transforming power systems in unprecedented ways. Understanding the impact and consequences of this resource mix transition is crucial to maintaining reliable grid operation. In this regard, this paper examines the impact of increased renewable energy sources on the oscillatory behavior of the U.S. Eastern Interconnection. From our analysis, we found that no single trend could be used to correlate the impact of increased RES penetration levels on the damping ratio of system modes. In fact, the impact of the increased RES penetration levels on a system mode was influenced by the group of synchronous machine models that were replaced with RES machine models. For example, replacing the synchronous generators in Florida significantly increased the damping ratio of the 0.21 Hz mode, whereas the impact was relatively small on the mode when synchronous machines were replaced in the North-eastern areas of the EI.

With an increase in the oscillation events observed in the U.S. Eastern Interconnection (EI), it has become increasingly important to have a good understanding of the EI system oscillatory behavior. However, as compared to the Western Interconnection system (WI), not much work has been done in this regard for the EI system. Therefore, in this paper, a thorough analysis is carried out to identify inter-area modes and their properties for the EI system using a 80000+ bus real EI model. Multi-channel Prony method is used in this paper for estimating system modes and mode-shapes.

Electromechanical modes are inherent to any interconnected power systems which provide a measure of the small-signal stability margin of the system. A number of algorithms have been developed for the estimation of these modes using synchrophasor measurements. However, most of these algorithms are not designed to operate in the presence of forced oscillations (FO). These FOs are results of periodic rogue input driving the system. When FOs are present, estimates of system modes can be biased depending on the frequency and the amplitude of the FOs. To tackle this problem, a new algorithm is proposed in this paper to estimate system modes in the presence of FOs. In the proposed method, the over-determined modified Yule–Walker method which is used to estimate autoregressive coefficients of an autoregressive moving average (ARMA) signal model is extended to an ARMA with exogenous input (ARMAX) model that incorporates the presence of FOs. Two versions of the proposed method are included in this paper based on the requirement of the information of the duration of FOs in the signal. Results obtained by implementing the proposed algorithm on simulated data and real-world data validate the effectiveness of both versions of the proposed method.

This paper derives an improved version of a periodogram detector for detecting forced oscillations (FOs) in power systems using synchrophasor measurements. FOs are usually periodic in nature and are often accompanied with multiple harmonic components. This harmonic information of FOs is incorporated into the proposed periodogram detector, which significantly improves its detection performance for a given probability of a false alarm. The original periodogram detector carries out detection of each component of FOs separately, even though they are related through their harmonics. In the proposed periodogram detector, the detection algorithm is re-derived by incorporating harmonic information such that the detector now looks for a combination of harmonic components of FOs for detection. This derivation shows a significant improvement in the detection performance of the proposed periodogram detector as compared to the original one. This is also illustrated by the results obtained by implementing the proposed periodogram detector on the simulated and real-world PMU measurements.

In this paper, a measurement-based approach is introduced to estimate the impact of operating conditions on the inter-area oscillations in the Western Electricity Coordinating Council (WECC) power grid using field measurement data. The correlation between oscillation modes and operating conditions is essential to ensure the grid's small-signal stability under increasing variability and uncertainty introduced by the accelerating penetration of renewable generation. Past studies have been focused on model-based methods, whose applications are limited by the model availability and accuracy. To overcome this limitation, the quantile regression method is proposed in this paper to establish a correlation model between the oscillation modes and operating conditions such as generation mix and load and tie-line flows using measurement data. To quantify the uncertainty of the correlation model, the bootstrap method is used to estimate the confidence intervals of the coefficients of the correlation model. Study results show that the high penetration of solar generation has decreased the damping ratio of a major inter-area oscillation Mode. The proposed approach is generic and can be applied to estimate other correlations using measurement data under a statistical framework.

As the number of Phasor Measurement Units (PMUs) deployed in power systems increases, so do the archives of synchrophasor measurements stored by utilities. Significant value can be extracted from these archives by using them to support studies such as frequency response analysis, small-signal stability analysis, and power plant model validation. To better enable utilities to perform these studies, the open-source Archive Walker tool was developed. This tool examines PMU data for grid disturbances and periods of interest. Once identified, the data can be further analyzed inside Archive Walker or exported to specialty tools. In this paper, the various disturbance detectors available in Archive Walker are described, and its interactions with other openly-available tools are highlighted.

High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.

High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real–world PMU measurements with the model-based simulated data. This paper proposes metrics to quantify the generator dynamic model validation results based on the response of generators to each system mode, which includes both local and inter-area, using modal analysis approach. The metrics provide information on the inaccuracy associated with the model in terms of the characteristics of each mode. Initial results obtained using the real-world data validates the effectiveness of the proposed metrics. In this paper, modal analysis was carried out using Prony method.

The Dynamic Contingency Analysis Tool (DCAT) is being developed to mimic the response of generation dynamics, protection, and load components to extreme contingencies in power systems. Currently, DCAT generates a huge data set, with the analysis results stored in multiple subfolders using a great many files in different formats (power flow cases, time series, figures, spreadsheets). To help power system engineers thoroughly understand and analyze system behavior under many scenarios and contingencies, we developed a database management module (DBM) that enables future integration of DCAT with industry-grade Big Data analytical and interactive visualization platforms. In this paper, we demonstrate the DBM framework with interactive analytics and visualization for DCAT on the synthetic South Carolina 500-bus power system model using hurricane contingencies that capture possibilities for an extreme contingency. DCAT’s advanced data management and interactive visualization can help planning engineers to visually explore and analyze system behavior, all in one view.

Protection equipment modeling is critical to power systems planning and operational studies; it enables more accurate system response when control actions, including protection relays and remedial action schemes (RASs), are adequately modeled and assessed. This paper incorporates a generic protection philosophy in the Dynamic Contingency Analysis Tool (DCAT), and demonstrates its effectiveness by modeling 2011 Pacific Southwest Blackout event autonomously.

This paper documents the performance of the Least Squares Autoregressive Moving Average plus Sinusoids (LS-ARMA+S) mode meter under non-sinusoidal oscillatory conditions. Originally developed as a way to eliminate the bias experienced by LS-ARMA mode meters in the presence of forced oscillations, the LS-ARMA+S mode meter was shown to be a promising upgrade under both ambient and forced oscillation conditions. In order to further validate it for real-world application, the LS-ARMA+S mode meter has been analyzed under two of the more common system events; ringdown-type transient responses, and hydro turbine rough zone oscillations. It is shown through extensive Monte Carlo simulations that the LS-ARMA mode meter is biased by each of these types of system events, and that the LS-ARMA+S mode meter can detect and compensate for them, thus reducing or even eliminating the bias. These results further suggest that the LS-ARMA+S mode meter extends the LS-ARMA mode meter beyond ambient conditions to include commonly observed oscillatory events.

Forced oscillations (FO) in power system are frequently an indication of the ill-operating behavior of different equipment in the system, such as an excitation system, turbine governor, control valve, etc. Corrective actions can be taken only after identifying the source of the disturbance causing these oscillations. In this paper, a novel method is proposed for locating the source of forced oscillations using PMU measurements and system model information. Simulation results obtained by implementing the proposed method on the minniWECC model validate the effectiveness of the proposed method.