Slava Maslennikov

The increasing penetration of inverter-based resources introduces new dynamic challenges to modern power grids, such as sub- and super-synchronous oscillations and other faster dynamics. These dynamics are typically fast in nature and are difficult to accurately model and analyze using standard transient stability (TS) methods, necessitating the need for electromagnetic transient (EMT) analysis. However, EMT simulations are notoriously slow for large-scale grids due to both equation formulations and computational limitations. To overcome this challenge, EMT-TS hybrid simulation is often used, since it offers a balanced trade-off between accuracy and speed, making it feasible to perform EMT analysis on large systems. One open question about EMT-TS hybrid simulation is the accuracy of the EMT-TS boundary or interface. This paper introduces an error index to quantify EMT-TS hybrid interface errors, identifies conditions where the hybrid simulation approach may become inaccurate, and suggests EMT region expansions to improve the simulation accuracy. Additionally, a three-sequence hybrid interface model is proposed to mitigate inaccuracies caused by unbalanced conditions.

[Most recently, there have been significant efforts by power system operators in studying generation and transmission uncertainties in operation planning. With the increasing penetration of weather-dependent renewable generation and frequent geo-climate events, power system operators have begun to predict weather changes and preventively mitigate their impacts on power generation and transmission. Uncertainty evaluation and subsequent preventive mitigation actions have become important tasks in operating regions with significant renewable generation. Amid these efforts, artificial intelligence and machine learning are playing instrumental roles in the development of new tools. Based on a handful of public reports, this article discusses the emerging new problem of operational uncertainties for power system operators and outlines a systematic approach that involves risk evaluation and mitigation in both generation scheduling and transmission operation. It is envisioned that risk-based operation will be the new paradigm for power system security assessment.

Phasor Measurement Units (PMUs) convert high-speed waveform data into low-speed phasor data, which are fundamental to wide-area monitoring and control in power systems, with oscillation detection and localization among their most prominent applications. However, representing electrical waveform signals with oscillations using PMU phasors is effective only for low-frequency oscillations. This paper investigates the root causes of this limitation, focusing on errors introduced by Discrete Fourier Transform (DFT)-based signal processing, in addition to the attenuation effects of anti-aliasing filters, and the impact of low reporting rates. To better represent and estimate waveform signals with oscillations, we propose a more general signal model and a multi-step estimation method that leverages one-cycle DFT, the Matrix Pencil Method, and the Least Squares Method. Numerical experiments demonstrate the superior performance of the proposed signal model and estimation method. Furthermore, this paper reveals that the phasor concept, let alone PMU phasors, can become invalid for waveform signals with high-frequency oscillations characterized by asymmetric sub- and super-synchronous components. These findings highlight the fundamental limitations of PMU data and phasor concept, and emphasize the need to rely on waveform data for analyzing high-frequency oscillations in modern power systems.

Power system oscillations are a phenomenon of periodic energy exchange at the oscillation frequency among participating dynamic components such as synchronous generators and inverter-based resources. Undamped or sustained power system oscillations are not desired in power system operations because they can cause equipment damage, reduced power transfer capacity, and large-scale blackouts. Therefore, any significant oscillations should be properly mitigated in a timely fashion. In recent decades, large-amplitude sustained oscillations have been observed in power grids around the world, thanks to widely deployed Phasor Measurement Units (PMUs). Many of the observed oscillations did not appear in traditional model-based planning studies such that no mitigation measures can be designed offline. Finding the originating source(s) of oscillations is crucial in real-time operations. Many oscillation source location (OSL) methods have been proposed but their effectiveness vary significantly. This PMU-OSL dataset provides a library of power system electromechanical oscillation test cases including both simulated cases and actual oscillatory events in power systems. By offering measurement and model data of realistic and challenging oscillation cases, this dataset can help facilitate the development and testing of algorithms, methods and tools for identifying the source of poorly damped or forced oscillation in power systems.

The Complex Dissipating Energy Flow (CDEF) method for locating sources of forced oscillations (FOs) has a solid theoretical foundation and has shown very good performance applied to low-frequency oscillations (LFOs) associated with synchronous generators. The CDEF method is successfully applied in ISO New England and uses phasor measurement unit (PMU) data. However, traditional PMU measurements may be inaccurate within the frequency range of subsynchronous oscillations (SSOs) associated with inverter-based resources (IBRs). In this paper, we present an extension of the CDEF method for locating the source of SSO, which we call SSO-CDEF. The SSO-CDEF is based on point-on-wave (POW) measurements and explicitly accounts the contribution of sub- and super-synchronous oscillations. The effectiveness of the method is demonstrated in IEEE 14-bus model with grid following and grid forming inverters and using EMT simulation results as synthetic POW measurements.

Understanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resiliency in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for the distribution system have been widely developed during the past decade, transmission system impact models lagged behind because of the scarcity of data. This study demonstrates a weather impact model for predicting the probability of failure of transmission lines. It builds upon a recently developed model and focuses on reducing model bias, through multi-model integration, feature engineering, and the development of a storm index that leverages upon distribution system data to aid the prediction of transmission risk. We explored three methods for integrating machine learning with mechanistic models. They consist of: (a) creating a linear combination of the outputs of the two modeling approaches, (b) including fragility curves as additional inputs to machine learning models and (c) developing a new machine learning model that uses the outputs of the weather-based machine learning model, fragility curve estimates and wind data to make new predictions. Moreover, due to the limited number of historical outages in transmission networks, a storm index was developed leveraging a dataset of distribution outages to learn about storm behavior to improve model skills. In the current version of the model, we substantially reduced the overestimation in the sum of predicted values of transmission line probability of failure that was present in the previously published model by 10 times. This has led to a reduction of model bias from 3352% to 14.46-15.43%. The model with the integrated approach and storm index demonstrates substantial improvements in the estimation the probability of failure of transmission line and their ranking by risk level. The improved model is able to capture 60% of the outages within the top 22.5% of the ranked power lines, compared to a value of 34.9% for the previous model. With an estimate of the probability of failure of transmission lines ahead of storms, power system planning and maintenance engineers will have critical information to make informed decisions to create better mitigation plans and minimize power disruptions. Long term, this model can assist with resilience investments as it highlights areas of the system more susceptible to damage.

Traditional grid analytics heavily rely on accurate power system models, especially dynamic ones for generators, controllers, and loads. However, obtaining comprehensive models is impractical in real operations due to inaccessible parameters and consumer privacy. This necessitates dynamic equivalencing for unknown subsystems, which employs physics-informed machine learning and neural ordinary differential equations (ODE-NET) to preserve dynamic behaviors post-disturbances. The contributions include: (1) A neural dynamic equivalence (NeuDyE) formulation enabling continuous-time, data-driven dynamic equivalence, eliminating the need for acquiring inaccessible system details; (2) Introduction of Physics-Informed NeuDyE learning (PI-NeuDyE) to actively control NeuDyE’s closed-loop accuracy; (3) Driving Port NeuDyE (DP-NeuDyE), a practical application of NeuDyE, reducing the number of inputs required for training. Extensive case studies on the 140-bus NPCC system validate the generalizability and accuracy of both PI-NeuDyE and DP-NeuDyE. These analyses cover various scenarios, including limitations in data accessibility. Test results demonstrate the scalability and practicality of NeuDyE, showcasing its potential application in ISO and utility control centers for online transient stability analysis and planning purposes.

The article describes the evaluation of the Complex Dissipating Energy Flow (CDEF) method's efficiency in locating the source of oscillation for 250+ simulated cases in 240-bus WECC system and 300+ actual oscillatory events in ISO New England power system. The paper proposes the CDEF implementation in the frequency domain and proposes a formalized Dissipating Energy pattern recognition (DR-PR) process significantly increasing the source locating capability even with limited system observability by PMU. The paper also discusses the efficiency of the CDEF in phasor domain for identifying the source of forced oscillation caused by Invertor Based Resource and using the electromagnetic type modeling.

This letter devises Neural Dynamic Equivalence (NeuDyE), which explores physics-aware machine learning and neural-ordinary-differential-equations (ODE-Net) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The contributions are threefold: 1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; 2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; 3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models. Extensive case studies in the NPCC system validate the efficacy of NeuDyE, and, in particular, its capability under various contingencies.