arXiv subjects

Panagiotis N. Papadopoulos

Publications and source records attributed to Panagiotis N. Papadopoulos.

5 recordsLinked to original sources

arXiv2026

Analytical Prediction of Voltage Collapse in Current-Limited Grid-Forming Inverters

The limited overcurrent capability of grid-forming (GFM) inverters makes current limiting essential during large disturbances. Activation of a circular current limiter (CCL) does not always cause the operating equilibrium to disappear. This paper develops an analytical framework to predict the grid-voltage boundaries at which the CCL is activated, determine whether the operating equilibrium persists as a saturated stable equilibrium point (satSEP), and identify the voltage at which it is lost. The CCL-based GFM inverter with frozen anti-windup is formulated as a piecewise-smooth system comprising normal-control and current-limited modes, so limiter activation is interpreted as a boundary-equilibrium bifurcation (BEB). A continuation formulation that switches to a reduced current-limited model when the CCL is activated is introduced to avoid the rank deficiency caused by frozen integrator states. An equivalent circuit that includes the filter capacitor yields closed-form expressions for the lower and upper boundary voltages at which the CCL is activated. A positive lower-boundary slope predicts that a satSEP persists in the current-limited mode and is lost at a later saddle-node, whereas a nonpositive slope predicts a non-smooth fold and equilibrium loss at the BEB. The upper-boundary slope is always negative under the assumed parameter conditions. Power-angle analysis, dynamic-model continuation in single-inverter and modified 9-bus systems, and time-domain simulations validate these predictions, showing that CCL activation can either cause immediate equilibrium loss through a non-smooth fold or allow a satSEP to persist until it is lost at a later saddle-node.

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arXiv2026

Bifurcation Analysis of Sub-Synchronous Oscillations Related to Grid-Forming Converter Inner Controllers

To ensure power system stability and security, it is vital to understand the complex nonlinear power system dynamics related to converter-interfaced generators. For example, grid-forming (GFM) converters are expected to be a key asset for maintaining a strong and stable power system, but might cause wide-bandwidth stability issues with underlying mechanisms heretofore unseen or understudied, including sub-synchronous oscillations (SSOs). This paper details a continuation-based bifurcation analysis of a GFM converter, revealing stability bounds with respect to operational conditions in addition to the time constant of the cascaded inner voltage and current controllers. We focus our analysis on the strong grid instability caused by an inner controller-related SSO, including continuation of the limit cycle past the Hopf bifurcation point, revealing rapid onset of unacceptably large oscillations. Furthermore, we investigate the impact of the circular current limiter, revealing spurious Hopf bifurcations in weak grids associated with the aforementioned SSO when adopting smooth approximations; this suggests the need for careful implementation of such approximations for GFMs, at least in bifurcation studies.

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arXiv2024

A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.

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arXiv2023

Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Machine learning (ML) for transient stability assessment has gained traction due to the significant increase in computational requirements as renewables connect to power systems. To achieve a high degree of accuracy; black-box ML models are often required - inhibiting interpretation of predictions and consequently reducing confidence in the use of such methods. This paper proposes the use of SHapley Additive exPlanations (SHAP) - a unifying interpretability framework based on Shapley values from cooperative game theory - to provide insights into ML models that are trained to predict critical clearing time (CCT). We use SHAP to obtain explanations of location-specific ML models trained to predict CCT at each busbar on the network. This can provide unique insights into power system variables influencing the entire stability boundary under increasing system complexity and uncertainty. Subsequently, the covariance between a variable of interest and the corresponding SHAP values from each location-specific ML model - can reveal how a change in that variable impacts the stability boundary throughout the network. Such insights can inform planning and/or operational decisions. The case study provided demonstrates the method using a highly accurate opaque ML algorithm in the IEEE 39-bus test network with Type IV wind generation.

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arXiv2022

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations

Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to showcase the benefits of SHapley Additive exPlanations (SHAP) for understanding the outcomes of ML models, which are increasingly being used. Second, we seek to demonstrate that SHAP explanations are able to capture the underlying physics of the power system. To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF) -- a physics-based linear sensitivity index -- can be derived from the SHAP values. To do so, we take the derivatives of SHAP values from a ML model trained to learn line flows from generator power injections, using a simple DC power flow case in the 9-bus 3-generator test network. In demonstrating that SHAP values can be related back to the physics that underpin the power system, we build confidence in the explanations SHAP can offer.

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