Real-Time Surrogate Modeling for Fast Transient Prediction in Inverter-Based Microgrids Using CNN and LightGBM

Osasumwen Cedric Ogiesoba-Eguakun; Kaveh Ashenayi; Suman Rath

eess.SYMar 31, 2026

Real-Time Surrogate Modeling for Fast Transient Prediction in Inverter-Based Microgrids Using CNN and LightGBM

Authors: Osasumwen Cedric Ogiesoba-Eguakun, Kaveh Ashenayi, Suman Rath

Organizations: Department of Electrical and Computer Engineering, The University of Tulsa, Tulsa, OK 74104, USA

Abstract

Real-time monitoring of inverter-based microgrids is essential for stability, fault response, and operational decision-making. However, electromagnetic transient (EMT) simulations, required to capture fast inverter dynamics, are computationally intensive and unsuitable for real-time applications. This paper presents a data-driven surrogate modeling framework for fast prediction of microgrid behavior using convolutional neural networks (CNN) and Light Gradient Boosting Machine (LightGBM). The models are trained on a high-fidelity EMT digital twin dataset of a microgrid with ten distributed generators under eleven operating and disturbance scenarios, including faults, noise, and communication delays. A sliding-window method is applied to predict important system variables, including voltage magnitude, frequency, total active power, and voltage dip. The results show that model performance changes depending on the type of variable being predicted. The CNN demonstrates high accuracy for time-dependent signals such as voltage, with an $R^2$ value of 0.84, whereas LightGBM shows better performance for structured and disturbance-related variables, achieving an $R^2$ of 0.999 for frequency and 0.75 for voltage dip. A combined CNN+LightGBM model delivers stable performance across all variables. Beyond accuracy, the surrogate models also provide major improvements in computational efficiency. LightGBM achieves more than $1000\times$ speedup and runs faster than real time, while the hybrid model achieves over $500\times$ speedup with near real-time performance. These findings show that data-driven surrogate models can effectively represent microgrid dynamics. They also support real-time and faster-than-real-time predictions. As a result, they are well-suited for applications such as monitoring, fault analysis, and control in inverter-based power systems.

Figures & tables

Reference	System / Focus	Model Type	EMT High- Frequency Data	Multiple Disturbances	OOD / Robustness Test	Main Limitation / Scope
[ 14 ]	Transient stability	CNN	No	Limited	No	Focused on stability classification, not multi-target surrogate regression
[ 27 ]	Transient security assessment	Deep learning	No	Limited	Partial	Emphasis on security assessment under partial observability
[ 22 ]	Transient stability	CNN + saliency map	No	Limited	No	Primarily classification and interpretability oriented
[ 29 ]	Small-signal stability	Deep CNN	No	Limited	No	Focused on small-signal stability, not EMT waveform prediction
[ 30 ]	Voltage vulnerability assessment	CNN	No	Limited	No	Application-specific vulnerability assessment
[ 19 ]	Small-signal stability correction	LightGBM	No	Limited	No	Structured stability features, not EMT multi-channel dynamics

TABLE I: Comparison of Representative Recent Studies on Data-Driven and Surrogate Modeling Approaches for Power-System Dynamics

Fig. 1: Internal structure of a representative distributed generation (DG) unit in the inverter-based microgrid. Each DG unit consists of a distributed energy source, a DC-link capacitor, and a voltage source inverter (VSI) that operates using PWM switching. The inverter output is filtered by an LCL filter and connected to the AC bus via a coupling transformer. A local controller regulates voltage, frequency, and power using control signals $(V^{*},f^{*},P^{*},Q^{*})$ , based on measurement feedback $(V_{DG},I_{DG},P_{DG},Q_{DG},f_{DG})$ . The same structure is assumed for all DG units.

Parameter	Value
Filter resistance, $R_{f}$	$0.1~\Omega$
Filter inductance, $L_{f}$	$4~\mathrm{mH}$
Filter capacitance, $C_{f}$	$200~\mu\mathrm{F}$
Line resistance, $R_{c}$	$0.1~\Omega$
Line parameters (2, 4, 6, 8)	$1.5~\mathrm{mH}+0.1~\Omega$
Line parameters (1, 3, 5, 7, 9)	$0.5~\mathrm{mH}+0.07~\Omega$

TABLE II: Key Parameters of the Inverter-Based Microgrid

Fig. 2: EMT digital twin microgrid and dataset generation framework. A grid-connected inverter-based microgrid is simulated under multiple disturbance scenarios. Synchronized measurements are collected and processed to extract key electrical variables $(V_{1},V_{2},V_{3},I_{1},I_{2},I_{3},P_{DG_{k}},Q_{DG_{k}},f_{DG_{k}})$ . Derived features $(V_{mag},P_{total},Q_{total})$ are computed and segmented using a sliding-window approach with window length $W$ and stride $S$ . The resulting dataset is organized into training, validation, and OOD test sets for surrogate modeling.

Fig. 3: Real-time surrogate modeling workflow using CNN and LightGBM. Multivariate time-series windows $X_{t}\in\mathbb{R}^{W\times d}$ are processed in parallel: CNN captures temporal waveform patterns, while LightGBM learns structured nonlinear relationships from statistical features. The models predict key variables $(V_{mag},f_{DG1},P_{total},V_{dip})$ , followed by OOD evaluation under noise and communication delay, and runtime benchmarking against EMT simulation to assess real-time capability.

CNN Architecture	LightGBM Hyperparameters
Conv1D (32): (100, 32)	Boosting: gbdt
Conv1D (64): (100, 64)	Learning rate: 0.05
MaxPool: (50, 64)	Estimators: 300
Conv1D (64): (50, 64)	Max depth: 6
GlobalAvgPool: (64)	Num leaves: 31
Dense (64)	Min child samples: 20

TABLE III: Model Architecture and Hyperparameters Used in This Study

Fig. 4: Training and evaluation of CNN and LightGBM models under OOD conditions, such as noise and communication delay scenarios. The digital twin simulation is used to generate the training and validation data for both models. CNN converges steadily across all targets, while LightGBM converges faster and stabilizes early. Both models are tested on noise and communication delay scenarios.

Fig. 5: Comparison of model predictions and ground truth under unseen conditions for key microgrid variables: voltage magnitude (

V_{\mathrm{mag}}

), frequency (

f_{\mathrm{DG1}}

), total active power (

P_{\mathrm{total}}

), and voltage dip (

V_{\mathrm{dip}}

). Results from CNN, LightGBM, and the hybrid model are shown. Insets show small local differences between the models. Smooth variables (

V_{\mathrm{mag}}

P_{\mathrm{total}}

) and frequency are predicted accurately, while

V_{\mathrm{dip}}

shows highly variable behavior with more visible deviations. The hybrid model provides consistent performance across both smooth and dynamic signals.

Fig. 6: Residual distributions and prediction accuracy for key microgrid variables: voltage magnitude ( $V_{\mathrm{mag}}$ ), frequency ( $f_{\mathrm{DG1}}$ ), total active power ( $P_{\mathrm{total}}$ ), and voltage dip ( $V_{\mathrm{dip}}$ ). Histograms display the residual distributions along with kernel density estimates, while the insets show predicted versus true values. Most variables have residuals that are tightly centered around zero with low bias, and they show strong linear agreement, especially for $f_{\mathrm{DG1}}$ and $P_{\mathrm{total}}$ . In contrast, $V_{\mathrm{dip}}$ shows higher variance and increased prediction spread due to its highly dynamic behavior.

Method	Sim (s)	Time (s)	Speedup	RT Ratio
Simulink	1.00	941.16	1.00	0.001
LightGBM	1.00	0.89	1053.58	1.12
CNN	1.00	5.09	185.04	0.20
Hybrid	1.00	1.80	522.65	0.56

TABLE IV: Runtime and Real-Time Performance Comparison

Fig. 7: Combined evaluation of prediction accuracy and computational efficiency. The top panel shows $R^{2}$ scores for LightGBM, CNN, and the hybrid model across key variables: $V_{\mathrm{mag}}$ , $f_{\mathrm{DG1}}$ , $P_{\mathrm{total}}$ , and $V_{\mathrm{dip}}$ . The bottom panel presents runtime on a logarithmic scale relative to the EMT simulation.

Power Systems

Power systems research studies the planning, operation, control, and protection of electrical grids and their integration with energy resources.

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A Unified Mamba--MoE Surrogate for Closed-Loop Simulation and Measurement-Window Forecasting of Inverter Transients

Haoguang Wang, Huy Hoang Le, Akhila Kandivalasa, Christian Moya+2

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Neural Surrogate Modeling Power Systems

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This paper proposes a Mamba surrogate model with mixture-of-experts (MoE) routing to represent the transient dynamics of inverter-based resources. A Mamba surrogate model is a predictive machine learning model built on the Mamba architecture. MoE routing uses a router network to assign data-dependent weights to specialized subnetworks (experts). The resulting Mamba--MoE surrogate can perform two tasks: (i) closed-loop simulation and (ii) measurement-window forecasting of inverter transients. A single Mamba backbone with task conditioning and expert routing serves both tasks, replacing two separate specialists. Task-matched objectives fit each prediction form, and an adaptive conformal layer provides prediction intervals for both tasks. For the considered grid-following inverter, the unified surrogate model remains in the same low-error regime as a Mamba specialist pair while using 13% fewer parameters. The prediction intervals achieve 94--96% empirical mean marginal coverage across the two tasks. For transient dynamics---that is, beyond the vicinity of an equilibrium point---our surrogate model with MoE routing yields lower errors across all outputs in both tasks compared to a shared Mamba backbone without expert routing. A controller hardware-in-the-loop simulation validates our results and shows that adapting only the shared output head with limited measured data reduces held-out forecasting error.

Abstract

Power Systems

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