Diffusion Model-based Parameter Estimation in Dynamic Power Systems

Amirthagunaraj Yogarathnam; Dmitrii Torbunov; Feiqin Zhu; Meng Yue; Tianqiao Zhao; Yihui Ren; Zhongjing Jiang

REVIEW 3 major objections 5 minor 14 references

Diffusion Model-based Parameter Estimation in Dynamic Power Systems

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A diffusion model conditioned on several fault-event observations at once cuts composite load-model parameter error by 58.6%.

desk verdict A solid, well-scoped simulation study of joint-conditioned diffusion for power-system load parameter estimation, with a real leak in the sensitivity screen and no real-data validation, but worth refereeing. read the letter →

arxiv 2411.10431 v3 pith:2AEWG5ZK submitted 2024-11-15 cs.AI cs.SYeess.SY

Feiqin Zhu , Dmitrii Torbunov , Zhongjing Jiang , Tianqiao Zhao , Amirthagunaraj Yogarathnam , Yihui Ren , Meng Yue This is my paper · ORCID

classification cs.AIcs.SYeess.SY

keywords compositeloadmodelCMPLDWGdiffusioninverseproblemparameterestimationnon-uniquenessjointconditioningdynamicpowersystems

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that the ill-posedness of inverse problems—different parameter sets producing identical observations—can be tamed by conditioning a generative diffusion model on several observations of the same system simultaneously. It introduces JCDI, a conditional denoising diffusion model that, instead of predicting parameters directly, generates samples from the posterior parameter distribution given measured active and reactive power trajectories. Tested on CMPLDWG composite load model parameterization in an IEEE 39-bus simulation, joint conditioning on three electrical fault events reduces the mean absolute range percentage error from 18.0% (single-event conditioning) to 7.46%, a 58.6% reduction. The same model reproduces post-fault power trajectories with root mean square errors below $4\times10^{-3}$ and generalizes to out-of-distribution faults with randomized bus locations and clearing times. The authors further claim that, being purely data-driven, the same joint-conditioning recipe applies to parameter estimation beyond power systems.

What carries the argument

The load-bearing object is the Joint Conditional Diffusion Model-based Inverse Problem Solver (JCDI): a conditional denoising diffusion probabilistic model run in parameter space rather than data space, with observed power trajectories as conditions. The denoising network is the Inverse Grid Transformer (IGT), a transformer encoder whose tokens are the 30 parameters being denoised, ResNet-encoded power trajectories, and a sinusoidally embedded diffusion time step; its attention mechanism is what lets the model learn correlations among parameters and between parameters and observations. The key innovation is multi-event joint conditioning: the reverse process is conditioned simultaneously on several fault-event trajectories, so the generated posterior is proportional to the product of the individual likelihoods, narrowing the set of non-identifiable parameter combinations. The training objective is standard DDPM noise prediction, minimizing $\mathbb{E}\|\varepsilon - \varepsilon_\omega(x_t, t, y)\|^2$, with inference by iterated denoising starting from Gaussian noise.

What would settle it

Record PMU data from real grid disturbances, feed the measured active and reactive power trajectories into the trained JCDI, and compare the estimated CMPLDWG parameters against independently verified values: if the parameter MARPE degrades toward the single-condition level of about 18% or the reconstructed trajectories exceed RMSE of roughly $10^{-2}$ under measurement noise and model mismatch, the synthetic-data premise fails. A complementary check that stays fully in simulation is to perturb the fault-clearing time or bus location of the three conditioning events and test whether the joint-conditioning benefit degrades continuously; if trajectory RMSE jumps to the CDI level for any perturbation, the posterior narrowing is specific to the three trained fault classes rather than a general resolution of non-identifiability.

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Extended reading notes

Core claim

The central discovery is that parameter non-identifiability in a complex dynamical system need not be removed by regularization or optimization; it is an uncertainty structure that a diffusion model can represent, and that additional observations can shrink. Working in the parameter space of the WECC composite load model CMPLDWG, JCDI learns the distribution of 30 sensitive parameters conditioned on observed power trajectories, so the posterior's spread encodes exactly which parameter combinations are interchangeable under a given disturbance. Conditioning the same model jointly on ordinary, trip, and stall fault trajectories multiplies the likelihood constraints, collapsing posterior width for correlated pairs such as Fmb/Fmc and CompPF/Qel0 and for parameters that are insensitive under any single fault, such as Rstall, Xstall, and frcel. The measured consequence is a drop in mean MARPE from 18.0% to 7.46%, and trajectory RMSEs of $7.84\times10^{-4}$, $8.61\times10^{-4}$, and $3.75\times10^{-3}$ under ordinary, trip, and stall faults, where single-condition estimation degrades to $4.44\times10^{-2}$ and $7.97\times10^{-1}$. The paper also reports that the joint-conditioned estimates generalize to held-out fault scenarios with randomized locations and clearing times.

Load-bearing premise

The load-bearing premise is that the synthetic pipeline—uniformly sampling 30 hand-selected parameters, simulating CMPLDWG on the IEEE 39-bus system under three fault classes, and defining the posterior against a uniform prior—faithfully stands in for the real-world inverse problem; if real PMU measurements bring noise, model mismatch, or a different prior over plausible parameters, the measured 58.6% error reduction and reported RMSE gains may not transfer.

Editorial extensions

If this is right

Joint conditioning on multiple disturbance events removes the need for iterative forward simulation during training and inference, since the forward operator is only used once to build the dataset.
Parameters estimated under joint conditioning reproduce system dynamics under untrained fault events with randomized bus locations and clearing times, giving cross-event generalization that single-event posteriors lack.
Because the posterior is a distribution rather than a point estimate, JCDI makes parameter correlations and identifiability limitations visible: mutual information between correlated parameter pairs drops sharply when multiple conditions are imposed.
For the CMPLDWG case, trajectory RMSEs of $7.84\times10^{-4}$, $8.61\times10^{-4}$, and $3.75\times10^{-3}$ under ordinary, trip, and stall faults are the paper's evidence that parameter accuracy translates directly into dynamic-response fidelity.
The same framework transfers, by design, to other electrical systems such as power electronic converters and energy storage, and to any dynamical system for which several observation records of the same unknown parameters are available.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural stress test the paper does not run is a sweep over the number of conditioning events: if posterior variance keeps shrinking as a fourth or fifth fault is added, the method would behave like an online sensor-fusion scheme, and the power-system results would be a special case of a general 'more constraints, tighter posterior' principle.
The method's practical value depends on how many distinct informative disturbances a utility can actually record, which suggests a natural extension: actively selecting which fault or operating condition to record next so as to maximally reduce the remaining parameter uncertainty.
The framework assumes the forward simulator is the correct model class; a direct extension would be to train it on simulator output corrupted by realistic PMU noise, or to force generated parameters to produce trajectories matching real measurements. The authors flag real-data validation as future work, and the transfer of the reported gains likely hinges on it.
Because the 30 estimated parameters were chosen by Sobol sensitivity ranking across the three chosen faults, the framework could be combined with an adaptive sensitivity analysis that re-selects parameters as new fault classes are added during deployment.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Quick take: this is a real and reasonably careful application of conditional diffusion models to a genuinely hard inverse problem in power systems — estimating ~200 CMPLDWG parameters from disturbance trajectories. The multi-event joint conditioning idea is the actual new bit, and the paper shows clearly, with 1000 posterior samples and re-simulated trajectories, that conditioning on three fault types narrows the posterior and cuts MARPE from 18.0% to 7.46%. The method comparison (DQN, supervised Res-TFR, CDI, JCDI) is internally consistent, and the OOD test with randomized faults is a good-faith attempt at generalization. The transformer-based denoiser and attention over parameter tokens is also a sensible architectural choice.

The soft spots are real but not fatal. The stress-test note is right: the 30 estimated parameters are picked by Sobol sensitivity ranking under the exact same three fault classes used for evaluation. So the headline numbers partly reflect the fact that the task was defined by what is identifiable under the test conditions. That is a leakage, not a contradiction — the paper is transparent about the selection, but the 'universal framework' and OOD claims should be read with that in mind. A clean test would hold out one fault class from both the sensitivity screen and the training, then evaluate on it.

The other weaknesses are the ones the authors themselves flag: all data come from one commercial simulator (ePHASORSIM), the prior is uniform over hand-picked ranges, and there is no real PMU validation. The scalability claims (100K+ parameters via diffusion) are optimistic extrapolation from image generation and are not demonstrated here. The discussion section is honest about these, which I respect.

Bottom line: within its stated synthetic setup, the central mechanism — joint conditioning narrows the posterior of non-identifiable parameters — is supported. The study is a credible proof-of-concept, but the absolute error numbers and the 'universal' framing should be treated as provisional until a held-out-fault and real-data test appear. No code or data released yet, which limits immediate reproducibility.

For review: yes, send it to a competent referee. It deserves a serious look, with the sensitivity-screen leakage addressed or acknowledged in revision. I would cite it as a solid reference for diffusion-based inverse solvers in power systems, and I would bring it to a reading group — the method is topical and the evaluation is unusually thorough for this area.

Best, [You]

Referee Report

3 major / 5 minor

Summary. The paper proposes JCDI, a conditional diffusion-model framework for parameter estimation in ill-posed inverse problems, and demonstrates it on WECC CMPLDWG composite load model parameterization in an IEEE 39-bus ePHASORSIM setup. The method trains a transformer-based denoiser to generate parameter samples conditioned on observed active/reactive power trajectories; joint conditioning on multiple fault-event trajectories is claimed to narrow the posterior of non-identifiable parameters. The headline results are a reduction in mean absolute range percentage error (MARPE) from 18.0% (single-condition CDI) to 7.46% (JCDI), a 58.6% relative improvement, and trajectory re-simulation RMSEs below 4e-3 for ordinary, trip, and stall faults. The paper also reports an out-of-distribution generalization test with randomized fault locations and clearing times, comparisons against DQN and a ResNet-transformer supervised baseline, and discusses scalability, inference speed, and real-world deployment limitations.

Significance. If the central claim holds, the paper makes a useful empirical contribution: it shows that a diffusion model can serve as an amortized probabilistic inverse solver for a high-dimensional, non-identifiable dynamical-system parameterization problem, and that conditioning on multiple fault-event observations sharpens the estimated parameter distribution. The strengths of the empirical design are the controlled CDI-versus-JCDI comparison, the 1000-sample posterior analysis, the use of re-simulated trajectory RMSE rather than only parameter error, and a separate generalization test with randomized fault parameters. The diffusion training objective and the joint-conditioning factorization are standard, and the quantitative reduction is an empirical measurement rather than an algebraic artifact. The main caveats are that all data are synthetic from the same simulator, the parameter set is selected using the same fault classes used for evaluation, and real-PMU validation is explicitly left to future work, which the authors acknowledge in Section 3.

major comments (3)

[Section 2, Supplementary Sections 3-4] The 30-parameter estimation task is defined by a Sobol sensitivity screen performed under exactly the three fault events (ordinary, trip, stall) that are later used for evaluation (Supplementary Fig. 6 and Supplementary Table 2). Parameters with low sensitivity under all three of these events are excluded from the estimation problem, so the reported 58.6% MARPE reduction and the trajectory RMSE gains measure performance on a task that is partly selected using the test events. This is not an internal contradiction, but it is a correctness risk for the out-of-distribution and 'universal framework' claims: a deployment setting may contain fault classes not represented in the sensitivity screen. A concrete remedy is to hold out one fault class from the sensitivity analysis and parameter-selection step and evaluate on that class; at minimum, the authors should report results on a version of the problem that includes some low-sensitivity parameters.
[Section 2, Table 1, Supplementary Section 4] The manuscript does not state whether CDI was trained on ordinary-fault trajectories only or on all three fault events. If CDI was trained only on ordinary-fault data, the comparison in Table 1 and the 58.6% reduction conflate the joint-conditioning mechanism with a difference in the size and diversity of the training set. The authors should specify the training data for each model and add an ablation trained on all three fault events but conditioned on a single event at inference, so that the effect of joint conditioning is isolated from the effect of additional training data.
[Section 2, Fig. 6, Section 3] The out-of-distribution test randomizes bus locations and fault clearing times, but every test scenario is assigned to one of the same three fault classes (ordinary, trip, stall) used in training and sensitivity selection. No truly unseen fault mechanism is tested, so the claim of 'favorable OOD generalization performance' in Section 3 is stronger than the evidence; this concern is closely related to the first major comment.

minor comments (5)

[Section 4, Eq. (9)] The factorization in Eq. (9) assumes conditional independence of observations given the parameters, and the text attributes this to independence of fault events; the authors should also state the assumed observation-noise model, since in a deterministic simulator the likelihood is a delta measure and the learned distribution is an amortized approximation of the posterior rather than a calibrated Bayesian posterior.
[Section 2, Table 1 paragraph] There is a typo in 'Due to high computational burdern'; it should read 'burden'.
[References] References [25] and [26] repeat references [22] and [20], respectively; the duplicates should be consolidated.
[Supplementary Section 2, Supplementary Table 1] The text says fault clearing times are selected from the empirical range of 3-6 cycles, but the listed values are 135 ms (about 8.1 cycles) and 44 ms (about 2.6 cycles); the statement and the table should be reconciled.
[Data availability / Code availability] Both statements say the data and code 'will be made available prior to publication'; for review purposes, the authors should provide an anonymized repository or a clear timeline, since all results are synthetic and cannot otherwise be reproduced.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity: the headline 58.6% reduction is an empirical held-out measurement, there are no self-citations, and the Sobol-based 30-parameter selection is a benchmark-design caveat rather than a circular reduction.

full rationale

The derivation chain is self-contained. Equations (6) and (9) are standard Bayesian identities (posterior proportional to likelihood times the uniform prior of Supplementary Section 4), and the DDPM objective (11) with noising (10) and sampling (12) are textbook Ho et al. (2020) formulas; none restates the claim that joint conditioning reduces error. The headline result (MARPE 18.0% to 7.46%, a 58.6% reduction, Table 1 and Fig. 4) is an empirical measurement on a held-out 50,000-sample test set at default parameter values, computed with the same RPE normalization for both CDI and JCDI; it is not forced by any equation, and no fitted parameter is relabeled as a prediction. The reference list contains no self-citations by the present authors, so no load-bearing uniqueness theorem or ansatz is imported from the authors' prior work. The one caveat is the Sobol sensitivity screen (Section 2 and Supplementary Section 3): the 30 estimated parameters are selected using rankings under the same three fault classes (ordinary, trip, stall) under which MARPE and RMSE are later evaluated, which can inflate absolute accuracy estimates and weaken the OOD generalization claim. This is a benchmark-design and correctness risk, not a circular reduction, because the JCDI-versus-CDI comparison is made on the identical parameter set and fault classes and the error reduction is an empirical outcome rather than an algebraic consequence. The paper also explicitly defers validation on real PMU data to future work (Section 3), so it does not claim the synthetic pipeline substitutes for real-world transfer. Overall the central derivation is independent of its inputs, giving a low circularity score of 1.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central empirical claims rest on domain assumptions about the simulator and the prior rather than on fitted physical parameters. Three hand-chosen design choices act as free parameters: the 30-parameter selection, the uniform sampling ranges, and the specific fault scenarios. No new physical entities are introduced.

free parameters (3)

Number of selected sensitive parameters = 30
The 30 most sensitive parameters by total Sobol index across the three fault events are estimated; the headline MARPE and RMSE numbers apply to this subset, not the full CMPLDWG parameter set.
Uniform sampling ranges for the 30 parameters = As in Supplementary Table 2 (e.g., Fma [0.1,0.3], LsA [1.5,3])
The training prior and all reported errors are defined relative to these hand-chosen ranges; different ranges would change identifiability and error magnitudes.
The three conditioning fault events = Bus 27, 135 ms; Bus 5, 135 ms; Bus 9, 44 ms
The choice and severity of the conditioning events determine how much the joint posterior narrows; the 58.6% reduction is specific to this event set.

assumptions (5)

domain assumption The CMPLDWG/ePHASORSIM pipeline faithfully represents real load dynamics for the studied faults
All data are synthetic; no real measurements are used. Section 2 and Supplementary Section 2 describe the simulator; Section 3 flags real-world deployment as future work.
domain assumption The 30 selected parameters are sufficient to capture the dynamic responses of interest
Sobol sensitivity analysis (Supplementary Section 3) discards the remaining ~170 parameters as low-sensitivity; this presumes their omission does not bias the parameter estimates for the three fault classes.
domain assumption Uniform sampling over the specified ranges defines the prior distribution
Training data are generated from uniform ranges (Supplementary Table 2); the diffusion posterior is the posterior under this synthetic prior, which may not match reality.
standard math Fault events are conditionally independent given the parameters
Used to factor the joint likelihood in Eq. (9); reasonable for separate simulations but not verified against real recordings.
standard math Standard DDPM and transformer machinery are valid for this conditional generation task
The method builds on the DDPM equations (Eqs. 10-12) and attention (Eqs. 7-8) without formal modification; any approximation error is inherited from those standard tools.

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Cite this review

Pith. "Pith review of Diffusion Model-based Parameter Estimation in Dynamic Power Systems." pith.science (2026). https://pith.science/paper/2AEWG5ZK

@misc{pith2026241110431,
  author       = {Pith},
  title        = {Pith review of: Diffusion Model-based Parameter Estimation in Dynamic Power Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2AEWG5ZK}},
  note         = {Machine review of arXiv:2411.10431}
}

abstract

Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness presents a critical barrier to accurate and unique identification. Here we introduce a parameter estimation framework to address such limits: the Joint Conditional Diffusion Model-based Inverse Problem Solver. By leveraging the stochasticity of diffusion models, it produces candidate solutions that capture underlying parameter distributions conditioned on the observations. Joint conditioning on multiple observations further narrows the posterior distributions of non-identifiable parameters. For composite load model parameterization, a challenging task in dynamic power systems, the proposed method achieves a 58.6% reduction in parameter estimation error compared to the single-condition model. It also accurately replicates system's dynamic responses under various electrical faults with root mean square errors below $4 \times {10^{ - 3}}$, exhibiting comprehensive advantages in calibration and efficiency over existing methods. Given its data-driven nature, it provides a general framework for parameter estimation while effectively mitigating the non-uniqueness problem across scientific domains.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 10 canonical work pages

[1]

Technical report, Western Electricity Coordinating Council (WECC) (2015)

WECC composite load model with DG speciﬁcation. Technical report, Western Electricity Coordinating Council (WECC) (2015)

work page 2015
[2]

Technical report, Western Electricity Coordinating Council (WECC) (2021)

WECC composite load model speciﬁcation technical report. Technical report, Western Electricity Coordinating Council (WECC) (2021)

work page 2021
[3]

Technical report, The Electric Power Research Institute (EPRI) (2020)

Technical reference on the composite load model. Technical report, The Electric Power Research Institute (EPRI) (2020)

work page 2020
[4]

Journal of Modern Power Systems and Clean Energy 8(5), 1015–1023 (2020)

Ma, Z., Wang, Z., Wang, Y., Diao, R., Shi, D.: Mathematical representation of WECC composite load model. Journal of Modern Power Systems and Clean Energy 8(5), 1015–1023 (2020)

work page 2020
[5]

Technical report, Electric Power Research Institute (2019)

The new aggregated distributed energy resources (der_a) model for transmission planning studies: 2019 update. Technical report, Electric Power Research Institute (2019)

work page 2019
[6]

https://opal-rt.atlassian.net/wiki/spaces/PEUD/pages/144472811/Positive+ Sequence+Fault (2024)

OPAL-RT TECHNOLOGIES, Inc.: ePHASORSIM User Document. https://opal-rt.atlassian.net/wiki/spaces/PEUD/pages/144472811/Positive+ Sequence+Fault (2024)

work page arXiv 2024
[7]

McGraw-Hill, New York (1994)

Kundur, P.: Power System Stability and Control. McGraw-Hill, New York (1994)

work page 1994
[8]

Tsinghua University Press, Beijing (2023)

Xinzhou, D., Bing, W., Shi, S.: Modern Power System Protection. Tsinghua University Press, Beijing (2023)

work page 2023

Show all 14 references

[9]

Mathematics and Computers in Simulation 55(1), 271– 280 (2001) https://doi.org/10.1016/S0378-4754(00)00270-6

Sobol, I.M.: Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates. Mathematics and Computers in Simulation 55(1), 271– 280 (2001) https://doi.org/10.1016/S0378-4754(00)00270-6 . The Second IMACS Seminar on Monte Carlo Methods

2001 doi
[10]

Networks in Systems Biology: Applications for Disease 13 Modeling, 93–118 (2020)

Tosin, M., Côrtes, A.M., Cunha, A.: A tutorial on sobol’global sensitivity analysis applied to biological models. Networks in Systems Biology: Applications for Disease 13 Modeling, 93–118 (2020)

2020
[11]

Computer Physics Communications 145(2), 280–297 (2002) https://doi.org/10.1016/S0010-4655(02)00280-1

Saltelli, A.: Making best use of model evaluations to compute sensitiv- ity indices. Computer Physics Communications 145(2), 280–297 (2002) https://doi.org/10.1016/S0010-4655(02)00280-1

2002 doi
[12]

Socio-Environmental Systems Modelling 4, 18155 (2022) https://doi.org/10.18174/sesmo.18155

Iwanaga, T., Usher, W., Herman, J.: Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling 4, 18155 (2022) https://doi.org/10.18174/sesmo.18155

2022 doi
[13]

The Journal of Open Source Software 2(9) (2017) https://doi.org/10.21105/joss.00097

Herman, J., Usher, W.: SALib: An open-source python library for sen- sitivity analysis. The Journal of Open Source Software 2(9) (2017) https://doi.org/10.21105/joss.00097

2017 doi
[14]

: Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al. : Human-level control through deep reinforcement learning. nature 518(7540), 529–533 (2015) 14

2015

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Technical report, Western Electricity Coordinating Council (WECC) (2015)

WECC composite load model with DG speciﬁcation. Technical report, Western Electricity Coordinating Council (WECC) (2015)

work page 2015

[2] [2]

Technical report, Western Electricity Coordinating Council (WECC) (2021)

WECC composite load model speciﬁcation technical report. Technical report, Western Electricity Coordinating Council (WECC) (2021)

work page 2021

[3] [3]

Technical report, The Electric Power Research Institute (EPRI) (2020)

Technical reference on the composite load model. Technical report, The Electric Power Research Institute (EPRI) (2020)

work page 2020

[4] [4]

Journal of Modern Power Systems and Clean Energy 8(5), 1015–1023 (2020)

Ma, Z., Wang, Z., Wang, Y., Diao, R., Shi, D.: Mathematical representation of WECC composite load model. Journal of Modern Power Systems and Clean Energy 8(5), 1015–1023 (2020)

work page 2020

[5] [5]

Technical report, Electric Power Research Institute (2019)

The new aggregated distributed energy resources (der_a) model for transmission planning studies: 2019 update. Technical report, Electric Power Research Institute (2019)

work page 2019

[6] [6]

https://opal-rt.atlassian.net/wiki/spaces/PEUD/pages/144472811/Positive+ Sequence+Fault (2024)

OPAL-RT TECHNOLOGIES, Inc.: ePHASORSIM User Document. https://opal-rt.atlassian.net/wiki/spaces/PEUD/pages/144472811/Positive+ Sequence+Fault (2024)

work page arXiv 2024

[7] [7]

McGraw-Hill, New York (1994)

Kundur, P.: Power System Stability and Control. McGraw-Hill, New York (1994)

work page 1994

[8] [8]

Tsinghua University Press, Beijing (2023)

Xinzhou, D., Bing, W., Shi, S.: Modern Power System Protection. Tsinghua University Press, Beijing (2023)

work page 2023