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REVIEW 5 major objections 4 minor 1 cited by

Cyber Resilience Assessment of Unbalanced Distribution System Restoration under Sparse Load Forecasting Attacks

T0 review · 5 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Twelve tampered weather inputs can make planned microgrid restoration infeasible under actual loads.

desk verdict Useful framework, but the missing clean-plan OPF baseline means the central causal claim is not yet established. read the letter →

arxiv 2510.03635 v3 pith:3NGUQ523 submitted 2025-10-04 eess.SY cs.SY

Chen Chao , Zixiao Ma , Ziang Zhang This is my paper
classification eess.SYcs.SY
keywords adversarialattackloadforecastingdistributionsystemrestorationcyberresilienceoptimalpowerflowmicrogridinverter-basedresourcesunbalancedthree-phase
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 aims to show that distribution-system restoration, which increasingly relies on AI-based load forecasting, is vulnerable to a new class of cyber attacks: sparse adversarial perturbations of weather inputs. The authors build a gradient-based attack that modifies only the most influential spatiotemporal input elements, then embed the compromised forecasts into a sequential restoration planner and test the resulting plans against a full three-phase unbalanced optimal power flow model. On a modified IEEE 123-bus feeder, perturbing as few as twelve weather inputs substantially increases forecast error and makes several microgrid restoration stages infeasible. The failures are traced to active-power-balance violations and insufficient grid-forming inverter ramping capability, meaning that a small, hard-to-detect input tampering can prevent critical loads from being restored. A sympathetic reader would care because it shifts the question from 'how much forecast error can an attack cause?' to 'can an attack actually stop restoration?'

What carries the argument

The central mechanism is the Sparse Adversarial Attack (SAA) algorithm: it computes the gradient of the forecasting loss with respect to the H×J weather/load input matrix, builds a binary mask marking the top-n entries by absolute gradient, and applies clipped sign-gradient updates only at those entries, preserving stealth. The other load-bearing component is the two-stage validation framework: a MILP restoration planner consuming attacked forecasts, followed by an unbalanced three-phase OPF that checks whether the resulting switching and dispatch plan stays feasible under un-attacked loads. The OPF includes nodal power balance, voltage, line-flow, GFL setpoint, and GFM ramping constraints (

What would settle it

Run the same MILP-plus-three-phase-OPF pipeline on the IEEE 123-bus feeder using measured (or noise-injected) actual load profiles as the validation input instead of the clean forecast; if no restoration stage becomes infeasible under the SAA-perturbed forecasts, the central claim that sparse weather-input perturbations cause system-level restoration failure would be refuted. A second check: set n=1 or n=2 and see whether any stage still fails.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that restoration planning built on forecasted loads fails under adversarial conditions even when the attack is extremely sparse. The proposed Sparse Adversarial Attack selects, at each iteration, the top-n gradient entries across the spatiotemporal weather-input matrix and perturbs only those elements within an epsilon ball, with a black-box variant that estimates gradients by querying the forecaster. The authors then validate the MILP restoration schedules in an unbalanced three-phase OPF under the clean forecast acting as 'true' load. In the IEEE 123-bus case, SAA with n=12 performs comparably to attacking an entire weather feature (72 elements), and n=72

Load-bearing premise

The load-bearing assumption is that the un-attacked forecast equals the true load; if the clean forecasting model has real error, the OPF comparison is model-vs-model, and the attack's attributed operational harm is not measured against ground truth (and the ramping-failure classification is further tied to user-set frequency thresholds in Eq. 25).

Editorial extensions

If this is right

  • If the claim holds, restoration planners cannot treat forecast accuracy as a proxy for cyber resilience; a forecast with small aggregate error can still produce infeasible switching and dispatch decisions.
  • Sparse attacks with a dozen perturbed weather entries can be as effective as attacking an entire weather feature while being far harder to detect, so defending only against broad data corruption is insufficient.
  • More complex forecasting architectures (CNN-LSTM over LSTM) appear more attack-susceptible, implying model choice affects downstream operational risk.
  • Operational failures in the validation concentrate in active-power balance and GFM ramping limits, so maintaining ramping reserves and dynamic headroom is a concrete mitigation.
  • Restoration outcome depends on which buses are attacked and when they are re-energized, so restoration sequencing itself becomes a security decision.

Reading between the lines

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

  • Because the validation treats the clean forecast as ground truth, the reported failure margins are relative to the model's own prediction; re-running the same pipeline against measured load data would reveal whether the attack's operational impact holds under realistic clean-model error.
  • The same attack-and-validate recipe generalizes beyond restoration: any grid decision process that feeds forecasts into an optimization (unit commitment, economic dispatch, volt-var control) could be stress-tested this way, a direction the paper does not pursue.
  • The dependence of the ramping constraint on user-chosen frequency thresholds (f_nadir, f_min) and the sensitivity coefficient alpha means the classification of 'insufficient ramping margin' as the failure cause is conditional on those parameters; a sensitivity sweep over them would sharpen the causal claim.
  • A defender could exploit the attack's own gradient signal: monitoring which weather inputs are most influential and screening for small but consistent changes in those cells is a testable detection scheme.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 4 minor

Summary. The paper develops a gradient-based sparse adversarial attack (SAA) on weather-related inputs to AI-based load forecasting, and evaluates downstream impact on distribution system restoration. The authors embed attacked forecasts into a MILP-based sequential restoration planner (from a prior published model) and validate the resulting plans under clean forecasts (treated as true loads) using an unbalanced three-phase OPF. Case studies on a modified IEEE 123-node feeder report that sparse perturbations (n=12 or 72 inputs) increase forecast MSE and cause OPF infeasibilities in some microgrid restoration stages, attributed mainly to active-power-balance violations and insufficient GFM ramping margins.

Significance. If the central causal claim is established, the paper addresses a timely and underexplored vulnerability: the coupling between AI-based load forecasting and cyber-physical restoration. The proposed SAA is clearly motivated, and the use of a three-phase unbalanced OPF validation is a strength over forecast-error-only studies. The results also point to actionable resilience insights (e.g., preserving GFM flexibility in early stages). However, the missing clean-plan counterfactual means the observed infeasibilities are not yet causally attributed to the attack; this must be fixed before the central claim can be accepted.

major comments (5)
  1. [§IV.B, §V.C, Fig. 4] The central claim requires that sparse weather-input perturbations are what make restoration plans OPF-infeasible. The validation only reports the OPF feasibility of the plan generated from attacked forecasts, evaluated under clean forecasts fθ(X) treated as 'actual'. The paper never reports the OPF result for the counterfactual clean-forecast plan. The statement in §V.C that the clean-forecast restoration sequence is the same as Table IV is not a substitute, because it compares only the switching/load sequence, not the GFL/GFM setpoints, and no OPF pass/fail is reported for that plan. Since the MILP planning model (Eqs. 10–11, from [3]) and the OPF validation model (Eqs. 13–25) are different approximations, it is possible that even the clean MILP dispatch is OPF-infeasible; then the failures in Table V would be model mismatch, not attack effects. Please run the OPF on the plan produced
  2. [Eq. (25), Table V] The GFM ramping constraint involves an unreported sensitivity coefficient α and user-defined thresholds f_nadir and f_min. These values directly determine whether 'insufficient ramping margins' become the binding cause of infeasibility. The paper does not list their numerical values or any sensitivity analysis. Moreover, the same symbol α is already used for the adversarial step size (Eqs. 3–8) and for the CLPU overshoot parameter (Eq. 12), causing ambiguity. Please report the values used for Eq. (25), clarify the notation, and test whether the qualitative results in Table V change under plausible variations of f_nadir and f_min.
  3. [Section III, Algorithms 1–2, Table III] Attack hyperparameters are not reported: the perturbation bound ε, step size α, number of iterations K, sparsity level n = 12/72, and (for black-box) the finite-difference δ are never given. Without these, the attack-performance comparisons in Table III are not reproducible, and the 'stealth' claim cannot be assessed because the actual magnitude of the perturbation is unknown. Please report these values and, ideally, show how the choice of ε affects both forecast MSE and downstream restoration feasibility.
  4. [§V.A, Fig. 5] The seven attacked buses are selected manually with no disclosed criterion. Because the restoration failures in Table V are stage- and location-specific (e.g., bus 46, bus 21, bus 66), the reader cannot tell whether the conclusions are robust to the choice of attacked buses. Please either provide a principled selection rule, or perform a sensitivity analysis over random/alternative attacked-bus sets and report the distribution of OPF infeasibility outcomes.
  5. [§V.C, Table V, Abstract] The text and the abstract do not match the evidence. The abstract claims 'voltage and power ramping violations', and §V.C states that 'buses 71, 92, and 99 in MG 4 are reported to violate certain constraints', but Table V lists only active-power-balance violations and lists bus 75, not 71, for MG 4. Either the OPF also produced voltage or ramping violations that are not shown, or the abstract/text overstate the findings. Please align the text, table, and abstract, and include all violated constraint types.
minor comments (4)
  1. [Eq. (12)] The text says 'The parameter α is the overshoot value' but Eq. (12) uses 'a' in the expression P0·(1 + a·e^(−(t−t0)/τ)). Please make the notation consistent.
  2. [§V.C] The sentence 'We use the generated restoration plans in Table 6 to conduct OPF validation' refers to Table 6, but the table is numbered Table IV. Please correct the cross-reference.
  3. [§V.C] The phrase 'Table III represents the increase in MSE from the attacked model predictions to clean condition' is imprecise: it is not clear whether the values are absolute MSE increases, relative increases, or ratios. Please define the metric.
  4. [§IV.B] The assumption that fθ(X) equals ground truth is stated but its limitation is not discussed. If the clean forecasting model has non-negligible error in practice, the OPF validation compares two model outputs, and the failure margin attributed to the attack is not measured against true loads. Please add a sentence acknowledging this and comment on how the conclusions might change.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: attack construction, restoration MILP, and OPF validation are mutually independent; the only self-citation is a planning tool, and the main validity caveat is a missing clean-plan OPF counterfactual, which is an experimental-control issue rather than circularity.

full rationale

The derivation chain is not circular. The sparse attack (Algorithms 1-2) is constructed from gradients of the forecasting loss and is evaluated against the same MSE-type objective it optimizes; that is an attack-performance metric, not a disguised prediction of restoration outcome. The restoration MILP (Eqs. 10-11) is imported from prior published work [3] and is used only as a planning tool; it does not contain OPF feasibility results. The OPF validation (Eqs. 13-25) is a separate unbalanced three-phase power-flow model with its own constraints; infeasibility is determined by an optimizer after seeing the attacked plan, so the 'attack succeeds' outcome is not encoded in the attack construction. The paper's stated assumption, 'Focusing on cyber resilience, this study assumes an accurate load forecasting model' (Section IV.B), makes f_theta(X) the validation truth; this is a modeling simplification and a limitation, not a circular definition. The only self-citation is [3], co-authored by Z. Ma, used as a tool for the MILP planner; the paper reproduces key equations rather than treating the citation as a black-box proof of the conclusion. The more substantive validity concern---the absence of a reported clean-plan OPF counterfactual, since the paper only states 'We also schedule the restoration sequence based on the normal forecasting loads f_theta(X), and the results are the same as in Table IV' (Section V.C), which is a sequence comparison, not an OPF pass/fail---could affect causal attribution if the MILP and OPF models mismatch, but it is an experimental-control issue, not a reduction of the result to its own inputs. No circular step is identified.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The paper contributes an attack+validation workflow; the main hidden overhead sits in unreported attack, frequency, and model parameters plus manual bus selection, none of which are independently verified. No new physical entities are introduced.

free parameters (6)
  • SAA sparsity level n and mask selection = n=12 and n=72 tested; no selection criterion
    The paper's 'sparse attack' claim depends on how many input elements are perturbed; n is user-set and strongly affects both stealth and effectiveness (Algorithm 2).
  • PGD/SAA step size α and perturbation bound ϵ = not reported
    These control attack magnitude and stealth in Eq. (2) and Algorithms 1-2; no values or sensitivity analysis are given, yet attack success depends on them.
  • Frequency/ramping coefficient α in Eq. (25) plus f_nadir and f_min = not reported
    Defines the GFM maximum load step and is the stated cause of restoration failures; these user-defined values are never disclosed, despite ramping violations being a primary result.
  • CLPU overshoot and decay parameters per load type/time = adopted from [28] (e.g., morning residential 1.33, 11.5 min)
    Shapes post-blackout load magnitudes in Eq. (12) and Table II; taken from prior measurements rather than fitted here, but central to the load-mismatch calculations.
  • Seven attacked bus locations = buses indicated in red in Fig. 5
    Manual selection without a disclosed criterion; the system-level failure results depend on which buses are compromised and when they are restored.
  • LSTM/CNN-LSTM training hyperparameters and random seeds = not reported
    No architecture details, epochs, or seeds are given; Table III shows attack performance differences between the two models, so these settings materially affect the results.
assumptions (6)
  • domain assumption The un-attacked forecast fθ(X) equals the true load during OPF validation.
    Section IV.B: 'Focusing on cyber resilience, this study assumes an accurate load forecasting model.' Without this, 'actual loads' in the OPF are model outputs, not ground truth.
  • domain assumption The MILP restoration model in [3] correctly captures unbalanced three-phase restoration feasibility and frequency dynamics.
    Section IV.A adopts the model and refers to [3] for full constraints; the current paper does not re-derive or independently verify it.
  • domain assumption GFL inverter outputs stay at dispatch setpoints (Eqs. (20)-(21)) and GFM outputs obey the ramp envelope in Eqs. (24)-(25).
    These flexibility assumptions define whether attacked plans are truly infeasible; real-time redispatch or load shedding could restore feasibility in practice.
  • domain assumption The attacker can query the forecasting model and modify four weather features within a small ϵ bound without being detected.
    Assumed in Section III for both white-box and black-box settings; no detection or defense mechanism is modeled, which simplifies the threat model substantially.
  • domain assumption Post-blackout load follows the exponential CLPU model with parameters in Table II, sourced from [28].
    Eq. (12) and Table II set the load magnitudes; alternative CLPU parameters would change the size of load mismatches and hence the failure margins.
  • domain assumption The modified IEEE 123-bus feeder with listed IBR sizes is representative of realistic distribution restoration.
    Section V.A uses a single modified test feeder; no sensitivity analysis over network topology, IBR sizing, or load mix is provided.

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Cite this review

Pith. "Pith review of Cyber Resilience Assessment of Unbalanced Distribution System Restoration under Sparse Load Forecasting Attacks." pith.science (2026). https://pith.science/paper/3NGUQ523

@misc{pith2026251003635,
  author       = {Pith},
  title        = {Pith review of: Cyber Resilience Assessment of Unbalanced Distribution System Restoration under Sparse Load Forecasting Attacks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3NGUQ523}},
  note         = {Machine review of arXiv:2510.03635}
}
read the original abstract

System restoration is critical for power-system resilience, but its growing reliance on artificial intelligence (AI)-based load forecasting creates a cyber-physical vulnerability in the restoration decision loop. Manipulated forecasts can cause infeasible restoration schedules, insufficient inverter-based-resource ramping margins, and unsuccessful recovery of de-energized segments, yet the resilience of restoration processes to such attacks remains largely unexplored. This paper evaluates restoration vulnerability at the system level rather than only measuring forecasting error. A gradient-based sparse perturbation method is developed as a stress-testing tool to identify influential forecasting inputs. We further create a restoration-aware validation framework that embeds these compromised forecasts into a sequential restoration model and evaluates operational feasibility using an unbalanced three-phase optimal power flow formulation. Case studies on a modified IEEE 123-bus feeder show that sparse input perturbations can substantially increase forecasting error and make selected microgrid restoration stages infeasible. The results reveal system-level failures caused by active-power-balance infeasibility and power ramping violations, which can prevent the restoration of critical loads. These findings provide actionable insights for designing cybersecurity-aware restoration planning frameworks.

Figures

Figures reproduced from arXiv: 2510.03635 by the authors.

Figure 1
Figure 1. Illustration of cyber attacks on load forecasting along with the impact over power system restoration. Weather data [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Illustration of three adversarial attack methods on load [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Vulnerabilities of power system restoration under ad [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Modified IEEE 123 node test feeder. data. The attack is implemented in the testing stage to simulate the real world black box attack scenario. TABLE I: Locations and capacities of GFL and GFM IBRs in IEEE 123 test feeder Type Locations Capacities GFL IBR (1-ϕ) 7, 15, 4…
Figure 6
Figure 6. Figure 6: Restoration results of the IEEE 123 bus test feeder, [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: OPF Validation of the proposed SAA. The microgrid in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

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Forward citations

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Reference graph

Works this paper leans on

28 extracted references · 1 canonical work pages · cited by 1 Pith paper

  1. [3]

    A two-level simulation- assisted sequential distribution system restoration model with frequency dynamics constraints,

    Q. Zhang, Z. Ma, Y . Zhu, and Z. Wang, “A two-level simulation- assisted sequential distribution system restoration model with frequency dynamics constraints,”IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 3835–3846, 2021

    2021

  2. [1]

    Department of energy releases report evaluating u.s. grid reliability and security,

    U.S. Department of Energy, “Department of energy releases report evaluating u.s. grid reliability and security,” U.S. Dept. of Energy,” Tech. Rep., 2025, [Online]. Available: https://www.energy.gov/articles/ department-energy-releases-report-evaluating-us-grid-reliability-and-security

    2025

  3. [2]

    Modernizing distribution system restoration to achieve grid resiliency against extreme weather events: An integrated solution,

    C. Chen, J. Wang, and D. Ton, “Modernizing distribution system restoration to achieve grid resiliency against extreme weather events: An integrated solution,”Proceedings of the IEEE, vol. 105, no. 7, pp. 1267–1288, 2017

    2017

  4. [4]

    Dis- tribution system blackstart and restoration using ders and dynamically formed microgrids,

    S. Maharjan, C. Bai, H. Wang, Y . Yao, F. Ding, and Z. Wang, “Dis- tribution system blackstart and restoration using ders and dynamically formed microgrids,”IEEE Transactions on Smart Grid, 2025

    2025

  5. [5]

    Multi-agent deep reinforce- ment learning for distributed load restoration,

    L. Vu, T. Vu, T. L. Vu, and A. Srivastava, “Multi-agent deep reinforce- ment learning for distributed load restoration,”IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1749–1760, 2023

    2023

  6. [6]

    Distribution service restoration: A distributionally robust reinforcement learning approach,

    S. S. Fazlhashemi, M. E. Khodayar, M. Khodayar, and J. Wang, “Distribution service restoration: A distributionally robust reinforcement learning approach,”IEEE Transactions on Smart Grid, 2025

    2025

  7. [7]

    Dy- namic frequency-constrained load restoration considering multi-phase cold load pickup behaviors,

    D. Xie, Y . Xu, S. Nadarajan, V . Viswanathan, and A. K. Gupta, “Dy- namic frequency-constrained load restoration considering multi-phase cold load pickup behaviors,”IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 107–118, 2023

    2023

  8. [8]

    Robust distribution system load restoration with time-dependent cold load pickup,

    M. Song, W. Sun,et al., “Robust distribution system load restoration with time-dependent cold load pickup,”IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3204–3215, 2020

    2020

Show all 28 references
  1. [9]

    Mpc-based black start and restoration for resilient der-rich electric distribution system,

    S. Konar and A. K. Srivastava, “Mpc-based black start and restoration for resilient der-rich electric distribution system,”IEEE Access, vol. 11, pp. 69 177–69 189, 2023

    2023

  2. [10]

    Situation-aware load restoration considering uncertainty and correla- tion,

    S. N. Edib, Y . Lin, V . M. V okkarane, F. Qiu, Y . Zhang, and P. Du, “Situation-aware load restoration considering uncertainty and correla- tion,”IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 2611– 2629, 2023

    2023

  3. [11]

    Short-term residential load forecasting based on lstm recurrent neural network,

    W. Kong, Z. Y . Dong, Y . Jia, D. J. Hill, Y . Xu, and Y . Zhang, “Short-term residential load forecasting based on lstm recurrent neural network,” IEEE transactions on smart grid, vol. 10, no. 1, pp. 841–851, 2017

    2017

  4. [12]

    A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network,

    C. Tian, J. Ma, C. Zhang, and P. Zhan, “A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network,”Energies, vol. 11, no. 12, p. 3493, 2018

    2018

  5. [13]

    Optimizing attention in a transformer for multihorizon, multienergy load forecasting in in- tegrated energy systems,

    J. Fan, W. Zhuang, M. Xia, W. Fang, and J. Liu, “Optimizing attention in a transformer for multihorizon, multienergy load forecasting in in- tegrated energy systems,”IEEE Transactions on Industrial Informatics, 2024

    2024

  6. [14]

    Vulnerability of machine learning approaches applied in iot-based smart grid: A review,

    Z. Zhang, M. Liu, M. Sun, R. Deng, P. Cheng, D. Niyato, M.-Y . Chow, and J. Chen, “Vulnerability of machine learning approaches applied in iot-based smart grid: A review,”IEEE Internet of Things Journal, vol. 11, no. 11, pp. 18 951–18 975, 2024

    2024

  7. [15]

    Enhancing cyber-resiliency of der-based smart grid: A survey,

    M. Liu, F. Teng, Z. Zhang, P. Ge, M. Sun, R. Deng, P. Cheng, and J. Chen, “Enhancing cyber-resiliency of der-based smart grid: A survey,” IEEE Transactions on Smart Grid, 2024

    2024

  8. [16]

    Is machine learning in power systems vulnerable?

    Y . Chen, Y . Tan, and D. Deka, “Is machine learning in power systems vulnerable?” in2018 IEEE International Conference on Communica- tions, Control, and Computing Technologies for Smart Grids (Smart- GridComm). IEEE, 2018, pp. 1–6

    2018

  9. [17]

    Exploiting vulnerabilities of load forecasting through adversarial attacks,

    Y . Chen, Y . Tan, and B. Zhang, “Exploiting vulnerabilities of load forecasting through adversarial attacks,” inProceedings of the tenth ACM international conference on future energy systems, 2019, pp. 1–11

    2019

  10. [18]

    Robust load forecasting towards adversarial attacks via bayesian learning,

    Y . Zhou, Z. Ding, Q. Wen, and Y . Wang, “Robust load forecasting towards adversarial attacks via bayesian learning,”IEEE Transactions on Power Systems, vol. 38, no. 2, pp. 1445–1459, 2022

    2022

  11. [19]

    Machine learning-based anomaly detection for load forecasting under cyberattacks,

    M. Cui, J. Wang, and M. Yue, “Machine learning-based anomaly detection for load forecasting under cyberattacks,”IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5724–5734, 2019

    2019

  12. [20]

    Power system restoration-a task force report,

    M. Adibi, P. Clelland, L. Fink, H. Happ, R. Kafka, J. Raine, D. Scheurer, and F. Trefny, “Power system restoration-a task force report,”IEEE Transactions on Power systems, vol. 2, no. 2, pp. 271–277, 1987

    1987

  13. [21]

    Power system restoration: a literature review from 2006 to 2016,

    Y . Liu, R. Fan, and V . Terzija, “Power system restoration: a literature review from 2006 to 2016,”Journal of Modern Power Systems and Clean Energy, vol. 4, no. 3, pp. 332–341, 2016

    2006

  14. [22]

    Ai platform - machine learning services by google cloud,

    Google Cloud, “Ai platform - machine learning services by google cloud,” https://cloud.google.com/ai-platform, 2025, accessed: Jul. 9, 2025

    2025

  15. [23]

    A survey on adversarial attacks and defences,

    A. Chakraborty, M. Alam, V . Dey, A. Chattopadhyay, and D. Mukhopad- hyay, “A survey on adversarial attacks and defences,”CAAI Transactions on Intelligence Technology, vol. 6, no. 1, pp. 25–45, 2021

    2021

  16. [24]

    Adversarial examples in the physical world,

    A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” inArtificial intelligence safety and security. Chapman and Hall/CRC, 2018, pp. 99–112

    2018

  17. [25]

    Bad data detection method for smart grids based on distributed state estimation,

    Y . Gu, T. Liu, D. Wang, X. Guan, and Z. Xu, “Bad data detection method for smart grids based on distributed state estimation,” in2013 IEEE International Conference on Communications (ICC). IEEE, 2013, pp. 4483–4487

    2013

  18. [26]

    The role of inverter-based resources during system restoration,

    Aurecon, “The role of inverter-based resources during system restoration,” CSIRO,” Final Report, Jun 2023, online. [On- line]. Available: https://www.csiro.au/-/media/EF/Files/GPST-Roadmap/ Stage3-Final/Topic-5 The-role-of-inverter-based-resources.pdf

    2023

  19. [27]

    Commercial and residential hourly load profiles for all tmy3 locations in the united states,

    S. Ong and N. Clark, “Commercial and residential hourly load profiles for all tmy3 locations in the united states,” Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory, https://doi.org/10.25984/1788456, 2014, accessed: 2025-06-12. [Online]. Available: http...

    2014 doi

  20. [28]

    Cold load pickup model parameters based on measurements in distribution sys- tems,

    C. Hachmann, G. Lammert, L. Hamann, and M. Braun, “Cold load pickup model parameters based on measurements in distribution sys- tems,”IET Generation, Transmission & Distribution, vol. 13, no. 23, pp. 5387–5395, 2019

    2019

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Reviewed August 4, 2026 · model on record in the stance chip above.