Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

Maryam Saeedifard; Qianxue Xia; Suman Debnath

REVIEW 3 major objections 3 minor 40 references

The paper claims that a machine-learning classifier can draw the stability boundary of a multiport solar power plant well enough to decide when to switch off the energy-balancing circulations, saving an average of 550 kW without destabilizi

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-03 14:08 UTC pith:GVWUTAPA

load-bearing objection Useful ML-gated EBC for MARS with solid cHIL work, but the claimed 'stability boundary' is a classifier fitted to simulation labels under an unspecified PV/ESS dispatch rule. the 3 major comments →

arxiv 2607.29072 v1 pith:GVWUTAPA submitted 2026-07-31 eess.SY cs.SY

Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

Qianxue Xia , Suman Debnath , Maryam Saeedifard This is my paper

classification eess.SY cs.SY

keywords energy balancing controlcapacitor voltage balancingmultiport solar power plantmodular converterstability boundaryneural network classifierhardware-in-the-looppower loss reduction

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper tries to establish that the boundary between stable and unstable operating regions of a multiport solar power plant (MARS) can be learned from simulation data rather than derived analytically, and that a neural-network classifier can use that boundary to switch an energy-balancing control (EBC) on or off. The EBC keeps submodule capacitor voltages balanced by injecting a circulating current, but that current costs power. The authors train a two-layer feedforward network on 6,158 simulated operating points labeled by whether capacitor voltages stay within prescribed bounds and dc ripple stays under 20%. The network reaches 96.5% testing accuracy, and when it deactivates the EBC across 1,104 operating conditions it reduces average power loss by about 550 kW. If true, this makes the MARS system more efficient across a wider operating range by turning on the balancing mechanism only where it is actually needed.

Core claim

The central discovery is that the stability boundary of the MARS system – defined by which combinations of ac active power, dc power, and ac reactive power keep all submodule capacitor voltages within ±15% of nominal and dc current ripple under 20% – can be mapped by a small artificial neural network. Trained offline on a high-fidelity simulation model of the plant and deployed in the control loop, the network outputs a single bit: whether the energy-balancing control should be activated. On held-out test data it classifies 96.5% of operating conditions correctly, outperforming both a simple threshold rule (80.57%) and a random-forest classifier (96.05%). When the classifier decides to switc

What carries the argument

The object carrying the argument is the energy-balancing control (EBC) and its learned activation criterion. The EBC injects a fundamental-frequency circulating current whose references are computed by minimizing the sum of squared deviations of submodule capacitor voltages from their arm averages; this redistributes power among submodules and keeps voltages inside the stability bounds (0.85–1.15 pu capacitor voltage, <20% dc ripple). The EBC criterion is a two-layer feedforward neural network with nine hidden neurons and sigmoid/softmax activations, trained offline on 6,158 simulated operating conditions (inputs: Pac, Qac, Pdc; output: EBC on/off). The network replaces an analytical derivat

Load-bearing premise

The stability labels that train the classifier come from the authors' own simulation model of the converter under chosen voltage and ripple bounds, so the learned boundary is only as trustworthy as that model's fidelity to the physical hardware; no independent measurements verify the boundary.

What would settle it

Run a hardware test or an independently verified, high-fidelity simulation of the MARS converter at the 17 test-set operating points where the ANN predicted 'stable without EBC' but the training sweep said 'unstable', and check whether any capacitor voltages exceed 1.15 pu or the dc ripple exceeds 20%; if any do, the classifier is unsafe in precisely the cases that matter. More generally, stepping Pac and Pdc along a fine path across the learned boundary while monitoring capacitor voltages would directly audit the learned stability region.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

Operating points previously kept in a conservative stable region can be used safely with the EBC activated, expanding the usable power range of the MARS plant.
Deactivating the EBC where the network says it is unnecessary suppresses the fundamental circulating current, cutting conduction losses; the paper quantifies the average saving at 550 kW over 1,104 conditions.
The classification overhead in the controller is negligible: CPU utilization rises from 12.7% to about 13.2% with the ANN criterion.
The approach is claimed to extend to modular converters of any size that mix submodules with external sources (PV, ESS) because it treats the plant as a black box and does not require a closed-form stability derivation.
The sorting frequency of the capacitor voltage balancing algorithm is shown to shift the stability boundary, so a data-driven boundary trained on the deployed algorithm is more accurate than a fixed analytical limit.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Because the classifier is trained on a grid with 40 MW / 30 Mvar resolution, the true boundary could cut through a cell; a safety margin around the learned boundary, or a third output class for "uncertain", would be a cheap extension to avoid the unsafe false negatives.
The paper's three stability labels (stable, voltage-stable, unstable) suggest that conditions that satisfy the voltage bounds but exceed the dc ripple limit might be usable with a ripple filter; the authors do not exploit this intermediate class.
The model is trained purely offline; an online adaptation loop that updates the network as the plant ages, or as new submodule types are added, could keep the boundary accurate without re-running the full 6,158-case sweep.
The 17 test-set false negatives are all in the dangerous direction (unstable predicted stable); readers of the paper who want to deploy this should ask what happens in those cases before trusting the 96.5%.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

Useful ML-gated EBC for MARS with solid cHIL work, but the claimed 'stability boundary' is a classifier fitted to simulation labels under an unspecified PV/ESS dispatch rule.

read the letter

Dear [Colleague],

Quick take: the paper does something useful and mostly honest. It trains an ANN/RF classifier to decide when to disable the energy balancing control (EBC) in a MARS converter, and demonstrates via cHIL that the system stays stable through transitions. The loss-saving number (550 kW average across 1,104 cases) is plausible, and the confusion matrices are reported. Credit where due: the cHIL setup is nontrivial, the sorting-frequency analysis (Sec. III.C) is a nice touch, and the comparison against a threshold-based benchmark is the right way to frame the ML contribution.

The soft spot is the central claim. The "stability boundary" is not derived from physics or extracted as a boundary; it is a binary classifier trained on labels produced by the authors' own high-fidelity simulation model [5]. That would be acceptable if the input features actually determined the labels. But the paper itself states (Sec. VI.B.3) that "the combinations of P_pv and P_ess can also impact the SM capacitor voltages," while the ANN features are only P_ac, Q_ac, P_dc (Fig. 6). Since P_pv and P_ess are not uniquely determined by those three (the power reference block presumably picks them by some dispatch rule, which is never stated), the same feature vector can map to different stability labels. The 96.5% test accuracy then only reflects the particular dispatch used to generate the 6,158-point grid. That is a real gap, not a nit.

Two more issues, smaller. First, the test-set confusion matrix shows 17 unstable conditions classified as stable; the paper never discusses what happens when the EBC is wrongly turned off under those conditions. For a control meant to preserve stability, that is the safety-critical number and it is missing. Second, the loss-reduction formula in Section V has a sign inconsistency: the relative reduction is defined with (with-criterion minus without-criterion) in the numerator, which would be negative for savings. Minor, but worth fixing.

Also, no dataset or trained models are shared, so the result is not independently reproducible. The benchmark thresholds (Pac_limit, Pdc_limit) are tuned on the same data, so the 80.57% vs. 96.5% comparison is somewhat favorable to the ML methods.

Overall: the engineering is real, the cHIL tests add value, and the paper is worth refereeing, but the "accurate stability boundary determination" framing overstates what is actually a dispatch-specific classifier. A serious revision should add P_pv/P_ess as features (or justify why the dispatch rule is fixed), analyze false-negative impact, and release the data or models.

I'd bring it to a reading group if someone works on MARS or ML-gated controls; otherwise it's a "maybe." I would not cite it yet in my own work until the feature sufficiency issue is addressed.

Recommendation: send to peer review, but expect major revision.

Referee Report

3 major / 3 minor

Summary. The paper proposes a machine-learning-assisted criterion for deciding when to activate or deactivate the energy balancing control (EBC) in the MARS system, a multiport modular converter with normal, PV, and ESS submodules. The authors generate 6,158 labeled operating points from a high-fidelity MARS simulation model, sweeping P_ac, P_dc, and Q_ac, and train ANN and random-forest classifiers to predict whether EBC is required to maintain capacitor-voltage and dc-ripple constraints. The ANN achieves 96.5% test accuracy, outperforming an if-else benchmark, and is integrated into PSCAD and a control-hardware-in-the-loop (cHIL) platform. The paper reports an average power-loss reduction of 550 kW across 1,104 operating conditions where EBC is deactivated, and cHIL tests demonstrate stable transitions and the influence of P_dc and Q_ac on capacitor-voltage disparity.

Significance. The data-driven EBC criterion is a practical alternative to analytical derivation of a stability boundary in a complex modular converter, and the cHIL implementation with negligible CPU overhead is a useful engineering contribution. The comparison of ANN, RF, and a simple benchmark, together with loss-reduction statistics, provides evidence of practical benefit. However, the claimed 96.5% accuracy is an agreement rate with labels produced by the authors' own simulation model, not with independent physical measurements, and the technical gaps identified below (feature sufficiency, false-negative cost, and label definition) currently prevent full acceptance of the central 'stability boundary determination' claim.

major comments (3)

[Sec. IV.A, Fig. 6, and Sec. VI.B.3] The ANN input space is (P_ac, P_dc, Q_ac), but Sec. VI.B.3 explicitly states that P_dc is dependent on both P_pv and P_ess and that the combinations of P_pv and P_ess can also impact the SM capacitor voltages. Unless the power reference calculation block of Fig. 4 uniquely maps each (P_ac, P_dc, Q_ac) triple to a single (P_pv, P_ess) pair in all 6,158 data points, identical feature vectors can correspond to different stability labels, and the 96.5% test accuracy would be an artifact of the particular dispatch rule used to generate the data. The paper does not report whether duplicate-feature/different-label cases exist, nor does it specify the dispatch rule. Please quantify the uniqueness of the mapping or, if it is not unique, include the additional features (or a dispatch identifier) in the input space.
[Sec. IV.C, Fig. 7] The test confusion matrix shows 17 false negatives: 17 operating conditions that require EBC activation (target class 1) are classified as stable (output 0). For a controller that gates a stabilizing action, these are safety-critical errors because the EBC would be left deactivated under potentially unstable conditions. The paper reports only the overall accuracy (96.5%) and does not analyze precision/recall, the location of these misclassified points relative to the boundary, or a possible asymmetric threshold to favor conservative EBC activation. Please provide per-class metrics and evaluate whether the 17 false negatives actually lead to voltage/ripple violations in simulation.
[Sec. IV.A, Fig. 5] Fig. 5 visualizes three categories — stable, voltage stable, and unstable — but the classifiers are binary (EBC on/off). The manuscript does not specify how 'voltage stable' cases (which satisfy capacitor-voltage bounds but violate the dc-ripple limit) are mapped to the binary labels. This ambiguity affects the ground-truth definition and the interpretation of the learned stability boundary. Please clarify the assignment of the three classes to the binary labels and report the class distribution used for training.

minor comments (3)

[Sec. V] The formula for relative loss reduction, (P_loss_with_EBC_criterion - P_loss_without_EBC_criterion)/P_loss_with_EBC_criterion, produces a negative value for the conditions where the criterion reduces losses, yet Table 4 reports positive reductions. Please correct the sign or the formula, and state clearly whether the comparison is with-minus-without or without-minus-with.
[Fig. 3] The stacked-bar labels in Fig. 3 are ambiguous: the rows '47% 37% 20% 0' and '53% 63% 80% 100%' can be read in conflicting ways, while the text claims a positive correlation between sorting frequency and the proportion of stable conditions. Please label the stable and unstable portions explicitly in the figure or caption.
[Fig. 7] The phrase 'grey diagonal cells' does not match the green/red confusion-matrix display shown in the figure. Please describe the matrix with the correct coloring and, as noted in the major comments, report per-class precision/recall in addition to overall accuracy.

Circularity Check

0 steps flagged

No significant circularity: the ML criterion is an empirical supervised classifier trained and tested on held-out simulation labels, with additional cHIL validation; no fitted quantity is relabeled as an independent prediction.

full rationale

The paper's derivation chain is an empirical supervised-learning workflow rather than a closed-form analytical derivation. Stability labels are generated offline from the authors' prior high-fidelity MARS model [5] under explicitly stated voltage and dc-ripple constraints; the ANN/RF classifiers are trained on those labels using a 70/15/15 split, and the reported 96.5% accuracy is on the held-out test set. The benchmark if-else thresholds are explicitly presented as a benchmark method, not as a predicted stability boundary, and the central loss-reduction claim is measured in simulation and cHIL. The cHIL tests provide a separate real-time simulator/controller implementation, so the paper is not purely self-referential. The acknowledgment in Section VI.B.3 that 'the combinations of P_pv and P_ess can also impact the SM capacitor voltages' identifies a potential feature-sufficiency/correctness limitation, not a circular step: the classifier is not claimed to be derived from physics, and no fitted parameter is renamed as an independent prediction. Self-citation to [5] supplies the simulation model, but the paper does not invoke it as a uniqueness theorem or as the sole justification for the learned boundary; the boundary is explicitly data-fitted and assessed on held-out data. Accordingly, concerns about simulation-fidelity and omitted features are external-validity risks rather than circularity.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 0 invented entities

No new physical entities are introduced. The EBC criterion is a control policy, not a new object. All free parameters are related to the ML models and the stability-limit choices; the central claim rests entirely on the fidelity of the authors' own simulation model and the safety of the classifier's false-negative rate.

free parameters (5)

ANN hidden-layer neuron count = 9
Selected by grid search as optimal; affects classification accuracy and is fitted to the dataset.
ANN weights and biases = Not disclosed
Trained on 6,158 simulation-labeled operating conditions; the resulting stability-boundary mapping is entirely determined by these fitted values.
RF hyperparameters = Not specified
Tuned via grid search; exact values not given, but affect the RF classifier's accuracy.
Benchmark thresholds Pac_limit and Pdc_limit = Not given
Extracted from the same 6,158-condition dataset (Algorithm 1), so they are fitted thresholds rather than derived from physics.
Capacitor voltage limits and dc ripple bound = 0.85–1.15 pu; <20%
Chosen design constraints defining 'stability'; not fitted to data, but they determine the labels.

axioms (5)

domain assumption The high-fidelity MARS model from [5] faithfully represents the physical MARS system.
All training labels, loss numbers, and cHIL plant dynamics derive from this model; no physical hardware validation is provided.
domain assumption The stability definition (capacitor voltage within ±15% of nominal and dc ripple <20%) is the correct stability criterion.
Used to label the 6,158 operating conditions; if the bounds are wrong, the learned boundary is wrong.
domain assumption The grid sweep over Pac, Pdc, Qac and the resulting 6,158 feasible points are representative of the operating space.
Training data is a filtered grid, not a random sample; generalization to random operating conditions is assumed.
domain assumption A 96.5% test accuracy is sufficient to safely disable EBC in the 1,104 deactivation cases.
The paper does not analyze the impact of the 17 false-negative test cases where unstable conditions would be classified as stable.
domain assumption The cHIL platform (OPAL-RT OP5707) replicates the PSCAD/EMTDC simulation behavior closely enough for validation.
Used to validate the ANN-based criterion; plant is still a real-time simulation, not physical hardware.

pith-pipeline@v1.3.0-daily-deepseek · 14157 in / 12174 out tokens · 121879 ms · 2026-08-03T14:08:55.578674+00:00 · methodology

0 comments

read the original abstract

The multiport autonomous reconfigurable solar power plant (MARS) is a promising solution to integrate renewable energy resources and energy storage systems into the ac power grid and HVdc links. In the MARS system, various input power sources are connected to the individual submodules (SMs) through dc-dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This paper aims to address the research gap by accurately determining the stability boundary for the MARS system. A novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. In conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both PSCAD/EMTDC simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criteria, efficient energy management becomes achievable for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high efficiency standards.

discussion (0)

Reference graph

Works this paper leans on

40 extracted references

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If 𝑃𝑎𝑐 > 𝑃𝑎𝑐_𝑙𝑖𝑚𝑖𝑡 and 𝑃𝑑𝑐 > 𝑃𝑑𝑐_𝑙𝑖𝑚𝑖𝑡
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Set 𝑖𝑐𝑖𝑟𝑐𝑞1𝑟𝑒𝑓 = 0 and 𝑖𝑐𝑖𝑟𝑑1𝑟𝑒𝑓 = 0
[3]

Set 𝑚𝑐𝑖𝑟𝑞1 = 0 and 𝑚𝑐𝑖𝑟𝑑1 = 0
[4]

end If where 𝑖𝑐𝑖𝑟𝑐𝑞1𝑟𝑒𝑓 = 0, and 𝑖𝑐𝑖𝑟𝑑1𝑟𝑒𝑓 = 0 are the reference values of the fundamental circulating current; 𝑚𝑐𝑖𝑟𝑞1 = 0 and 𝑚𝑐𝑖𝑟𝑑1 = 0 are the modulation ind ices of the fundamental circulating current. Table 1 – CPU utilization in real time simulation w/o EBC criterion RF ANN CPU Utilization 12.7% 13.4% 13.2% Table 2 – Testing accuracy of the confusio...
[5]

Transition from Conventional Sorting Algorithm to EBC under Unstable Operating Conditions. This test case aims to demonstrate the limitations of the conventional sorting algorithm in balancing the capacitor voltages of the MARS system under certain operating conditions, and how the activation of the EBC addresses these limitations. Specifically, in the ti...
[6]

Impact of Operating Condition Changes with EBC Criterion This section presents cyclic test results that assess the system’s stability across varying steady -state operating conditions, managed by the integration of a NN -based EBC criterion within the control architecture. Fig. 12 illustrates the system’s response to a series of dispatched command steps, ...
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This section presents two test cases designed to evaluate the influence of 𝑃𝑑𝑐 and 𝑄𝑎𝑐 on the voltage disparity across SM capacitors

Influence of 𝑃𝑑𝑐 and 𝑄𝑎𝑐 on Capacitor Voltage Disparity These two test case is to test the influence of Pdc and Qac on SM capacitor voltage disparity, thus the system is operated without the EBC nor EBC criterion. This section presents two test cases designed to evaluate the influence of 𝑃𝑑𝑐 and 𝑄𝑎𝑐 on the voltage disparity across SM capacitors. During th...
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P. Poblete, G. Pizarro, G. Droguett, F. Núñez, P. D. Judge and J. Pereda, "Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters," in IEEE Transactions on Power Electronics , vol. 37, no. 9, pp. 10306 -10318, Sept. 2022

2022
[36]

Machine Learning Based Operating Region Extension of Modular Multilevel Converters Under Unbalanced Grid Faults,

S. Wang, T. Dragicevic , Y. Gao, S. K. Chaudhary and R. Teodorescu, "Machine Learning Based Operating Region Extension of Modular Multilevel Converters Under Unbalanced Grid Faults," in IEEE Transactions on Industrial Electronics , vol. 68, no. 5, pp. 4554 -4560, May 2021

2021
[37]

Modulation, losses, and semiconductor requirements of modular multilevel converters,

S. Rohner, S. Bernet, M. Hiller, and R. Sommer, “Modulation, losses, and semiconductor requirements of modular multilevel converters,” in IEEE Transactions on Industrial Electronics , vol. 57, no. 8, pp. 2633 –2642, Aug. 2010

2010
[38]

Modular Multilevel Converters and Hvdc/Facts: A Success Story,

H. Knaak , “Modular Multilevel Converters and Hvdc/Facts: A Success Story,” in Proceedings of the 14th European Conference on Power Electronics and Applications , Aug 2011, pp. 1–6

2011
[39]

Advanced High-Fidelity Lumped EMT Grid Modelling & Comparison ,

S. Debnath, Q. Xia, M. Saeedifard, and M. Arifujjaman, “Advanced High-Fidelity Lumped EMT Grid Modelling & Comparison ,” CIGRE Grid of the Future, 2019

2019
[40]

Power Electronic Hardware -in-the-Loop (PE -HIL): Testing Individual Controllers in Large -Scale Power Electronics Systems,

S. Debnath, P. R. V. Marthi, Z. Dong, Q. Xia and S. Chakraborty, "Power Electronic Hardware -in-the-Loop (PE -HIL): Testing Individual Controllers in Large -Scale Power Electronics Systems," IEEE Energy Conversion Congress and Exposition (ECCE) , 2022, pp. 1 -8. Qianxue Xia ( Member, IEEE ) She received the M.S. degree in electrical engineering from Arizo...

2022

[1] [1]

If 𝑃𝑎𝑐 > 𝑃𝑎𝑐_𝑙𝑖𝑚𝑖𝑡 and 𝑃𝑑𝑐 > 𝑃𝑑𝑐_𝑙𝑖𝑚𝑖𝑡

[2] [2]

Set 𝑖𝑐𝑖𝑟𝑐𝑞1𝑟𝑒𝑓 = 0 and 𝑖𝑐𝑖𝑟𝑑1𝑟𝑒𝑓 = 0

[3] [3]

Set 𝑚𝑐𝑖𝑟𝑞1 = 0 and 𝑚𝑐𝑖𝑟𝑑1 = 0

[4] [4]

end If where 𝑖𝑐𝑖𝑟𝑐𝑞1𝑟𝑒𝑓 = 0, and 𝑖𝑐𝑖𝑟𝑑1𝑟𝑒𝑓 = 0 are the reference values of the fundamental circulating current; 𝑚𝑐𝑖𝑟𝑞1 = 0 and 𝑚𝑐𝑖𝑟𝑑1 = 0 are the modulation ind ices of the fundamental circulating current. Table 1 – CPU utilization in real time simulation w/o EBC criterion RF ANN CPU Utilization 12.7% 13.4% 13.2% Table 2 – Testing accuracy of the confusio...

[5] [5]

Transition from Conventional Sorting Algorithm to EBC under Unstable Operating Conditions. This test case aims to demonstrate the limitations of the conventional sorting algorithm in balancing the capacitor voltages of the MARS system under certain operating conditions, and how the activation of the EBC addresses these limitations. Specifically, in the ti...

[6] [6]

Impact of Operating Condition Changes with EBC Criterion This section presents cyclic test results that assess the system’s stability across varying steady -state operating conditions, managed by the integration of a NN -based EBC criterion within the control architecture. Fig. 12 illustrates the system’s response to a series of dispatched command steps, ...

[7] [7]

This section presents two test cases designed to evaluate the influence of 𝑃𝑑𝑐 and 𝑄𝑎𝑐 on the voltage disparity across SM capacitors

Influence of 𝑃𝑑𝑐 and 𝑄𝑎𝑐 on Capacitor Voltage Disparity These two test case is to test the influence of Pdc and Qac on SM capacitor voltage disparity, thus the system is operated without the EBC nor EBC criterion. This section presents two test cases designed to evaluate the influence of 𝑃𝑑𝑐 and 𝑄𝑎𝑐 on the voltage disparity across SM capacitors. During th...

[8] [8]

Multi-Port DC-AC Converter with Differential Power Processing DC -DC Converter and Flexible Power Control for Battery ESS Integrated PV Systems,

J. Wang, K. Sun, C. Xue, T. Liu and Y. Li, "Multi-Port DC-AC Converter with Differential Power Processing DC -DC Converter and Flexible Power Control for Battery ESS Integrated PV Systems," in IEEE Transactions on Industrial Electronics , vol. 69, no. 5, pp. 4879 -4889, May 2022

2022

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Active Distribution Management System Based on Smart Inverter Control of PV/ESS Integrated System,

S. Kang, J. Noh and J. -W. Park, "Active Distribution Management System Based on Smart Inverter Control of PV/ESS Integrated System," in IEEE Transactions on Industrial Electronics , vol. 69, no. 8, pp. 7994 - 8003, Aug. 2022

2022

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Design and Performance Evaluation of the Modular Multilevel Converter (MMC) -based Grid-tied PV-Battery Conversion System,

L. Zhang, Z. Zhang, J. Qin, D. Shi and Z. Wang, "Design and Performance Evaluation of the Modular Multilevel Converter (MMC) -based Grid-tied PV-Battery Conversion System," IEEE Energy Conversion Congress and Exposition (ECCE) , 2018, pp. 2649 -2654

2018

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Integration of PV Panels and EV Chargers on the Modular Multilevel Converter Based SST,

Y. -C. Su, H. -M. Li, P. -L. Chen and P. -T. Cheng, "Integration of PV Panels and EV Chargers on the Modular Multilevel Converter Based SST," in IEEE Transactions on Industry Applications , vol. 58, no. 5, pp. 6428 -6437, Sept. -Oct. 2022

2022

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Renewable Integration in Hybrid AC/DC Systems Using a Multi -Port Autonomous Reconfigurable Solar Power Plant (MARS),

S. Debnath et al ., "Renewable Integration in Hybrid AC/DC Systems Using a Multi -Port Autonomous Reconfigurable Solar Power Plant (MARS)," in IEEE Transactions on Power Systems , vol. 36, no. 1, pp. 603-612, Jan. 2021

2021

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Improved Control of Capacitor Voltage Balancing in Modular Multilevel Converter Submodules,

Y. Lyu, Y. Zi and X. Li, "Improved Control of Capacitor Voltage Balancing in Modular Multilevel Converter Submodules," in IEEE Access, vol. 12, pp. 19510 -19519, 2024

2024

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A Modular Multilevel Converter With Self - Voltage Balancing Part II: Y -Matrix Modulation,

Y. Liu and F. Z. Peng, "A Modular Multilevel Converter With Self - Voltage Balancing Part II: Y -Matrix Modulation," in IEEE Journal of Emerging and Selected Topics in Power Electronics , vol. 8, no. 2, pp. 1126 -1133, June 2020

2020

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A Robust Capacitor Voltage Balancing Method for CPS -PWM-Based Modular Multilevel Converters Accommodating Wide Power Range,

L. Zhou, C. Chen, J. Xiong and K. Zhang, "A Robust Capacitor Voltage Balancing Method for CPS -PWM-Based Modular Multilevel Converters Accommodating Wide Power Range," in IEEE Transactions on Power Electronics , vol. 37, no. 12, pp. 14306 -14316, Dec. 2022

2022

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A Hybrid Modular Multilevel Converter for Solar Power Integration,

T. S. Basu and S. Maiti , "A Hybrid Modular Multilevel Converter for Solar Power Integration," in IEEE Transactions on Industry Applications, vol. 55, no. 5, pp. 5166 -5177, Sept. -Oct. 2019

2019

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Modular Multilevel Converter for Large -Scale Multistring Photovoltai c Energy Conversion System,

S. Rivera, B. Wu, R. Lizana, S. Kouro, M. Perez and J. Rodriguez, "Modular Multilevel Converter for Large -Scale Multistring Photovoltai c Energy Conversion System," IEEE Energy Conversion Congress and Exposition , 2013, pp. 1941 -1946

2013

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A Novel Grid -Connected PV System Based on MMC to Get the Maximum Power Under Partial Shading Conditions,

F. Rong, X. Gong and S. Huang, "A Novel Grid -Connected PV System Based on MMC to Get the Maximum Power Under Partial Shading Conditions," in IEEE Transactions on Power Electronics , vol. 32, no. 6, pp. 4320 -4333, June 2017

2017

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A Power Mismatch Elimination Strategy for an MMC -Based Photovoltaic System,

H. Bayat and A. Yazdani, "A Power Mismatch Elimination Strategy for an MMC -Based Photovoltaic System," in IEEE Transactions on Energy Conversion , vol. 33, no. 3, pp. 1519 -1528, Sept. 2018

2018

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SOH Balancing Control Method for the MMC Battery Energy Storage System,

N. Li, F. Gao, T. Hao, Z. Ma and C. Zhang, "SOH Balancing Control Method for the MMC Battery Energy Storage System," in IEEE Transactions on Industrial Electronics , vol. 65, no. 8, pp. 6581 -6591, Aug. 2018

2018

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Assessment of Fault Tolerance in Modular Multilevel Converters with Integrated Energy Storage,

T. Soong and P. W. Lehn, "Assessment of Fault Tolerance in Modular Multilevel Converters with Integrated Energy Storage," in IEEE Transactions on Power Electronics , vol. 31, no. 6, pp. 4085 -4095, June 2016

2016

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Fault -Tolerant Operation of The Modular Multilevel Converter in an Energy Storage System Based on Split Batteries,

A. Hillers and J. Biela, "Fault -Tolerant Operation of The Modular Multilevel Converter in an Energy Storage System Based on Split Batteries," 16th European Conference on Power Electronics and Applications , 2014, pp. 1 -8

2014

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Analytical Derivation of Intersubmodule Active Power Disparity Limits in Modular Multilevel Converter -Based Battery Energy Storage Systems,

G. Liang et al., "Analytical Derivation of Intersubmodule Active Power Disparity Limits in Modular Multilevel Converter -Based Battery Energy Storage Systems," in IEEE Transactions on Power Electronics , vol. 36, no. 3, pp. 2864 -2874, March 2021

2021

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Operation and Control of M MCs Using Cells with Power Transfer Capability,

F. Briz, M. López, A. Zapico , A. Rodríguez and D. Díaz -Reigosa, "Operation and Control of M MCs Using Cells with Power Transfer Capability," IEEE Applied Power Electronics Conference and Exposition (APEC), 2015, pp. 980 -987. 10

2015

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Analysis of Battery Faul t Tolerance in Modular Multilevel Converter with Integrated Battery Energy Storage System,

Y. Ma, H. Lin, X. Wang, Z. Wang and Z. Ze, "Analysis of Battery Faul t Tolerance in Modular Multilevel Converter with Integrated Battery Energy Storage System," 20th European Conference on Power Electronics and Applications (ECCE Europe) , 2018, pp. P.1-P.8

2018

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Improved Capacitor Voltage Balancing Control for Multimode Operation of Modular Multilevel Converter with Integrated Battery Energy Storage System,

Z.Wang, H. Lin, and Y.Ma, “Improved Capacitor Voltage Balancing Control for Multimode Operation of Modular Multilevel Converter with Integrated Battery Energy Storage System,” IET Power Electron. , vol. 12, no. 11, pp. 2751 –2760, Sep. 2019

2019

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Internal Power Flow of a Modular Multilevel Converter with Distributed Energy Resources,

T. Soong and P. W. Lehn, "Internal Power Flow of a Modular Multilevel Converter with Distributed Energy Resources," in IEEE Journal of Emerging and Selected Topics in Power Electronics , vol. 2, no. 4, pp. 1127 -1138, Dec. 2014

2014

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A Hybrid MMC -Based Photovoltaic and Battery Energy Storage System,

H. Bayat and A. Yazdani, "A Hybrid MMC -Based Photovoltaic and Battery Energy Storage System," in IEEE Power and Energy Technology Systems Journal , vol. 6, no. 1, pp. 32-40, March 2019

2019

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Modelling, simulation and analysis of a modular multilevel converter for medium voltage applications,

S. Rohner, S. Bernet, M. Hiller, and R. Sommer, “Modelling, simulation and analysis of a modular multilevel converter for medium voltage applications,” in Proc. IEEE Int. Conf. Ind. Technol ., 2010, pp. 775 –782

2010

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Modulation, losses, and semiconductor requirements of modular multilevel converters,

S. Rohner, S. Bernet , M. Hiller, and R. Sommer, “Modulation, losses, and semiconductor requirements of modular multilevel converters,” IEEE Transactions on Industrial Electronics , vol. 57, no. 8, pp. 2633 –2642, Aug. 2010

2010

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Neural Network Based Model Predictive Controllers for Modular Multilevel Converters,

S. Wang, T. Dragicevic, Y. Gao and R. Teodorescu, "Neural Network Based Model Predictive Controllers for Modular Multilevel Converters," in IEEE Transactions on Energy Conversion , vol. 36, no. 2, pp. 1562 - 1571, June 2021

2021

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Predictor -Based Neural Network Finite -Set Predictive Control for Modular Multilevel Converter,

X. Liu et al ., "Predictor -Based Neural Network Finite -Set Predictive Control for Modular Multilevel Converter," in IEEE Transactions on Industrial Electronics , vol. 68, no. 11, pp. 11621 -11627, Nov. 2021

2021

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Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters,

S. Wang, T. Dragicevic, G. F. Gontijo, S. K. Chaudhary and R. Teodorescu, "Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters," in IEEE Transactions on Industrial Electronics , vol. 68, no. 11, pp. 11628 -11634, Nov. 2021

2021

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Neural Predictor -Based Low Switching Frequency FCS - MPC for MMC With Online Weighting Factors Tuning,

X. Liu et al ., "Neural Predictor -Based Low Switching Frequency FCS - MPC for MMC With Online Weighting Factors Tuning," in IEEE Transactions on Power Electronics , vol. 37, no. 4, pp. 4065 -4079, April 2022

2022

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Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters,

P. Poblete, G. Pizarro, G. Droguett, F. Núñez, P. D. Judge and J. Pereda, "Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters," in IEEE Transactions on Power Electronics , vol. 37, no. 9, pp. 10306 -10318, Sept. 2022

2022

[36] [36]

Machine Learning Based Operating Region Extension of Modular Multilevel Converters Under Unbalanced Grid Faults,

S. Wang, T. Dragicevic , Y. Gao, S. K. Chaudhary and R. Teodorescu, "Machine Learning Based Operating Region Extension of Modular Multilevel Converters Under Unbalanced Grid Faults," in IEEE Transactions on Industrial Electronics , vol. 68, no. 5, pp. 4554 -4560, May 2021

2021

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Modulation, losses, and semiconductor requirements of modular multilevel converters,

S. Rohner, S. Bernet, M. Hiller, and R. Sommer, “Modulation, losses, and semiconductor requirements of modular multilevel converters,” in IEEE Transactions on Industrial Electronics , vol. 57, no. 8, pp. 2633 –2642, Aug. 2010

2010

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Modular Multilevel Converters and Hvdc/Facts: A Success Story,

H. Knaak , “Modular Multilevel Converters and Hvdc/Facts: A Success Story,” in Proceedings of the 14th European Conference on Power Electronics and Applications , Aug 2011, pp. 1–6

2011

[39] [39]

Advanced High-Fidelity Lumped EMT Grid Modelling & Comparison ,

S. Debnath, Q. Xia, M. Saeedifard, and M. Arifujjaman, “Advanced High-Fidelity Lumped EMT Grid Modelling & Comparison ,” CIGRE Grid of the Future, 2019

2019

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Power Electronic Hardware -in-the-Loop (PE -HIL): Testing Individual Controllers in Large -Scale Power Electronics Systems,

S. Debnath, P. R. V. Marthi, Z. Dong, Q. Xia and S. Chakraborty, "Power Electronic Hardware -in-the-Loop (PE -HIL): Testing Individual Controllers in Large -Scale Power Electronics Systems," IEEE Energy Conversion Congress and Exposition (ECCE) , 2022, pp. 1 -8. Qianxue Xia ( Member, IEEE ) She received the M.S. degree in electrical engineering from Arizo...

2022