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REVIEW 4 major objections 4 minor 24 references

Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that fractional technical loss on a distribution line can be estimated from voltage magnitude readings at the two ends alone, with no power-flow or load measurements.

desk verdict A sensible voltage-drop heuristic undermined by a load-bearing mismatch between complex-power loss and active technical loss. read the letter →

arxiv 2506.21311 v1 pith:SV3GUTGG submitted 2025-06-26 eess.SY cs.SY

Mohini Bariya , Genevieve Flaspohler This is my paper · ORCID
classification eess.SYcs.SY
keywords technicallossestimationdistributiongridsvoltagemagnitudesensinglocalizationsparsesensorcoverageradialfeedersvossestimatorlow-andmiddle-incomecountries
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 fractional technical loss on a distribution line can be estimated from voltage magnitude readings at the line's two ends, with no power-flow or load measurements. The voss estimator, derived in Theorem 2.1 as $\mathrm{lossfrac}(m) \approx (v_m^2 - v_m v_{m+1})/v_m^2$, turns a simple voltage drop into an estimate of energy lost to Joule heating. A corrected version extends the idea to sparse sensing across multi-segment lines whose intervening loads are handled by a symmetric uniform-leakage model. Simulations on radial test feeders show the estimates track true losses, and a deployment of plug-point voltage sensors yields plausible medium-voltage loss fractions. If this holds, loss localization becomes accessible to utilities that cannot afford comprehensive power-flow sensing.

What carries the argument

The mechanism is the voss estimator, a ratio-symmetric expression that reduces loss to a voltage-magnitude drop: $\mathrm{lossfrac}(m)\approx (v_m^2 - v_m v_{m+1})/v_m^2$. The proof rewrites input and output power using the same line current, so the current cancels and only the voltage phasors remain; the small-angle approximation then removes the phase difference. For multi-segment lines, the correction factor $\hat{c}$ in Eq. (5) rescales the single-segment estimate according to a continuous model with uniform impedance and uniform current leakage, so the measured endpoint voltages alone, together with an engineering estimate of $\rho_s$, yield the corrected loss fraction. This object carries the argument because it converts a quantity normally obtained from power-flow measurements into a function of easily measured magnitudes.

What would settle it

Take a set of distribution feeders with known per-line metered losses, compute the corrected voss estimate using only endpoint voltages and a $\rho_s$ chosen by the paper's uniform-load heuristic, and compare the estimated loss fraction with the metered one wherever the phase-angle difference across the line is not small. A systematic gap on long, heavily loaded lines would show where the small-angle approximation, or the need for a power-derived $\rho_s$, breaks the method's no-power-flow promise.

Watch

Extended reading notes

Core claim

The central discovery is that the fraction of input power lost on a line segment is approximately the relative voltage-magnitude drop, $\mathrm{lossfrac}(m)\approx (v_m^2 - v_m v_{m+1})/v_m^2$, once the phase-angle difference across the line is small. Because only magnitudes appear, the estimate is computable from sparse, low-cost voltage sensors. For the common case where sensors bracket several load nodes, Theorem 2.2 multiplies the single-segment estimate by a correction factor $\hat{c}=1-\frac{\rho_v-\rho_s}{\rho_v+\rho_s}\frac{\rho_v+2\rho_s}{3\rho_v}$, derived from a uniform-leakage line model and parameterized by the measured voltage ratio $\rho_v$ and an engineering estimate $\rho_s$ of the ratio of real power leaving to entering the line. In simulation on 13-node and 34-node radial test feeders, the estimates match true line losses closely enough to localize lossy lines; on real grid data from plug-point sensors, they produce loss fractions consistent with high-loss systems. The authors' claim is that this makes technical-loss estimation and localization practical for under-instrumented grids.

Load-bearing premise

The multi-segment version depends on an engineering guess for $\rho_s$, the fraction of real power that exits the line relative to what enters it; voltage sensors alone do not measure that fraction.

Editorial extensions

If this is right

  • Utilities can rank lines by lossiness using voltage sensors at endpoints, focusing repair and replacement budgets on the worst feeders.
  • Because voltage readings carry less privacy weight than consumption data, the method sidesteps a key barrier to smart-meter-based loss estimation.
  • If line input power is known, the voss loss fraction converts directly to an estimate of kilowatt-hours lost, enabling loss-reduction program evaluation.
  • The multi-segment correction makes sparse sensor coverage workable, so a utility does not need a sensor at every load tap.

Reading between the lines

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

  • A deployment-ready version of the method needs a principled rule for choosing $\rho_s$ from network topology and voltage readings alone; the paper treats $\rho_s$ as an engineering estimate, and its accuracy in practice will hinge on that rule.
  • The derivation estimates the magnitude of complex-power loss; converting that to active energy loss implicitly assumes the power factor is similar at both ends of the line, a condition worth checking on heavily reactive feeders.
  • Applying the estimator to feeders with distributed generation or meshed topology would test whether the uniform-leakage and small-angle assumptions hold outside radial, passively loaded networks.
  • The real-world demonstration lacks ground-truth losses; a controlled deployment with metered losses on a set of lines would turn the estimator from plausible into validated.
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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

4 major / 4 minor

Summary. The paper proposes 'voss', a voltage-magnitude-only estimator of the fractional loss on distribution lines. Theorem 2.1 derives voss(m) = (v_m^2 - v_m v_{m+1})/v_m^2 for a single line segment by invoking the small-angle approximation e^{jΔθ}≈1. Theorem 2.2 extends the estimator to multi-segment lines by modeling the line as a uniform impedance with uniform current leakage and introducing a correction factor that depends on ρ_s, the ratio of real power leaving to entering the line. The method is tested on the IEEE 13- and 34-node test feeders in OpenDSS, and an illustrative application to voltage data from Accra, Ghana is presented. The claimed contribution is a low-cost, privacy-friendly tool for localizing technical losses in under-instrumented LMIC distribution grids.

Significance. If the central claim were correct, this would be a genuinely useful and novel tool for loss localization in grids where power-flow sensing is unavailable. The paper is clearly written, the single-segment algebra is transparent, and the multi-segment correction is an interesting attempt to handle sparse sensing. The real-world illustration with GridWatch data is valuable as a proof of concept. However, the method as stated does not estimate active technical loss: the estimand in Theorem 2.1 is the magnitude of complex-power loss divided by input complex-power magnitude, not the I^2R Joule-loss fraction, and the paper never bridges that gap. The multi-segment correction further requires an unmeasured parameter ρ_s whose provenance in the validation is not disclosed. These issues strike at the paper's central claim, so the contribution is not presently supported.

major comments (4)
  1. [II.A, Eq. (1)] The estimand is mis-specified. The definition before Theorem 2.1 sets lossfrc(m)=|(s_m-s_{m+1})/s_m|, i.e., the ratio of the magnitude of the complex-power loss to the magnitude of the input complex power, but technical loss is active power loss Re(s_m-s_{m+1})/Re(s_m). Writing the line impedance as R+jX and the load current as I at angle φ behind the voltage, the voss expression of Eq. (1) approximates (R cosφ + X sinφ)I/V_m, whereas the active-loss fraction is R I/(V_m cosφ). These two quantities differ by the factor cosφ(cosφ+(X/R) sinφ), which is not small for typical distribution X/R ratios and power factors. The paper never states or proves a condition under which the magnitude of complex-power loss equals active technical loss; the introduction explicitly identifies technical loss with Joule heating. Consequently, Eq. (1) does not estimate the claimed quantity.
  2. [II.B, Eq. (5) and Table I] The multi-segment correction factor requires ρ_s, the ratio of real power leaving the line to real power entering it. Voltage magnitude measurements do not determine ρ_s. The paper suggests an 'engineering estimate' but never reports how ρ_s was set for any row of Table I. If ρ_s was chosen from the same OpenDSS power-flow solution that defines the 'true loss', the near-exact agreement in Table I is circular; if it was chosen by some other rule, that rule must be stated. A sensitivity analysis over plausible ρ_s values is needed before the estimator can be claimed to work without power-flow information.
  3. [II.B, Eqs. (6)-(7)] The multi-segment model assumes uniform current leakage per unit length (∂i/∂x=ι), a uniform line impedance (∂z/∂x=ζ), and takes i(x) to be real. Real distribution feeders have discrete, often constant-power loads with reactive components, so current is neither uniform in space nor phase-aligned with the source current. The derivation also treats the line impedance ζ as a scalar that cancels in the correction factor, which sidesteps the R/X phase issue of Eq. (1). The correction factor is therefore untested outside the symmetric uniform-leakage model, and no simulation or sensitivity analysis is provided for concentrated-load or reactive-load scenarios.
  4. [III, Fig. 2 and Table I] The 'true loss' used in the validation is never defined. If it is the active-power loss reported by OpenDSS, the near-perfect agreement with voss is inconsistent with the estimand mismatch identified above unless the simulated lines have X/R≈0 and unity-power-factor loads; if it is an apparent-power or complex-power loss magnitude, then the comparison does not validate the paper's stated goal of estimating technical (Joule) loss. The paper must state the definition of true loss, report the line R/X ratios and load power factors for the tested feeders, and disclose any processing of the OpenDSS output.
minor comments (4)
  1. [Table I] The row for line 816-822, phase A reports a 'Single segment' value of 0.010 that is inconsistent with the displayed correction factor 0.75 and corrected value 0.073; the value 0.10 would be consistent. Please correct this typo.
  2. [Throughout] There are several typographical errors: 'mult-segment' in the Fig. 1 caption, 'lossf rac' in the theorem statement, and 'disti=ribution' in reference [5].
  3. [IV] The statement that the transformer turns ratio 'is cancelled out in the estimator equation' should be shown explicitly; because voss uses voltage magnitudes, a constant scaling of both measurements does cancel, but this deserves a sentence or equation to avoid ambiguity.
  4. [Abstract and I] The wording 'voltage magnitude measurements alone' overstates the multi-segment case, since Theorem 2.2 also requires the unmeasured parameter ρ_s; the claim should be qualified consistently throughout.

Circularity Check

2 steps flagged · score 6.0 of 10

The voltage-only estimator is derived for a complex-power-magnitude ratio, not for Joule-heating technical loss; the paper's identification of the two is unproven, and the multi-segment correction depends on an unmeasured real-power-flow ratio.

  1. self definitional [Sec. II-A, Theorem 2.1 (Eq. (1)) and proof; cf. Introduction's definition of technical loss]
    "Let the fraction of loss occurring on a single line segment m with input and output power sm and sm+1 respectively be denoted as: lossf rac(m) ≜ |(sm−sm+1)/sm|. The line loss on segment m can be approximated as: lossf rac(m) ≈ voss(m) ≜ (v2m − vmvm+1)/v2m"

    The theorem defines the estimand lossfrac as the magnitude of complex-power loss divided by the magnitude of input complex power, |ΔS|/|S|. The paper's Introduction defines technical loss as Joule heating, i.e., active power loss. For a line impedance R+jX and current I at angle −φ, the small-angle voss reduces to (R cosφ + X sinφ)|I|/V, whereas the true active-loss fraction is R|I|/(V cosφ). These coincide only when X/R = tanφ, not in general. Thus Eq. (1) is derived, by construction, for the paper's own definition of lossfrac, and that quantity is then relabeled as technical loss without proof. The central claim therefore reduces to a re-labeled definition rather than an independent prediction about Joule losses.

  2. other [Sec. II-B, Eq. (5) and Sec. III, Table I]
    "ρs can be reasonably chosen through an engineering estimate. For example, if the line of interest starts at the feeder head and ends 1/3 of the way down, we can reasonably estimate ρs = 2/3, assuming load is uniformly distributed along the feeder length."

    The multi-segment correction factor c-hat in Eq. (5) depends on ρs, the ratio of real power leaving to entering the line. Voltage magnitude measurements determine only ρv, not ρs, so the correction imports a power-flow quantity (or a load-distribution assumption) that the paper's abstract says is unnecessary. In Table I, the corrected estimates agree with 'True Loss' to about 0.001–0.005, but the paper never reports the ρs values used or any voltage-only procedure for choosing them. Without that, the close agreement cannot be attributed to the voss method; if ρs was taken from the same OpenDSS power-flow solution that produced the 'true' losses, the multi-segment validation is a fitted input presented as a prediction.

full rationale

The basic single-segment voss formula is a legitimate first-principles algebraic rearrangement for the quantity the paper defines as lossfrac, namely the magnitude ratio of complex power loss to input complex power. However, the paper's advertised target is technical loss defined as Joule heating (active power loss), and it never proves that the complex-power-magnitude ratio equals or approximates the active-loss fraction. The proof is therefore self-definitional: it establishes a result about the paper's own definition and then labels that definition as technical loss. The multi-segment correction adds a second load-bearing dependency on ρs, a real-power-flow ratio that voltage sensors do not measure; the simulation validation does not disclose how ρs was selected, so the near-exact agreement in Table I is not independently demonstrated. No load-bearing self-citation or imported uniqueness theorem is involved; the circularity is in the definitional identification and the unstated power-flow input, yielding a partial but central reduction of the claimed contribution.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central estimator rests on a small-angle approximation and on a definition of loss fraction as complex-power magnitude. The multi-segment refinement adds an unmeasured power-ratio parameter and an idealized uniform-load model. No new physical entities are introduced.

free parameters (1)
  • ρ_s (power ratio estimate) = Not disclosed for Table I; text suggests 2/3 for a line spanning the first third of a uniformly loaded feeder.
    The multi-segment correction factor in Eq. (5) depends on ρ_s. The paper says it can be estimated by engineering judgment, but the simulation validation does not reveal the values used; the close match with true losses hints that they were taken from the OpenDSS power-flow solution, making the estimator's multi-segment output depend on a fitted or borrowed value.
assumptions (4)
  • domain assumption Small-angle approximation: e^{j(θ_m - θ_{m+1})} ≈ 1
    Invoked in Theorem 2.1, Eq. (2). Common in distribution networks but not always valid, especially with distributed generation or high-reactance lines; no error bound is given.
  • ad hoc to paper Uniform impedance and uniform current leakage along multi-segment line
    Sec. II-B models the multi-segment line as a continuous line with constant ζ and ι (Eq. 6). This idealized symmetry is used to derive the correction factor and is not tested against unequally loaded real feeders.
  • ad hoc to paper Loss fraction is defined as the magnitude of complex power loss
    Theorem 2.1 defines lossf rac(m) = |(s_m - s_{m+1})/s_m|. This is not the same as active energy loss, the standard meaning of technical loss; the paper offers no proof of equivalence.
  • domain assumption Outlet voltage approximates line voltage and transformer ratios cancel
    In Sec. IV, wall-outlet voltage is used as a proxy for medium-voltage line voltage, assuming a constant transformer turns ratio. No quantitative error analysis is provided for this approximation.

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Cite this review

Pith. "Pith review of Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids." pith.science (2026). https://pith.science/paper/SV3GUTGG

@misc{pith2026250621311,
  author       = {Pith},
  title        = {Pith review of: Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SV3GUTGG}},
  note         = {Machine review of arXiv:2506.21311}
}
read the original abstract

Electric grids in low- and middle-income countries (LMICs) across the world face an acute challenge. To support global decarbonisation efforts and raise millions from energy poverty, these grids must shoulder substantial load growth while integrating distributed renewable generation. However, decades of rapid and poorly funded infrastructure expansions have led to national grids in many LMICs that are strained and weak, composed of aging, faulty, and undersized infrastructure. A cause and symptom of this weakness is excessive technical loss within the grid infrastructure during energy delivery, particularly at the distribution level; network losses are regularly estimated to be well over 20 percent, compared to a baseline of 5 percent in higher-income nations. Addressing technical loss through targeted interventions is essential for bolstering grids' physical and economic strength. Unfortunately, current approaches for estimating and localizing technical loss require expensive, extensive power flow sensing, which is essentially absent in LMIC distribution systems. We present a novel approach to technical loss estimation without power flows, which leverages more readily available voltage magnitude measurements at sparse locations in the grid. This estimator puts loss estimation and localization within reach for LMIC grids globally, and provides a critical tool for the effective design, implementation, and evaluation of loss-reduction interventions.

Figures

Figures reproduced from arXiv: 2506.21311 by the authors.

Figure 1
Figure 1. The models for deriving the single segment (top) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Comparison of single-segment voss estimates in Eq. (1) to true loss fraction for individual line segments on two IEEE distribution test networks. Results are shown for each phase of each line. Lines 20 and 26 for the 34 node network and line 671680 for the 13 node network are excluded from the results as they have ∼ 0 input power, rendering the loss fraction noise-sensitive and meaningless, especially for the purpos… view at source ↗
Figure 3
Figure 3. The voss estimator applied to real-world voltage magnitude measurements made nLine’s GridWatch sensors in Accra, Ghana. Each example is from a different part of Accra and uses measurements from three sensors situated linearly down a network, as demonstrated by the clear voltage drop between each sensor’s voltage reading. For each example, we plot the daily voltage curve recorded by the sensor (top) and a correspondi… view at source ↗

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Works this paper leans on

24 extracted references · 23 canonical work pages

  1. [1]

    World energy outlook 2022

    IEA, “World energy outlook 2022.” IEA, Paris, 2022. [Online]. Available: https://www.iea.org/reports/world-energy-outlook-202

    work page 2022

  2. [2]

    Underutilized potential: The business costs of unreliable infrastructure in developing countries,

    J. E. Maruyama Rentschler, M. G. M. Kornejew, S. Hallegatte, J. M. Braese, and M. A. B. Obolensky, “Underutilized potential: The business costs of unreliable infrastructure in developing countries,” World Bank policy research working paper , no. 8899, 2019

    work page 2019

  3. [3]

    Electric power transmission and distribution losses overview and minimization in pakistan,

    S. S. Bhatti, M. U. U. Lodhi, S. ul Haq, E. Gardezi, E. M. A. Javaid, M. Z. Raza, and M. I. U. Lodhi, “Electric power transmission and distribution losses overview and minimization in pakistan,” International Journal of Scientific & Engineering Research , vol. 6, no. 4, pp. 1108– 1112, 2015

    work page 2015

  4. [4]

    Sizing electricity losses in transmission and distribution systems in latin america and the caribbean,

    R. Jim ´enez, T. Serebrisky, and J. Mercado, “Sizing electricity losses in transmission and distribution systems in latin america and the caribbean,” Published by Inter-American Development Bank , 2014

    work page 2014

  5. [5]

    (2022) How much electricity is lost in electricity transmission and disti=ribution in the united states? [Online]

    E.I.A. (2022) How much electricity is lost in electricity transmission and disti=ribution in the united states? [Online]. Available: https://www.eia.gov/tools/faqs/faq.php?id=105&t=3

    work page 2022

  6. [6]

    Survey of regulatory and technological developments concerning smart metering in the european union electricity market,

    J. Vasconcelos, “Survey of regulatory and technological developments concerning smart metering in the european union electricity market,” Florence School of Regulation , 2008

    work page 2008

  7. [7]

    Watching the grid: Utility-independent mea- surements of electricity reliability in accra, ghana,

    N. Klugman, J. Adkins, E. Paszkiewicz, M. G. Hickman, M. Podolsky, J. Taneja, and P. Dutta, “Watching the grid: Utility-independent mea- surements of electricity reliability in accra, ghana,” in Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) , 2021, pp. 341–356

    work page 2021

  8. [8]

    Technical loss calculation by distribution system segment with corrections from measurements,

    A. Meffe and C. C. B. de Oliveira, “Technical loss calculation by distribution system segment with corrections from measurements,” in CIRED 2009-20th International Conference and Exhibition on Electric- ity Distribution-Part 1. IET, 2009, pp. 1–4

    work page 2009

Show all 24 references
  1. [9]

    Analysis of technical losses in electrical power system (nigerian 330kv network as a case study),

    M. Anumaka, “Analysis of technical losses in electrical power system (nigerian 330kv network as a case study),” Int. J. Res. Rev. Appl. Sci , vol. 12, no. 2, pp. 320–327, 2012

    2012

  2. [10]

    Calculating line losses in smart grid: A new rule of thumb,

    T. Hong and J. J. Burke, “Calculating line losses in smart grid: A new rule of thumb,” in IEEE PES T&D 2010 . IEEE, 2010, pp. 1–5

    2010

  3. [11]

    Probabilistic methodology for technical and non-technical losses estimation in distribution system,

    E. A. A. Neto and J. Coelho, “Probabilistic methodology for technical and non-technical losses estimation in distribution system,” Electric Power Systems Research, vol. 97, pp. 93–99, 2013

    2013

  4. [12]

    Mon- itoring technical losses to improve non-technical losses estimation and detection in lv distribution systems,

    H. Henriques, R. Corr ˆea, M. Fortes, B. Borba, and V . Ferreira, “Mon- itoring technical losses to improve non-technical losses estimation and detection in lv distribution systems,” Measurement, vol. 161, p. 107840, 2020

    2020

  5. [13]

    Investigat- ing the effects of bus numbering in a radial transmission network using load-flow study,

    A. Abdulkareem, A. Alayande, T. E. Somefun, and E. Ette, “Investigat- ing the effects of bus numbering in a radial transmission network using load-flow study,” Heliyon, vol. 7, no. 5, p. e07098, 2021

    2021

  6. [14]

    Energy losses estimation in power distribution systems,

    L. M. Queiroz, M. A. Roselli, C. Cavellucci, and C. Lyra, “Energy losses estimation in power distribution systems,” IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1879–1887, 2012

    2012

  7. [15]

    Energy loss computation by using statistical techniques,

    A. Shenkman, “Energy loss computation by using statistical techniques,” IEEE Transactions on Power Delivery, vol. 5, no. 1, pp. 254–258, 1990

    1990

  8. [16]

    Development of simplified loss models for distribution system analysis,

    C. Chen, J. Hwang, M. Cho, and Y . Chen, “Development of simplified loss models for distribution system analysis,” IEEE Transactions on Power Delivery, vol. 9, no. 3, pp. 1545–1551, 1994

    1994

  9. [17]

    System wide mv distribution network technical losses estimation based on reference feeder and energy flow model,

    K. A. Ibrahim, M. T. Au, C. K. Gan, and J. H. Tang, “System wide mv distribution network technical losses estimation based on reference feeder and energy flow model,”International Journal of Electrical Power & Energy Systems , vol. 93, pp. 440–450, 2017

    2017

  10. [18]

    Smart meter data: Balancing consumer privacy concerns with legitimate applications,

    E. McKenna, I. Richardson, and M. Thomson, “Smart meter data: Balancing consumer privacy concerns with legitimate applications,” Energy Policy, vol. 41, pp. 807–814, 2012

    2012

  11. [19]

    On the existence and linear approxima- tion of the power flow solution in power distribution networks,

    S. Bolognani and S. Zampieri, “On the existence and linear approxima- tion of the power flow solution in power distribution networks,” IEEE Transactions on Power Systems , vol. 31, no. 1, pp. 163–172, 2015

    2015

  12. [20]

    Radial distribution test feeders,

    W. H. Kersting, “Radial distribution test feeders,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp. 975–985, 1991

    1991

  13. [21]

    Real time opendss framework for distribution systems simulation and analysis,

    D. Montenegro, M. Hernandez, and G. Ramos, “Real time opendss framework for distribution systems simulation and analysis,” in 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Confer- ence and Exposition (T&D-LA) . IEEE, 2012, pp. 1–5

    2012

  14. [22]

    The smart grid and pmus: Operational challenges and opportunities,

    T. J. Overbye and J. D. Weber, “The smart grid and pmus: Operational challenges and opportunities,” in IEEE PES General Meeting . IEEE, 2010, pp. 1–5

    2010

  15. [23]

    Evaluation of the benin power com- pact’s electricity generation and distribution projects: Baseline report,

    S. Hughes, C. Ksoll, K. Bos, A. Harris, S. K. Wongla, D. Bernstein, C. Seaton, and G. Lapadatova, “Evaluation of the benin power com- pact’s electricity generation and distribution projects: Baseline report,” Mathematica Policy Research, Tech. Rep., 2020

    2020

  16. [24]

    Sensing and measurement for advanced power grids,

    J. D. Taft and P. Martini, “Sensing and measurement for advanced power grids,” Resnick Sustainability Institute, California Institute of Technology, Tech. Rep, 2012

    2012

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