Routing power flows in distribution networks using locally controlled power electronics

Hamelink, J. B.; Nguyen, Phuong H.; Kling, W.L.; Ribeiro, Paulo F.; Groot, R.J.W. de

doi:10.1109/upec.2012.6398619

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The signal is added with Gauss white noise so that its mean is 0 and the variance is δ 2 . The θ is solved by a three-phase signal matrix S [s[0], · · · , s[N − 1]] to obtain the phasor value, and N is the number of samples.…”

Section: S[n] = G(a[n] φ[N]) A[n] Cos(φ[n]) A[n] Sin(φ[n])mentioning

confidence: 99%

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Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

et al. 2018

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Micro phasor measurement units (µPMU) installed in active distribution networks are very useful for improving observability by acquiring system real-time data. However, three-phase imbalance and harmonic power flows adversely impact the accuracy of synchronous measurements, which implies the importance of phasor estimation errors. This paper proposes a new phasor estimation algorithm for µPMU in active distribution networks that uses a conditional maximum likelihood (CML) estimation method. Firstly, the signal model of three-phase, three-wire and four-wire imbalance systems is established. Then, the probability distributions of the magnitude and phase angles are derived from the geometric characteristics of the CML method by solving the geometric equation. Simulation results show that the proposed CML based method is effective for estimating phasor and impedance models of active distribution networks by using µPMU measurement data.

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Section: S[n] = G(a[n] φ[N]) A[n] Cos(φ[n]) A[n] Sin(φ[n])mentioning

confidence: 99%

“…With the increasing popularity of distributed energy sources in power systems, the operation of distribution systems becomes more complex [1]. A great number of renewable energy resources and controllable loads are integrated into active distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

et al. 2018

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Smart Electrical Infrastructure for AC-Fed Railways With Neutral Zones

Pilo

Mazumder

González-Franco

2014

IEEE Trans. Intell. Transport. Syst.

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Improving the Controllability of the Infrastructure in AC-Fed Railways With Neutral Zones

Pilo

Mazumder

2014

2014 Joint Rail Conference

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This paper presents an enhancement to the electrical infrastructure that applies to those AC railway power supply systems which have neutral zones. The proposed upgrade allows an improved degree of controllability of the infrastructure, making it possible to perform power routing in traction electrical grids. The modified system consists on the addition of controlled power electronic devices connected in parallel to both sides of the neutral zones. The paper discusses the advantages of such a system in a real section of the high-speed Madrid-Barcelona.

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Routing power flows in distribution networks using locally controlled power electronics

Cited by 3 publications

References 10 publications

Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks

Smart Electrical Infrastructure for AC-Fed Railways With Neutral Zones

Improving the Controllability of the Infrastructure in AC-Fed Railways With Neutral Zones

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