Automatic identification of service phase for electric utility customers

Seal, Brian; McGranaghan, Mark

doi:10.1109/pes.2011.6039623

Cited by 26 publications

(16 citation statements)

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“…To control and operate three-phase systems with active components, three-phase models are needed to adequately represent the network; however, validation of such models is not trivial because phase labels are often unknown [3]. For lack of better information, utilities often assume their network is balanced and assign one-third of loads to each phase [4]. They may also use manual approaches for phase identification based on signal injection techniques.…”

Section: Introductionmentioning

confidence: 99%

Phase identification in distribution networks with micro-synchrophasors

Wen¹,

Arghandeh

Meiery

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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Abstract-This paper proposes a novel phase identification method for distribution networks where phases can be severely unbalanced and insufficiently labeled. The analysis approach draws on data from high-precision phasor measurement units (micro-synchrophasors or uPMUs) for distribution systems. A key fact is that time-series voltage phasors taken from a distribution network show specific patterns regarding connected phases at measurement points. The algorithm is based on analyzing crosscorrelations over voltage magnitudes along with phase angle differences on two candidate phases to be matched. If two measurement points are on the same phase, large positive voltage magnitude correlations and small voltage angle differences should be observed. The algorithm is initially validated using the IEEE 13-bus model, and subsequently with actual uPMU measurements on a 12-kV feeder.

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Section: Introductionmentioning

confidence: 99%

Phase identification in distribution networks with micro-synchrophasors

Wen¹,

Arghandeh

Meiery

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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“…Correct phase labeling is crucial in order to avoid excessive losses or reduced life cycle of network components as a result of imbalanced loads or insufficient harmonic mitigation. Phase mislabeling is also a major source of error in diagnostic processes such as topology detection, state estimation, and fault location [19]. Phase identification is a nontrivial subset of topology detection that can be performed with substantially lower accuracy in phasor magnitude and angle.…”

Section: A Diagnostic Applicationsmentioning

confidence: 99%

Micro-synchrophasors for power distribution systems

Arghandeh

Brady

2016

Engineering &Amp; Technology Reference

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The smart grid revolution is creating a paradigm shift in distribution networks that is marked by new, significant intermittencies and uncertainties in power supply and demand. These developments include the dramatic increase in the adoption of distributed energy resources (DER), electric vehicles, energy storage, and controllable loads. This transformation imposes new challenges on existing distribution infrastructure and system operations for stockholders, engineers, operators and customers. Unfortunately, distribution networks historically lag behind transmission networks in terms of observability, measurement accuracy, and data granularity. The changes in the operation of the electric grid dramatically increase the need for tools to monitor and manage distribution networks in a fast, reliable and accurate fashion. This paper describes the development process of a network of high-precision micro phasor measurement units or µPMUs, beginning with an overview of the µPMU technology that provides synchronous measurements of voltage phase angles, or synchrophasors. Next, the µPMU network and communications infrastructure are discussed, followed by an analysis of potential diagnostic and control applications of µPMU data in the electric grid at the distribution level.

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“…Another one, although costly, is by using special per phase signal injection and receiver devices [22,23] that can match the non-unified phases with reference ones based on the phase with injected signal. The last method is by post-analysis of measured data and finding some type of correlation between series of measurement with non-unified phases and reference measurement [21,[24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dense-Mesh Distribution Network Operation Using Long-Term Monitoring Data

et al. 2019

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The technical and economic aspects and the possibility of the mesh network topology offering many radial configurations lead to the fact that large municipal networks are generally under radial operation. However, it is very important to analyze the operation and control of the mesh networks, especially in terms of their safety and durability and in the frame of the smart grid concept, respectively. The article deals with the analysis of the operation of the dense-mesh municipal distribution network of E.ON Distribuce a.s. based on the long-term data from power quality monitors. It also shows a brief view of the current lack of data usability from monitors installed in distribution networks in the context of smart grid.

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Automatic identification of service phase for electric utility customers

Cited by 26 publications

References 0 publications

Phase identification in distribution networks with micro-synchrophasors

Phase identification in distribution networks with micro-synchrophasors

Micro-synchrophasors for power distribution systems

Analysis of Dense-Mesh Distribution Network Operation Using Long-Term Monitoring Data

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