Indices for assessing utility distribution system RMS variation performance

Brooks, D.; Dugan, R.C.; Waclawiak, M.; Sundaram, A.

doi:10.1109/61.660886

Cited by 131 publications

(39 citation statements)

References 3 publications

(2 reference statements)

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“…Another set of methods adopted by Electric Power Research Institute (EPRI) known as EPRI 2D and 3D Histograms [7] and EPRI indices (e.g. SARFIx) [8].…”

Section: B Present Characterization Schemesmentioning

confidence: 99%

Power Quality (PQ) Survey Reporting: Discrete Disturbance Limits

Herath

Gosbell

Perera

2005

IEEE Trans. Power Delivery

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Abstractiscrete or event type power quality (PQ) disturbances mainly include voltage sags, swells, and the transients. An extensive literature survey suggests that there is no generally accepted method for characterization of these disturbances and suitable limits are not yet found in any international standard. One of the reasons for the lack of characterization methods is the difficulty of defining suitable site indices for each discrete disturbance type. In this paper existing characterization methods are reviewed and discussed. A new generalized approach is then given to show a better way of characterizing voltage sags, swells and transients. This is followed by a proposed new method of defining MV/LV distribution discrete disturbance limits for general utility networks and their suitability is shown by an examination of some Australian sites.

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“…Another set of methods adopted by Electric Power Research Institute (EPRI) known as EPRI 2D and 3D Histograms [7] and EPRI indices (e.g. SARFIx) [8].…”

Section: B Present Characterization Schemesmentioning

confidence: 99%

Power Quality (PQ) Survey Reporting: Discrete Disturbance Limits

Herath

Gosbell

Perera

2005

IEEE Trans. Power Delivery

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“…In this case, number of propagated voltage sags (N sag ) and the total number of interruptions (N int ) are calculated by monitoring each bus's voltage magnitude (V i ) for the test system. Annual N sag can be obtained as follows [13,14]:…”

Section: Pq and Reliability Assessment Indicesmentioning

confidence: 99%

“…The total number of continued interruptions for sensitive buses is equal to N sag and the total number of interruptions per year for non-sensitive loads is assumed as follows [13,14]:…”

Section: Pq and Reliability Assessment Indicesmentioning

confidence: 99%

Optimum Network Reconfiguration to Improve Power Quality and Reliability in Distribution System

Bashardoust¹,

Farrokhifar²,

Fard³

et al. 2016

IJGDC

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In this paper, network reconfiguration has been conducted to improve the power quality and reliability of distribution systems in presence of distributed generations. In this case, network reconfiguration is analyzed for different indices. The most important indices are the number of propagated voltage sags (N sag), sustained average interruption frequency index, average system interruption frequency index and momentary average interruption frequency index. The Imperialist competitive algorithm(ICA) is hired to find the optimal configuration of the distribution system in order to minimize the mentioned indices. Simulation result is discussed with and without distributed generation.

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“…Sag severity at certain location can be presented through probability density and distribution functions [3]. Furthermore, the sag information can be compressed into a sag table which groups the sags based on the interval of residual voltage and duration [4][5][6][7][8][9], or by using modified reliability based SAIFI index [10][11][12]. These single-site indices simply give the number of events per year within a certain range of magnitude and duration, resulting in discrete representation of sag characteristics.…”

Section: Introductionmentioning

confidence: 99%

Identification of Weak Areas of Network Based on Exposure to Voltage Sags—Part II: Assessment of Network Performance Using Sag Severity Index

Liao

Abdel-Rahman

Guo

et al. 2015

IEEE Trans. Power Delivery

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Abstract--This paper presents a new stochastic approach to comprehensive assessment of the impact of voltage sags in large scale power networks. The approach takes into account the stochastic nature of power system operation including load variation, uncertainty of fault clearing time by protection relays, fault rates of network components and the variation/uncertainty in equipment sensitivity to voltage sags. A new duration zone division method is used to derive sag duration and occurrence frequency based on stochastic distribution of clearing time required by specific protection systems. The adopted probabilistic representation facilitates estimation of network performance based on a single-event characteristic, sag severity index (SSI), developed in the companion paper (Part I). Based on SSI, a new single-site index with respect to voltage sags (Bus Performance Index, BPI S ) is developed to comprehensively represent the overall bus performance with respect to voltage sags. The index is used to identify the areas of the network, named weak areas of the network in this paper, containing buses that are most exposed to potentially disruptive voltage sags. The application, robustness and sensitivity of BPI S are thoroughly analysed and discussed in the paper.Index Terms-Voltage sags, equipment susceptibility, voltage tolerance curves, sag severity index. I. INTRODUCTIONoltage sags cause frequent disruptions to modern industrial processes and malfunction of electronic equipment, resulting in substantial financial losses [1]. This issue, as one of the most critical power quality problems, has become a major focal point for many utilities and industries [2]. To reduce overall financial consequences of voltage sags, it is necessary to assess voltage sag performance of network buses as accurately as possible and consequently to identify the buses that are most affected by voltage sags. Assessing voltage sag performance at certain location requires two steps: calculating single-event characteristic for each sag event; and then calculating single-site indices based on the single-event characteristics of all sag events occurring at the location [3]. Various single-site indices have been proposed in literature to assess voltage sag performance. Sag severity at certain location can be presented through probability density and distribution functions [3]. Furthermore, the sag information can be compressed into a sag table which groups the sags based on the interval of residual voltage and duration [4][5][6][7][8][9], or by using modified reliability based SAIFI index [10][11][12]. These single-site indices simply give the number of events per year within a certain range of magnitude and duration, resulting in discrete representation of sag characteristics. The aforementioned indices are not suitable for comparing the actual voltage sag performance of different buses as they typically do not include multiple sag characteristics at the same time nor, and more importantly, sensitivity of equipment connected at these buses to vo...

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Indices for assessing utility distribution system RMS variation performance

Cited by 131 publications

References 3 publications

Power Quality (PQ) Survey Reporting: Discrete Disturbance Limits

Power Quality (PQ) Survey Reporting: Discrete Disturbance Limits

Optimum Network Reconfiguration to Improve Power Quality and Reliability in Distribution System

Identification of Weak Areas of Network Based on Exposure to Voltage Sags—Part II: Assessment of Network Performance Using Sag Severity Index

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