Estimation of Voltage Unbalance Attenuation Caused by Three-Phase Induction Motors: An Extension to Distribution System State Estimation

With the increased interest in controlling and managing voltage unbalance (VU), network operators are now increasingly being motivated to obtain the state of their networks with greater details. Distribution system state estimation (DSSE) is a prominent tool that enables network operators to estimat...

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Veröffentlicht in:IEEE transactions on power delivery 2019-10, Vol.34 (5), p.1853-1864
Hauptverfasser: Kahingala, Thisandu Dulhara, Perera, Sarath, Jayatunga, Upuli, Agalgaonkar, Ashish Prakash, Ciufo, Philip
Format: Artikel
Sprache:eng
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Zusammenfassung:With the increased interest in controlling and managing voltage unbalance (VU), network operators are now increasingly being motivated to obtain the state of their networks with greater details. Distribution system state estimation (DSSE) is a prominent tool that enables network operators to estimate the level, location, and related impacts of VU. DSSE techniques developed to date which use the polynomial or exponential load models are deficient in recognizing VU attenuation associated with three-phase induction motors (IMs) that are directly connected to the ac network. Adoption of adequate three-phase models of IMs, which are used in full unbalanced load flow, into the existing DSSE techniques is not feasible due to several factors including requirement of extensive data, additional computational efforts, and major constraints related to incorporating the necessary modifications. As a solution, this paper proposes a novel formulation for postprocessing the results of current DSSE techniques to reflect the aforementioned behavior of IMs. Simulations are carried out using the IEEE 13 bus distribution network for a range of scenarios to verify the developed methodology. The work presented in relation to VU attenuation in interconnected networks also complements the existing treatise in IEC documentation that only deals with radial networks.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2019.2904505