Real-time reduced steady state model synthesis of active distribution networks using PMU measurements

Abstract

Real-time reduced steady state model synthesis of active distribution networks using PMU measurements. IEEE Transactions on Power DeliveryAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. Permanent link to this version:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192943 1  Abstract--Due to the increase of generation sources in distribution networks, it is becoming more and more complex to develop and maintain models of these networks. Network operators need to determine reduced models of distribution networks to be used in grid management functions. This paper presents a novel method that synthesizes steady state models of unbalanced active distribution networks by the use of dynamic measurements (time series) from PMUs. As PMU measurements may contain errors and bad data, the paper presents the application of a Kalman Filter technique for real-time data processing. In addition, PMU data captures the power system's response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter has been improved to extract the steady state component of the PMU measurements to be fed to the steady state model synthesis application. Performance of the proposed methods has been assessed by real-time hardware-in-the-loop simulations on a sample distribution network.Index Terms-active distribution network, Kalman Filter (KF), model synthesis, PMU.

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

“…However, µPMU measurements can be impacted by noise, outliers, and missing samples, particularly in distribution networks [7]. Therefore it is normal to estimate the quasi-steady state components of the µPMU data to improve parameter estimation before using advanced estimation techniques, such as symmetrical component methods.…”

A SGAM-based architecture for synchrophasor applications facilitating TSO/DSO interactions

“…from the PMU measurements in real-time. Hence, both input and output of this function are synchrophasors [8,9].…”

“… Derivation of Key Information: This is actually a family of functions including Steady State Model Synthesizer [9], Oscillatory Mode Meter [10], Voltage Stability Analyzer [11], Sub-synchronous Oscillation Detector [12], and Feeder Dynamic Rating [13]. The inputs to all of these functions are curated voltage and current synchrophasors whereas their outputs consist of different number sets in floating point format, often having lower reporting rate compared to that of the synchrophasors.…”

Synchrophasor applications facilitating interactions in transmission and distribution operations

“…In order to overcome the abovementioned problems, an enhanced Kalman Filter (KF) technique has been utilized in the data processing unit for both bad data removal and extraction of the proper signal feature from the PMU data [14]. Figure 2 compares the raw data of a voltage phasor with its curated version, i.e.…”

“…In addition, the system's dynamic response (oscillations) and the steady state component of the signals have been extracted from the curated phasor data. More details on the enhanced KF can be found in [13,14].…”