Search

With the power system operating close to its stability margin, the increased penetration of renewable resources and loads with diverse characteristics, appropriate stability studies, and control decisions have become crucial. Due to the importance of accurate representation of aggregate loads in such studies, research interests for load modeling have increased. This paper puts forward a novel measurement-based load modeling approach that adapts itself based on the complex dynamics exhibited by the installed aggregate loads. The proposed approach primarily utilizes the collected measurements to gain insight regarding the dynamic trend in the measurements and subsequently determines the optimal order of an equivalent system or load model capable of effectively representing the aggregate load dynamics. An advanced signal pre-processing technique is employed, which effectively suppresses the noise and also preserves the transients present in the measurements. Afterward, an adaptive dynamic load model (ADLM) accurately represents the installed aggregate load. Lastly, the Refined Instrumental Variable (RIV) approach estimates the equivalent dynamic load model parameters. The results on a 2 bus and the IEEE 118 bus networks in DIgSILENT Powerfactory and a 7 bus network in Real-Time Digital Simulator (RTDS) reveal the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

With the increased penetration of intermittent distributed energy resources (DERs) and the inclusion of complex loads, the states of an active distribution network (ADN) can experience sudden variations. Most distribution networks are only partially observable using available measuring devices. A distribution system state estimator (DSSE) uses pseudo, i.e., forecasted power injection measurements for the unobserved buses to perform state estimation. The pseudo measurements may fail to capture the sudden change in DERs and loads’ power. Consequently, the conventional DSSEs may fail to estimate the states when there is a sudden change in the loads and DERs connected to the unobserved buses. This paper proposes a forecasting-aided state estimator for ADNs, which can capture the sudden changes in states caused due to intermittent variations in DERs and loads’ power. The estimator requires measurements from a limited number of $\mu $ PMUs, which are placed according to the depth-of-one unobservability criterion. The present work proposes to split the state vector into two parts. The first part captures the gradual Spatio-temporal variations in the states. The second part captures temporally uncorrelated variations in the states that may occur due to the sudden change in power injections. The proposed estimator tracks both state vector components using an l1-regularization-based estimation model. The algorithm is tested on an unbalanced IEEE benchmark test feeder using real load profiles. [ABSTRACT FROM AUTHOR]

This work proposes an M-estimator of scatter matrix for data-driven topology detection in Active Distribution Networks (ADNs) using bus voltage phase angle measurements available from Micro Phasor Measurement Units ($\mu$ PMUs). The proposed method is robust to the presence of bad measurements and non-Gaussian $\mu$ PMU noise. [ABSTRACT FROM AUTHOR]

This article proposes a parameter estimation scheme for three-phase untransposed distribution lines. The estimation process is carried out using voltage magnitude (rms) and power-flow measurements from both ends of the line. The parameter estimation is carried out by considering the influence of measurement and instrument transformer uncertainties. A novel effective variance-based reweighted nonlinear least-squares scheme is proposed. Moreover, for estimation of untransposed line parameters under balanced loading conditions, the present work proposes a parameter estimation scheme using a reduced order model of the untransposed line. The accuracy and stability of the estimator are verified for the IEEE-13 bus test system. The algorithm is also tested with time-tagged remote terminal unit (RTU) measurements from a real distribution feeder. [ABSTRACT FROM AUTHOR]

With the integration of intermittent renewable energy sources in the distribution network, the number of network reconfiguration events has increased significantly. In a medium voltage distribution network, most of the critical circuit breakers’ (CBs’) statuses are monitored by remote terminal units (RTUs). However, in many cases, some of the CBs’ status may not be correctly updated by the supervisory control and data acquisition (SCADA) system because of communication failure and data packet loss issues. Thus, the distribution system operator (DSO) cannot solely rely on CB status provided by the SCADA for topology detection. This paper proposes a data-driven topology tracking algorithm for active distribution networks. The topology of the distribution network is represented with a time-varying connectivity matrix. The changes in network topology are detected by estimating the elements of the bus connectivity matrix using voltage phase angle measurements provided by a sparse set of micro phasor measurement units ($\mu$ PMUs). The algorithm extracts information from previous network topology using an $l_1$ norm regularization on the difference of consecutive connectivity matrices. The changes in topology can be detected by observing a few $\mu$ PMU phasor samples. The algorithm is tested on IEEE benchmark test feeders with real load profiles. [ABSTRACT FROM AUTHOR]