A Hardware-in-the-Loop test platform for the performance assessment of a PMU-based Real-Time State Estimator for Active Distribution Networks

Abstract

Abstract-The paper describes the development of a Hardwarein-the-Loop (HIL) test platform for the performance assessment of a PMU-based sub-second linear Real-Time State Estimator (RTSE) for Active Distribution Networks (ADNs). The estimator relies on the availability of data coming from Phasor Measurement Units (PMUs) and can be applied to both balanced and unbalanced ADNs. The paper first illustrates the architecture of the experimental HIL setup that has been fully designed by the Authors. It consists of a Real-Time Simulator (RTS) that models the electrical network model as well as the measurement infrastructure composed by virtual PMUs. These virtual devices stream their data to a real Phasor Data Concentrator (PDC) suitably coupled with a Discrete Kalman Filter State Estimator (DKF-SE). By using this experimental setup, the paper discusses the performance assessment of the whole process in terms of estimation accuracy and time latencies. In the RTS, a real ADN located in the Netherlands has been modeled together with the associated PMUs.

Performance Assessment of Linear State Estimators Using Synchrophasor Measurements

“…In the second stage, the SE combines the estimated voltage phasors calculated in the first stage with conventional RTU data processed by a nonlinear state estimator. Pignati et al [18], Jones et al [21], and Sarri et al [33] have used purely PMU-based LSEs implemented in real electrical networks. In particular, in [21], the LSE is based on the WLS algorithm with a refresh rate of 30 frames-persecond and is applied to a transmission network (subsection of Virginia Electric and Power Company's 500 kV network).…”

Hardware-in-the-Loop validation of the Grid Explicit Congestion Notification mechanism for primary voltage control in Active Distribution Networks

“…Next, they are passed to a Kalman Filter (KF)-based SE. A detailed description of both the PDC and the RTSE process can be found in [18,24]. In this work we rely on a PMU-based RTSE.…”

“…In particular, the median value of the solution time is 1.12ms in RT with a corresponding 95-th percentile of 2.70ms, where as off-line these values are 17.37ms and 32.82ms respectively. In [18] the authors characterize the latency of the whole process from the moment the data is arrived to the PDC until the state is available from the RTSE for the case of the BML 2.10 feeder and they find that this latency is in the order of 20ms. Therefore, taking into account the time latencies shown in Fig 12, within roughly 35ms from the moment the data is available to the PDC we are able to solve the centralized optimization problem and compute the GECN signals.…”

“…• A RTS model composed of: (i) the electrical network, represented by a real distribution feeder [17], as well as the controllable resources and the GECN local controllers [1], and (ii) all the measurement infrastructure composed PMUs (i.e., [18]- [20]);…”

“…• A Phasor Data Concentrator (PDC), which receives the PMU data, decapsulates it and forwards it to a Discrete Kalman Filter (DKF)-based RTSE (e.g., [18]) and a GECN network controller that uses the estimated state to compute optimal power set-points for voltage control and the corresponding GECN broadcast control signals [1];…”