Abstract-We describe the real-time monitoring infrastructure of the smart-grid pilot on the EPFL campus. We experimentally validate the concept of a real-time state-estimation for a 20 kV active distribution network. We designed and put into operation the whole infrastructure composed by the following main elements: (1) dedicated PMUs connected on the medium-voltage side of the network secondary substations by means of specific current/voltage transducers; (2) a dedicated communication network engineered to support stringent time limits and (3) an innovative state estimation process for real-time monitoring that incorporates phasor-data concentration and state estimation processes. Special care was taken to make the whole chain resilient to cyber-attacks, equipment failures and power outages. The achieved latency is within 65ms. The refresh rate of the estimated state is 20ms. The real-time visualization of the state estimator output is made publicly available, as well as the historical data (PMU measurements and estimated states). To the best of our knowledge, the work presented here is the first operational system that provides low-latency real-time stateestimation by using PMU measurements of a real active distribution network.
Abstract-This paper aims to assess the performance of linear state estimation (SE) processes of power systems relying on synchrophasor measurements. The performance assessment is conducted with respect to two different families of SE algorithms, i.e., static ones represented by weighted least squares (WLS) and recursive ones represented by Kalman filter (KF). To this end, this paper firstly recalls the analytical formulation of linear WLS state estimator (LWLS-SE) and Discrete KF state estimator (DKF-SE). We formally quantify the differences in the performance of the two algorithms. The validation of this result, together with the comprehensive performance evaluation of the considered state estimators, is carried out using two case studies, representing distribution (IEEE 123-bus test feeder) and transmission (IEEE 39-bus test system) networks. As a further contribution, this paper validates the correctness of the most common process model adopted in DKF-SE of power systems. Index Terms-Discrete Kalman filter (KF), linear state estimation (LSE), performance evaluation, phasor measurement unit (PMU), weighted least squares (WLS).
Abstract-The evolution toward emerging active distribution networks (ADNs) can be realized via a real-time state estimation (RTSE) application facilitated by the use of phasor measurement units (PMUs). A critical challenge in deploying PMU-based RTSE applications at large scale is the lack of a scalable and flexible communication infrastructure for the timely (i.e., sub-second) delivery of the high volume of synchronized and continuous synchrophasor measurements. We address this challenge by introducing a communication platform called C-DAX based on the information-centric networking (ICN) concept. With a topicbased publish-subscribe engine that decouples data producers and consumers in time and space, C-DAX enables efficient synchrophasor measurement delivery, as well as flexible and scalable (re)configuration of PMU data communication for seamless full observability of power conditions in complex and dynamic scenarios. Based on the derived set of requirements for supporting PMU-based RTSE in ADNs, we design the ICN-based C-DAX communication platform, together with a joint optimized physical network resource provisioning strategy, in order to enable the agile PMU data communications in near real-time. In this paper, C-DAX is validated via a field trial implementation deployed over a sample feeder in a real-distribution network; it is also evaluated through simulation-based experiments using a large set of real medium voltage grid topologies currently operating live in The Netherlands. This is the first work that applies emerging communication paradigms, such as ICN, to smart grids while
Abstract-The paper proposes a specific algorithm for the pre-estimation filtering of bad data (BD) in PMU-based power systems linear State Estimators (SEs). The approach is framed in the context of the so-called real-time SEs that take advantage of the high measurement frame rate made available by PMUs (i.e., 50 -60 frames per second). In particular, the proposed algorithm examines PMU measurement innovations for each new received set of data in order to locate anomalies and apply countermeasures. The detection and identification scheme is based on: (i) the forecasted state of the network obtained by means of a linear Kalman filter, (ii) the current network topology, (iii) the accuracy of the measurement devices and (iv) their location. The incoming measurement from each PMU is considered reliable, or not, according to a dynamic threshold defined as a function of the confidence of the predicted state estimated by using an AutoRegressive Integrated Moving Average (ARIMA) process. The performances of the proposed algorithm are validated with respect to single and multiple bad data of different nature and magnitudes. Furthermore, the algorithm is also tested against faults occurring in the power system to show its robustness during these unexpected operating conditions.
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