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-We intend to prove that PMU-based state estimation processes for active distribution networks exhibit unique time determinism and refresh rate that make them suitable to satisfy the time-critical requirements of protections as well as the accuracy requirements dictated by faulted line identification. In this respect, we propose a real-time fault detection and faulted line identification functionality obtained by computing parallel synchrophasor-based state estimators. Each state estimator is characterized by a different and augmented topology in order to include a floating fault bus. The selection of the state estimator providing the correct solution is done by a metric that computes the sum of the weighted measurement residuals. The proposed process scheme is validated by means of a real-time simulation platform in which an existing active distribution network is simulated together with a PMU-based monitoring system. The proposed process is shown to be suitable for active and passive networks, with solid-earthed and unearthed neutral, for low and high impedance faults of any kind (symmetric and asymmetric) occurring at different locations.

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-We intend to prove that PMU-based state estimation processes for active distribution networks exhibit unique time determinism and refresh rate that make them suitable to satisfy the time-critical requirements of protections as well as the accuracy requirements dictated by faulted line identification. In this respect, we propose a real-time fault detection and faulted line identification functionality obtained by computing parallel synchrophasor-based state estimators. Each state estimator is characterized by a different and augmented topology in order to include a floating fault bus. The selection of the state estimator providing the correct solution is done by a metric that computes the sum of the weighted measurement residuals. The proposed process scheme is validated by means of a real-time simulation platform in which an existing active distribution network is simulated together with a PMU-based monitoring system. The proposed process is shown to be suitable for active and passive networks, with solid-earthed and unearthed neutral, for low and high impedance faults of any kind (symmetric and asymmetric) occurring at different locations.

Abstract-The accuracy of state estimators using the Kalman Filter (KF) is largely influenced by the measurement and the process noise covariance matrices. The former can be directly inferred from the available measurement devices whilst the latter needs to be assessed, as a function of the process model, in order to maximize the KF performances. In this paper we present different approaches that allow assessing the optimal values of the elements composing the process noise covariance matrix within the context of the State Estimation (SE) of Active Distribution Networks (ADNs). In particular, the paper considers a linear SE process based on the availability of synchrophasors measurements. The assessment of the process noise covariance matrix, related to a process model represented by the ARIMA [0,1,0] one, is based either on the knowledge of the probabilistic behavior of nodal network injections/absorptions or on the aposteriori knowledge of the estimated states and their accuracies. Numerical simulations demonstrating the improvements of the KF-SE accuracy achieved by using the calculated matrix Q are included in the paper. A comparison with the Weighted Least Squares (WLS) method is also given for validation purposes.