Lorenzo Zanni

Real-time state estimation of the EPFL-campus medium-voltage grid by using PMUs

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 state-estimation by using PMU measurements of a real active distribution network.

Probabilistic assessment of the process-noise covariance matrix of discrete Kalman filter state estimation of active distribution networks

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 a-posteriori 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.

Performance Assessment of Linear State Estimators Using Synchrophasor Measurements

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.

A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

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.

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming measurement set and the KF predicted state) to estimate the prediction-error covariance matrix by means of a constrained convex optimization problem. The latter is designed to ensure the symmetry and the positive semidefiniteness of the estimated covariance matrix, so that the KF numerical stability is guaranteed. Our proposed method is straightforward to implement and requires the setting of one parameter only, i.e., the number of past innovations to be considered. It relies on the knowledge of a linear and stationary measurement model. The ability of the method to track state step-variations is validated in ideal conditions for a random-walk process model and for the case of power-system state estimation. The proposed approach is also compared with other methods that estimate the KF stochastic parameters and with the well-known linear weighted least squares. The comparison is given in terms of both accuracy and computational time.

Architecture and characterization of a calibrator for PMUs operating in power distribution systems

In recent years, the Phasor Measurement Unit (PMU) technology is rapidly evolving towards the potential deployment also in power distribution systems (DSs). In general, this specific field of applications requires PMUs whose accuracy levels are beyond those required by the IEEE Std. C37.118. Additionally, there is the need to define the architecture of an associated calibration system capable to assess the metrological performances of these devices. In this paper, we first analyse the impact of the uncertainties (in term of phase and magnitude) introduced by arbitrary PMUs on a state estimation (SE) process performed on the IEEE 13-bus distribution test feeder. The outcomes of this analysis are used to infer the most stringent steady-state performances of PMUs for DSs monitoring and, consequently, to define the requirements and the hardware architecture of a PMU calibrator presently developed at the Authors laboratories. A preliminary metrological characterization of the proposed calibrator is presented in the paper.

Fault Detection and Faulted Line Identification in Active Distribution Networks using Synchrophasors-based Real-Time State Estimation

We intend to prove that phasor-measurement-unit (PMU)-based state estimation processes for active distribution networks exhibit unique time determinism and a refresh rate that makes 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 performed 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 where 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.

Enabling resilient smart grid communication over the information-centric C-DAX middleware

Limited scalability, reliability, and security of todays utility communication infrastructures are main obstacles to the deployment of smart grid applications. The C-DAX project aims at providing and investigating a communication middleware for smart grids to address these problems, applying the information-centric networking and publish/subscribe paradigm. We briefly describe the C-DAX architecture, and extend it with a flexible resilience concept, based on resilient data forwarding and data redundancy. Different levels of resilience support are defined, and their underlying mechanisms are described. Experiments show fast and reliable performance of the resilience mechanism.

Fault detection and faulted line identification in active distribution networks using synchrophasors-based real-time state estimation

We intend to prove that phasor-measurement-unit (PMU)-based state estimation processes for active distribution networks exhibit unique time determinism and a refresh rate that makes 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 performed 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 where 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.

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

The paper describes the development of a Hardware-in-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.