Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible.

The availability of accurate measurements is the prerequisite for the actual implementation of many monitoring and management applications in smart distribution networks. Phasor measurement units (PMUs) can provide synchronized voltage and current measurements, referred to a common time reference [usually the coordinated universal time (UTC)]. This feature, as well as the high accuracy and reporting rate of PMUs, can be exploited for an accurate network monitoring. At the same time, the smartness of the grid can include the possibility for the measurement system to self-detect its weak points and improve its performance. In this perspective, a technique for the estimation and the compensation of systematic errors existing in the components of a PMU-based distributed measurement system suitable for monitoring three-phase distribution networks is presented. The uncertainty induced by the components of the measurement system, mainly instrument transformers and PMUs, is included in the model, along with the uncertainty affecting the values of the network line parameters. The method exploits the possible constraints introduced by the grid topology (presence of multiple lines, injected currents, etc.) to improve the accuracy of the estimation so that a proper compensation of the estimated errors can be allowed. The validity of the approach is verified though simulations performed on a small portion of a test medium voltage distribution grid.

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient compressive sensing harmonics detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid.

This paper presents a new Kalman filter approach to Power System State Estimation based on PMUs, in which the knowledge of the system frequency is exploited to ensure the accuracy of the estimated quantities even under off-nominal conditions. In the proposed solution, the frequency is added as a new state variable to be estimated, so that its value can be known with lower uncertainty, thus leading to more accurate estimates also for node voltages and branch currents. All the frequency measurements available from PMUs can be exploited through the presented method to improve the estimation. In order to assess the benefits given by the integration of the frequency knowledge, the performance of the new approach is compared to different state estimation methodologies, by means of simulations carried out on the New England IEEE 39-bus system under different realistic operating conditions and measurement configurations. Performed tests take into account, in particular, the possible occurrence of off-nominal frequency conditions, highlighting the issues associated to traditional PMUbased Kalman filter approaches and proving the effectiveness of the proposed solution.

Identifying the prevailing polluting sources would help the distribution system operators in acting directly on the cause of the problem, thus reducing the corresponding negative effects. Due to the limited availability of specific measurement devices, ad-hoc methodologies must be considered. In this regard, Compressive Sensing-based solutions are perfect candidates. This mathematical technique allows recovering sparse signals when a limited number of measurements are available, and thus overcoming the lack of Power Quality meters. In this paper, a new formulation of the ℓ1-minimization algorithm for Compressive Sensing problems, with quadratic constraint, has been designed and investigated in the framework of the identification of the main polluting sources in Smart Grids. A novel whitening transformation is proposed for this context. This specific transformation allows the energy of the measurement errors to be appropriately estimated and thus better identification results are obtained. The validity of the proposal is proved by means of several simulations and tests performed on two distribution networks for which suitable measurement systems are considered along with a realistic quantification of the uncertainty sources.

The widespread diffusion of nonlinear power electronics-based devices in distribution grids has raised the interests towards power quality monitoring. In this respect, the estimation of synchronized harmonic phasors would enable the employment of algorithms that allow identifying the sources of harmonic disturbances, which, until now, have been only tested through numerical simulations or implemented in small-scale. However, the impact of the uncertainty due to the employed voltage transformers and to the other devices that compose the measurement chain has to be analyzed. In this framework, the present paper proposes a class of harmonic synchrophasor estimation algorithms that allows compensating the frequency response function of the instrument transformer. The technique has been tested and validated under realistic conditions considering an inductive voltage transformer. Experimental results highlight its effectiveness, especially when taking into account its ease of implementation. The black-box approach allows abstracting from the operating principle of the voltage transducer.