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.

Several monitoring, protection, and control applications designed for modern power grids are based on detailed grid models and thus require an accurate knowledge of line parameters. A synchronized monitoring infrastructure based on Phasor Measurement Units (PMUs) may be of great support to the task of line parameters estimation, but the accuracy of the estimated parameters may be largely affected by the uncertainty of all the elements of the PMU-based measurement chain. Thus, an accurate parameters estimation must appropriately consider the metrological behavior of all these elements, and in particular that of instrument transformers. To address this challenge, the paper proposes an enhanced multi-branch method for accurate estimation of the line parameters and of the systematic measurement errors introduced by the instrument transformers when measurements for multiple operating conditions are considered. Indeed, multiple operating conditions are dealt with properly, thanks to an in-depth analysis of the problem modeling within the framework of Tikhonov regularization. The validity of the proposed approach is confirmed by the results obtained on the IEEE 14 bus test system.

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Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

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 1-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.