Monitoring systems are expected to play a major role in active distribution grids, and the design of the measurement infrastructure is a critical element for an effective operation. The use of any available and newly installed, though heterogeneous, metering device providing more accurate and real-time measurement data offers a new paradigm for the distribution grid monitoring system. In this paper the authors study the meter placement problem for the measurement infrastructure of an active distribution network, where heterogeneous measurements provided by PhasorMeasurement Units (PMUs) and other advanced measurement systems such as Smart Metering systems are used in addition to measurements that are typical of distribution networks, in particular substation measurements and a-priori knowledge. This work aims at defining a design approach for finding the optimal measurement infrastructure for an active distribution grid. The design problem is posed in terms of a stochastic optimization with the goal of bounding the overall uncertainty of the state estimation using heterogeneous measurements while minimizing the investment cost. The proposedmethod is also designed for computational efficiency so to cover a wide set of scenarios

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Distribution system state estimation (DSSE) is one of the key elements of the monitoring activity of an active distribution network, and is the basis for every control and management application. The DSSE relies on real measurements collected by the distributed measurement system and on other available information, mainly obtained from historical data that help in obtaining observability. This prior information is necessary to derive the so called pseudomeasurements. Accurate input data are fundamental for an accurate estimation, as well as knowledge on possible correlation in the measured and pseudomeasured data. A degree of correlation can exist in the measured data, due to measurement devices, and among power consumptions or generations of some particular nodes. This paper presents an exhaustive analysis on the influence of correlations on the quality of the estimation. The importance of including correlation in the weighted least square estimation approach is discussed using both traditional and synchronized measurements. Results obtained on a 95-bus distribution network are presented and discussed

Synchrophasor measurements, performed by phasor measurement units (PMUs), are becoming increasingly important for power system network monitoring. Synchrophasor standards define test signals for verification of PMU compliance, and set acceptance limits in each test condition for two performance classes (P and M). Several PMU algorithms have been proposed to deal with steady-state and dynamic operating conditions identified by the standard. Research and discussion arising from design, implementation, testing and characterization of PMUs evidenced that some disturbances, such as interharmonic interfering signals, can seriously degrade synchrophasor measurement accuracy. In this paper, a new compressive sensing (CS) approach is introduced and applied to synchrophasor measurements using a CS Taylor-Fourier (TF) multifrequency (CSTFM) model. The aim is to exploit, in a joint method, the properties of CS and the TF transform to identify the most relevant components of the signal, even under dynamic conditions, and to model them in the estimation procedure, thus limiting the impact of harmonic and interhamonic interferences. The CSTFM approach is verified using composite tests derived from the test conditions of the synchrophasor standard and simulation results are presented to show its potentialities

This paper presents a new approach to the distribution system state estimation in wide-area networks. The main goal of this paper is to present a two-step procedure designed to accurately estimate the status of a large-scale distribution network, relying on a distributed measurement system in a multiarea framework. First of all, the network is divided into subareas, according to geographical and/or topological constraints and depending on the available measurement system. Then, in the first step of the estimation process, for each area, a dedicated estimator is used, exploiting all the measurement devices available on the field. In the second step, data provided by local estimators are further processed to refine the knowledge on the operating conditions of the network. To improve the accuracy of the estimation results, correlation arising in the first step estimations has to be suitably evaluated and considered during the second step. Performed analysis shows that existing correlations can be included in the estimation process with very low data exchange among areas, thus involving minimum communication costs. Both first and second steps can be performed in a decentralized way and with parallel processing, thus leading to reduced overall execution times. Test results, obtained on the 123-bus IEEE test network and proving the goodness of the proposed method, are presented and discussed