No abstract

No abstract

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

The distribution grid is the infrastructure that transports electrical energy generated by large plants that are long distances away to the final user. A typical distribution grid consists of High-to-Medium Voltage (HV/MV) transformation centers; the MV grid; Medium-to-Low Voltage (MV/LV) transformation centers; and the LV grid, both three-phase and single-phase. Residential and commercial customers are mainly connected to the LV, while large industrial facilities are connected to the MV

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