R. Chiumeo

A protection coordination scheme for active distribution networks

New problems concerning the distribution grid protection system arise from the large diffusion of installed distributed energy resources. In particular, unwanted trips, unwanted island operations and worsening of power quality are the main issues coming from the interactions among active networks and grid protections. A possible new protection scheme, which can be employed in active networks, is proposed and discussed in the paper. Logic selectivity is implemented introducing communications among grid and distributed generator systems protections. Digital simulations confirm the suitability of the proposed solution for a large set of grid faults.

Fault Ride Through strategies for Permanent Magnets Wind Generators

The paper presents three different approaches for the control of grid connected inverters for Permanent Magnet Wind Generators. Their performances in facing grid unbalances are compared through digital simulations. For each control scheme, three alternative Fault Ride Through (FRT) strategies are considered and tested. From simulations, it emerges that, in addition to control expedients, some hardware devices are required for satisfying FRT requirements.

Conventional and reverse droop control in islanded microgrid: Simulation and experimental test

Local control of Distributed Energy Resources (DER) in microgrids usually relies on droop algorithm in order to ensure stability to the system in islanded operation. Conventional and reverse droop control are applied respectively to Voltage Control Mode (VCM) inverters and to Current Control Mode inverters (CCM). In low-voltage microgrid, traditional P-ω and Q-V droop characteristic is subject to the power coupling and could lead to unstable operating conditions due to dependence of load on the voltage and on the frequency. Therefore P-V and Q-ω droop characteristic, named resistive droop, is used in simulations and in experimental tests to show its effectiveness for a stable and decoupled regulation of islanded LV microgrid.

Self-tuning Kalman filter and machine learning algorithms for voltage dips upstream or downstream origin detection

In this paper, self-tuning Kalman Filter (KF) is applied to a significant sample of full waveforms associated to the voltage dips monitored in the Italian distribution network by the QuEEN system, with the aim of events detection and waveforms segmentation. Segmentation is done in order to extract more features and information from the original voltage waveforms, to make easier voltage dips classification, based on the events source location (upstream/downstream from the point of measurement). The aforementioned classification is achieved by Machine Learning algorithms. The evaluation of the obtained results is based on the computation of a “confusion matrix”.