Due to the proliferation of new architectures for distribution networks (commonly defined as Smart Grids, SGs), the number of monitored quantities in the networks is exponentially increasing. This will require deploying many sensors and instruments, including

• Smart meters, with power quality monitoring capability.
• Advanced synchronized measurement devices, such as Phasor Measurement Units.
• Low-Power Instrument Transformers.
• Other heterogeneous sensors and instruments, installed in the context of Internet of Things.

In addition, other non-measured data, e.g. weather forecasts, will be used and merged along with all other data from the field for improving the power network reliability. As a consequence, massive volumes of data will be generated every second in a SG. The main concept to be taken into account is that this huge amount of data becomes actual information (i.e., decisional tasks can rely upon them) only when they are accompanied by an indication about their trustworthiness.

The first step of this research project consists in assessing the impact on information quality of each measurement device. Then, the study of their combined effects is performed. By considering the size and heterogeneity of the available data, a new approach is required to merge traditional methods, based on the propagation of uncertainty, with new techniques needed suitable for big data analytics.

On this basis, a smart self-improving measurement system will be defined to actively supervise and optimize the quality of the information, as well as to solve contingencies and criticalities. To this purpose, an innovative approach will be proposed to make the measurement system able to self-detect lack of quality of information provided by some remote measuring instruments and mitigate their impact, thus improving the resilience of the whole SG.

In the final step, the benefits enabled by the qualified information provided by such an extensive and pervasive smart measurement system on major applications for SGs will be analyzed.

A last aspect that will be considered in the project is relevant to its practical affordability. Distribution System Operators (DSOs) are experiencing a transition from traditional power networks to SGs, by planning huge middle and long-term investments for assets revamping and replacement. At the same time, they demand for a complete observability of the infrastructure, through a capillary measurement infrastructure.