The Digital Calibration Certificate (DCC) has been the first metrological quality-related document that has undergone a thorough analysis and requirements assessment process in order to develop a fully machine-actionable digital process that fulfills the requirements of ISO 17025. During this, still ongoing, process, the demand for a more modular approach to digital quality documents has become obvious. The paper introduces the DX schema, which is the conceptual basis for the future DCC version 4 and other digital certificates, reports, and documents related to metrology, conformity assessment, and a process-oriented digital quality infrastructure. In addition, the paper introduces some examples of digital certificates and reports which will potentially benefit from the DX.

Several strategic initiatives undertaken by the Measurement Standards Laboratory of New Zealand in response to the digital transformation of metrology are discussed. While the laboratory does not currently perceive an external demand for the digital delivery of its metrological services, it recognises that change will be required as global support for digital services becomes the norm. To support the longer-term vision of semantic representation of metrological data, a shift will be required from procedural, process-based knowledge capture towards abstract scientific forms. MSL has several initiatives in place to prepare for and facilitate this transition.

One of the most important root causes of non-optimal efficiency in the conversion of wind kinetic energy into electricity is represented by static errors affecting wind turbines. These can simply involve sensors (like the anemometers) or might deal with the set up of the rotor (as when the three blades do not have the same pitch reference). Based on such premise, in this work, a comprehensive framework is proposed for diagnosing static errors affecting wind turbine performance, based on SCADA data analysis. The three types of considered static errors are: static yaw error; absolute or relative blade pitch misalignment; anemometer bias. It is desirable to exploit as much as possible the SCADA-collected data for diagnosing such types of static errors. Yet, given the mediation of the control system, the SCADA data might even be misleading and, for example, might indicate a correct alignment of the rotor to the wind flow, while it is not correct. Hence, the objective of the work is identifying phenomena observable through SCADA data which can be related to such static errors. Acknowledged that some manifestations can be similar for the various errors, a work flow is set up for distinguishing between them. The selected phenomena are: under-performance, which is associated to all the errors; increased vibrations; shift in the nacelle anemometer measurements. Real-world test cases, namely wind farms operated by the ENGIE Italia company, are discussed. A combination of single wind turbine and fleet-wide methods is proposed. The latter involve dividing the fleet in potentially anomalous and normal wind turbines and modeling quantities of interest (like the power) as a function of the corresponding quantities of the healthy wind turbines. The collected case studies indicate that the proposed method is effective and allows distinguishing between the various types of errors related to the anemometer, the yaw and the pitch systems.

In modern power systems the replacement of large synchronous generators with distributed inverter-based resources is lowering the power system inertia, making electrical grids more vulnerable to dynamic perturbations. For this purpose the European Network of Transmission System Operators for Electricity promoted the enhancement of the grid operation tools with specific functions for on-line estimation of the power system inertia, which are extremely useful in detecting critical thresholds and triggering proper mitigation strategies. For this purpose, the development of reliable frequency predictive models represents an essential requirement for enabling system inertia estimation. To try and address this issue, this paper explores the role of Convolutional Neural Networks (CNN) for on-line frequency estimation from grid measurements. The main idea is to model the grid frequency deviations by a CNN-based identification technique, which allows inferring the main parameters ruling the power system dynamics. Detailed simulation results obtained on several case studies demonstrate that the CNN model is able to detect data patterns, and discover hidden relationships maintaining low estimation errors even for multi-step ahead frequency predictions, thus offering a valuable tool for inertia estimation in modern power systems, especially with the increasing share of renewable energy.

Digital transformation in metrology will entail digitalisation of work practices in national metrology institutes (NMIs). This article argues that communities of practice (CoPs)—informal social structures—play a key role in how NMIs operate, making their dynamics a critical consideration in managing digitalisation. NMI work is empirical in nature, and the knowledge required to perform measurements at the highest level is embedded in practice-based forms within CoPs. However, digitalisation necessitates the development of abstract representations of processes and information. Moreover, scientific knowledge is usually communicated in a formal language using abstract concepts. So, digitalisation of NMI work products may be challenged by the distinction between empirical and abstract modes of knowledge capture. This article explores the conceptual tension between abstract and empirical modes of thought in metrology and the challenge of aligning them in the context of digital transformation. It draws on published anecdotal accounts and the author’s professional experience.