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The paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together frequency signals, measured by means of phasor measurement units (PMU), exhibiting similar oscillatory behavior after the occurrence of a fault or disturbance. Unfortunately, the increasingly massive presence of renewable energy sources is undermining the clustering methods defined so far, requiring new solutions to the problem. To overcome the considered drawbacks, the authors propose hereinafter to ( ${i}$ ) improve the grouping capabilities of an iterative spectral clustering method thanks to the definition of new parameters for similarity estimation (Modified Bray Curtis index) and cluster thresholding (weighted Fiedler value) as well as (ii) enhance its robustness with respect to both measurement noise and uncertainty affecting the PMUs by means of a deep test procedure. To this aim, particular attention is paid in the design and assessment stage to the definition of both filtering algorithm and measurement parameters (e.g., the length of the analysis window). Once defined these parameters, the method is capable of 100% correctly separating transmission network sections oscillating with similar trends in a number of tests conducted on simulated and actual signals, so highlighting the promising performance of the method highlighting its reliability and efficacy in different test conditions.

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving.

Laboratory experiences have proved to be a key moment of the educational path in most of the so-called Sciences, Technology, Engineering and Mathematics (STEM) subjects. Having the opportunity of practicing on actual experiments about the theoretical knowledge achieved during the classroom lectures is a fundamental step from a didactic point of view. However, lab activities could be forbidden in the presence of tests characterized by safety issues, thus limiting students’ cultural growth; this is particularly true for physics experiments involving radioactive materials, sources of dangerous radiations. To face the considered problems, the authors propose hereinafter a mixed-reality solution involving augmented reality (AR) at students-side and actual instrumentation at laboratory-side. It is worth noting that the proposed solution can be applied for any type of experiment involving the remote control of measurement instruments and generic risk conditions (physical, chemical or biological). As for the considered case study on gamma radiation measurements, an ad-hoc AR application along with a microcontroller-based prototype allows students, located in a safe classroom, to (i) control distance and orientation of a remote actual detector with respect to different radioactive sources and (ii) retrieve and display on their smartphones the corresponding energy spectrum. The communication between classroom equipment and remote laboratory is carried out by means of enabling technologies typical of Internet of Things paradigm, thus making it possible a straightforward integration of the measurement results in cloud environment as dashboard, storage or processing.

In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver’s position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle.

The topics of virtual, mixed, and extended reality have now become key areas in various fields of scientific and industrial applications, and the interest in them is made tangible by the numerous papers available in the scientific literature. In this regard, the Special Issue “VR, AR, and 3-D User Interfaces for Measurement and Control” received a fair number of varied contributions that analyzed different aspects of the implementation of virtual, mixed, and extended reality systems and approaches in the real world. They range from investigating the requirements of new potential technologies to the prediction verification of the effectiveness and benefits of their use, the analysis of the difficulties of interaction with graphical interfaces to the possibility of performing complex and risky tasks (such as surgical operations) using mixed reality viewers. All contributions were of a high standard and mainly highlight that measurement and control applications based on the new models of interaction with reality are by now increasingly ready to leave laboratory spaces and become objects and features of common life. The significant benefits of this technology will radically change the way we live and interact with information and the reality around us, and it will surely be worthy of further exploration, maybe even in a new Special Issue of Future Internet.

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; therefore, motor diagnostics is an issue that assumes great importance. To prevent their failures and face the considered service outages in a timely manner, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on an artificial neural network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminarily validated on a set of 28 electric motors, and its performance is compared with common state-of-the-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98% in identifying anomalous conditions of three-phase asynchronous motors.

Ensuring service continuity has become a fundamental issue for companies involved in electricity distribution; in particular, isolating the smallest possible portion of the network as a result of faults has long been a primary objective. To this aim, solutions based on logic selectivity have been defined and implemented for an efficient search for the network branch affected by the fault and its subsequent isolation. The authors have recently presented a proposal for the implementation of logic selectivity that exploits the LoRa transmission protocol, an ideal solution in the case of areas not reachable by the currently exploited communication technologies. The present paper, instead, deals with the optimization of some LoRa parameters, which made it possible to exploit network configurations in terms of coverage range, sensitivity and signal-to-noise ratio. The performance of the new configuration has been assessed through a number of tests conducted in the laboratory and on-field, highlighting promising results in terms of both intervention times and reliability. In particular, tests conducted in both rural and urban areas have assured fault isolation times as low as 33 ms (fully compliant with the current regulations) in the presence of the most challenging fault condition.

In the paper, an analytical method for determining the optimal positioning of intelligent electronic devices in medium voltage grids is proposed. Intelligent electronic devices are automated devices able to communicate one with each other and command the circuit breaker in order to localize and isolate a line fault as fast as possible. However, the number of intelligent electronic devices to install has to be limited, due to the relevant installation costs and the reduction in the transmission bandwidth caused by the increased number of exchanged messages. So, the electrical distributor has to carefully detect the nodes of the grid where the intelligent electronic devices have to be installed. The authors propose a method based on integer linear programming, which, given the number of intelligent electronic devices to install, finds their optimal position, i.e., the one that minimizes the penalties associated with the power down experienced by customers. In order to highlight the offered advantages in terms of computational effort, the proposed approach has been assessed with a real medium voltage grid.

The Distributed Maximum Power Point Tracking (DMPPT) approach is a promising solution to improve the energetic performance of mismatched PhotoVoltaic (PV) systems. However, there are still several factors that can reduce DMPPT energy efficiency, including atmospheric conditions, the efficiency of the power stage, constraints imposed by the topology, the finite rating of silicon devices, and the nonoptimal value of string voltage. In order to fully explore the advantages offered by the above solution, the implementation of a Boost based DMPPT emulator is of primary concern, especially if it behaves as a controlled voltage or current source. The repeatability of experimental tests, the tighter control of climatic conditions, the closing of the gap between the physical dimensions of a PV array and the space available in a university lab, the simplicity with which new algorithms can be tested, and the low maintenance costs are just some of the benefits offered by an emulator. This paper describes the realization and use of a Boost based Distributed Maximum Power Point Tracking (DMPPT) emulator and shows its high flexibility and potential. The device is able to emulate the output current vs. voltage (I-V) characteristics of many commercial PhotoVoltaic (PV) modules with a dedicated Boost DC/DC converter. The flexibility is guaranteed by the ability to reproduce both I = f ( V ) and V = g ( I ) characteristics at different values of not only the irradiance levels but also the maximum allowed voltage across the switching devices. The system design is based on a commercial power supply controlled by a low-cost Arduino board by Arduino (Strambino, Torino, Italy). Data acquisition is performed through a low-cost current and voltage sensor by using a multichannel board by National Instruments. Experimental results confirm the capability of the proposed device to accurately emulate the output I-V characteristic of Boost based DMPPT systems obtained by varying the atmospheric conditions, the rating of silicon devices, and the electrical topology.

The paper deals with the problem of improving the maximum sample rate of analog-to-digital converters (ADCs) included in low cost wireless sensing nodes. To this aim, the authors propose an efficient acquisition strategy based on the combined use of high-resolution time-basis and compressive sampling. In particular, the high-resolution time-basis is adopted to provide a proper sequence of random sampling instants, and a suitable software procedure, based on compressive sampling approach, is exploited to reconstruct the signal of interest from the acquired samples. Thanks to the proposed strategy, the effective sample rate of the reconstructed signal can be as high as the frequency of the considered time-basis, thus significantly improving the inherent ADC sample rate. Several tests are carried out in simulated and real conditions to assess the performance of the proposed acquisition strategy in terms of reconstruction error. In particular, the results obtained in experimental tests with ADC included in actual 8- and 32-bits microcontrollers highlight the possibility of achieving effective sample rate up to 50 times higher than that of the original ADC sample rate.

The paper deals with a novel method to measure inter-area oscillations, i.e., electromechanical oscillations involving groups of generators geographically distant from one another and ranging within the frequency interval from 0.1 Hz up to 1 Hz. The estimation of the parameters characterizing inter-area oscillations is a crucial objective in order to take the necessary actions to avoid the instability of the transmission electrical system. The proposed approach is a signal-based method, which uses samples of electrical signals acquired by the phasor measurement unit (PMU) and processes them to extract the individual oscillations and, for each of them, determine their characteristic parameters such as frequency and damping. The method is based on Hilbert transformations, but it is optimized through further algorithms aiming at (i) improving the ability to separate different oscillatory components, even at frequencies very close to each other, (ii) enhancing the accuracy associated with the damping estimates of each oscillation, and (iii) increasing the robustness to the noise affecting the acquired signal. Results obtained in the presence of signals involving the composition of two oscillations, whose damping and frequency have been varied, are presented. Tests were conducted with signals either synthesized in simulated experiment or generated and acquired with actual laboratory instrumentation.

In this paper, a logic selectivity system based on Long Range (LoRa) technology for the protection of medium-voltage (MV) networks is proposed. The development of relays that communicate with each other using LoRa allows for the combination of the cost-effectiveness and ease of installation of wireless networks with long-range coverage and reliability. The realized demonstrator to assess the proposed system is also presented in the paper; based on different types of faults and different locations, the times needed for clearing a fault and restoring the network were estimated from repeated experiments. The obtained results confirm that, with an optimized design of transmitted packets and of protocol characteristics, LoRa communication grants fault management that meets the criteria of logic selectivity, with fault isolation occurring within the maximum allowed time.

Reducing or optimizing electrical power consumption is one of the most fundamental goals within the current frameworks of smart energy management. This approach would be spread from the industrial plants, characterized by high consumptions often well distributed throughout the day, down to the domestic utilities, typically discontinuous and with limited consumption at the single user level. More specifically, it is desirable for the latter case to be able to control in a simple and effective way the power consumption of typical household appliances by means of technologies that are already used and spread (such as tablets and smartphones) to become aware of their actual impact, both economic and environmental. To this aim, the authors present the proof-of-principle of user-friendly monitoring system for power consumption awareness based on the recent technologies of Internet of Things (IoT) and Augmented Reality (AR). In particular, common devices such as smartphones associated along with appropriate measurement nodes and a suitable app, developed to the purpose, allow consumers to view in AR environment electrical consumption of their domestic electrical loads to consciously decide whether to switch them off. Performance of both sensor nodes and AR environment were preliminarily assessed in either laboratory experiments or actual household context, highlighting the promising effectiveness of the proposed approach.