• Energy Research

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.

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.

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.

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.

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.

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.

Modeling and analysis of the functioning of power systems is very difficult because of time varying and non-linear behaviors. Often power systems are described trough simplified approaches. The most widespread one recognizes load conditions that can be considered stationary within limited time intervals, in order to apply thus standard harmonic analysis techniques. Here a data segmentation approach to distinguish the time intervals in which stationary load conditions can be recognized is discussed and assessed.