Converters for photovoltaic (PV) systems usually consist of two stages: a dc/dc booster and a pulsewidth modulated (PWM) inverter. This cascade of converters presents efficiency issues, interactions between its stages, and problems with the maximum power point tracking. Therefore, only part of the produced electrical energy is utilized. In this paper, the authors propose a single-phase H-bridge multilevel converter for PV systems governed by a new integrated fuzzy logic controller (FLC)/modulator. The novelties of the proposed system are the use of a fully FLC (not requiring any optimal PWM switching-angle generator and proportional-integral controller) and the use of an H-bridge power-sharing algorithm. Most of the required signal processing is performed by a mixed-mode field-programmable gate array, resulting in a fully integrated System-on-Chip controller. The general architecture of the system and its main performance in a large spectrum of practical situations are presented and discussed. The proposed system offers improved performance over two-level inverters, particularly at low-medium power.

Electrical load planning and demand response programs are often based on the analysis of individual load level measurements obtained from houses or buildings. The identification of individual appliances’ power consumption is essential, since it allows improvements, which can reduce appliances’ power consumption. In this paper, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a non-intrusive load monitoring (NILM) technique. A NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this paper. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the paper.

Electric motors and the systems they drive are the single largest electrical end-use; it is estimated that they consume between 43% and 46% of all global electricity consumption. Recent studies show that efficiency values of induction motors can be successfully increased, with improvements up to 20%-30%. In 2009, the European Commission published a new regulation (640/2009) concerning requirements for the eco-compatible design of electric motors. In such a scenario, the experimental determination of induction motor efficiency is getting more and more important, because of the need of placing these motors in the right energy efficiency levels defined by international regulations. The correct motor classification strictly depends on the uncertainty associated with the efficiency determination. According to the IEC 60034-2-1 standard, the efficiency of three-phase induction motors can be determined by applying the direct or indirect efficiency technique. The direct procedure is suggested for low uncertainty efficiency measurements of single-phase induction motors and three-phase motors with rated power ≤1 kW. In this paper, we propose a comparative analysis between direct and indirect efficiency determination for three-phase induction motors, according to the IEC 60034-2-1 standard. The contribution of this paper consists in the comparison between the uncertainty calculations in indirect and direct efficiency determination for induction motors, to investigate the advisability of adopting the direct efficiency determination as reference method, because it is easier to implement and it has a good accuracy. This task is carried out starting from the accuracy requirements for measurement equipment, as demanded by the Guide to the Expression of Uncertainty in Measurement GUM. Some experimental results obtained with the indirect and direct efficiency determinations on a 3-kW three-phase two-pole induction motor are presented and discussed. A final suggestion related to a future version of IEC 60034-2-1 is also reported.

Nonintrusive load monitoring (NILM) systems are used to identify the energy consumption patterns of individual devices in an electrical system, but broadening their market availability is a significant challenge. In this paper, a NILM system using edge processing is proposed, in which energy consumption data are processed directly on the device installed at the monitored facility. Specifically, it uses a sequence-to-point approach based on a convolutional neural network implemented on an Arm Cortex-M7 microcontroller. This paper also reports the results of an extensive 12-month testing phase. The NILM system was installed in two real houses in central Italy to evaluate its installation and potential application in real-world scenarios. This study presents a promising solution that enables the widespread adoption of NILM systems by reducing their implementation cost and complexity and addresses the privacy concerns associated with cloud-based data processing. The results of our real-world testing provide compelling evidence of the potential of the proposed NILM system in various applications, including smart homes, building automation, and industrial energy management.

Load monitoring systems make it possible to obtain information on the status of the various loads powered by an electrical system. The term “electrical load” indicates any device or circuit that absorbs energy from the system to which it is connected, and which therefore influences electrical quantities such as power, voltage, and current. These monitoring systems, designed for applications related to energy efficiency, can also be used in other applications. This article analyzes in detail how the information derived from Non-Intrusive Load Monitoring (NILM) systems can be used in order to create Energy Management Systems (EMS), Demand Response (DR), anomaly detection, maintenance, and Ambient Assisted Living (AAL).

The paper deals with a low-cost transducer that implements an improved technique for wind speed measurement in open air. The instrument is a 2-axis ultrasonic anemometer, capable of monitoring both wind speed and direction. Its main advantage is that using the ultrasonic technique it has no moving parts, allowing for the utilization in a variety of applications requiring low maintenance and fast response to rapid wind gusts. Wind speed measurement for power turbine control can be considered as a reference application of it. The proposed instrument presents a simple design and is constructed using commercially available components, to reduce development costs. In the paper the proposed architecture is presented, discussing the algorithm applied to process the transduced ultrasonic signals. Some results obtained during the first tests are also presented and discussed, suggesting for successive improvements.