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.

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).

Classification systems based on machine learning (ML) models, critical in predictive maintenance and fault diagnosis, are subject to an error rate that can pose significant risks, such as unnecessary downtime due to false alarms. Propagating the uncertainty of input data through the model can define confidence bands to determine whether an input is classifiable, preferring to indicate a result of unclassifiability rather than misclassification. This study presents an electrical fault diagnosis system on asynchronous motors using an artificial neural network (ANN) model trained with vibration measurements. It is shown how vibration analysis can be effectively employed to detect and locate motor malfunctions, helping reduce downtime, improve process control and lower maintenance costs. In addition, measurement uncertainty information is introduced to increase the reliability of the diagnosis system, ensuring more accurate and preventive decisions.

Arcing is a harmful condition that can lead to electrical fires. The U.S. National Electrical Code requires the installation of electric arc breakers in all residential areas. However, such devices can fail, and therefore, may not work when needed. At present, there are no tests to confirm the correct operation of this type of device. This study aimed to propose an experimental test method to verify the proper functioning of magneto-thermal switches with an electric arc protection function for the occurrence of arc faults. The results show that the proposed method adequately detected the correct operations and tripping time by using different surge suppressors. The proposed system lays the foundations for a portable test system for the periodic checking of electrical systems in which arc fault detection devices (AFDDs) are installed.

Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique’s performance. The results show that the online SFRA could be of interest for monitoring induction motors’ health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400.