Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

Series arc faults cause the majority of household fires involving electrical failures or malfunctions. Low-fault current amplitude is the reason for the difficulties faced in implementing effective arc detection systems. The paper presents a novel arc detection and faulty line identification method. It can be easily used in the low-voltage Alternate Current (AC) household network for arc detection in the Non-Intrusive Load Monitoring (NILM). Unlike existing methods, the proposed approach exploits both current and voltage signal time domain analysis. Experiments have been conducted with up to six devices operating simultaneously in the same circuit with an arc fault generator based on the IEC 62606:2013 standard. Sixteen time-domain features were used to maximize the arc-fault detection accuracy for particular appliances. Performance of the random forest classifier for arc fault detection was evaluated for 28 sets of features with five different sampling rates. For the single period analysis arc, detection accuracy was 98.38%, with F-score of 0.9870, while in terms of the IEC 62606:2013 standard, it was 99.07%, with F-score of 0.9925. Location of a series arc fault (line selection) was realized by identifying devices powered by the faulty line. The line selection was based on the Mean Values of Changes feature vector (MVC₅₀), calculated for absolute values of differences between adjacent current signal periods during the arc fault. The fault location accuracy was 93.20% for all cases and 98.20% for cases where the arc fault affected a single device.

The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the common supply current signal. Analysis of the instantaneous changes in the aggregated current on the output of the analyzed circuit in the power network is exploited here. The signal is processed using the time alignment of the current and voltage signals samples represented in the array form. The scheme includes filtering, event detection and identification, which is performed by comparing parameters of the detected event against previously determined signatures of monitored appliances. The analysis is performed in the time domain; therefore (unlike other existing methods), the information contained in the original signal is not lost. The approach was tested in the laboratory designed specifically for this purpose. All tests have been conducted with up to 12 appliances operating at the same time in the single power supply circuit. The measurement setup was developed and used to record appliances’ switching on/off events. During tests, 2300 events for devices were recorded. Collected data were processed to identify particular devices with the accuracy of 98.8% and macro-averaged F-score measure of 0.9874. High identification accuracy was achieved despite the high number of devices operating in the background.

The paper presents the application of the Wavelet Transform (WT) analysis to identify electrical appliances operating in the end-user household. The generic approach to the non-intrusive devices identification, i.e. using aggregated current and voltage signals is described. Next, the novel approach to extract features from the signals using WT coefficients is introduced. This allows for monitoring the appliance in the transient state, during the on-off event or while changing its mode (in the multi-state devices). This approach may replace or support the traditional steady-state analysis. Features extracted during switching the specific appliance in the laboratory conditions are then used to train the artificial intelligence-based classifier. It is further responsible for identifying the devices in the on-line mode. As the classifier, the random forest was applied. Its evaluation on the available data proves the usefulness of the proposed approach.

The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach.

Celem pracy jest wykazanie skuteczności nowo opracowanych metod detekcji uszkodzeń opartych na prostym podejściu statystycznym obejmującym liniową analizę dyskryminacyjną i przetwarzanie sygnałów. Przeprowadzone badania potwierdzają wysoką skuteczność wykrywania uszkodzeń we wszystkich rozpatrywanych obszarach. (Wykrywanie zdarzeń awaryjnych w sieciach SN na podstawie analizy danych z rejestratorów zakłóceń.