An innovative non-intrusive load monitoring system for commercial and industrial application

Abstract

Over more than 30 years development of Non-intrusive Load Monitoring (NILM), many technologies were developed from sampling frequency I Hz to hundreds kHz to extract from simple information such as difference power dP, dQ; shape of transient signatures to complex information such as harmonics or electromagnetic interference (EM I). There are now many real NILM systems in residential applications but there is lack of researches to apply NILM to commercial and industrial application. This site uses so much energy in business and production so that analyzing power consumption to optimize their operation is very important to produce competitive products on the market. Unfortunately, it is very difficult to recognize commercial and industrial machine that are composed of many electric elements. This paper provides a new NILM approach to monitor commercial and industrial machines based on basic electrical appliances inside architecture of machine. All power differences dP, dQ, dS are used to analyze and classify basic electrical appliances. Adaptive filter CUSUM is used to improve removing noise in preprocessing phase of the NILM system. Finally, Field Program Gate Array (FPGA) is discussed to implement our NILM system.

A Non-Intrusive Motor Load Identification Method Based on Load Transient Features

“…Therefore, if a set of load identification methods for induction motors is proposed and installed in industrial sites, it means that more than 75% of the industrial load is monitored at the equipment level, and more than 50% of the electric load of the whole Chinese society is monitored at the equipment level. However, in reality, since the power consumption data of each motor are related to a company's production and involves commercial secrets, and many motors are integrated into complete industrial machines (Kien Nguyen Trung et al, 2012), energy service providers often do not have access to the power consumption data of the motors. Nevertheless, to achieve the goal of energy conservation, we still have to rely on industrial load identification methods to obtain such data as the actual running time (turn-on/off time), running power, and power consumption of each motor.…”

“…There have been few works on industrial load identification (Leeb and Kirtley, 1993;Leeb et al, 1995;Khan et al, 1997;Chang et al, 2007;Shaw et al, 2008;Chang et al, 2011;Kien Nguyen Trung et al, 2012;Adabi et al, 2015;Holmegaard and BaunKjaergaard, 2016;Renaux et al, 2018;Yi et al, 2019Yuan et al, 2019Yu et al, 2020;Faustine et al, 2021;Yang et al, 2021), and fewer on the circuit mechanism of three-phase induction motors (Leeb and Kirtley, 1993;Leeb et al, 1995;Khan et al, 1997;Chang et al, 2007;Shaw et al, 2008;Chang et al, 2011;Yi et al, 2019). Leeb et al (Leeb and Kirtley, 1993;Leeb et al, 1995;Khan et al, 1997;Shaw et al, 2008) used the spectral envelope of instantaneous power in the induction motor starting the transient process as the identification feature but did not clarify that the shape and duration of the envelope are related to the size of the mechanical torque; for instance, the duration and the shape of the envelope may be different for each start of the mixer.…”

Estimating Machine Power Consumptions through Aggregated Measurements and Machine Data Acquisition

“…In the context of private households NILM is already being used by a number of companies [7]. However, employing NILM in industry poses significant difficulties due to the large quantity of basic electric appliances inside one machine and a high noise level [8].…”