A New Event-detection Method Based on Composite Windows in NILM for Industrial Settings

Yi, Shuhui; Yin, Xiaodong; Diao, Yinglong; Wang, Binwu; Wu, Peng

doi:10.1109/ispec48194.2019.8975265

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…e electricity demand of industrial users can be characterized as a sequence fluctuating with time. Modeling and analysis of this sequence can achieve its inherent traits [25,26]. e equipment-side of a concrete plant is selected as the monitoring target in this paper.…”

Section: Characteristic Analysis Of Industrial Usersmentioning

confidence: 99%

“…RMS is a feature that is very sensitive to the window length; hence, here, we select the composite-window method based on the cumulative sum (CUSUM) algorithm to calculate three steady-state feature indices. e composite-window method is applicable for the feature indices of both steady-state and transient-state [26].…”

Section: Steady-state Features Extractionmentioning

confidence: 99%

“…In this paper, transient feature indices are extracted according to the composite-window based on CUSUM [26].…”

Section: Transient Features Extraction Detection Of Transientmentioning

confidence: 99%

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Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

Yi¹,

Diao²,

Liu³

et al. 2022

Journal of Control Science and Engineering

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Nonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a nonintrusive load identification method is proposed, combining the least square QR (LSQR) with the sequential leader clustering algorithm. Firstly, regarding accurate depiction of industrial loads, some appropriate load feature indices of steady-state and transient processes are extracted, respectively. For steady-state processes, the active power, the reactive power, and the root mean square (RMS) current value are selected as the feature indices. In the case of transient processes, ten feature indices of three stages are employed: before, during, and after transient events, consisting of the duration of transient events, the RMS current value before and after transient events, the average value of active power before and after transient events, the average value of reactive power before and after transient events, the maximum RMS current value of transient events, etc. On this base, the LSQR algorithm is proposed to decompose unknown composite power to access the operation of various loads at steady-state. The sequential leader clustering algorithm is propounded to classify transient events of typical industrial loads and further identify which kind of loads had switched. Finally, to validate the effectiveness of the presented model, data of industrial loads from a concrete plant are collected, including blender, cement screw, sewage dump, and inclined belt conveyor, and simulation analysis is fulfilled. The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.

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Section: Characteristic Analysis Of Industrial Usersmentioning

confidence: 99%

Section: Steady-state Features Extractionmentioning

confidence: 99%

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Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

Yi¹,

Diao²,

Liu³

et al. 2022

Journal of Control Science and Engineering

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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Motor load accounts for more than 50% of the total electric power load in China. Identifying the load of induction motors non-intrusively is of great importance for the design of energy-saving schemes and formulation of demand-side response strategies in industrial enterprises. Based on the transient mechanism of the induction motor, the present work first defines some motor load start-up transient feature parameters with clear physical meanings and proposes a set of non-intrusive motor load identification methods applicable to industrial settings. In addition, a case study that applied the proposed method to the industrial setting was performed to verify its effectiveness. The results showed that the proposed method can overcome the problem of misidentification caused by the fact that the start-up transient process is affected by its mechanical load characteristics and hence can identify motors with similar running power and has good anti-interference capacity despite power quality disturbances.

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“…And there are relatively few studies on industrial load identification. Since it is not convenient to collect transient characteristics for modeling of industrial load due to production demand, how to extract data for modeling identification without affecting the operation of industrial load becomes very critical [23,24]. In this paper, the operation of industrial load is studied.…”

Section: Introductionmentioning

confidence: 99%

Nonintrusive Industrial Load Identification Combined with Random Forests and Steady-State Waveform

Yi¹,

Zhu

Liu³

et al. 2021

Journal of Electrical and Computer Engineering

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Nonintrusive industrial load identification can accurately acquire the operation data of each load in the plant, which is the benefit of intelligent power management. The identification method of the industrial load is complicated and difficult to be realized due to the difficulty in collecting transient data for modeling, and high-precision measuring equipment is required. Aiming at this situation, the article proposes a nonintrusive industrial load identification method using a random forest algorithm and steady-state waveform. Firstly, by monitoring the change of the industrial load power state, when the load changes and becomes stable, the steady-state waveform is extracted. Due to different electrical characteristics of industrial loads, the current waveform of loads is different to some extent. We can construct characteristic data for each industrial load to construct its own current steady-state waveform. Then, using the high-dimensional data of the steady-state waveform as the sample data, the bootstrap sampling method and the CART algorithm in the random forest algorithm are used to generate multiple decision trees. Finally, the industrial load types are identified by voting multiple decision trees. The actual operating load data of a factory are used as the sample data in the simulation, and the effectiveness and rapidity of the proposed identification algorithm are verified by the combined load method simulation comparison. The simulation results show that the accuracy of the proposed identification algorithm is more than 99%, the identification time is 3.36 s, which is much higher than that of other methods, and the operation time is less than that of other methods. Therefore, the proposed identification algorithm can effectively realize the nonintrusive industrial load identification.

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A New Event-detection Method Based on Composite Windows in NILM for Industrial Settings

Cited by 12 publications

References 7 publications

Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

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

Nonintrusive Industrial Load Identification Combined with Random Forests and Steady-State Waveform

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