2011 Eighth IEEE International Conference on e-Business Engineering
Feature Selection of Non-intrusive Load Monitoring System using STFT and Wavelet Transform Yi-Ching Su, Kuo-Lung Lian
Hsueh-Hsien Chang
Dept. of Electrical Engineering National Taiwan University of Science and Technology Taipei, Taiwan
[email protected],
[email protected]
Dept. of Electronic Engineering Jin-Wen University of Science and Technology New Taipei, Taiwan
[email protected]
Compared with the traditional load monitoring system, non-intrusive load monitoring is easy to install, and does not need to install sensor at each load point. Therefore, nonintrusive load monitoring system has the potential to become a widely used load monitoring scheme, or important part of energy management system [3-5]. The focus of this paper is on signatures selection of nonintrusive load monitoring system. Voltage and current signal measured from the utility service entrance is called raw signals. Characteristics such as harmonic, turn-on transient power, extracted from the raw signals of loads can be recognized as the power signatures. In this paper, the turn-on transient current is chosen as the power signature. The mathematical tools we use are the short-time Fourier transform and wavelet transform to identify the turn-on time of load, duration of the transient and the frequency components [6-9]. The theory of shorttime Fourier transform and wavelet transform, and the purposes of using them in non-intrusive load monitoring as the data selection are described in Section II. Section III presents two case studies of non-intrusive load monitoring systems. Finally, a conclusion is given in Section IV.
Abstract— This paper proposes a concept of non-intrusive load monitoring system for smart meter to monitor the situation of loads. In this study, the user can clearly know the power consumption of loads by observing the operation and time of use of loads, and then improve the habit of consumption to complete the goals of saving energy and reducing carbon. This paper employs a scheme of non-intrusive load monitoring system by extracting the significant and representative power signatures of voltage and current at utility service entry in identifying loads and analyzing the characteristics of loads, and then finds out the physical behavior of operation of loads to establish the model of loads. This paper uses short-time Fourier transform (STFT) and wavelet transform (WT) of time-frequency domain to analyze and compare different loads in the experiments. In the experiments, the results reveal wavelet transform is better than STFT on transient analysis of loads. Choice of power signatures affects the results of load recognition and computation time. Keywords- Non-intrusive load monitoring; signature selection; wavelet transform; short-time Fourier transform
I.
INTRODUCTION
The traditional method of load monitoring is based on Supervisory Control and Data Acquisition (SCADA). Sensors are installed at load points and communication network is required for monitoring communication, and control purpose. Essentially, sensors will detect the changes of switches/breakers, and send a message to load recorder. Once the load recorder receives the message that sensors delivered, load recorder will record the load data immediately, and deliver them to the data center for further analysis of load conditions. However, such load monitoring method is too expensive to accomplish in an ordinary household [1]. Fred Schweppe and George Hart proposed the concept of Non-Intrusive Load Monitoring (NILM) [2]. Nonintrusive load monitoring means no installation of any voltage or current sensors at any load point, and it, determines the operating schedule of the major electrical loads in a building from measurements made at the utility service entry.
978-0-7695-4518-9/11 $26.00 © 2011 IEEE DOI 10.1109/ICEBE.2011.49
II.
PROPOSED METHODS
The traditional non-intrusive load monitoring system uses active and reactive power as the power signature. However, such monitoring is not instantaneous due to the limitations of sampling speed. In addition, when a certain load’s power is equal to the total power of the other loads in the system, false identifications will occur [1]. To counteract such a problem, this paper proposes to use the turn-on transient waveforms as the basis of the situation of loads and use it in identifying loads and analyzing the characteristics of loads. The physical behavior of loads is different from each other and the turn-on transient waveforms are inherently different [2]. A. Short-Time Fourier Transform The discrete Fourier transform or fast Fourier transform does not account for any temporal information of the original signal. In order to solve this drawback, Dennis Gabor proposed to break the sampled signal into several shorter blocks or frames and apply the discrete Fourier transform (DFT) to each block. This transformation is called short-time Fourier transform (STFT), and is represented by (1).
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and (8) is resulted. where x(t) is the original signal, w(t) is the window function. Commonly used window functions are rectangular, hamming, hanning and gauss windows. Hamming window is selected in this paper due to its good capability in reducing the leakage effect. The definition of hamming window is given in (2).
DWT essentially decompose the original signal into detailed and approximate signals via high-pass filter (h[n]) and low-pass filter (g[n]), respectively. as shown in Figure 1. And the relationship between high-pass and low-pass filters is as shown in (9).
Although, the short-time Fourier transform (STFT) contains the time information, which the fast Fourier transform lacks of, STFT have some inherent drawbacks, such as difficult choice of window functions and proper range of frequency [10].
This paper uses Daubechies as the mother wavelet for wavelet transform.
B. Wavelet Transform To overcome the problems of STFT, Jean Morlet and Alex Grossman proposed the concept of wavelet transform.
Where
is called the daughter wavelet, and
the mother wavelet. Moreover, parameter a is the scale factor, b is the shift factor. Wavelet transform essentially uses scale and shift factors to decompose the signal to be analyzed into a series of mother wavelet. Therefore, it is much suitable for analyzing transients [11]. Wavelet transform can be classified into continuous wavelet transform (CWT), discrete wavelet transform (DWT), stationary wavelet transform (SWT) and wavelet packet analysis. CWT is defined as:
Figure 1. Discrete wavelet transform scheme
III.
EXPERIMENTAL RESULTS
STFT and DWT are applied to three different loads, which are 160 HP and 123 HP induction motors and a 119 W dehumidifier. The induction motors, driven by variable voltage drives, are simulated by EMTP-ATP program while the dehumidifier is the actual load. For NILM system, two cases are studied. In case 1, a simulated NILM system monitors the voltage and current waveforms of utility service entry, supplying representative loads in an industrial building. In case 2, the NILM system monitors voltage and current waveforms of utility service entry, supplying a collection of loads representative of the major loads in a commercial building. The active and reactive powers and turn-on transient energy are chosen as the power signatures for load recognition.
where x(t) is the original signal, wa,b is the daughter wavelet. In order to get the DWT, discretization of the scale factor and shift factor is needed:
Then, substituting (5) into equation (4), one will get (6).
A. Data Acquisition and Measurement For non-intrusive load monitoring system, the main data are the voltage and currents of aggregate loads coming from utility service entrance. The simulation of the load turn-on transients is fulfilled by EMTP-ATP. The results of STFT and DWT, and the recognition of non-intrusive load monitoring system are done in MATLAB program.
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B. Analysis and Comparsion The result of STFT and DWT of the turn-on current transients of three loads are compared and analyzed. Figs 2(a),(b),(c) respectively show the waveform of turn-on current transients of the 160 Hp, 123 Hp induction motors , and a 119W dehumidifier. . Figs 3(a),(b),(c) respectively show the results of STFT of turn-on current transients. Figs 4(a), (b),(c) respectively show the results of DWT of turn-on current transients. Fig 3 (a) shows that the turn-on transient last from 0 to 0.16 sec before steady state occurs. Fig 3 (b) show that the steady-state occurs after 0.18 sec; Fig 3 (c) shows the steady-state occurs after 0.09 sec. Fig 4 clearly shows that parameter a5 is the contour of the original signal. Fig 4 (a) shows that the signal reaches to steady-state at about the 2500th sampled data point. Fig 4 (b) shows that the signal also reaches to steady-state at about the 2500th sampled data point. Fig 4 (c) shows that the signal reaches to steady-state at about the 400th sampled data point. From the results of STFT and DWT, it is found that wavelet transform can clearly show the point to begin steady-state. However, due to fixed window frame, STFT can not accurately find out the steady-state point occurred. Moreover, any window function has leakage effect accompanied. Wavelet transform use the scale factor and shift factor to make the result close to the original signal. Therefore, the analysis results of wavelet transform are much better than those of the STFT. We also can obtain the load character from the subfrequency levels provided by the wavelet transform.
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(c) Figure 2. The current waveform of turn-on transients, (a) a 160 Hp induction motor, driven by a variable-voltage drive (b) a 123 Hp induction motor, driven by variable-voltage drives (c) a 119W dehumidifier
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(c) Figure 3. The results of STFT of turn-on current transients, (a) a 160Hp induction motor, driven by a variable-voltage drive (b) a 123Hp induction motor, driven by a variable-voltage drive (c) a 119W dehumidifier
(c) Figure 4. The results of wavelet transform of turn-on current transients, (a) a 160Hp induction motor, driven by a variable-voltage drive (b) is a 123Hp induction motor, driven by a variable-voltage drive (c) a 119W dehumidifier
C. Case Study of NILMs This section provides two cases of the non-intrusive load monitoring system. In case 1, a simulated NILM system monitors the voltage and current waveforms of utility service entry supplying representative loads in an industrial building. In case 2, the NILM system monitors voltage and current waveforms of utility service entry, supplying a collection of typical major loads in a commercial building. Cases 1 and 2 both consist of circuit breaker number 1 to number 3 for multiple combinations of loads. Hence, seven combinations are possible. Each data set includes a voltage variation from -5% to +5% during one interval. Then, every load consists of 11 sampling data set. Thus, the total number of data sets is 77. This paper uses neural network as the tool for type reorganization. In each case, the network is trained until the mean square error is less than 0.0001 or the maximum of epoch is 3000. Thirty-nine data sets out of the above mentioned 77 data sets are used for training while the other 38 are used for testing. In this case study, we set the active and reactive powers as the power signatures for load identifications. 1) Case 1:The neural network in the NILM system identifies three loads with transient and steady-state signatures observed during operation of the 480V common bus. These loads include a 160Hp induction motor, a 123 Hp induction motor, driven by line frequency variablevoltage drives, and a hank of loads supplied by a six-pulse thyristor rectifier. Fig 5 is the turn-on transient energy waveform of the three loads. Different physical character for these three loads can be clearly observed from the different turn-on transient waveforms. Comparing the turn-on transient
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waveforms with the steady-state ones for the same load, the turn-on transient can provide more physical characters than the steady-state waveform. Hence, it can be the basis for load identification. In Figs. 5 (a) and (b), both of the loads have different turn-on transient waveforms. After 0.1 sec, both of them have the identical power. In Fig 5 (c), the load is combined with a bank of resistance and inductance, driven by a six-pulse thyristor rectifier. Because of the controller of the six-pulse thyristor rectifier, the power, which the load needs, is increasing with the time, as shown in the figure. Table 1 shows that values for training and test recognition accuracy of load identification in multiple operations are 100% for real and reactive powers (PQ) as power signatures, and/or with turn-on transient energy (UT) as the power signature. 2)Case 2:The neural network in the NILM system identifies three loads with transient and steady-state signatures operating on a 220V common bus. These loads include a 2.6Hp induction motor, a 4.7 Hp induction motor, and an R-L linear load with real power and reactive power equivalent to that of a 4.7 Hp induction motor. Fig 6 is the turn-on transient energy waveform of the three loads. Figs. 6 (a) and (b) clearly show that the there have different turn-on transient waveforms for the small and medium-size induction motors. Also, the powers they need are different. Figs. 6 (b) and(c) show the waveforms of different loads with the same powers. The figure shows when the two loads start at 0 sec, they have different turn-on transient waveform. Table 2 shows that values for the training and test recognition accuracy of load identification in multiple operations are all 100% if real and reactive powers, as well as the turn-on transient energy (PQUT) are all selected as power signatures. However, the training and test recognition accuracy of load identification in multiple operations are only 58.97% and 39.47%, respectively if only real and reactive powers (PQ) are selected as power signatures. Those who cannot be identified by real and reactive powers are second and third loads or combinations of the first and second loads and combinations of the first and third loads. In other words, test recognition for those loads in multiple operations is quite low when using only real and reactive powers as power signatures.
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TABLE I. THE RESULTS OF LOAD IDENTIFICATION IN CASE STUDY 1
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supporting this research under Contract No. NSC 100-2221E-228-006.
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REFERENCES
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C. Langhman, K. Lee, R. Cox, S. Show, S. B. Leeb, L. Norford, and P. Armstrong, "Power Signature Analysis," IEEE Power & Energy Analysis Magazine, pp.56-63, 2003. [3] G. W. Hart, "Nonintrusive appliance load monitoring," Proceedings of the IEEE Conference ,pp.1870.1891,1992. [4] S. B. Leeb, A conjoint pattern recognition approach to nonintrusive load monitoring, Ph.D.Thesis , MIT,1993. [5] A. I. Cole, and A.Albicki, "Nonintrusive identification of electrical loads in a three-phase environment based on harmonic content," Proceedings of the IEEE Instrumentation and Measurement Technology Conference, pp.24-29 2000. [6] A. I. Cole and A. Albicki, "Data extraction for effective non-intrusive identification of residential power loads," Proceedings of the IEEE Instrumentation and Measurement Technology Conference, pp.812815,1998. [7] A. I. Cole and A. Albicki, ̌ Algorithm for non-intrusive identification of residential appliances,” Proceedings of the IEEE International Symposium on Circuits and Systems, pp.338-341,1998. [8] L. K. Norford and S.B.Leeb, "Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithm," Energy and Buildings, Vol. 24, pp.51-64,1996. [9] S. R. Shaw, S. B. Leeb, L. K. Norford, and R.W.Cox, "Nonintrusive load monitoring and diagnostics in power systems," IEEE Trans. on Instrumentation and Measurement. Vol. 57, No.7, pp.14451454,2008. [10] C. Zhao, M. He, Xia Zhao, "Analysis of Transient Waveform based on Combined Short Time Fourier Transform and Wavelet Transform," IEEE International Conference on Power System Technology, pp.21-24,2004. [11] D. C. Robertson, O. I. Camps, J. S. Mayer, and W. B. Gish, Sr., "Wavelets and electromagnetic power system transients," IEEE Trans. on Power Delivery, Vol.11, No.2, pp.1050-1056,1996.
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H. H. Chang, "Genetic Algorithms and Non-intrusive Energy Management System Based Economic Dispatch for Cogeneration Units," Energy, 36 (1), pp. 181-190, 2011.
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(c) Figure 6. Simulation of load turn-on transient energy for case 2, (a) a 2.6 Hp induction motor (b) a 4.7 Hp induction motor (c) an R-L linear load with real and reactive powers equivalent to those of a 4.7 Hp induction motor.
TABLE II. THE RESULTS OF LOAD IDENTIFICATION IN CASE STUDY 2
IV.
CONCLUSIONS
This paper focus on power signature selection of nonintrusive load monitoring system, using wavelet transform and short-time Fourier transform. Then, the results indicate that wavelet transform is much better than short-time Fourier transform. This paper also shows via two case studies that the choices of power signatures can strongly influence the accuracy of load identifications. ACKNOLEDGEMENTS The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially
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