A. Moonis et al. (Eds.): IEA/AIE 2014, Part II, LNAI 8482, pp. 189–198, 2014. © Springer International Publishing Switzerland 2014

A Novel Method for Extracting Aging Load and Analyzing Load Characteristics in Residential Buildings

Hsueh-Hsien Chang1, Meng-Chien Lee2, and Nanming Chen2

1 Department of Electric Engineering, Jinwen University of Science and Technology, New Taipei, Taiwan

2 Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

Abstract. This study proposes a Hellinger distance algorithm for extracting the power features of aging load based on a non-intrusive load monitoring system (NILM). Hellinger distance algorithm is used to extract optimal features for load identification and the back-propagation artificial neural network (BP-ANN) is employed for the aging load detection. The proposed methods are used to analyze and identify the load characteristics and aging load in residential building. The result of aging load detection can provide the demand information for each load. The recognition result shows that the accuracy can be improved by using the proposed feature extraction method. In order to reduce the con-sumption of energy and send a real-time alarm of aging load to the user, the system provides the information of energy usage from the data analyses.

Keywords: Non-intrusive load monitoring system (NILM), aging load detec-tion, Hellinger distance, back-propagation artificial neural network (BP-ANN).

1 Introduction

In traditional load monitoring methods, the sensors which connect to the main system for inner signal exchange have to be installed on each load [1]. When a load or a breaker state changes, the sensor will detect the switch state and send the message to load recorder, then the recorder transmit the data to the information center for load analysis, and the system will show the load energy information. Non-intrusive load monitoring system (NILM) does not only need no sensor on each load but also it has only one sensor to connect in the power service entry between the utility system and the residential building. It combines the energy monitoring device, which is installed on the power entrance, with household meter. The system shows the ON and OFF information of each load by analyzing the voltage and current waveforms data from the data acquisition device (DAQ). Comparing traditional load monitoring methods with NILM, the latter does not require many devices, complex installation, and high cost for every sensor [2] [3].

In the household system, the aging of electric appliances may happen because of over time or over limitation for appliances. The aging may cause the change of load characteristics in the power and harmonic components, which will increase the energy

190 H.-H. Chang, M.-C. Lee, and N. Chen

consumption and risk. Therefore, it is necessary to detect efficiently the aging of elec-tric appliances for saving the energy and reducing the risk.

This study proposes a Hellinger distance algorithm for extracting the power fea-tures of aging load based on a NILM system. In this paper, the optimal features are used for load identification and the back-propagation artificial neural network input (BP-ANN). BP-ANN, which imitates animal central nervous information process system, is employed for the aging load detection. By adjusting the weight and bias between the neurons, the neural network can generate network outputs that are cor-rectly corresponding to the optimal inputs [4].

There are three experiments based on Hellinger distance algorithm for detecting and simulating the aging of loads. The experiment implements the algorithm into the NILM system. This research measures a lot of power signatures including the root mean square value of voltage (Vrms), the root mean square value of current (Irms), real power (P), reactive power (Q), total harmonic distortion of voltage (VTHD), total harmonic distortion of current (ITHD) and odd order current harmonics (In) for each load by using Power Quality Monitoring Device ADX30000. Hellinger distance algo-rithm is used to calculate the distance between the power signatures of new and old load and to find the optimal features. The result shows that aging load detection based on non-intrusive load monitoring system can detect and diagnose the aging of loads in the residential buildings.

2 Basic Theory

In the household system, the aging of appliances may cause the increasing of energy consumption and the change of characteristics in power. Hellinger distance algorithm is used for detecting the aging of loads by measuring the power signatures, and to find the optimal features. The optimal features are used for load identification, especially for serving as input in the BP-ANN. The following sections will describe the power signatures and Hellinger distance algorithm.

2.1 Power Signatures

Power signatures include Vrms, Irms, P, Q, VTHD, ITHD and In. They are calculated by the voltage and current waveforms measured from load side. The harmonic compo-nents are calculated by Fourier series. The equation (1) and (2) is the real power and reactive power, respectively [5].

= =

−+==N

n

N

nIVnnn nn

IVIVPP0 1

00 )cos(2

1 ϑϑ

(1)

= =

−==N

n

N

nIVnnn nn

IVQQ1 1

)sin(2

1 ϑϑ (2)

where n is the order of harmonic; V0 and I0 are the average voltage and average current, respectively; Vn and In are the effective n th harmonic components of the

A Novel Method for Extracting Aging Load and Analyzing Load Characteristics 191

voltage and current, respectively; and θVn and θIn are the n th harmonic components of the voltage and current phase angles, respectively.

The total harmonic distortion of voltage and current are as shown in equation (3) and (4), respectively.

%100||

...||||||%

24

23

22 ×

+++=

FV V

VVVTHD (3)

%100||

...||||||%

24

23

22 ×

+++=

FI I

IIITHD (4)

where FV and

FI are the fundamental voltage and current, respectively; Vn and

In are the n order harmonic components of voltage and current, respectively.

2.2 Hellinger Distance Algorithm

The Hellinger distance algorithm (HD) is a type of divergence which is used to quan-tify the similarity between two probability distributions in probability and statistics [6]. To define the Hellinger distance algorithm, let I and J denote two probability distributions, where I and J are N positive (I1, I2 ,I3 ,…, IN) and (J1, J2 ,J3 ,…, JN) to satisfy =1I and = 1J , respectively. The square of HD between I and J are

shown in equation (5).

=

−=N

nnnH JIJId

1

22 )(21

),( (5)

The expansion of equation (5) is defined as:

)2(2

1),(

1

2nn

N

nnnH JIJIJId −+=

=

(6)

Since =1I and = 1J ,

==

−=−=N

nnn

N

nnnH JIJIJId

11

2 1)22(21

),( (7)

where Hellinger distance satisfies 10 2 ≤≤ Hd . The Hellinger distance is mini-

mum if 02 =Hd , or I = J; The Hellinger distance is maximum if 12 =Hd or the

difference between I and J is large. While I and J are normal distributions as shown in equation (8):

),(~ 2IINI σμ

),(~ 2JJNJ σμ (8)

Hellinger distance is defined as following Equ. (9).

22

222 )(

4

1

222 2

1),( JI

JI

eJIdJI

JIH

σσμμ

σσσσ +

−−

+−=

(9)

192 H.-H. Chang, M.-C. Lee, and N. Chen

where Iσ and Jσ are the standard deviation of I and J, respectively; I

μ and Jμ

are the average values of I and J. Equ. (9) shows the results calculate the difference between two probability distributions based on Hellinger distance algorithm.

3 Loads Specification and Study Cases

3.1 Loads Specification

The experiment in this study uses three kinds of electric appliances. They are total six loads including new and old rice steamer, hair dryer and electrical oven to show that Hellinger distance algorithm can extract the power features of aging load. The speci-fication of new and old appliances is the same. The modes of loads used in this expe-riment are the heating mode of rice steamer, the low speed mode of hair dryer, and the 4th power of electrical oven. Table 1 shows that the specification of loads.

Table 1. Specification of loads

Load Specification New Rice Steamer

(Heating Mode) 1Φ110V, 800W, Made in 2012

New Hair Dryer (Low Speed)

1Φ110V, 355W, Made in 2013

New Electrical Oven (4th Level Power)

1Φ110V, 900W, Made in 2012

Old Rice Steamer (Heating Mode)

1Φ110V, 800W, Made in 1998

Old Hair Dryer (Low Speed)

1Φ110V, 370W, Made in 2001

Old Electrical Oven (4th Level Power )

1Φ110V, 900W, Made in 2006

3.2 Study Cases

The power signatures of each load including Vrms, Irms, P, Q, VTHD, ITHD and In are measured by power quality monitoring device ADX30000. The Hellinger distance is calculated between new and old load after measuring all loads, and then to extract the optimal features which have the longest distance based on the Hellinger distance algo-rithm. The optimal features are used to input the BP-ANN for load identification for network training and test. Fig. 1 is the process of aging load identification. The expe-riments implement the proposed method to test the optimal features for detecting aging load, to simulate the aging load by adjusting the voltage, and to recognize aging load in NILM system.

A Novel Method for Extracting Aging Load and Analyzing Load Characteristics 193

Fig. 1. The flow chart of aging load detection

4 Experimental Results

This study performs three experiments based on Hellinger distance algorithm to test the optimal features for identifying the aging of the load, to simulate the aging load by adjusting the voltage, and to implement Hellinger distance algorithm into NILM sys-tem.

4.1 Case 1 Study: Feature Extraction

In this case study, this case uses proposed method to extract optimal power signatures for indentifying a new and old rice steamer. For each rice steamer, power signatures are recorded every fifteen second. The total data is 30 in fifteen minutes. According to Hellinger distance algorithm, the average and standard deviation are calculated to find the optimal features. Table 2 shows the results of the proposed method for a new and old rice steamer. The average and standard deviation for the P of the old rice steamer is higher than that of the new rice steamer. Thus, the power consumption of the old load is higher than that of the new one. In Table 2, the optimal features are real power (P), reactive power (Q) and fundamental current (IF), the HD are 1, 0.9999, and 1, respectively.

194 H.-H. Chang, M.-C. Lee, and N. Chen

Fig. 2 is the power distributions of the new and old rice steamer in heating mode. Fig. 2 shows that the power distributions of the new and old rice steamer have a lot of difference, however P and Q. Table 3 shows the identification results of the new and old rice steamer. The average and standard deviation of optimal features P, Q and PQ are inputted into the artificial neural network. The average recognition accuracy can reach 99%

Table 2. The results of proposed method for the new and old rice steamer

Features New Rice Steamer Old Rice Steamer Hellinger Distance μ σ μ σ

P(W) 766.955 2.729 803.186 10.573 1

Q(Var) -2.502 0.014 -2.592 0.06 0.999

THDV(%) 1.923 0.024 1.899 0.034 0.714

THDI(%) 0.123 0.001 0.129 0.002 0.079

IF(A) 7.033 0.021 7.403 0.084 1

I3rd(A) 0.01 0.0007 0.009 0.0006 0.001

I5th(A) 0.094 0.002 0.098 0.002 0.014

I7th(A) 0.041 0.0008 0.043 0.001 0.014

I9th(A) 0.016 0.0004 0.015 0.0007 0.042

I11th(A) 0.056 0.002 0.061 0.001 0.053

Fig. 2. Power distributions of the new and old rice steamer in heating mode

Table 3. Identification results of the Case 1 study

Features Average Training Recognition (%)

Average Testing Recognition (%)

Average Training Time (s)

P 100 99.09 0.462 Q 100 100 0.464

PQ 100 99.09 0.458

A Novel Method for Extracting Aging Load and Analyzing Load Characteristics 195

4.2 Case 2 Study: The Simulation of the Aging Load

In this case study, the case simulates the aging of loads by adjusting the voltage. To efficiently simulate the aging of loads, an assumption is executed for aging load. The increased power of the old load is related with the increased current or voltage of the new one. Fig. 3 and Fig. 4 are the power waveform of the new rice steamer operated in 110V and 114.4V, respectively. Fig. 5 is the power waveform of the old rice steamer operated in 110V. The figure shows that the real power waveform of the new rice steamer operated in 114.4V is similar with the real power waveform of the old rice steamer operated in 110V. As the result, the aging of loads can be simulated by adjusting the voltage.

Table 4 shows the simulation results of the new rice steamer operated in 110V and 114.4V. The average real power of the new rice steamer which simulates the old rice steamer is higher than that of the new one. The optimal features in Table 4 are real power (P), reactive power (Q), and fundamental current (IF) while the Hellinger dis-tance values are all 1.

Fig. 3. Power waveform of the new rice steamer operated in 110V

In this study, the aging simulations of the new rice steamer from 110V to 110V

20% are performed. The results show that the voltage at +4% can be used to simu-

late the aging of the old rice steamer. Table 5 is the identification results of the case 2 study. The input files of average of optimal features P, Q and PQ are inputted to the artificial neural network. The average recognition accuracy can reach 85%.

196 H.-H. Chang, M.-C. Lee, and N. Chen

Fig. 4. Power waveform of the new rice steamer operated in 114.4V

Fig. 5. Power waveform of the old rice steamer operated in 110V

Table 4. Simulation results of the new rice steamer operated in 110V and 114.4V

Features New Rice Steamer in 110V

New Rice Steamer in 114.4V

Hellinger

Distance μ σ μ σ

P(W) 766.955 2.729 812.633 1.502 1

Q(Var) -2.502 0.014 -2.632 0.039 1

THDV(%) 1.923 0.024 1.969 0.025 0.998

THDI(%) 0.123 0.001 0.125 0.001 0.026

A Novel Method for Extracting Aging Load and Analyzing Load Characteristics 197

Table 4. (Continued)

IF(A) 7.033 0.021 7.229 0.016 1

I3rd(A) 0.01 0.0007 0.01 0.001 0.025

I5th(A) 0.094 0.002 0.098 0.001 0.043

I7th(A) 0.041 0.0008 0.035 0.001 0.042

I9th(A) 0.016 0.0004 0.016 0.0006 0.012

I11th(A) 0.056 0.002 0.057 0.0009 0.198

Table 5. Identification results of the Case 2 study

Features Average Train-ing Recognition

(%)

Average Testing Recognition (%)

Average Train-ing Time (s)

P 100 90.5 0.59 Q 100 92.5 0.611

PQ 100 85 0.626

4.3 Case 3 Study: Aging Load Detection in NILM System

In this case study, the results of Hellinger distance algorithm are employed to imple-ment into NILM system. In the system, six loads are divided into the new and old of three kinds of electrical appliances. Each load is measured for voltage from 0% to

5%. Table 6 is the identification results of this case study. The average of optimal

features P, ITHD and IF are inputted to the artificial neural network. The average rec-ognition accuracy can reach 75%.

Table 6. Identification results of Case 3 study

Features Average Train-ing Recognition

(%)

Average Test-ing Recognition

(%)

Average Train-ing Time (s)

P、ITHD、IF 100 75 14.161

5 Conclusions

This study proposes a Hellinger distance algorithm to extract the power features of aging load based on a NILM system. The optimal features are used for load identifica-tion and the back-propagation artificial neural network training. By adjusting the weight and bias between the neurons, the neural network can produce an output that is correctly corresponding to the optimal input and to identify the aging. The results show that the proposed method of aging load detection in NILM system can be used successfully to detect and simulate the aging of loads in household system.

198 H.-H. Chang, M.-C. Lee, and N. Chen

Acknowledgements. The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 102-2221-E-228-002.

References

1. Bruce, A.G.: Reliability Analysis of Electric Utility SCADA Systems. IEEE Transactions on Power Systems 13(3), 844–849 (1998)

2. Chang, H.H.: Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses. Energies 5, 4569–4589 (2012)

3. Chang, H.-H., Lian, K.-L., Su, Y.-C., Lee, W.-J.: Energy Spectrum-Based Wavelet Trans-form for Non-Intrusive Demand Monitoring and Load Identification. In: IEEE Industry Applications Society 48th Annual Meeting 2013 (IAS Annual Meeting 2013), Orlando, FL USA, October 6-11, pp. 1–9 (2013)

4. Kosko, B.: Neural networks and fuzzy systems. Prentice- Hall, New Jersey (1992) 5. Chang, H.-H., Lin, L.-S., Chen, N., Lee, W.-J.: Particle Swarm Optimization Based Non-

Intrusive Demand Monitoring and Load Identification in Smart Meters. In: IEEE Industry Applications Society 47th Annual Meeting 2012 (IAS Annual Meeting 2012), Las Vegas, NV USA, October 7-11, pp. 1–8 (2012)

6. Rahul, A., Prashanth, S.K., Suresh Kumar, B., Arun, G.: Detection of Intruders and Flood-ing In Voip Using IDS, Jacobson Fast And Hellinger Distance Algorithms. IOSR Journal of Computer Engineering (IOSRJCE) 2, 30–36 (2012)

7. Wang, Z., Zheng, G.: Residential Appliances Identification and Monitoring by a Nonintru-sive Method. IEEE Transactions on Smart Grids 3(1) (March 2012)