WSN Based Utility System for Effective Monitoring and Control of Household Power Consumption

Munhaw Kam, N. K. Suryadevara, S. C. Mukhopadhyay, S.P.S.Gill Massey University, Palmerston North, New Zealand

Contact email: S. C. Mukhopadhyay@massey.ac.nz

Abstract

In this paper, an effective and improved intelligent electrical

power measurement and control operations of household appliances

has been documented. The System has been designed and developed

to monitor the electric parameters including voltage, current, phase

angle and electric power consumption of a residence. The system is

made up of intelligent realizing model which detects and regulates

the household electric equipments for way of life by means of

pursuing the contract price costs. It could keep costs low for the

people and also boost the smart grid stability. The developed

prototype has been carefully tested in the real home environment

context for typical household appliances usages and the results are

encouraging to apply in the real environment.

Keywords - Wireless Sensor Networks, Smart Home

Environment, Power Consumption, ZigBee, Consumer Electronics.

I. INTRODUCTION

A smart grid is usually a power grid that uses Information and Communication Technologies (ICT) to collect energy related information as well as act in an automated trend to enhance system’s dependability and performance [1]. The intelligent process in the grid permits information technology to be an origin to help permeate in present today’s power supply process by means of technological know-how by making use of Wireless Sensor Network (WSN) [2]

The smart grid utility system encapsulates online metering system intended for aiding consumers to help optimally utilize power consumption usage. The importance of an integrated ingenious grid monitoring system with sensor network meters help in additional productivity and stability for the consumers [3] .

Initiatives like smart grids employing wireless sensor community engineering as a way of handling vitality, self-reliance in addition to unexpected emergency resilience difficulties [4]. A written report with expenditure regarding smart grid by gross sales involving smart grid realizing, supervising, control methods to connected software offered for the globally smart grid industry tend to be $13 billion dollars by 2018 [5].

Wired sensor networks have already been deployed to use in many applications over the last decade [6]; due to the extendable environment of smart grid utility systems, smart grids have seen a huge upsurge with different interests [7]. The spanking new technologies of the ICT include cutting-edge breakthroughs with details in information technology, sensors, electrical metering, transmission and distribution, in addition to electrical energy storage technology; provide new details to providers of electrical energy.

The ZigBee connections, with wireless platform is analyzing Japan’s new wise house instant program effects with a new initiative that could consider use of this future ZigBee Net Process (IP) specs along with the IEEE 802. 15. 4g regular to aid Japan create wise households that will increase electricity supervision in addition to efficiency [8] .

It is estimated that 65 million home owners in US will probably have smart meters by 2015 and it's also an authentic estimate of how big is the strength of the smart grid utility system [9]. Smart Grid as well as wireless sensor communities offers an brilliant functions that improve interactions associated with agents including telecommunication, control and optimization to realize flexibility, self-healing, productivity, cyber security as well as consistency associated with power systems whilst decreasing the cost as well as giving useful resource managing and utilization.

Many smart meter researches have been done in the past. Numerous system designs along with improvement ways of smart grid electric method for properly handling the household appliances for optimal energy harvesting have been introduced [6] [10].

To be able to link several household appliances and have wireless systems to monitor and control while using effective power tariffs have been suggested [1] [2], though their prototypes are verified using test bed scenarios. Additionally, smart meter techniques similar to [11], have been made to specific uses in particular associated with geographical uses and so are restricted to specific areas.

Distinct ICT integrated with smart meter equipment’s are already proposed and also examined in a residential context pertaining to ideal electric power operation [12], nevertheless particular equipment’s are usually limited to specific houses. Contemplating efficiency and price factors have linked to effective design of smart grid utility system. However, low-cost, reliable and powerful methods to help continually check and manage power consumption depending on customer requirements are in first stages involving progress.

The research outcomes present in this paper is an extension to the work presented in [13][15-19]. The improvement in this paper is in the energy consumption measurement by considering the additional factors of power measurements such as power factor angle determination and harmonic distortion measurements. The work presented in this paper more accurate due to the influence of power factor of the load when compared with similar works such as [14]. The requirement of this task will be useful for the research works such as [15][16-23].

978-1-4673-6386-0/14/$31.00 ©2014 IEEE

The rest of this paper is organized as follows, the improved power monitoring system is described in Section II and The system implementation and experiments results are presented in section III, conclusion with future works are in Section IV.

II. SYSTEM DESCRIPTION

The basic components of the household energy consumption system consist of voltage and current transformers to detect the sinusoidal waveform from an electrical appliance. The waveforms are then fed into Zero Crossing Detector (ZCD) to convert the sinusoidal waveforms into square waveforms. The ZCD is basically a hysteresis comparator. The output of the ZCD is then connected to the external interrupt of 8051 microcontroller [24].

Once the external interrupt signal is received, a timer is enabled to determine the time difference between two square waveforms to calculate the power factor angle. On the other hand, the same sinusoidal waveforms from the sensors are fed into the designed circuit to perform signal filtering and conditioning. The functional block diagram of the household electrical parameters monitoring is as shown in Fig.1.

Figure 1: Functional block diagram of the improved electrical parameter measurement and control system.

The filtered signals are then sent to the microcontroller to obtain the Root Mean Square (RMS) values of current and voltage signals respectively. The signals are then sent wirelessly through the XBee [25] to a computer system for computing the measurements. The determination of power factor angle, harmonic distortions along with the basic electrical parameters voltage and current are presented in the following sections:

The fabricated system is shown in Fig.2. The XBee radio module is embedded into the integrated circuit and it is connected to a microcontroller. All data that processed by the microcontroller are sent through the XBee via serial port communication.

Figure. 2 Fabricated smart power monitoring and control unit.

A. Power Factor Angle

In general the concept of determining the power factor angle graphically is shown in Fig.3. The power factor angle is determined from the zero cross detections of the current and voltage square waveforms.

Figure. 3 Conceptual power factor angle determination.

The Fig.4 depicts the flow chart of practical power factor angle measurement as done in the present research work. Two external interrupts of microcontroller have been set as rising edge triggered mode and an 8 bit timer of microcontroller is generated at 390 Hz of Pulse Width Modulation (PWM) to provide 10 micro second as pulse ticks. Once the first external interrupt sense the rising edge of the voltage signal, the timer is programmed to tick. A separate counter then starts to count the number of PWM until the second external interrupt sense the rising edge of current signal. The time difference of the waveforms is computed to determine the Power Factor Angle (PFA).

Figure 4: Flow chart of power factor measurement

The Fig.5 is the screen shot of an oscilloscope that showing the output signals of ZCD of a household appliance 400watts room heater. It was observed that there is a time offset between current and voltage signals as shown in the Fig.5. In order to get the power factor angle, the time differences is divided by the period of the waveform which is 20ms and multiply by 360 degree.

Figure 5: Outputs of ZCD.

B. Voltage Measurement

The measurement of the voltage is performed by using a step down transformer to reduce 230V to 9V RMS. The output signal was then fed into voltage divider to further step down the signal voltage below 3.3V. The signal is shifted up above the reference setting, before the signal is sent to microcontroller. The reason for this is because the Analog to Digital Convertor (ADC) of microcontroller only can take the positive values of signal and the signal has to be less than 3.3V which is safe for microcontroller. After that, the ADC discretizes the sinusoidal waveform with 2 KHz sample frequency. The reason of selecting 2 KHz is because it will provide 500 micro second time spaced in between the samples and it is equal to 9 degree of interval by considering one cycle comprises as 360 degree.

The discretized values are stored in an array and implemented the Discrete Fourier Series as given in eq. (1) and eq. (2) to obtain the Vrms values.

�� � ����� sin �� � 1 ∗ ∆������

����1

�� � � ���40 �2

bm denotes mth harmonic, n is the sample number,

∆� is the angle between two consecutive samples,

N is the total samples in one period,(N=40 for our experiment).

The Fig.6 shows the flow chart and the schematic diagram of the implemented voltage measurement.

Figure 6: Flow chart and schematic diagram of voltage measurement

C. Current Measurement

The measurement of the current is done with the help of a current transformer. The output signal of the current transformer is very small; hence the signal is amplified after it is level shifted. The signal is then fed into ADC of microcontroller to calculate its Irms value. The following Fig.7 shows the flow chart and schematic diagram of current measurement.

Figure.7 Circuit schematic for Current measurement.

The Fig.8 is the screen shot of an oscilloscope that shows the input and output of the current measurement circuit. The channel 2 is output of the current transformer which also the input of the circuit and channel 1 is the signal that already been amplified and level shifted.

Figure. 8 Current signal before and after level shifted.

Before Shifted

After Shifted

D. Power Measurement

The power measurement is calculated from the obtained power factor angle and RMS values of current and voltage signals as discussed in the above sections. In order get the real power values of the household appliances; the power factor angle is multiplied with the current RMS (Irms) and voltage RMS (Vrms) as shown in eq (3). The computation of the power measurement is done in the computer system which is programmed by using C Sharp.

Power = Vrms * Irms * Power Factor Angle (PFA) (3)

E. Harmonic Distortion Measurement

The harmonic distortion measurement is used to characterize the linearity and power quality of the household appliances. The Total Harmonic Distortion (THD) is computed as shown below. The eq. (4) and eq. (5) are used to obtain the fundamental value of the respective waveforms of voltage and current. Considering the determined RMS values as discussed in the previous section THD is obtained by following the eq.(6) and eq.(7).

"� � ����� sin�� � 1 ∗ ∆����

������4

�#$�% � "�40�5

�'() � ��*+,� � ��-.�/��6

123 � �'()�-.�/4 100%�7

�#$�% denotes the fundamental component of the supply

voltage,�'() is the total harmonic components.

III. EXPERIMENTAL RESULTS The electrical parameter measurements such as voltage,

current, power factor angle and the harmonic distortions as designed and developed according to the above mentioned sections have been tested on several regularly used household appliances. The voltage measurement system of the developed prototype has been verified by connecting the household appliances to a variant transformer and by varying the input voltage from 40V to 260V. The obtained values are plotted in the form of a graph as shown in Fig.9.

Figure.9 Voltage measurement in terms of ADC value.

To verify the current drawn, the electrical appliances with different power rates from 60W to 2200W were considered. The Fig.10 shows the measured voltage values versus the current drawn values.

Figure.10 EMF voltage vs Current drawn by the current circuit.

The computed ADC values that were obtained from various

household appliances are plotted in relation to the respective current drawn and is shown in Fig.11.

Figure.11 ADC value of current vs. the actual current drawn.

The Fig.12 shows the designed prototype system that has 3.3V DC supply to the circuit this includes current measurement, voltage measurement and triac controller circuit. Fabrication of the triac circuit for control operation is similar to the method provided in [13].

Figure 12. Overall schematic of the improved power monitoring and control circuit.

The Fig13 shows the demonstration of THD measurement

by attaching the electrical drill and microwave household appliances with the prototype system. In the computer Graphical User Interface, the calculated value of THD is displayed.

Figure.13 THD measurements of different household appliances.

The Fig.14 shows the connection of household appliance heater attached on the prototype system and the GUI on the computer displaying the values of voltage, current, power factor and power consumption of the heater in real-time.

Figure 14. Prototype of the smart power monitoring system and GUI.

The prototype system has been tested by attaching different

electrical appliances with variant power rates. Table.I shows the percentage of the errors for the measured power, voltage, current and power factor. Table.I Percentage error of received voltage, current and measured power.

It is observed that the errors between the results obtained and the measurements verified with a standard device are less than 10 per cent. The results are also compared with the obtained measurements of [13] and there is a significant improvement in the accuracy of power computations.

V. CONCLUSIONS AND FUTURE WORK

A smart power monitoring and control system has been designed and developed towards the implementation of smart home energy consumption. The developed system effectively monitors and controls the electrical appliance usages at an elderly home. The presented outcomes of this paper consider the computational factors like power factor angle and harmonic distortion for effective calculation of power consumption related to the regular electrical appliances used in a smart home.

In near future, the system will be integrated with co-systems

like smart home inhabitant behavior recognitions systems to

determine the wellness of the inhabitant in terms of energy

consumption.

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