IET Science, Measurement & Technology Case Study
Real-time power monitoring using field-
programmable gate array with IoT technology
ISSN 1751-8822
Received on 31st December 2018 Revised 11th March 2019 Accepted on 11th April 2019 doi: 10.1049/iet-smt.2018.5692 www.ietdl.org
Kok Tong Lee
1, Hou Kit Mun
21Faculty of Engineering and Information Technology, Southern University College, 81300 Skudai, Johor, Malaysia
2School of Engineering, Taylor's University, No. 1 Jalan Taylor's, 47500 Subang Jaya, Selangor, Malaysia E-mail: [email protected]
Abstract: Automation technologies are often impacted undesirably by downtime caused by machine failures. An approach to overcome machine failures is through failures prediction, achievable via the monitoring of power consumption. Unusual power consumption could serve as an alert for possible machines faults. Real-time power consumption monitoring is made possible with the deployment of Internet of Things (IoT), which brings together digital cloud computing and conventional automation technology to rapidly increase the efficiency of production. In this work, the authors have developed an IoT-based power monitoring system to detect unusual power consumption of machines and present the data to users remotely through Android application and cloud storage. The competent integration of Wi-Fi module ESP8266, CT sensor module and field-programmable gate array DE1-SoC board allows the system to accurately measure power consumption and instantaneously store the data in cloud storage. Performance of the developed system was characterised by current measurement of five different loads. The absolute errors, given by the difference between current measured by digital multimeter and the developed system, range from 0.01 to 0.26 A. The close agreement between the two measurements endorses the accuracy and reliability of the real-time power monitoring system, on top of its easy-accessibility gifted by IoT.
1 Introduction
Production losses can adversely impact a company. There are several risk factors that could drive production loss in an industry, one of them being the failure of machines. The direct cost of equipment failures is the loss of efficiency; it was estimated that factories lost ∼5–20% of their productivity due to downtime [1], which in turn contributes to significant monetary loss. Take the automotive manufacturing industry in the United States as an example, the production costs of downtime was about $1.3 million per hour or $22,000 per minute [2]. This project aims to develop a power monitoring system that is able to monitor the power consumption of machines in real time for failure prediction. The unusual power consumption of machines can be easily observed and it could serve as an alarm for any possible faulty conditions.
This early warning could provide judicious information on machine maintenance requirements and timely maintenance would ensure the smooth flow of productions with minimal downtime.
In the past few decades, there have been a lot of researches [3–
6] on wireless communication, thanks to its beneficial features such as the reduction of cable restriction, the formation of the dynamic network, the reduction of cost and the ease of deployment [6]. The wireless technology has catalysed the development of automation technology. An automated system can integrate various disciplines that consist of mechanical machines and drive, electronic sensors and actuators, electrical instruments and control, information communication and computer. Owing to the rapid research on wireless communication, Internet of Things (IoT) is currently shaping the future of automation. IoT is extremely useful in real-time monitoring. IoT technology has been widely used in many applications such as environment monitoring [7–10], weather information accessing [11], infrastructural health monitoring [12]
and power distribution network status monitoring and warning system [13]. The cloud environment is gaining popularity [14, 15]
because it allows users to access and process data through the Internet. Data collection and storage is crucial in a power monitoring system because it facilitates data analysis. In the past, data storage incurred a high cost and the capacity of data storage was often limited. Opportunely, the advancement in IoT cloud storage provides a quantum leap in data storage, in terms of
capacity and cost. Data storage is made cheaper and faster with IoT; and this advantage further enhances the abundant benefits of using IoT [16–18]. Some researchers had attempted to develop the power monitoring system. The Arduino-based power monitoring system developed by Vergara and Villaruz [19] is without the IoT technology and the processing speed is slow. The power monitoring system developed by Sindhuja and Balamurugan [20] is lack of wireless monitoring capability and slow in processing speed. Baviskar et al. [21] and Makwana et al.[4] had also developed a wireless power monitoring system but the system is unequipped with the cloud storage and web server connection. In addition, all the existing power monitoring systems are unable to monitor the power remotely through the mobile device [4, 19–21].
With the employment of wireless technology and the cloud environment, the authors’ developed field-programmable gate array (FPGA)-based power monitoring system addresses the need of measuring electrical parameters via computer or mobile devices wirelessly; or in other words, the authors’ system offers rapid and remote power monitoring which was absent in conventional or existing power monitoring systems.
2 Proposed system and methods 2.1 System architecture
The developed power monitoring system is alternating current (AC) power based and it consists of a current transformer (CT) which is used to measure the power consumption of the machines.
The measured data is gathered and processed in Intel Altera DE1- SoC FPGA development board to compute the power consumption.
In addition, the system is capable of establishing a Wi-Fi link with Android phones and web servers in order to allow remote monitoring of machines power consumption. Fig. 1 shows the top- level architecture of the proposed FPGA-based power monitoring system with IoT Technology.
2.2 Development of sensor module
CT is a transformer that is used to induce an AC in its secondary which is proportional to the AC current in its primary. To enable connection between a CT to an FPGA board, the output signal
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from the CT needs to be conditioned to meet the input requirements of FPGA analogue inputs. Therefore, in the authors’
design, a burden resistance circuit was connected to the CT sensor to condition the signal to the range of 0–4 Vdc since the FPGA analogue reference voltage is 4 Vdc. The working principle of the burden resistance circuit is shown in Fig. 2. Resistor R1 and resistor R2 formed a voltage divider to divide the 4 V supply and introduce a 2 Vdc bias that was superimposed on the AC voltage of the burden resistance circuit. The resultant signal was a sinusoidal signal that centred at 2 V and oscillated between 0 and 4 V.
The value of burden resistance (141.4 Ω) was calculated using (1)–(3) [22] as follows:
Rb= 0.5Vr
I2p , (1)
I2p= I1p
N2, (2)
I1p= 2Irms, (3)
where Rb is the burden resistance, Vr is the FPGA analogue reference voltage, I2p is the secondary peak-current, I1p is the primary peak-current, Irms is the root mean square (RMS) current and N2 is the number of turns at the secondary side. Fig. 3 illustrates the hardware of the burden resistance circuit.
2.3 Computation of power consumption
Fig. 4 illustrated the protocol implemented in the computation of power consumption. The sampling frequency of the system was determined to be 10 kHz. The measured signal from the CT sensor was used to compute the power consumption, in which the measured analogue signal that ranged from 0 to 4 Vdc corresponded to the current that ranged from 0 to 20 A RMS.
Conversion of the measured CT signal to digital signal was achieved by an analogue-to-digital converter (ADC) on the FPGA board. Power consumption was calculated with (4)–(6) [23] and eventually displayed on the Eclipse console.
P = 240Irms, (4)
Irms= Itotal k N2
Rb, (5)
Itotal=
∑
n = 1
k Vdc(n) −Vr 2 × Vr
4095
2, (6)
where P is the calculated power, Rb is the burden resistance, Vdc is the digital signal converted by the ADC, Vr is the FPGA analogue reference voltage and n is the number of samples.
2.4 Development of Wi-Fi communication module
The most imperative module to enable remote interaction between the power monitoring circuit to the Internet is the low-cost microchip ESP8266 that is produced by Espressif Systems.
ESP8266 is a Wi-Fi device capable of running self-contained application by means of its integrated reduced instruction set computer (RISC) processor and on-chip memory [24]. The authors’
developed power monitoring system exploited the on-chip transmission control protocol/internet protocol (TCP/IP) protocol stack of ESP8266 Wi-Fi module to establish wireless communication between the FPGA and the cloud. An ESP8266 Wi-Fi module breakout board was constructed and shown in Fig. 5.
The Wi-Fi module enables the delivery and storage of power consumption data in the cloud, making it possible for users to remotely monitor power consumption through Android-based applications. The Android application and cloud employed by the authors’ power monitoring system are Virtuino and ThingSpeak, Fig. 1 Proposed system architecture
Fig. 2 Burden resistance circuit principle
Fig. 3 Burden resistance circuit
Fig. 4 The workflow of power computation
Fig. 5 Breakout board for ESP8266 Wi-Fi module
respectively. Power consumption is presented as a continuous, real- time line-chart that is straightforwardly understandable. Examples of the Virtuino interface and graph in ThingSpeak are shown in Figs. 6 and 7, respectively.
2.5 Performance evaluation
Performance of the as-developed system was evaluated, in which accuracy was presented as the key performance index. Five different appliances (loads) were used to test the system. The current of the appliances was measured using both the digital multimeter (DMM) and the FPGA-based system. The readings from the DMM and the system were compared. Accuracy in the authors’ context was given by the absolute error that illustrated the
difference between the current measured by DMM and the FPGA- based system. Fig. 8 shows the complete system prototype which mainly consists of a CT sensor, a Wi-Fi module, a burden resistance circuit and a DE1-SoC FPGA board. The FPGA board was powered by an external 12 V DC jack.
3 Results and discussion
Current consumptions measured using DMM and FPGA-based system for five different loads were illustrated in Figs. 9 and 10.
The numerical results were recorded in Table 1. As shown in the table, the current measurement by both the DMM and FPGA-based system was in close agreement.
It is observed that the performance of the as-developed power monitoring circuitry is comparable to a conventional multimeter.
By taking five common electronic appliances as the test subjects, the absolute error between DMM and the as-developed system was at an acceptable range of 0.01–0.261 A.
The difference in current reading from DMM and FPGA-based system is explainable by discrepancy caused by the serial connection between the DMM probe and the tested appliances, in which the internal impedance of DMM and the resistance of the probe (0.5 Ω each) affect the current readings significantly if the impedance of the appliance is relatively low.
The impedance of vacuum machine is low due to its high current consumption. Consequently, 1 Ω of resistance from the DMM probe could present a significant change in the total Fig. 6 Screenshot of the Virtuino interface Android apps
Fig. 7 Screenshot of the ThingSpeak graphs
Fig. 8 The complete FPGA-based power monitoring system
Fig. 9 The comparison of current consumption measured by DMM and FPGA-based system for
(a) Vacuum machine, (b) Soldering iron, (c) Drill machine
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impedance of the testing circuit and affect the measured value of the current consumption. This explains the relatively larger absolute error between DMM and FPGA-based system for vacuum machine. Contrary, both the soldering iron and fan are of high impedance (consume <1 A of current), making the 1-Ω resistance from DMM probe negligible. Current consumption measurement from DMM is relatively more precise. As a result, the absolute errors between DMM and FPGA-based system for soldering iron and fan are small.
Similarly, the absolute errors between DMM and FPGA-based system for hair dryer are as well low due to the resistive nature of hair dryer. The inductive part of the reactive load is negligible.
Most of the currents drawn by the hair dryer are by the heating element. Therefore, the 1-Ω resistance from DMM does not significantly alter the current consumption reading, giving close agreement with FPGA-based system.
The accuracy of the as-developed system is demonstrated by the measurement of current drawn by five different loads. The close agreement between readings taken by DMM and the FPGA-based
system gives the authors’ developed system an upper hand as the authors’ system is capable of not only producing accurate power measurement but it is also capable of wireless communication for remote monitoring. The use of ESP8266 Wi-Fi module, Virtuino and ThingSpeak makes the designed system a real-time, compact, easy-accessible and reliable wireless power monitoring system.
The comparison between the as-developed system against other power monitoring systems reported in the literature is shown in Table 2. The developed system shows better in terms of accuracy, processing speed and accessibility.
4 Conclusions
In summary, this work presents an FPGA-based power monitoring system enhanced with IoT technology. The system is capable to establish Wi-Fi communication between the DE1-SoC FPGA board, Android phones and web servers with the cloud storage. The use of Virtuino, ThingSpeak and ESP8266 Wi-Fi module makes the system a real-time, compact, easy-accessible and reliable Fig. 10 The comparison of current consumption measured by DMM and FPGA-based system for
(a) Fan, (b) Hair dryer
Table 1 Current consumption reading for different devices
No Appliances Average current reading Absolute error, A
Developed system, A DMM, A
1 vacuum 5.636 5.375 0.261
2 soldering iron 0.24 0.23 0.01
3 drill machine 0.596 0.494 0.102
4 fan 0.21 0.193 0.017
5 hair dryer 7.058 6.973 0.085
Table 2 Comparison of power monitoring systems
Ref. Web server Mobile app Cloud storage Wireless Processing speed Average error, A
[4] no no no yes moderate NA
[19] no no no no slow 0.3
[20] yes no yes no slow NA
[21] no no no yes moderate NA
this work yes yes yes yes fast 0.1
wireless power monitoring system. The designed system was applied to five different appliances which are vacuum, soldering iron, drill machine, fan and hair dryer for real-time power monitoring. Low absolute error of 0.01–0.26 A was observed, endorsing the feasibility of the system. The comparison between the as-developed system against other power monitoring systems showed that the system is better in terms of processing speed, accessibility and accuracy. For future works, a load failure prediction using machine learning will be investigated.
5 Acknowledgment
The authors would like to express their gratitude towards Mr. Lim Cheng Yen for providing assistance with facilities usage in the laboratory.
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