Published at : 19 Apr 2021
Volume : IJtech Vol 12, No 2 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i2.4302
|Erwin Sutanto||Biomedical Engineering, Faculty of Science and Technology, Universitas Airlangga, Kampus C, Jalan Mulyosari, Surabaya60115, Indonesia|
|Silvi Nurwahyuni||Department of Engineering, Faculty of Vocational, Universitas Airlangga, Campus B, Jalan Srikana 65, Surabaya 60286, Indonesia|
|Riky Tri Yunardi||Department of Engineering, Faculty of Vocational, Universitas Airlangga, Campus B, Jalan Srikana 65, Surabaya 60286, Indonesia|
|Guillermo Escrivá-Escrivá||Institute of Energy Engineering, Universitat Politècnica de València, Camino de Vera, s/n, Edificio 8E, escalera F, 2 opiso, 46022 Valencia, Spain|
Thepurpose of this study was to determine the link between induction
voltages from various electrical loads. We used a residual current device (RCD)
circuit that operates with a capacitor as a DC voltage reading tool. The
circuit reads the value of the leakage current generated by the sensing coil
from the RCD. It also uses the Blynk framework as an online monitoring system
and a WeMos D1-R2 microcontroller to connect to the server using Wi-Fi. Using
this system, the dataset was collected in a Python server and utilized with a
machine learning technique to draw a correlation between the load power and
reading voltage. This will help improve the mistakes of a common RCD cut-off
point, which is usually defined only at a specific induced voltage. For the
different types, an LED lamp and typical electric fan were used as loads in the
experiment. Meanwhile, for a similar type of load, three different LED lamps
were characterized using machine learning to show the correlation. From the
comparison, a threshold voltage of around 1V and three different gradients with
increases of more than 10% are found for LED lamps with loads of 3W, 5W, and
9W.The results show that the relationship depends on the type of its power
Circuit breaker; Electrical loads; Filtered signal; Leakage current; Machine learning
In our modern daily activities, it is undeniable that we depend on electronic devices. With the technological advances that are rapidly being developed, it is easy to use electrical utilities. It may start with a refrigerator for food storage and a rice cooker as a cooking helper; we may then add an air conditioner as a room temperature controller, and finally, a digital television (TV) for entertainment. There are many other common devices that may be included in the long list of our electrical utilities. However, there may be leakage currents from those electrical loads that we are unaware of. The current could flow in the unarranged path of our electrical wiring, for example, through poor electrical insulation or ungrounded chassis (Lee and Lin, 2005). This may be due to the changed value of capacitance against the alternating voltage or the lifespan of the installation.
The measurement of electrical devices could be useful in determining the possible danger of household electricity. This measurement can be conducted using technologies, such as the Internet of Things (IoT) and machine learning, as in concentrations measurement (Chong et al., 2016). The concept of a system includes three main elements.
Specifically, physical objects are integrated with a sensor module, internet connection, and data center using a server to store information from the measurement (Muhammad Asraf et al., 2018). In this case, the measured object is the leakage current, which is measured using a capacitor as a filter circuit (Sutanto et al., 2019). The connectivity through the internet uses a common Blynk IoT framework, as in Sutanto et al. (2020). Finally, the server as data storage is based on Python, which will also be useful for analysis of the data, for example, using TensorFlow (Piponi et al., 2020).
Machine learning could be seen as a tool in computers that allows them to learn without requiring an explanation or being explicitly programmed, as they are in normal software development. Thus, in this study, machine learning is used to learn the leakage current behavior from the residual current device (RCD). This could help us develop a system capable of identifying the possible function in responding to the leakage current using only the induction voltage. Meanwhile, the model training requires datasets before giving out a prediction model that best fits the system. This kind of application is usually applicable in a specific domain and cannot be applied to all possibilities (Shukla, 2018). Similarly, the results of this work will be used only to identify leakage currents from specific electrical loads.
In this work, the approach is applied to Light-Emitting
Diode (LED) based
lamps in comparison with Compact
Fluorescent Lamp(CFL), as done in Latief et al. (2019), because of the low cost of
these lamps. We start by describing a filter design for the RCD before
outlining our monitoring system with the discussed IoT system and Python
server. The system is evaluated with different kinds of loads by comparing the
LED lamp and electric fan. It is also tested with different loads using three
different wattages of LED lamps.
Using a filtered RCD circuit, an IoT-based electric machine leakage learning application was developed to detect the leakage current in a new way. The output sensing coil was rectified using a capacitor circuit to be read by the WeMos D1-R2 module in a DC value. By utilizing the Blynk application, it was possible to monitor the induction’s voltage from various loads.
The leakage current of various components only seems to have a relationship with the power consumption if used with similar types of loads. Thus, the correlation might be missed if the leakage current is compared with different kinds of loads. This issue was seen between the LED lamp and electric fan loads, and it was explained according to the waveforms of induction voltage signals.
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