• International Journal of Technology (IJTech)
  • Vol 12, No 2 (2021)

Filtered Leakage Current Measurement for Various Loads

Filtered Leakage Current Measurement for Various Loads

Title: Filtered Leakage Current Measurement for Various Loads
Erwin Sutanto, Silvi Nurwahyuni, Riky Tri Yunardi, Guillermo Escrivá-Escrivá

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Cite this article as:
Sutanto, E., Nurwahyuni, S., Yunardi, R.T., Escrivá-Escrivá, G., 2021. Filtered Leakage Current Measurement for Various Loads. International Journal of Technology. Volume 12(2), pp. 401-411

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
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Filtered Leakage Current Measurement for Various Loads

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 supply.

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.

    We were able to use the gathered data for machine learning. With the employed technology, we could define the adjusted threshold voltage for the best transition from the normal condition over the leakage current condition. The demonstration was given by similar kinds of LED lamps with increasing loads of 3W, 5 W and 9 W. The results indicated that there should be a change in the cut-off point for each different load.


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