Articles

Design and Development of Overcurrent Protection Relay Inverse Definite Minimum Time Type Based on Arduino Uno

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An overcurrent protection relay is an essential component in electrical system to protect devices from damage due to excessive current. The Inverse Definite Minimum Time (IDMT) type has a trip time that depends on the magnitude of the overcurrent, with faster trip times for higher overcurrent levels. Arduino Uno can be used as a microcontroller platform to build an IDMT with relatively low cost and ease of implementation. This research aims to design and construct an Arduino Uno-based IDMT. The system consists of a current sensor, Arduino Uno, and a relay. The current sensor is used to detect the current flowing through the load. The Arduino Uno processes the data from the current sensor and determines if the current exceeds a predefined limit. If the current exceeds the limit, the Arduino Uno will activate the relay to cut off the current flow to the load. The IDMT trip time is implemented using an algorithm that considers the magnitude of the overcurrent and the minimum trip time. The system is tested using a simulator and actual load. Test results show that the Arduino Uno-based IDMT system works well and can protect the load from damage due to overcurrent.

A Linguistic Communication Interpretation Wearable Device for Deaf and Mute User

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There is a segment of society, which does not have access to today’s sophisticated acoustics, but gesture-based sign language, such as using the hands or the shoulders of the eyes, can be a vital tool for making sure their audio is audible. The most widely used sign language in the world, known as ALC—American Linguistic Communication—varies slightly depending on the nation. Deaf and mute people can communicate effectively by using hand gestures to convey their message. The wearable good glove we developed for this study will translate ALC motions into the proper alphabets and words. It makes use of a glove with a number of flex sensors on the fingers’ distal and proximal interphalangeal joints as well as the metacarpophalangeal joint to detect finger bending. The complete system is divided into three units: a wearable hand glove unit with a flexible device that records user-created ALC gestures, a processing unit in charge of taking sensor data, and a final unit that uses a machine classifier to identify the appropriate alphabet. In order to receive known alphabet data in text form through a wired channel via the mobile “Sign to Speech App,” which presented that text data into this app, the smartphone unit is linked to the processing unit. Its user-friendly design, low cost, and availability on mobile platforms give it an edge over traditional gesture language techniques.