The diploma project is devoted to the development of a hardware and software controller for a local energy node of a decentralized network of automated electrical substations. The relevance of the work is determined by the need to improve the resilience of power supply, reduce the impact of peak loads, and use distributed energy sources and storage systems to support the power grid.
The project considers the principles of decentralized power supply structures, the features of a local energy node, and the methods of monitoring, local protection, and dispatch control. The physical principles of on-grid inverter operation, methods of external inverter control, and methods for measuring direct current, voltage, and temperature are analyzed.
Кафедра автоматизації та системнеруйнівного контролю
The aim of this diploma project is to develop a portable thermal imaging system designed to visualize the spatial distribution of thermal radiation of objects in real time and to detect temperature anomalies.
The work includes an analysis of the physical principles of infrared radiation, justification of the choice of an uncooled microbolometer array MLX90640 and a powerful dual-core ESP32-S3 microcontroller with additional external memory. The system architecture has been developed, an electrical schematic has been designed, and software for data acquisition, filtering, and temperature data processing has been implemented. Two-dimensional interpolation algorithms have been realized to improve the visual resolution of thermal images and to generate pseudo-color images on a TFT display.
The practical value of this work lies in creating an accessible and affordable solution for home use. Unlike large enterprises, ordinary consumers are constrained by budgets, so the development is focused on maximum cost-effectiveness and simplicity. The main emphasis is on creating a system that any family can easily afford and use in daily life during power outages without significant expenses.
The aim of the project is to develop an energy-efficient automated lighting control system that ensures an automatic transition to an emergency power source during a blackout and optimizes energy consumption to maximize autonomous operation time.
The proposed solution is based on the principle of directly powering LED devices from the low-voltage ports of a portable power station, which prevents inefficient energy losses. To maximize charge conservation, the system is equipped with sensitive presence and ambient light sensors. The device automatically analyzes room conditions and detects human presence, even if a person remains completely motionless. Artificial lighting smoothly turns on only when genuinely needed and automatically turns off when the user leaves the room.
The result of the project is the creation of a reliable, comfortable, and financially affordable household system. Its implementation allows for the complete automation of the lighting control process, improves everyday user comfort, and extends the autonomous operation time of lighting from a single charging station by several times during prolonged emergency blackouts.
Research advisor: I.Lysenko
The diploma project consists of an introduction, four main chapters, conclusions, and a list of references. The project contains 72 pages of main text, 32 figures, 1 table, and 16 references.
The purpose of this work is to describe the design of an automated defect detection system using machine learning methods and computer vision algorithms capable of ensuring optimal output product quality in manufacturing processes.
To accomplish the objectives of the diploma project, the following tasks were defined:
- to describe the general principles and concepts of automation and automated control;
- to develop a concept for an automated quality control system with visual defect detection and to create a structural diagram of the automated visual defect detection system based on the proposed concept;
- to select the components of the automated system, including the central computing unit, camera, actuators, and other elements, and to develop a block electrical diagram of the system with a detailed description of the connections between the blocks;
- to develop a training algorithm for an artificial neural network for visual defect recognition, evaluate the obtained results using new input data, and create a flowchart of the automated visual defect detection system algorithm.
Research advisor: I.Lysenko
A universal hardware platform for CNC machine control has been developed in this work. The main advantage of the system compared to other solutions in its class is that all necessary features and interfaces are integrated onto a single board: a built-in graphical touch-controlled display, a mechanical encoder for manual positioning, support for USB flash drives and SD cards, galvanic isolation of STEP/DIR/ENABLE signals, and 0–10 V analog outputs for controlling servo drives and frequency converters. At the same time, the system remains affordable within its class, making it suitable for educational institutions, small workshops, and DIY projects.