Measurements, Models, Systems and Design

531 s.

Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach

Introduction. Cascaded H-bridge multilevel inverters (CHB-MLI) are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of CHB-MLI. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of 5 level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation experiment for the modulation index value of 0.85, one second open and short circuit faults are created for the place of faulty switch. Fault is identified automatically by means of artificial neural network (ANN) technique using sinusoidal pulse width modulation based on distorted total harmonic distortion (THD) and managed by its own. The novelty of the proposed work consists of a fast Fourier transform (FFT) and ANN to identify faulty switch. Purpose. The proposed architecture is to identify faulty switch during open and short failures, which has to be reduced THD and make the system in reliable operation. Methods. The proposed topology is to be design and evaluate using MATLAB/Simulink platform. Results. Using the FFT and ANN approaches, the normal and faulty conditions of the MLI are explored, and the faulty switch is detected based on voltage changing patterns in the output. Practical value. The proposed topology has been very supportive for implementing non-conventional energy sources based multilevel inverter, which is connected to large demand in grid.Вступ. Каскадні багаторівневі інвертори H-bridge все частіше використовуються в таких пристроях, як розподільні системи, електричні тягові системи, системи прямого перетворення високої напруги та багато інших. Незважаючи на те, що багаторівневі інвертори містять велику кількість перемикачів, що управляють, виявлення несправності займає значний час. У конфігурації аварійного вимикача увімкнено діод для роботи в режимі вільного ходу в умовах обриву несправності. При короткому замиканні аварійні стани виконуються запобіжником, який може визначити напрямок струму вільного ходу. Категорія несправності може бути визначена самостійно, а також відмова силових вимикачів, що порушує функціонування та надійність каскадних багаторівневих інверторів H-bridge. У цій статті досліджуються наслідки та характеристики обривів та коротких замикань багаторівневих інверторів. Характеристики вихідної напруги 5-рівневого інвертору часто визначаються характерними несправностями перемикача, при цьому при аналізі моделювання використовується значення індексу модуляції 0,85. В імітаційному експерименті значення індексу модуляції 0,85 в місці несправного перемикача створюються односекундні обриви і коротке замикання. Несправність ідентифікується автоматично за допомогою методу штучної нейронної мережі з використанням синусоїдальної широтно-імпульсної модуляції на основі спотвореного повного гармонійного спотворення та керується самостійно. Новизна запропонованої роботи полягає у застосуванні швидкого перетворення Фур’є та штучної нейронної мережі для ідентифікації несправного перемикача. Мета. Пропонована архітектура призначена для виявлення несправного комутатора при розмиканні та короткочасних відмовах, що має знизити повне гармонійне спотворення та забезпечити надійну роботу системи. методи. Запропонована топологія має бути спроектована та оцінена з використанням платформи MATLAB/Simulink. Результати. Використовуючи підходи швидкого перетворення Фур’є та штучної нейронної мережі, досліджуються нормальні та несправні стани багаторівневих інверторів, і несправний перемикач виявляється на основі моделей зміни напруги на виході. Практична цінність. Запропонована топологія дуже сприятлива для реалізації нетрадиційних джерел енергії на основі багаторівневого інвертора, пов'язаного з великим попитом у мережі

Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks

In real-world applications - to minimize the impact of failures - machinery is often monitored by various sensors. Their role comes down to acquiring data and sending it to a more powerful entity, such as an embedded computer or cloud server. There have been attempts to reduce the computational effort related to data processing in order to use edge computing for predictive maintenance. The aim of this paper is to push the boundaries even further by proposing a novel architecture, in which processing is moved to the sensors themselves thanks to decrease of computational complexity given by the usage of compressed recurrent neural networks. A sensor processes data locally, and then wirelessly sends only a single packet with the probability that the machine is working incorrectly. We show that local processing of the data on ultra-low power wireless sensors gives comparable outcomes in terms of accuracy but much better results in terms of energy consumption that transferring of the raw data. The proposed ultra-low power hardware and firmware architecture makes it possible to use sensors powered by harvested energy while maintaining high confidentiality levels of the failure prediction previously offered by more powerful mains-powered computational platforms

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Artificial Intelligence-based Control Techniques for HVDC Systems

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PD

Machine Learning-based Predictive Maintenance for Optical Networks

Optical networks provide the backbone of modern telecommunications by connecting the world faster than ever before. However, such networks are susceptible to several failures (e.g., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. It is challenging to accurately and quickly detect and localize such failures due to the complexity of such networks, the time required to identify the fault and pinpoint it using conventional approaches, and the lack of proactive efficient fault management mechanisms. Therefore, it is highly beneficial to perform fault management in optical communication systems in order to reduce the mean time to repair, to meet service level agreements more easily, and to enhance the network reliability. In this thesis, the aforementioned challenges and needs are tackled by investigating the use of machine learning (ML) techniques for implementing efficient proactive fault detection, diagnosis, and localization schemes for optical communication systems. In particular, the adoption of ML methods for solving the following problems is explored: - Degradation prediction of semiconductor lasers, - Lifetime (mean time to failure) prediction of semiconductor lasers, - Remaining useful life (the length of time a machine is likely to operate before it requires repair or replacement) prediction of semiconductor lasers, - Optical fiber fault detection, localization, characterization, and identification for different optical network architectures, - Anomaly detection in optical fiber monitoring. Such ML approaches outperform the conventionally employed methods for all the investigated use cases by achieving better prediction accuracy and earlier prediction or detection capability