Bulletin of Electrical Engineering and Informatics
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Intelligent maximum power point tracking control for solar photovoltaic systems using fuzzy and neuro-fuzzy techniques
Get PDFA solar-photovoltaic (PV) system cannot optimize power transfer from the generator to the load due to the nonlinear characteristics of the PV arrays. Maximum power point tracking (MPPT) approaches are necessary to optimize the power output of PV arrays. This study introduces a dual intelligent MPPT framework using fuzzy-logic controller (FLC) and neuro-fuzzy controller (NFC) to enhance solar PV efficiency under dynamic environmental conditions. The FLC uses 49 fuzzy rules with seven membership functions (MFs) in a fuzzy interface system (FIS). The NFC is an extension of FLC and is constructed using the artificial neuro-fuzzy interface system (ANFIS). The work analyzes the simulation results and performance realization, including % power loss, system efficiency, and MPPT efficiency under variable irradiance and temperatures. The solar-PV system utilizes FLC and NFC to achieve MPPT efficiencies of 97.89% and 98.61%, respectively. Similarly, the solar-PV system employing FLC and NFC yields system efficiencies of 98.24% and 99.23% respectively. The proposed system using both FLC and NFC is compared with existing MPPT approaches, with better improvement in system efficiency
Application of two inductors with single magnetic core in a two-level current source inverter
Get PDFCurrent source inverter (CSI) transforms DC current into a predetermined AC current. In practice, the DC current are acquired by connecting inductors with the DC power source. Common-emitter current source inverter (CE-CSI) is an inverter where the emitter terminals of the insulated gate bipolar transistors (IGBTs) or metal oxide semiconductor field effect transistors (MOSFETs) switches are connected at a common voltage. This inverter requires two non-isolated DC current sources as input power. The two level CE-CSI is the simplest circuit of the CE-CSIs. The circuit was able in simplifying inverter circuits compared to the three-level CE-CSI in case of device number, i.e., diodes, IGBTs/MOSFETs, and gate drive circuits. This paper studied the basic characteristics of the two-level CE-CSI when two reactors with a single magnetic core were used. The inverter circuit was examined and evaluated through computer tests, and experimentally. The two-level CE-CSI was able to generate a low distortion of sinusoidal AC load current with total harmonic distortion (THD) value 1.92%. Test data showed that the magnitudes of low order harmonics were less than 0.3% of the fundamental frequency. Moreover, the inverter efficiency can be increased due to reduction of the power losses caused by power switching devices
Comparative analysis of 5G network performance at Thailand's premier shopping centers
Get PDFThis paper evaluates 5G network performance across three well-known shopping malls in Bangkok: Icon Siam, Siam Paragon, and CentralWorld. The study focuses on assessing key quality of service (QoS) metrics, consisting of download (DL) speed, upload (UL) speed, and latency. Measurements were taken in various zones within each mall; including high, ground, and outdoor areas through field tests using two different mobile network operators (MNO-1 and MNO-2). The findings indicate noticeable differences in performance, with Icon Siam recording the highest average DL speed of 273.6 Mbps (MNO-1) and the outdoor zone at Siam Paragon having the lowest at 11.2 Mbps (MNO-2). While MNO-1 provided more stable UL speeds, MNO-2 showed greater variability. Latency results also highlighted MNO-1’s stronger network efficiency, often staying below 20 ms, apart from a slight increase in outdoor areas. Statistical analyses, using ANOVA and t-Test, revealed significant disparities in QoS parameters depending on location and MNO, with outdoor areas often underperforming. These results underline the importance of in-building distributed antenna systems (IB-DAS) and improved infrastructure for boosting 5G performance. Furthermore, this study offers insights that can be useful to improve network quality in high-traffic locations
Artificial intelligence based on fuzzy logic for a long-range radio frequency identification reader antenna
Get PDFThe radiation pattern of radio frequency identification (RFID) antennas is influenced by various factors such as design, operating frequency, and polarization, which determine characteristics like directionality, omnidirectionality, beam width, and gain. Achieving precise readings over extended distances is crucial for the effectiveness of RFID systems, enabling faster item retrieval and delivery. The read distance, a critical aspect of RFID system performance, depends on factors like transmitted power, frequency, and antenna gain. Passive backscatter RFID setups particularly benefit from optimizing read distance for efficient operation. Fuzzy logic, as a soft computing technique, addresses uncertainties inherent in RFID systems effectively. This paper presents a novel approach to RFID antenna design, utilizing fuzzy logic to dynamically adjust frequency and power transmission. By enhancing field distribution, polarization, and received signal strength, this approach aims to optimize tag readings at extended distances, thereby improving overall system effectiveness. The methodology involves implementing algorithms in a C program to control the long-range distance aspects of the RFID system. Incorporating fuzzy rule algorithms into the RFID system's control logic enhances its ability to respond intelligently to changes in the operating environment, contributing to improved performance and reliability in long-range RFID applications
An internet of things-based weather system for short-term solar and wind power forecasting using double moving average
Get PDFThis article presents the design and implementation of an internet of things (IoT)-based weather forecasting system aimed at optimizing operational planning for renewable energy generation. The system leverages a Raspberry Pi as its central controller, integrating pyranometer and anemometer sensors for real-time data collection and predictive analytics. Utilizing the double moving average method, the system provides accurate short-term forecasts of solar and wind power outputs, which are crucial for addressing the intermittency challenges of renewable energy sources. The integration with the Blynk platform ensures user-friendly data visualization and accessibility. Results from a three-day testing phase reveal the system's high accuracy, with prediction errors of 8.79% for solar power and 16.49% for wind power. These findings underscore the system's potential to enhance energy planning, improve efficiency, and support sustainability goals. By enabling data-driven decision-making, this IoT-based forecasting system offers a scalable solution for advancing renewable energy integration into the power grid
Doppler radar-based pothole sensing using spectral features in k-nearest neighbors
Get PDFPotholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning
A common vocabulary for semantic interoperability of Moroccan e-government services
Get PDFInteroperability is a critical factor for the success of e-government services, as it enables different public information systems to communicate in a consistent and accurate manner. Governments are making significant efforts to improve their public e- services interactions and promote e-government interoperability. Morocco has developed an e-government interoperability framework that lists compliance rules and references for the development of public information systems. Unfortunately, Moroccan public administrations still work independently and operate as siloed organizations. To deal with this problem, it is essential to implement a common vocabulary (CV) for public services that public administrations can share to formalize public data, enhance exchange between information systems, and ensure data interoperability. In this light, this work presents a CV to standardize public services data, define concepts and relationships. The standardized vocabulary is defined using RDF/XML serialization format and incorporates fundamental declarations to ensure digital communication in Moroccan public services. The approach is illustrated through a case study of e-health service. The study shows the potential added value of creating a national vocabulary. It helps public administrations to structure data, interoperate more effectively and accelerate digital transformation
Development and experimental study of an intelligent water quality monitoring system based on the internet of things
Get PDFThe goal of this work is to create an intelligent internet of things (IoT)-based water quality monitoring system that will effectively monitor and analyze water parameters, collect real-time data, and provide critical information for decision-making in water management and environmental issues. Provide data transfer over wireless networks such as Wi-Fi or Bluetooth. The scientific novelty of the project lies in the development of an innovative system that combines modern IoT technologies and machine learning methods to provide comprehensive and accurate water quality monitoring, which is a significant contribution to water management and environmental safety. Five sensors are connected to Arduino-Mega 2560, ESP-32-E in a discrete manner to determine water parameters. The extracted sensor data is transferred to a desktop application developed on the Blynk App platform and compared with World Health Organization (WHO) standard values. Based on the measurement results, the proposed system can successfully analyze water parameters using the fast forest binary classifier to determine whether the tested water sample is potable or not. An intuitive user interface has been created that will allow users to monitor and analyze water quality data in real time. Provide the ability to create graphs, charts, and reports for visual presentation of data
Modeling 6(10)-35 kV electrical network for fault location via negative correlation
Get PDFIn order to maintain the technical leadership of the economic sector in any nation, there is currently a greater focus on guaranteeing the fail-safe operation of electrical networks and electrical equipment. This paper presents a model for evaluating the fault location procedure based on computer simulation in MATLAB/Simulink of complex 6(10)-35 kV power line systems. The proposed algorithm for preprocessing electrical network signals in normal and emergency modes uses a negative statistical correlation of all possible electrical parameters, while the resulting percentage errors when estimating the location of the fault are within acceptable limits. Algorithms and significant parameters have been determined for effectively carrying out the procedure for searching for the location of a fault through the use of modeling programs, namely: zero-sequence voltage, negative-sequence voltage, initial current value. and the positive sequence voltage is the transition resistance at the accident site. An assessment of the results of preliminary modeling may indicate that devices for finding the location of a fault in the 6(10)-35 kV electrical network will be able to use information obtained about the object using the developed methodology, adjust calculation algorithms and take into account the operating modes of the electrical network
A simplified approach to establishing the impact of software source code changes on requirements specifications
Get PDFThis paper proposes an improved approach to establishing the impact of source code changes in software features. An association of the methods affected by the changes made and functional requirements of the software reveals the likely impact of the changes made in the software. Changes in source code are exhibited in the operational behaviour of the software. Functional requirements and source code artefacts play a key role in assessing the impact of the changes made. In this study, we investigated the possibility of establishing the association between the changed methods and the functional requirements. The study found out that changes made in the methods can be mapped to the functional requirements that the methods are implementing. The motivation in this endeavour was to assess the impacted software requirements which translate to the likely software features affected by the changes made in the software. With the intent of the software users being seen in the requirements statements and the method naming by the developers, a mapping of the two software artefacts would help developers find out the impacted software features when assessing the overall effect of the committed changes