2022

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A Comparative Analysis On Finding Out Electrolytes From Effective Data Sets In Human Sweat
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Sadid, Md. Shahriar; Shahriar, Sayef; Mahamat, Abba
This paper explores the non-invasive method of identifying electrolyte levels of human sweat. A non- invasive micro patch antenna-based sensor is used to determine the electrolyte level of human sweat. This device is designed to be wearable so as to have close proximity to the human skin in order to have great advantage in detecting the electrolyte (NaCl) level from sweat. A 1.8 mm thick copper patch antenna that operate in a frequency range of 0.5 GHz -3.5 GHz is used. Paper based substrate is preferred due to the fact that it’s a good absorber and cost effective. For testing the sensitivity, the antenna is structured to have a frequency of 1.57 GHz with several dielectric constants ranging between 1.0 F/m - 2.0 F/m, thereby allowing the substrate dielectric to be controlled by the properties of the absorbed sweat. The paper looks at the Shift in resonant frequency and also the magnitude of reflection coefficient which is employed on a concentration range of approximately 0.001 mol/L - 5 mol/L of NaCl. For various levels of electrolyte, the resonant frequency, the reflection magnitude are fluctuating, the first resonance is regarded for analysis of data.
A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Al-Monsur, Abdullah; Ratul, Ishrak Jahan; Ar-Rafi, Abrar Mohammad
The chronic cardiac condition myocardial infarction (heart failure) is characterized by decreased blood supply to the body as a result of the heart muscles’ impaired contractile properties. Patients with heart failure, like those with any other cardiac disorder, have difficulty performing daily activities and have a shorter life expectancy, with the vast majority of cases resulting in death at some point during the patient’s lifetime. Treatment outcomes and patient quality of life improve significantly when patients with heart failure are identified early and are likely to survive. As a result, machine learning techniques can be extremely beneficial in this situation because they can be used to predict the survival of heart failure patients in advance, allowing patients to receive the most appropriate treat- ment at the earliest possible stage. As a result, six supervised machine learning algorithms were applied to a dataset of 299 people from the University of California, Irvine Machine Learning Repository in order to predict their chances of surviving heart failure. There were a variety of algorithms used in this study including Decision Tree Classifier, Logistic Regression, Gaussian Nave Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine, among others. Prior to scaling the data, a preprocessing step was carried out, and both the standard and min-max scaling methods were employed. When it came to optimizing the hyperparameters, the techniques grid-search cross validation and random search cross validation were combined. Data resampling techniques such as the edited nearest neighbor (SMOTE-ENN) and synthetic minority oversampling (SMOTE) data resampling are also employed (SMOTE-ENN). It has been thoroughly compared and analyzed the outcomes of all of the different approaches. As a result of these findings, the Random Forest Classifier (RFC) outperforms all other approaches, achieving a test accuracy of 90 percent when compared to the other approaches when SMOTE-ENN and the standard scaling technique are employed. With the help of an imbalanced dataset, this comprehensive investigation vividly illustrates the application and compatibility of sev- eral machine learning algorithms. Among the methods for improving the performance of machine learning algorithms discussed in this investigation are the SMOTE-ENN algo- rithm and hyperparameter optimization.
A Dissertation on Detection of Autism Spectrum Disorder by Machine Learning Algorithm
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Bristy, Afsana Hossain; Hasan, Tasnimul; Shawon, Md Minhajul Islam
This work proposes an investigative strategy to examining the efficacy of alternative boosting algorithms in terms of improving the accuracy of diagnosing Autism Spectrum Disorder (ASD). When it comes to autism spectrum disorder (ASD), the average number of cases per 10,000 people has increased from 1.9 in 1980 to 14.8 in 2010, according to data from Asia. Early detection and identification are crucial for improved treatment outcomes in ASD. Different boosting machine learning algorithms have been shown to be an effective technique for detecting autism spectrum disorder (ASD) when it is still in its early stages. This research utilized a dataset containing a total of 1100 instances that was amalgamated from three datasets, with 104 instances being teenagers and 704 instances being adults, with the remainder of the instances being kid instances, from the University of California Irvine (UCI) repository. This dataset was used to train and test the model classification classifier. To begin the functioning of the classifiers, several methodologies were used to build distinct data frames and correlation heatmaps, which were then compared. Eight machine learning methods were investigated, and their performance parameters such as the confusion matrix and accuracy were measured and compared to one another. Furthermore, a comprehensive comparative research was carried out by simulating the precision, sensitivity, F1 score, and ROC-AUC of each algorithm and comparing the results.
A novel Charging Capacity Model (CCM) for electric vehicle charging
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-31) Aurko, Shafquat Yasar; Salehdin, Riyad; Rafi, Faiaz Allahma
On the cusp of the fourth industrial revolution, the worldwide adoption of electric cars (EVs) is likely to cause grid overload. owing to uncoordinated charging of a high number of EVs. As this figure continue to rise, this problem will continue to grow worse. This difficulty is likely to be mitigated by coordinated charging of electric vehicles. Using a modified Particle Swarm Optimization (PSO) method, this research brings forward the Charging Capacity Model (CCM), which limits the number of EVs that may be charged at different times of the day. The CCM is also implemented using IoT, data analytics, communication, and networking. The proposed concept was tested in Bangladesh using an electric vehicle called the Easy-Bike, a popular three-wheeler EV in the nation.
An Efficient Short Term Load Demand Forecasting Using a Novel Parallel CNN-BiLSTM Hybrid Neural Network for Bangladesh Perspective
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Rahman, Md. Abdur; Hossain, Al-Amin; Jawad, Tahmid
STLF (Short Term Load Forecasting) has traditionally become one of the most crucial, delicate, and precise demanding variables in energy systems. An efficient STLF enhances not only the financial feasibility of the system, but also its safety, consistency, and dependability in performance, allowing for the realization of a prospective Smart Electricity System. The state-of-the-art models exhibit significant nonlinearity in load information from available projections, as well as limited applicability in real-world circumstances. However, for real-world application, the energy forecasting area requires better resilience, improved prediction accuracy, and adaptability capacity. The study given in this paper supports the case for a hybrid strategy, in which the complimentary qualities of several cognitive methodologies are merged to provide a superior solution to the STLF problem. The deep learning models for STLF integrated with statistical techniques are presented in this paper for an accurate load forecasting. Temperature, humidity, and day type are all taken into account since they have a substantial effect on the overall performance of an appropriate STLF. The load demand data has been collected from the PGCB database, the weather data has been collected from the rp5 archive and the holidays are considered from the government calendar which excludes the data collection part of our research. The data refinement process has been done where many preprocessing techniques are applied on the raw data. With the proper data analysis and scaling the further process fed into the deep learning models where the training, validation and testing were done. In terms of computing complexity and prediction accuracy, the suggested model outperforms the prior hybrid models. The proposed technique of our methodology against existing power prediction information reveals that it performs better in terms of precision and accuracy. The evaluation has been done considering the Mean Absolute Percentage Error (MAPE) and R-squared score which outperforms the existing literature reviews. When compared to existing baseline models, the suggested technique had the lowest error rate on the Power Grid Company Bangladesh dataset.
An evolutionary game theoretic charging mechanism aimed at incentivizing charge sharing without any change in infrastructure
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-31) Kabir, MD Rizwanul; Muhaimin, Muhammad Mutiul; Mahir, Md. Abrar
This thesis presents a novel mechanism for charge sharing in between Electric Vehicles or EVs. Electric vehicles face some obstacles in the face of adoption over conventional cars. Electric vehicles (EVs) have a limited driving range due to battery limits. EV charging stations are also sometimes rather far apart, and they are not widely available in many areas. Battery depletion entails traveling to remote places or even taking detours, both of which increase the total driving time of EVs. Under the proposed network design, an EV that does not have enough energy to finish its route can ask for energy. Other EVs close to it may respond. It is to be kept in mind that every EV is selfish about its own charge. The model utilizes Evolutionary Game Theory (EGT) and replicator equation on graphs. The EV that needs extra energy and makes the initiative to ask for such is the receiver. The respondents may either be givers or non-givers. Givers choose to share their energy, whereas non-givers don’t. Givers get a fixed incentive that topples the potential cost of driving to the receiver, whereas nongivers neither gain or lose anything. In the model proposed in this thesis, an attempt is made to control this incentive, thus controlling the total number of givers in the world. The results show that an equilibrium can be established in a system where givers are consistently created. This balance is achieved by altering the incentive provided by EVs with decreased energy levels. Thus, an effective energy sharing system is proved to be sustainable utilizing a theoretical and numerical approach as well as a simulation model to substantiate the theoretical model.
An Investigative Approach To Employ Different Machine Learning Algorithms in Detecting Brain Cancer
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Mohtasim, Md. Akib; Islam, Zahidul; Alam, Ashraful
This thesis takes an exploratory method to investigate the performance of several machine learning algorithms in more accurately detecting brain tumors. It primarily accomplishes this by detecting both malignant and benign lesions in the brain. In recent years, brain cancer has been linked to the highest newborn cancer fatality rates worldwide. Various machine learning techniques have been shown to be an excellent tool for detecting brain while it is still in its early stages. To train and test the model classifier, a dataset containing 700 instances and 1500 features from the Kaggle Data repository was used. Thirteen Eleven machine learning algorithms were studied and their performance parameters like confusion matrix and accuracy were analyzed. Furthermore, a thorough comparison was carried out through the compuration of precision, sensitivity, F1 score, recall, specificity, cross validation score and error rate of each algorithm.
Analysis of FPS and DPS in noma for real-time and non-real time applications under different mimo techniques
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Alavi, Abdullah; Rafique, Moontasir; Farhad, Md. Aadnan
In 5G wireless communication, Non-Orthogonal Multiple Access (NOMA) is a preferred approach for accommodating a large number of users while also providing significant capacity. The same data is sent to all users via a technique called cooperative relaying, and one user can relay data to another. Energy harvesting systems have been devised to provide enough power for users, with Simultaneous Wireless Information and Power Transfer (SWIPT) gaining popularity in recent years. A comparison of two different power allocation systems in NOMA, Fixed Power Allocation Scheme (FPS) and Dynamic Power Allocation Scheme (DPS), is presented in this work (DPS). The comparisons were developed based on how well they performed and what they were like while undergoing SWIPT. When compared to FPS, it has been discovered that employing DPS results in a nearly 25% boost in peak spectral efficiency. DPS, on the other hand, has a larger risk of outage since increased power causes the signal bandwidth to fall below the goal rate a substantial number of times. Conclusions were reached based on the comprehensive data as to which power allocation coefficient scheme will be employed in real-time and non-real-time communication standards. The findings imply that FPS is better for real-time communication, while DPS appears to work better for non-real-time communication. After incorporating MIMO techniques with NOMA, it was found that the system performed better for far users consistently. And for near users, MIMO performed better in high power region.
Analysis of Micro-Strip Patch Sensor of Various Sizes and Parameters for Non-invasive Electrolyte Sensing
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Md Toushique, Fardeen; Saikot, Shoeb Mahmud; Nizam, Talha Bin
Electrolyte imbalance is a major health issue that can go unnoticed due to the lack of frequent testing. Most prevalent electrolyte monitoring measures require direct blood testing which is invasive in nature. Invasive monitoring measures are not only painful but also time-consuming. So non-invasive electrolyte monitoring is slowly gaining popularity. In this paper, non-invasive micro-strip patch antennas of different sizes and parameters have been analyzed. Micro-strip patch antennas of five different sizes and five different parameters have been analyzed for their sensitivity, linearity, and accuracy for a frequency range of 0.01GHz to 0.2GHz. Seven different concentrations of NaCl have been used for this analysis. Various research papers have found that there is a correlation between changing concentrations of NaCl and reflection coefficient S11 and resonant frequency. From this analysis, it has been observed that changing antenna size causes changes in antenna characteristics.
Anomaly Detection System in Industrial Control System using Machine Learning
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Nabil, Ahammed Sakir; Rahman, Ahnaf Akif; Ahmed, Imtihan
An industry is composed of various types of machines and instruments interconnected through a system of network performing in harmony following specific instructions assigned to specific nodes or equipment. Industrial control system refers to the whole environment that keeps everything included in the industrial system in order. Like any other system, industrial control system is also prone to attacks which might result in massive loss. In this paper, six machine learning algorithms have been applied for detecting the presence of anomaly in industrial control system using HIL-based Augmented ICS (HAI 21.03) Security Dataset. The dataset has been analyzed using analysis of variance to extract 50 of the most important features from each sample in the dataset. All the machine learning models' performances are recorded, and a full comparative analysis for hyperparameter optimization, downsampling-upsampling with hyperparameter tuning, and without hyperparameter tweaking is shown. Random search cross validation has been employed for hyperparameter optimization, and synthetic minority oversampling technique has been used for upsampling. In terms of several evaluation metrics like accuracy, recall, precision, F1-score, Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and specificity, satisfactory performances have been observed. In addition to these evaluation metrics, which have also been used by other researchers in previous studies, we have evaluated the performance of our models using Geometric Mean(G-Mean) and Matthews Correlation Coefficient (MCC), which are considered two of the most important evaluation metrics in imbalanced datasets. Using our proposed approach, a maximum recall score of 99.77% and an F1-score of 99.50% have been achieved, which are significantly higher than previous studies. Maximum G-Mean of 99.89% and MCC of 0.9950 have been obtained by the application of K-Nearest Neighbors (KNN) model. Therefore, our proposed approach has the prospect to be an efficient method for detecting anomalies in industrial control systems and taking appropriate actions.
Cellular Wireless Network Communication for Smart Grid
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Sillah, Abu Bakarr; Awa, Coulibaly; Aichetou, Pamboundom Njoya; Hamid, Mohamed
Since the introduction of Long Term Evolution (LTE) networks and now 5th Generation, cellular mobile networks are turning into a great platform for universal massive data capture, communication, storage, and processing. Cellular Wireless Network will offer more acceptable services for operation and realworld applications, especially the anticipation of the upcoming Smart Grids. Throughout this article, we describe how the cellular wireless network, with its rise of Machine-Type Connectivity and the notion of Mobile Edge Computing, provides a suitable setting for dispersed monitoring and control operations in Smart Grids. In particular, we demonstrate in detail how Smart Grids could benefit from enhanced distributed State Prediction models implemented within a cellular network setting. We present an overview of highly scalable State Estimation techniques, focusing on those highly distributed optimizations and likely statistical models, and explore their inclusion as part of the Cellular Smart Grid activities. We also show the prototype utilizing both the software and the hardware implementation. As a consequence, it was evident that the integration of the newest cellular wireless technologies into the conventional power grid would boost its smartness, efficiency, security, power quality, and intelligent communications amongst the distribution substations.
Cervical Cancer Behavior Risk Prediction Using Machine Learning
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Tabassum, Bushra; Hasan, Nafis Jabid; Azim, Md. Muhibul
Cervical cancer is a serious public health concern that affects women all over the world. Early risk prediction of cervical cancer can play an essential role in prevention by boosting public awareness of this disease, because it is a fatal disease. Both healthcare professionals and persons at risk can benefit from early prediction utilizing a Machine Learning (ML) model. Using a dataset from the UCI ML repository, eleven supervised machine learning algorithms are used to predict early risks of cervical cancer in this work. Accuracy, precision, F1, recall, and ROC_AUC are among the performance metrics used to predict the early risks of cervical cancer using machine learning models. Finally, a comparison analysis reveals that using the Multi-Layer Perceptron (MLP) method with default hyperparameters, this study achieved 93.33% prediction accuracy. Decision Tree Classifier (DTC), Random Forest Classifier (RFC), K- Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi- Layer Perceptron (MLP) all showed accuracy of 93.33% when using the hyperparameter tuning strategy.
Characteristics and Applications of Plasmonic Materials Alternate to Gold and Silver
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Alvi, Rafiuddin; Shahriair, Toukir; Shohdy, Abdelrahman
In our modern world, plasmonic materials are used in many scientific sectors. They are mostly used in chemical sensing, information processing, further size reductions, miniaturize optical components etc. Gold and silver are the conventional materials used in plasmonic materials. But these materials also have some problems regarding their characteristics. There are some unconventional materials that show similar characteristics. The real as well as the imaginary parts of the refractive index of these atypical materials employed in the doping element in the proposed model of COMSOL simulations can be obtained using the Lorentz-Drude model. From the simulations, transmittance vs wavelength graphs are generated which are compared to the outputs of gold and silver. A comparative analysis is included for a better insight.
Demand Based Electricity Pricing in Bangladesh: in a Deregulated Market Scenario
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Sonet, Nazmul Haider; Rahman, Shadman Saqlain; Jahan, Insan Arafat
Deregulation in electricity market allows for competition and makes the system economically efficient. In Bangladesh, electricity is centrally controlled and regulated where the electricity prices vary on the type and amount of usage. This study reports the prospect of demand-based electricity pricing in the context of a deregulated electricity market in Bangladesh. The market clearing price is determined from the supply and the demand curve on half hourly basis. To determine the supply curve, the generating power plants capacity are placed in ascending order according to their per unit power generating cost. The demand is considered inelastic and is determined from the daily load curve. The effect of seasonal variation on demand-based electricity price is further analyzed. The demand-based electricity prices are generally found to be lower than the existing prices. Furthermore, generating electricity from the units with least generating cost can save significant amount of cost which can be utilized in transmission and distribution network. The demand-based dynamic price can be useful in implementing demand response in the system.
Design & Investigation of Different Highly Sensitive PCF Sensors Based on Surface Plasmon Resonance
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Rafid, Rahbar Al; Rashid, Shahriar; Zerin, Nausheen
Many Researchers have proposed many designs on SPR based PCF sensors. This field is outgrowing very rapidly due to its label free, real time monitoring detection technique and all other applications. So, sensors with better performance are discovered and on the way to be discovered, The sensitivity and accuracy are quite high in SPR sensors and it has fabrication friendly size. But in case of higher sensitivity, complex structures and fabrication complications arrive. That is why designing a PCF sensor includes some goals & others need to be compromised. Studying many PCF sensors, our design was proposed for an improved sensing performance and detecting magnetic and temperature changes. Also, the target was so that this design can be practically implemented. We proposed a circular lattice structure with gold coating. It was surrounded with a thin PML layer & stack and draw method was adapted for easy fabrication. We optimized various parameters whichever gave the best results. In all cases we used COMSOL Multiphysics 5.3a and Matlab for our researched work. Amplitude Sensitivity (AS) of 7223.62 RIU-1 and wavelength sensitivity (WS) of 28500 RIU-1 was noted. The Sensor resolutions are 1.38×10-6 and 3.50×10-6 RIU for amplitude and wavelength respectively and the sensor has a FOM of 914 with an analyte sensing range from Refractive indices 1.33 to 1.42. Aside from this, the sensor can perform as a temperature sensor with a maximum temperature sensitivity of 1.25 nm/ °C and also as a magnetic field sensor with a maximum sensitivity of 0.16 nm/Oe. Thus, denoting the high sensing capabilities, this proposed sensor can prevail as a potential asset in the bio-detection field.
Design and performance analysis of different photonic crystal fibers
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Rahman, Muntaha; Siraz, Sadia; Anzum, Mariea Sharaf
Over the years, countless designs of traditional PCFs and SPR-PCFs have been proposed by researchers with various structures, sensitivities, and confinement losses. However, a majority of such propositions depicted either large sensitivities but high confinement losses or low sensitivity and low losses. After an in-depth analysis of several previous works, keeping in mind their imperfections, we constructed our sensors and polarization filters with outstanding performance characteristics using Comsol Multiphysics 5.3a. We started our research by designing a typical hexagonal Photonic Crystal Fiber Sensor for milk detection showing a maximum sensitivity of 81.16% and 81.32% for camel and cow milk. We then went on to develop two different SPR-PCF sensors; a quadrature cluster SPR-PCF sensor attaining an outstanding ultra-high figure of merit (FOM) of 4230.42 RIU−1 and a Bent Core PCF-SPR sensor for broadband double peak sensing gaining maximum amplitude resolution of 1.18×10-6 RIU and a supreme wavelength resolution of 2.16×10-6 RIU. After working with sensors for an immense amount of time, we shifted our focus to other application aspects of PCFs, more specifically, polarization filters. We lastly suggested a D-structured single-polarization filter for S and U band applications. All of our designs have been optimized to achieve remarkable sensing and filtering characteristics like high sensitivity, meager losses, impressive FOM, satisfactory crosstalk and insertion loss.
Design and Stability Analysis of DC-DC Unidirectional, Bidirectional and Interleaved SEPIC Converters with Swarm Intelligence Algorithms Based Optimized PID Controller
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Mahmud, Al Jaber; Mithun, Mehedi Hasan; Khan, Md. Ashik
This thesis represents an investigative analysis of the closed-loop stability of the Unidirectional SEPIC (Single-Ended Primary Inductor Converter) converter, Bidirectional SEPIC converter, and Interleaved SEPIC Converter by implementing Swarm Intelligence Algorithms (SIA) for designing an optimized PID controller. The applicability and compatibility of three Swarm Intelligence Algorithms, which are Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Ant Colony Optimization for continuous domain (ACOR), are analyzed in optimizing the control mechanism of the power converters. The improvement of performance parameters is observed, such as Percentage of Overshoot (%OS), Rise Time (Tr), Settling Time (Ts), and Peak Amplitude. The outcomes are compared with the help of various fitness functions. The thesis focuses on higher-order SEPIC Converters and its variants (fourth-order). Higher-order converters benefit from smaller ripple currents, easier EMC filtering, and avoiding current spikes owing to resistive losses. The converters were developed using State Space Averaging (SSA), and the transfer function of the converter's open-loop system was determined using MATLAB's system identification toolbox. By using the PID controller, the closed-loop system of the converter is introduced. For the tuning purposes of the PID Controller, the PID Tuner App of MATLAB has been used. Nevertheless, for the better performance of the controller, the algorithms are evaluated in the system through different fitness functions: IAE, ITAE, ISE, and ITSE. MATLAB is used to carry out all the simulations. After analyzing the performances for the case of the Unidirectional SEPIC converter, ACOR-PID (ITSE) is the most optimized controller among all the algorithms based PID controllers in terms of performance parameters. In this case, values of overshoot (1.8603%), settling time (2.3414 sec), and peak amplitude (1.0186) are lower than FA-PID and PSO-PID for each of the error functions. For rise time, the value of ACOR-PID (ITAE) is better (0.3798 sec). Again, for the case of the Bidirectional SEPIC converter, PSO-PID (ITSE) is the most optimized controller among all the algorithms based PID controllers in terms of performance parameters. In this case, values of overshoot (0.2674%) and peak amplitude (1.0027) are lower than FA-PID and ACOR-PID for each of the error functions. For rise time, the value of ACOR-PID (ITAE) is better (0.3798 sec), and for settling time, the value of ACO¬¬R-PID (IAE) is better (0.1134 sec). Furthermore, for the case of the Interleaved SEPIC converter, PSO-PID (ITSE) is the most optimized controller among all the algorithms based PID controllers in terms of performance parameters. In this case, values of overshoot (5.2104%) and peak amplitude (1.0521) are lower than FA-PID and ACOR -PID for each of the error functions. For rise time and settling time, the values of PSO-PID (ITAE) are better (0.1471 sec and 1.3354 sec, respectively). Hence, Swarm Intelligence Algorithm based optimized PID controller provides more optimized results and performs far better than Conventional PID controller for SEPIC converter and its variants.
Distribution System Performance Improvement using Meta-heuristic Optimization Algorithms
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Apon, Hasan Jamil; Morshed, Khandaker Adil; Abid, Md. Shadman
The critical challenge for an efficient islanding operation of a distribution system having Distributed Generation (DG) is preserving the frequency and voltage stability. Contemporary load shedding schemes are inefficient and do not adequately assess the optimum amount of load to shed which results in either excessive or inadequate load shedding. Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Additionally, maintaining frequency and voltage stability is crucial for optimal functioning of an islanded network connected to DGs. Conventional load shedding schemes do not effectively identify the optimal amount of load to shed, culminating in either excessive or insufficient load shedding. Hence, the first part of this work presents an optimal load shedding technique using Chaotic Slime Mould Algorithm (CSMA) with sinusoidal map in order to achieve greater efficiency. A constrained function with static voltage stability margin (VSM) index and total remaining load after load shedding was applied to accomplish the evaluation. A total of three islanding scenarios of IEEE 33 bus and IEEE 69 bus radial distribution systems were used as test systems to assess the efficacy of the proposed load shedding approach using MATLAB software. To identify performance enhancements, the developed method was compared to Backtrack Search Algorithm (BSA) and the original SMA. According to the results, CSMA outperforms both BSA and SMA in terms of remaining load and voltage stability margin index values in all the test systems. Moreover, the second part of this work proposes Chaotic Equilibrium Optimizer (CEO) with iterative map to achieve an optimal solution for multiple DG sizing and placement in distribution networks, as well as an optimal load shedding approach. Regarding DG placement, the objective function was to minimize total active power loss and voltage deviation of the network nodes. The proposed method was compared with Modified moth flame optimization (MMFO), Teaching learning based optimization (TLBO) and the original Equilibrium optimizer (EO). Moreover, to assess the optimal load shedding technique, a constrained function with total remaining load and static voltage stability margin (VSM) index was used. In addition, the proposed CEO algorithm is compared with some of the recent metaheuristics algorithms applied in this domain such as Grasshopper optimization algorithm (GOA), Backtrack search algorithm (BSA) and the original Equilibrium optimizer (EO). In the last part of the work, based on a new metaheuristic known as the Artificial hummingbird algorithm (AHA), this work provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.
Driver audacity evaluation based on environment-specific embedded cyber-physical systems using low-level computation platform.
(Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh, 2022-05-30) Khan, Md. Asifuzzaman; Ashfaq, Md. Arean; Ahmed, Md. Kazi Sazzad
Reckless driving of public transport in third world countries like Bangladesh has long been an unsolved issue. One of the reasons being the lack of constant monitoring of the driver’s driving style. Present solution to this is to use a constant monitoring device onboard the vehicle to assess the driver’s performance. They use onboard neural networks to analyze various driving data pattern collected from the suite of sensors on board the car. However the use of onboard neural network increases the computational complexity and demands for expensive processor. Moreover the suite of sensors used such as GPS, Steering angle sensor, IMU adds to the cost. This makes them a not so viable option for mass use in the roads of a underdeveloped country like Bangladesh. Here in this paper we propose a plug and play device that can analyze the driving behavior in real-time using an onboard low level processor and 2 low cost sensors. This process eliminates the need of onboard neural network. A test bench is setup for simulating the various driving patterns and the collected data is compared with the published studies to validate the method. Then the methodology is explained in details. Finally the whole system is tested with the collected data and result is analyzed.
Electrical Home Automation Using IoT and Arduino
(Department of Electrical and Electronic Engineering(EEE), Islamic University of Technology(IUT),, 2022-05-30) Alshaer, Hussein M. A.; Odowa, Abdullahi Ali Abdullahi; Hassan, Abdirahman Omar; Omar, Soumaya Musse
Internet of things (IoT) is a system which has several devices i.e. home appliances and any other gadgets which sensors and other various sensing devices are implanted with them to perform a task which is to interconnect and also transmit data in between neighboring devices and networks by using the internet. The inter-connected system of IoT provides and gives the possibility and the capability of controlling and monitoring devices automatically and remotely, each device is given a very unique IP address and they are recognized and communicated through those IP addresses. The IoT gadgets or devices are smart and intelligent and they take control from Web server that is installed in the cloud, the data that the gadgets gather from the environment is sent through an IoT gateway for analyzing and then perform the required task accordingly and sometimes those data are sent in to the cloud. The home automation is referred as a way of managing and monitoring the home appliances/devices remotely by either manually or automatically. Home automation gives the owner of the house the capability of controlling his home devices. Home Automation design refers to the ways of automating typical home appliance functions with not requiring for human input. Simultaneously, a home automation android application and an automated web page script have been built for deployment of the internet of things architecture. Home automation is effective because it helps users to utilize their home equipment smoothly and efficient. The cutting edge technologies that has developed these days allows the technologies like Wi-Fi and Bluetooth to provide connection of several gadgets without the need of physical wired connection which reduces the cost. One of the advantages of this system of automation is that it gives you the ability to control and monitor your home appliances from one centered place, which means you, can control your home appliances all at once and see their status.