In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

To enhance the production of solar photovoltaic (SPV)-based cleaner energy, the maximum power point (MPP) tracking (MPPT) schemes are utilized. To ensure a reliable and effective MPP extraction from SPV systems, the exploitation and implementation of different MPPT schemes are of great significance. This article intends to present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN). In recent years, modern optimization-based global MPP (GMPP) extraction schemes are gaining much attention from researchers. In this review article, thirteen modern optimizations and ANN-based GMPP tracking techniques are vividly described with their flowchart and detailed mathematical modeling. This work assesses all the schemes according to parameters like tracking efficacy, tracking time, application, sensed parameters, converter utilized, steady-state oscillations, experimental setup, and key notes. Based on the rigorous review, a novel GMPP extraction scheme based on a recently introduced meta-heuristic approach named artificial gorilla troops optimizer is proposed. This review work serves as a source of comprehensive information about applying these MPPT techniques to extract GMPP from the SPV system under PS conditions; furthermore, it can be considered a one-stop handbook for further study in this field.

A state-of-the-art review on shading mitigation techniques in solar photovoltaics via meta-heuristic approach

Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.

/pdf/hybrid-and-deep-learning-approach-for-early-diagnosis-of-2sk8kp5s.pdf

Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC; 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is further developed for data monitoring on a smartphone. All in all, this self-powered WSN node can interface with the users of the SHBs for displaying ambient data, which demonstrates its promising applicability and stability.

https://www.mdpi.com/2072-666X/12/6/653/pdf

Self-Sustained Autonomous Wireless Sensor Network with Integrated Solar Photovoltaic System for Internet of Smart Home-Building (IoSHB) Applications.

An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.

/pdf/african-vulture-optimization-algorithm-based-pi-controllers-udedlec8.pdf

African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application ‘MQTT dashboard’ is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.

An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications.

This paper proposes an Internet of Thing (IoT)-enabled smart plug socket (SPS) to monitor and control energy consumption in a home/building energy management system. The device can wirelessly transmit data to a home energy management (HEM) controller. Additionally, the transmitted data can visualize, store, and analyze through the connected web server and smartphone. A customized android application is developed to interface the proposed SPS with the webserver and smartphone. According to experimental results, the proposed SPS can correctly interpret the power usage of wirelessly attached devices from anywhere without data loss. The proposed system has experimented with for 40-minute in the laboratory. The experiment shows an energy consumption of 0.124 kWh with an electricity bill of 0.697 RM. As a result, with the correct scheduling algorithm, the suggested smart socket framework can be utilized entirely in a home energy management system.

Design and Implementation of an IoT-Enabled Smart Plug Socket for Home Energy Management

This study proposes a telehealth device that can be used by nurses, attendants and physicians to differentiate between patients who need timely or urgent consideration. Home-bound patients with minimal portability and decreased capacity can use this gadget to call for appropriate assistance. The proposed telehealth-internet of things (IoT) device automatically generates caution for supplying patients with auspicious health treatment under incessant clinical circumstances. A real-time android app, namely ‘village doctor app (VDA)’ is developed and connected to a cloud network server. This App is associated with a few wireless sensors. Based on the sensor data, a doctor can take the appropriate measures to look at specific patients' details.

Md. Rokonuzzaman

Papers

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications.

Design and Implementation of an IoT-Enabled Smart Plug Socket for Home Energy Management

Design and Implementation of Telehealth Device: Linking IoT Sensors to Cloud Networks