Need for off-grid electric vehicle (EV) charging solutions, using intelligent control systems, such as machine learning (ML)-based maximum power point tracking (MPPT), to harness solar energy, offers a means of optimizing efficiency even in the face of fluctuations. This innovative strategy combines clean energy, cutting-edge power electronics, and practical application, which makes it perfect for fostering innovation in areas with inadequate infrastructure. For rural areas without grid infrastructure, this paper presents a novel design and performance assessment of a portable solar-powered EV charging system. To maximize solar energy harvesting and charging efficiency, the system combines an interleaved buck-boost converter with an ML-driven MPPT algorithm. It is appropriate for small electric vehicles (EVs) like auto rickshaws because it uses a 48 V lithium iron phosphate (LiFePO₄) battery. A supervised regression model trained on real-time electrical (voltage, current, and power) and environmental (temperature, irradiance) parameters is used to implement the MPPT algorithm. The system was created using MATLAB/Simulink, and the key performance parameters were evaluated using real-time information. Analyses of the key performance metrics like charging efficiency, converter stability, and tracking accuracy show a superior energy harvesting efficiency of 97%.

This paper discusses the difficulties in charging electric vehicles (EVs) in office settings. Challenges include manual cable connections, limited charging possibilities, safety concerns around wire management, the absence of variable charging features. This work concentrates on cutting-edge wireless power transmission (WPT) technology made especially for commercial parking lots. This technology combines sustainable energy resources with the revolutionary strength of the IoT. It uses a combination of storage batteries and photovoltaic technologies to enable an economical and reliable power source for EVs. IoT technology enables automatic charging when an electric vehicle is parked. Furthermore, the Blynk application gives customers instant access to data about the PV system's operational state and the status of the EVs' batteries. RFID and IoT methods are added to the platform to give real-time information on charging slot status and rigorous security rules for user verification.

This paper presents an Intelligent Digital Twin (IDT) framework designed for real-time Structural Health Monitoring (SHM) and optimization of mechanical systems. The proposed approach integrates high-frequency sensor data with a physics-based digital model to provide accurate, continuous assessment of structural integrity. Sensor inputs from vibration, strain, and temperature measurements are preprocessed and transformed into damage-sensitive features, which are assimilated into a high-fidelity finite element digital twin using Kalman filtering for precise state estimation. An anomaly detection module evaluates residuals between measured and predicted responses to identify potential faults, while a reinforcement learning (RL) agent operates within the updated digital twin to learn optimal maintenance and control strategies that minimize structural degradation and operational costs. The framework is implemented and tested on a scaled mechanical testbed subjected to dynamic loading. Experimental results demonstrate significant performance improvements compared to conventional SHM methods, achieving 96.8% detection accuracy, 95.6% precision, 96.1% recall, and a 14.7% optimization gain, along with reduced response latency of 135 ms. These outcomes highlight the effectiveness of combining digital twin technology with RL-driven decision-making to create adaptive, proactive, and efficient SHM systems suitable for long-term industrial deployment.

The convergence of Artificial Intelligence and the Internet of Things has paved the way for the development of smart environments that are efficient, adaptive and sustainable. The IoT enables vast networks of interconnected sensors, device and actuators to capture real-time data, AI provides the intelligence needed to process, analyze and act upon this information. Together, they transform reactive systems into proactive context-aware ecosystems capable of decision-making and automation. This chapter presents a comprehensive framework for integrating AI with IoT highlighting its architecture, component and applicability across domains such as smart cities, healthcare, agriculture, transportation and industrial automation. The framework emphasizes enabling technologies including edge, fog and cloud computing, communication protocols and open platforms like FIWARE, which collectively support scalability, interoperability and real-time analytics. the study also critically addresses challenges related to data management, security, privacy, ethical considerations and workforce readiness.

A hybrid tree is a synthetic construction that look a lot like a real tree, with solar panels or wind turbines installed on its branches. It will enable the delivery of electricity to lighting loads, cell phones, household devices, laptops, and electric vehicles, making it a suitable source of energy for green cities or smart cities. A 0.7-kW hybrid tree development with 200 W of solar and 500 W of wind is used in this study. To maximize energy output, PV panels should be oriented perpendicular to the sun's rays. This research includes an automated two-axis intelligent solar tracking system that automatically positions PV panels to achieve optimum energy output from any part of world. Tracking system is developed on a computational model that incorporates a controller, GPS, gyro-orientation sensor and digital-compass. The developed technology accurately tracks the sun while using minimal electricity, increasing the efficiency of PV panels. Solar panel parameters were measured at different temperature and irradiance range. The data taken from three distinct days is used for simulation to further evaluate it. Research has been conducted on wind turbine power attributes at different wind speeds and power coefficients at several tip speed ratios. The hybrid tree's power generation was studied on various days. The acquired findings revealed that the designed system can follow the sun on any day, optimizing energy use by operating tracker within particular times that allow assembling optimum feasible power of solar panels while assuring minimal energy usage by the tracking system. The suggested solar-wind hybrid tree generates 444.5 Wh/day using a two-axis tracking system.

This paper presents the best modeling and control strategies for a grid-connected hybrid wind-solar power system to maximize energy production. For variable wind speeds, determine the optimal power point using fuzzy logic control, adopt an adaptive hill climb searching method, and compare it with an optimal torque control method for large inertia wind turbine (WT). The role of fuzzy logic controller (FLC) is to adjust the hill climbing search (HCS) technique's step-size according to the operating point. The doubly-fed induction generator (DFIG) control system has two subsystems: rotor-side and grid-side converters. The active and reactive power have been indirectly regulated by adjusting the current on the d-q axis. The rotor side converter (RSC) controllers are responsible for controlling the WTs rotational speed to achieve the maximum power output. The grid side converter (GSC) manages the voltage at the DC link and keeps a unity power factor between the grid and GSC. Optimal hybrid power point tracking technique for use with photovoltaic systems in both constant and variable shade circumstances, based on particle swarm optimization (PSO) and perturb and observe (P&O). The optimal power point tracking (OPPT) approach is compared to three other methods: PSO, P&O, and hybrid P&O-PSO. The model has a total capacity of 2.249 MW, with wind capacity of 2 MW and solar capacity of 0.249 MW, and its efficiency is analyzed.

An optimal control of a grid-connected solar-wind hybrid scheme for the electricity generation system by utilizing both wind and solar renewable energy in a remote region that is inaccessible to the electricity grid. The control and assessment of a hybrid sustainable energy generation system power system that supplies three-phase, four-line loads as well as a battery array are presented in this research work. Wind energy conversion system (WECS) is comprised of a doubly-fed induction generator (DFIG) and two pulse width modulation (PWM) voltage source converters, namely the grid side converter (GSC) and the rotor side converter (RSC), which are linked together via a DC-link and are equipped with a technique for maximum power point tracking (MPPT). The grid voltage-oriented control strategy is employed to provide a consistent DC-bus voltage for the GSC and to regulate the reactive power on the power grid. Even the difference in voltage and frequency can be controlled with this novel strategy. The stator voltage-oriented vector technique is designed in the RSC control strategy, resulting in effective regulation of reactive and active power at the stator as well as an MPPT obtained by controlling the optimal torque. The hybrid sustainable energy generating system (HSEGS) simulation model is designed to have a capacity of 5 kW, and its efficiency is evaluated using the MATLAB/ Simulink platform and demonstrated in a variety of circumstances.

A smart grid is a structure that regulates, operates, and utilizes energy sources that are incorporated into the smart grid using smart communications techniques and computerized techniques. The running and maintenance of Smart Grids now depend on artificial intelligence methods quite extensively. Artificial intelligence is enabling more dependable, efficient, and sustainable energy systems from improving load forecasting accuracy to optimizing power distribution and guaranteeing issue identification. An intelligent smart grid will be created by substituting artificial intelligence for manual tasks and achieving high efficiency, dependability, and affordability across the energy supply chain from production to consumption. Collection of a large diversity of data is vital to make effective decisions. Artificial intelligence application operates by processing abundant data samples, advanced computing, and strong communication collaboration. The development of appropriate infrastructure resources, including big data, cloud computing, and other collaboration platforms, must be enhanced for this type of operation. In this paper, an attempt has been made to summarize the artificial intelligence techniques used in various aspects of smart grid system.

The performance analysis of the multistring single cell H-bridge inverter for grid connected Photovoltaic (PV) system has been presented in this paper. Based on power conversion stages the grid connected PV systems have been classified as a single stage and two stage grid connected PV system. In this paper comparison of performance of both single stage and two stage systems has been carried out to address the technical challenges of grid current, grid voltage, DC link voltage and grid synchronization. The simulation studies have been carried out in Matlab/Simulink environment. Finally, the simulation results are analyzed based on performance parameters to authenticate the possibility of system.

Real-time biomedical signal transmission requires IoTs and cloud infrastructure. In this work, we investigate feasible lossy compression approaches that leverage the temporal and spatial dynamics of the signal along with current algorithms based on Compressive Sensing (CS) that use signal correlation in space and time. These techniques are altered so they may be applied efficiently to a distributed WSN. To achieve this, we proposed Convolution Neural Network (CNN) based Optimized Bio-Signals Compression using Auto-Encoder (BCAE), which integrates auto-encoder and feature selection. Instead of using the entire signal as an input, we encode the main part of the signal and send it to the desired location. Reconstruction decrypts without signal loss. Realistic aggregation and data collection procedures can improve data reconstruction accuracy. We compare various techniques' reconstruction error vs. energy requirements. The simulation results reveal that packet loss is 40% and data reconstruction error is 5%. Data forwarding time is lowered by 16.36%, while network energy usage is cut by 23.59%. The proposed method outperforms with existing techniques and the results are validated using MATLAB.

Abstract In recent years, there has been a significant demand for lightweight converters that provide exceptional control performance while generating minimal acoustic noise. This has resulted in higher switching signals for hard- switched 2-level low voltage 3-phase inverters. However, the increased switching frequencies have a negative impact on the inverters’ efficiency, requiring the use of expensive switch technology to maintain high efficiency. This paper aims to tackle these challenges by introducing the design and implementation of a extremely effective 3-Level T-Type Neutral Point Clamped (NPC) inverter designed for Electric Vehicle (EV) applications. 3-Level T-Type NPC inverter associates the strengths of both NPC and T-Type inverters. Similar towards the NPC, it offers a lower breakdown voltage for the switching devices, thereby reducing switching losses. Additionally, it shares the characteristics of the T-Type inverter, resulting in fewer conduction losses. To validate the feasibility of the converter, simulation is performed using MATLAB software, followed by the development and testing of a lab-scale prototype. The proposed three-level T-type NPC inverter have several merits over traditional 2-level inverters, including reduced distortion, switching losses, and improved efficiency. These benefits contribute to enhanced motor drive performance, reduced electromagnetic interference (EMI), and extended battery life in EVs.

Abstract Presently, there are many more commercial applications for thermoelectric Peltier coolers, notably portable air/water refrigerators, electronics cooling processes, thermal management systems in medical applications, and others. Thus, the goal of this project is to develop an experimentally-based optimisation procedure for powered by sunlight peltier-based air conditioners and heaters for use in hospitals that are capable of running continuously. This enables patients to cook meals in the heating element and keep drugs in the cold compartment inside the cooler, both functions being incorporated into one model while also reducing the device’s dimensions and requirement for space. In this study, a model that employs perturb and observe (P&O) approaches applied to solar systems under constant and partially shady circumstances was powered by the Maximum Power Point Tracking (MPPT) methodology. Additionally, the Internet of Things modules offer user and autonomous management, while the entire system is tracked and shown on an OLED. An appropriate number of modules also reduces the expense per cooling unit for Peltier coolers and warmers.

Deployment of small cells over the existing cellular network is an effective solution to improve the system coverage and throughput of fifth generation (5G) mobile communication networks. The arrival of the 5G mobile networks have demonstrated the importance of advanced scheduling techniques to manage the limited frequency spectrum available while achieving 5G transmission requirements. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that mitigate the interference between the different cells. In this research work, we formulate an optimization problem for heterogenous networks (HetNets) for resource allocation to maximize the system throughput among the cell center users (CCUs) and cell edge users (CEUs). We solve the optimization problem by effective utilization of the weight factors distribution for resource allocation. A novel Utility-based Resource Scheduling Algorithm (URSA) optimizes the resource sharing among the users with better delay budget of each application. The designed URSA ameliorates fairness along with reduced cross layer interference for real and non-real time applications. Performance of the URSA has been evaluated and compared most relevant state of art algorithms using the matlab based simulators. Furthermore, simulation results validate the superiority of the proposed scheduling scheme against conventional techniques in terms of throughput, fairness, and spectral efficiency.

In our country near about 1/4 of the patients lose their lives due to interrupted health monitoring system. In most of the hospitals, doctor visits patients once or twice in a day. A situation might arise where the patient's health becomes critical in between that interval when a doctor is not available with a patient and the patient might lose their life. Therefore, to overcome these issues of existing system we are proposing health monitoring by using Light Fidelity (Li-Fi) technology, where patient's health is monitored and it will be updated to the doctor throughout the day. Li-Fi technology is a new emerging technology which uses wireless optical networking phenomenon for data transmission with data rate of 224Gbps. The use of Li-Fi technology for health-care monitoring system is that it can measure various physiological parameters of the human body and is secure, reliable and better than conventional Wireless Fidelity (Wi-Fi) technology. Results of the prototype model show better performance in terms of accessibility and portability of the physiological sensor. Proposed system is user friendly, cost effective and will have impact on the upcoming days in the hospitals.

This paper presents the design, control and evaluation of an Autonomous Hybrid Wind Solar System (AHWSS) energy system feeding into three-phase, four-line loads and an array of batteries. Wind Energy Conversion System Connected to the Grid (WECS) contains Doubly Fed Induction Generator (DFIG) and two PWM voltage source converters i.e. Grid Side Converter (GSC) and Rotor Side Converter (RSC) connected back to back at DC-link and are provided with an algorithm for Maximum Power Point Tracking (MPPT). The grid voltage-oriented control algorithm is used to maintain a steady DC bus voltage for the GSC and to balance the reactive power at the power grid even the divergence in frequency and voltage can be regulated with this novel approach. The stator voltage-orientated vector control is implemented in the RSC control strategy, delivering effective controlling of active and reactive power at the stator, and also a MPPT is achieved through controlling the Tip Speed Ratio. The photovoltaic (PV) system along with the boost converter is fed to the DC link. A Perturb & Observe method is used for tracking maximum power in a solar PV system. The model is implemented in MATLAB's Sim-power-system toolkit with ode3 solver and is presented in different scenarios, e.g., solar irradiation, differing wind velocity, dynamic, and unbalanced nonlinear loads. In these all constraints, DFIG's stator winding currents are balanced with low Total Harmonics Distortion (THD), value less than 4% in all scenarios.

In recent years, Hybrid Wind-Solar Energy Systems (HWSES) comprised of Photovoltaic (PV) and wind turbines have been utilized to reduce the intermittent issue of renewable energy generation units. The proposed research work provides optimized modeling and control strategies for a grid-connected HWSES. To enhance the efficiency of the maximum power tracking of a grid-connected wind-driven Doubly Fed Induction Generator (DFIG) integrated with solar Photovoltaic (PV) system, connected to the DC link of the back-to-back converters of the Hybrid Wind-Solar Energy System (HWSES). Stator Flux-Oriented control is utilized to regulate the Grid Side Converter and Rotor Side Converter. The main objective of this paper is to apply the Maximum Power Point Tracking (MPPT) strategy to wind and solar PV systems to maximize the power extraction and to provide better integration of the hybrid systems into the electrical grids. Perturb and Observe (P&O) and Incremental Conductance (IC) MPPT algorithms are implemented to the solar PV system with varying solar insolation and their performances and efficiencies are compared. For varying wind speeds, Tip Speed Ratio (TSR) and Optimal Torque (OT) MPPT algorithms are implemented and their performances and efficiencies are compared for the hybrid system considering and integrating solar PV system. The optimal torque MPPT algorithm shows better responses when compared to the TSR method. A 2MW simulation model of the HWSES is developed and its performance is analyzed using MATLAB/Simulink environment. The implemented schemes have the advantage of tracking the optimal power output of the HWSES rapidly and precisely. Additionally, the provided schemes effectively control the power flowing through the HWSES and the utility grid, resulting in a quick transient response and enhanced stability performance.

Numerous cattle calves do not return to the sheds after grazing because they become disoriented, resulting in the loss of that specific cattle. If the cattle calves are not counted before and after grazing, it is impossible for the person to manually count them. Cattle calves are sensitive to a range of illnesses/diseases, most of them may reduce the production and quality of milk products and, if not discovered early, could even result in the cattle's death. Diseases have an influence on-farm production, therefore ends up in low production, low income, and low quality. This research work provides a methodology where different sensors and image processing techniques are used to monitor the health of the cattle and alert the user regarding its health condition, also an automated counting system is implemented to count the total number of cattle in the shed.

Nowadays sleeping disorder is common among the people who work in metropolitan cities due to stress, pollution which often cause difficulty during sleeping. Diagnosing patients who have obstructive sleep apnea requires lab-based polysomnography, but over the years now more gadgets are available for sleep test. Today's technology has made life easier by offering many intelligent solutions to every problem in society, but when it comes to Sleeping Obstructive Disorder(SOD) still its in the initial stage. With the growing population for every year the number of patients are also increasing day by day. Hence a solution is required to automatically monitor and control the health status of a person. Hence, we proposed an IOT based Sleep Monitoring Device that has features like a fast WiFi module with local storage. The cardiac information is measured with the use of electro-resistive sensors and an accelerometer. In an effort to fix these problems, we've invented a new IOT-ready sleep monitoring gadget, which takes use of a new way of measuring cardiac and respiratory data with polymer-based innovation, and lets us record ECG and accelerometer results with only one lead. The NODEMCU allows the transfer of data in real time using a wireless internet connection, and it does so in line with industry standards.

This paper describes the architecture and control of an autonomous hybrid solar-wind system (AHSWS) powered distributed generation system supplying to a 3ϕ-4 wire system. It includes a nonlinear controlling technique for maximum power point tracking (MPPT) used in doubly fed induction generator dependent wind energy translation scheme and solar photovoltaic system (SPVS). In the hybrid model, the DC/DC converter output from the PV system is explicitly coupled with the DC-link of DFIG's back-to-back converter. An arithmetical model of the device is developed, derived using a suitable d-q reference frame. The grid-voltage-oriented vector regulation is required to manage the GSC to keep the steady-state voltage of the DC bus and to adjust reactive power on the grid side. Also, the stator-voltageoriented control scheme offers a stable function of DFIG to regulate the RSC on the stator edge for reactive and active power management in this approach. DC/DC converter is being used to maintain the maximum power from SPVS. A Perturb & Observe method is used for tracing maximum power in an SPVS. The simulation designs of 4.0kW DFIG and 4.5kW solar array simulator are built-in SIMPOWER software kit of MATLAB, it is shown to achieve optimum efficiency under various mechanical and electrical circumstances. It can produce rated frequency and voltage in both scenarios.