bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling a Pluse-Width Modulation(PWM) Rectifier in 3D Space
Authors: Prasad Reddy M. Ram , Deepak Karanam , Pagidala Shiva Shankar , Pikkili Bhuvanesh , Rayaveera Nitin Narasimha , Shaik Moin Basheer
Published: June 2024

Power electronics pertains to a category of devices that convert electrical power from one form to another. Semiconductor devices act as switches in power electronics circuits, serving as the central components responsible for voltage regulation. Power electronics circuits strive to convert electrical energy from one form to another, from source to load, while minimizing cost, size, and weight as much as possible. The term “rectification” refers to the power circuit which alters the ac characteristic of the line electric power in order to supply “rectified” ac power with a dc value at the load side. The rectifier is utilized to convert alternating current (AC) voltage into direct current (DC) or low-frequency AC in order to power different load configurations. The AC mains are subjected to the injection of low order current harmonics by the Conventional Phase Controlled Rectifier. These harmonics necessitate the use of filtering components of considerable size for effective attenuation. The paper explores the simulation of the Converter through various PWM techniques, including space vector pulse width modulation and sinusoidal pulse width modulation.
Real time Capacitance and Absorption Based Voltage Regulation Model for Improved Power Stabilization with Cuk Converter
Authors: Vij Priya , Divya
Published: June 2024

The problem of voltage regulation in power systems has been well studied. There exist numbers of stabilization model discussed in literature which consider input and output voltage. The methods suffer to achieve higher performance in power stabilization. To handle this issue, an efficient Real-time Capacitance and Absorption based voltage regulation model (RCAVRM) is presented in this paper. The model has been fabricated with different MOFSET and cuk converter connected in serial where the Mofset are designed to maintain the traces of voltage present in different capacitors and inductors. At each duty cycle, the method collects the number of circuits being used and number of other circuits not triggered. Based on these details, the method computes Capacitance Power Stabilization Factor (CPSF) and Voltage Absorption Factor (VAF). Using these two factors, the method computes the value of power stabilization weight (PSW) for different circuits. Based on the value of PSW, a unique circuit is selected to regulate the power supply to the electric system. The proposed model improves the performance of power stabilization with least voltage loss.
A New Symmetrical Cascaded Single Phase H Bridge Inverter With Reduced Switch Count
Authors: Kumar Dinesh , Kumar Pandey Vijay , Nachappa M.N. , Goswami Sachin
Published: June 2024

An imaginative multilevel inverter topology is presented with the intent of deploy few series connected switches, ameliorate modularity and derogatory voltage stress collate to unremarkable topologies which is published in literature [1] and [2]. This paper mainly focuses on symmetrical configuration. Modified sinusoidal pulse width modulation (MSPWM) technique is developed in this paper with less number of carrier requirement compared to traditional sinusoidal pulse width modulation. This generalized structure and MSPWM technique will give the advantage of less cost of device, compact size.
A Novel Methodology to Implement Non-Ideal Boost Inverter Using Genetic Algorithm based Sliding-PI Controller
Authors: Guputa Rajesh , Singh Akhilendra , Sharma Bhuvnesh , Prakash Febin
Published: June 2024

The paper introduces a Non-Ideal Boost Inverter with Linear Load as an alternative to the traditional Voltage Source Inverter. Unlike the traditional inverter, this new design is capable of generating a sinusoidal AC output voltage that can be either higher or lower than the DC input voltage, depending on the duty cycle. This eliminates the need for a second power conversion stage. To enhance the performance of the Boost Inverter, a Sliding-PI controller, a relatively new control topology, is employed. The Kp and Ki constants for the controller are determined using a Genetic Algorithm. The paper then proceeds to present the operations, analysis, control strategy, and simulation results of the proposed inverter design.
Simulation of SIMO based DC-DC Converter for Multi Level Solar PV Inverter
Authors: Agrawal Ajay , Chaudhary Pavan , Kumar Ravindra , Kulhar Kuldeep Singh
Published: June 2024

Simulation of SIMO based DC-DC Converter for multi level Solar PV Inverter is presented in this paper. In order to reduce the number of distinct DC supplies for an H-bridge inverter SIMO DCDC converter is applied which in turn allows the usage of single PV system for a multi level H-bridge inverter. Simulation model of three level and five level inverters is built and results are presented to validate the proposed ideas.
SVPWM based double loop control method of a three phase inverter for Microgrid Application
Authors: Ram Prasad Reddy M. , Deepak Karanam , Rami Reddy M. Venkata , Mounika Jangiti , Rohith S. , Singh B. Rashmitha
Published: June 2024

Many AC Microgrids required an inverter for converting power from DC to AC. Many control techniques are available and need a flexible control method which can able to regulate both the voltage at DC and AC side. The DQ method is developed in this paper by incorporated with space vector pulse width modulation (SVPWM) technique. A distribution generator (DG) is considered in this paper for connecting to utility grid through an inverter controlled by proposed double loop control technique. One voltage controlled loop and one current controlled loop are used in proposed control method to regulate both voltage and current. This paper showcases comprehensive findings using MATLAB/Simulink.
Industrial Energy Efficiency and Climate Change Mitigation in Smart Cities: A Comprehensive Review
Authors: Bargavi Manju , Saraswat Bhawna , Naval Preeti , Kulhar Kuldeep Singh
Published: June 2024

In the context of escalating urbanisation and climate change, smart cities emerge as a beacon of sustainable urban development, leveraging cutting-edge technology and data analytics to enhance municipal services and the well-being of residents. This review article, focusing on industrial energy efficiency within smart cities, underscores the pivotal role of these urban environments in mitigating climate change impacts. It highlights the industrial sector’s substantial contribution to global greenhouse gas emissions, driven by energy-intensive processes predominantly fuelled by fossil fuels. The study presents a comparative analysis of emissions across continents, revealing the industrial activities’ significant environmental footprint. It advocates for energy efficiency as a strategic imperative to reduce energy consumption, curb emissions, and foster sustainability. The paper concludes by recommending policy interventions that incentivise eco-friendly industrial practices, endorse the circular use of materials, and promote sustainable economic models. These recommendations are contextualised within Iceland’s CAP 2020 initiative, which aims for a significant reduction in emissions by 2030, underscoring the need for sustainable material management, particularly in metallic ores and fossil fuels, to align with environmental sustainability goals. The article calls for a global collaborative effort, beyond individual national policies, to address the urgent challenges posed by climate change, advocating for international cooperation, investment in renewable energy, and a transition towards a more sustainable future.
Perspective-smart energy management system using machine learning
Authors: Balmiki Vinod , C Santhosh Kumar , Sharma Praveen , Fezaa Laith H.A. , Almusawi Muntather , Lakumanan M.
Published: June 2024

In today’s rapidly evolving world, the demand for energy is steadily increasing, while the need for sustainability and efficient resource utilization becomes ever more critical. Smart Energy Management Systems (SEMS) are poised to play a pivotal role in addressing these challenges. Leveraging the power of Machine Learning (ML), SEMS offer a promising avenue to optimize energy consumption, enhance grid reliability, and reduce carbon footprints. This review article provides an in-depth exploration of the current state of Smart Energy Management Systems empowered by Machine Learning, highlighting their key components, applications, challenges, and future prospects.
Comparative Analysis between DVR and D - STATCOM for DFIG Based WECS Integrated to Grid
Authors: Rang Reddy G. Pandu , Reddy M. Koteswara , Reddy M. Mahendra , Raju V. Bharath , Sekhar Reddy M. Chandra
Published: June 2024

The instabilities in the power supply, such as voltage spikes, transients, and distorted waves, can cause consumer electronics to malfunction or even fail altogether. It is one of the biggest worries for gridconnected WECS in the present and future.In a variety of ways, specialized power electrical devices can improve power quality. These days, power applications make use of a broad variety of flexible controllers that take use of advanced power electronic components.In order to effectively minimize various forms of power quality concerns in a grid-connected wind energy conversion system, this article evaluates the reliability of two distinctive electrical devices, the Distribution Static Compensator (DSTATCOM) and the Dynamic Voltage Restorer (DVR). MATLAB/SIMULINK is used to examine the devices’ performance.
Neuro-Fuzzy Controllers for Power Quality Improvement of Grid Connected PV-Battery-Diesel based Hybrid Supply System
Authors: Sharma Khushboo , Menaka C. , Saraswat Nidhi , Kulhar Kuldeep Singh
Published: June 2024

Diesel generators are commonly operated to supply electric power for many apartments, hospitals, institutes and industries during off grid mode. However, consumption of heavy diesel can causes harmful effect to atmosphere and surroundings. Under these circumstances, integrated photovoltaic system (PVS) to the diesel generators can able to give some viable solutions for many problems. In order to store the energy from PVS during off grid mode as well as to provide uninterrupted power supply into load bus, a battery bank is also incorporated into the system in this paper.These kinds of system are mainly connected in distribution system. Hence, a proper inverter controller needs to be implemented for maintaining power quality. Neuro-Fuzzy based controllers are implemented to use in the control block of the inverter to achieve the fast and precious responses during quick variations happens in the system. In addition with this, a sliding mode controller is also incorporated with inverter controller to regulate invert input current.A hardware- in the -loop by OPAL-RT devices is established to collect the results under various operating conditions.
Enhancement of Power Quality of Single Stage Grid Connected PV System by Using Takagi-Sugeno-Kang Fuzzy Controllers
Authors: Faujdar Pramod Kumar , T Thiruvenkadam , Awasthi Aishwary , Kulhar Kuldeep Singh
Published: June 2024

Grid connected solar power plants are widely established in many places worldwide. Photovoltaic (PV) based grid connected solar plants are attracting recently due to improved in controlling of power converters. Single stage grid connected systems can reduce number of converters connected in power plants which resultant in reduce cost of the system. However, DC to DC converters are generally used in PV systems to enhance the operation of maximum power point for best utilization. The inverters also can be using to extract maximum power from PV systems through new controlling techniques in power electronics devises. Therefore an extraDC to DC converter is not required to make PV at its maximum power point condition. However, this technology can be used for small scale solar power plants since all PV arrays in solar power plant cannot be received same irradiance. Takagi-Sugeno-Kang (TSK) fuzzy controlleris having significant priority than proportional plus integral controllers when rapid changes are having in input. Hence, TSK based single stage controller is developed in this paper for grid connected 1MW solar plant. Generally distribution system is connected with unbalanced loads, hence these unbalanced loads will create forcefully unbalanced currents in electrical grid. Unbalanced grid currents further create many problems to other loads. Therefore, the proposed controller is designed to help making grid currents balanced during unbalanced local loads. Further, the inverter can compensate reactive power demanded by local loads to minimize reactive power supplied by grid. Extensive results are presented and evaluated through hardware-in-loop on the platform of OPAL-RT to enhance the performance of proposed controller for 1MW grid connected solar plant.
LSTM Controllers for Power Quality Improvement in Grid Connected Hybrid Wind-PV-Battery based Power Supply System
Authors: Agrawal Ajay , Das Neeraj , Jain Sandeep Kumar , Kulhar Kuldeep Singh
Published: June 2024

Nowadays, the popularity of grid integrated systems primarily supplied by renewable energy sources based hybrid Microgrids have increased. This is due to their ability to reduce global warming and meet the increasing electrical load demand worldwide. In order to guarantee an uninterrupted power supply to loads when operating in the operation of off-grid, the system incorporates a storage device, thereby establishing an effective energy management system. A new energy management system has been developed to enhance the stability of the proposed system. The system has the ability to stabilize voltage at the load bus, compensate for reactive power, and mitigate the harmonics as well as the effect of unbalanced load. In order to attain quick reactions amidst swift alterations, the suggested control technique has integrated LSTM controllers. Practical outcomes have been achieved by leveraging Hardware-in-the-Loop using OPAL-RT modules. Numerous case studies have been carried out to further confirm the efficacy of the suggested approach.
A PSO-based Approach to Determine the Market Clearing Price by Considering System Losses and Reactive Power Constraints in a Pool Market
Authors: Rani R Hannah Jessie , Gupta Sheryl , Chaudhary Pavan , Kulhar Kuldeep Singh
Published: June 2024

The deregulation of electricity has led to significant transformations in the market structure and policies governing the industry. The primary responsibility of a power system operator in this market is to establish the Market Clearing Price (MCP). The MCP is established by evaluating the incremental bids submitted by generators in various markets. But the quadratic bid functions have more information about the price structure and are more realistic. The Independent System Operator (ISO) determines the clearing price by analyzing the bids submitted. Conventional methods do not provide an accurate calculation of MCP for the spot market. This may lead to poor allocation of generation. This paper uses the basic Particle Swarm Optimization technique to maximize the generator bid function. By calculating MCP, the optimum generation with least cost is determined. Losses in the system with reactive power constraints are considered while the loads are kept in-elastic. The result of the proposed technique is compared with the classical approach. IEEE 9 bus system is taken to illustrate the proposed model.
Enhancing Power Grid Resilience against Cyber Threats in the Smart Grid Era
Authors: Padmavathy R. , Singh Sanjeev Kumar , Sindhu M. , Jasim L. Hussien , Saxena Archana , Singh Dari Sukhvinder
Published: June 2024

In the age of smart grids, fortifying power grids against cyber threats has become of utmost importance. This paper reviews endeavours in the realms of defining, measuring, and scrutinising the resilience of smart grids. An exhaustive overview of both qualitative frameworks and quantitative metrics for resilience study is provided, underscoring the ideal characteristics of a resilience metric. The complexities in formulating and crafting such metrics in practical settings are also broached. Another focal point is the hierarchical outage management scheme, crafted to enhance the robustness of smart distribution systems, particularly those encompassing multi-microgrids. This scheme introduces a two-tiered approach: the preliminary stage revolves around resource scheduling via a model predictive control-based algorithm, whilst the subsequent stage centres on coordinating power transfers amongst microgrids. Moreover, the paper probes into the vulnerabilities of smart grids owing to the incorporation of information technology. A thorough exploration of security prerequisites, accounts of severe cyber-attacks, and a strategic methodology to detect and counteract these threats are elucidated. The paper wraps up by spotlighting future research trajectories, especially in forging a comprehensive framework for resilience and addressing challenges tied to multi-modal cyber/physical attacks and big data concerns.
Solar Energy Forecasting Models for Grid Integration and Power Balancing
Authors: B Sivadharshini , Saxena Archana , Saritha G. , Alawady Ahmad , Lall Sweta , Dhabliya Dharmesh
Published: June 2024

This paper offers a comprehensive review of the advancements in the domain of solar energy forecasting models, emphasizing their significance for grid integration and power balancing. The increasing inclusion of renewable resources in global energy portfolios underscores the urgency for precise forecasting of variable resources like solar energy. Solar generation technologies have witnessed remarkable growth, leading to heightened grid penetration rates. However, the ground-level solar resource is characterized by high variability, primarily influenced by factors such as cloud cover changes, atmospheric aerosol levels, and certain atmospheric gases. This variability, especially at elevated grid penetration levels, introduces challenges related to reserve costs, dispatchable generation, and overall grid reliability. To address these challenges, there’s a pressing need for forecast systems with high accuracy across multiple time horizons, catering to regulation, dispatching, scheduling, and unit commitment. Furthermore, the variability of renewable energy stands as a significant barrier to its broader adoption. Energy storage emerges as a potential solution to mitigate power imbalances arising from the disparity between available renewable power and load demands. Through an analytical model, this review explores the potential of storage in reducing power imbalances and the requisite storage capacity to achieve this balance. The paper delves into the theory behind these forecasting methodologies and highlights successful applications in solar forecasting for utility-scale solar plants.
Improving Renewable Energy Operations in Smart Grids through Machine Learning
Authors: Muralidharan P. , Subramani K. , Habelalmateen Mohammed I. , Pant Rajesh , Mishra Aishwarya , Ikhar Sharayu
Published: June 2024

This paper reviews the work in the areas of machine learning’s role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future.
Strategic Grid Integration of Renewable Using Electric Vehicles for Smart Cities Stability
Authors: Gill Amandeep , T Ramachandran , Upadhyay Ramakant , Agarwal Gautam Ankita
Published: June 2024

This review article delves into the strategic integration of renewable energy sources within smart cities, emphasizing the role of electric vehicles (EVs) in ensuring stability. Smart cities, at the forefront of sustainable urban development, prioritize energy systems that are both clean and renewable. The integration of various renewable energy resources, including solar, wind, geothermal, hydropower, ocean, and biofuels, into the energy systems of smart cities is explored, considering both technical and economic aspects. The rapid adoption of these technologies provides a foundation for a low-carbon economy, ensuring a cleaner and more sustainable urban environment. However, challenges persist, particularly in optimizing energy systems for renewable integration, stability, operational range, and cost-effectiveness. Additionally, the evolution of smart cities has brought forth advanced energy conservation systems, addressing energy management at both building and city levels. Innovative solutions, such as advanced infrastructure and energy trading strategies in distributed systems, are proposed. The role of EVs in the smart grid is also highlighted, addressing challenges posed by renewable energy’s intermittent output. The potential of EVs to counteract these fluctuations, especially under the vehicle-to-grid (V2G) framework, is discussed, offering insights into future research avenues for a harmonious and sustainable energy ecosystem in smart cities.
Solar Energy Forecast for Integration of Grid and Balancing Power Using Profound Learning
Authors: Shwetabh Kumar , Pathrotkar Nikita
Published: June 2024

The rapid and unexpected advancements in solar photovoltaic (PV) technology pose a future challenge for power sector experts responsible for managing the distribution of electricity, given the technology’s direct reliance on atmospheric and weather conditions. Therefore, the development of reliable predictive models for short-term solar PV generation forecasts becomes critically important to maintain a stable power supply and ensure seamless grid operations. With the evolution of deep learning and its intricate models, its application in this domain offers a more efficient means of achieving precise forecasts. As a result, the proposed system undergoes the following stages: a) Collecting data from the Sky Images and Photovoltaic Power Generation Dataset (SKIPDD) hosted on a GitHub repository, which contains one-minute intervals of 64x64 sky images and concurrent PV power generation data. b) Enhancing the PV input data through processes such as geometric correction, ortho rectification, pan sharpening, block adjustment, and histogram equalization. c) Extracting PV-related features from these images using an Autoencoder. d) forecasting using integration of CNNbased Bi-LSTM. Experimental evaluation states that the proposed system (ACNN-BiLSTM) outperforms better on various measures (accuracy:0.95, MSE:0.08, MAE: 0.02).
Efficient Power centric Resilience Maximization Model for Distribution System Using Adaptive Configuration
Authors: Shwetabh Kumar , Pathrotkar Nikita
Published: June 2024

The resilience is the most dominant factor in any disaster condition which has been approached by various techniques in literature. Some of the methods consider only the power availability and grid states for the resilience maximization. However, they suffer to achieve higher performance in voltage distribution. To solve this issue, an efficient Power Centric Resilience Maximization Model (PCRMM) is presented in this paper. The model monitors the incoming voltage and generates adaptive configuration for the distribution system by considering number of grids, their states, emergency units, essential units, service sectors and so on. By considering all these factors, the method computes the resilience support for different micro grids according to the available voltage. Also, the method computes the functional support for the cycle and based on that dynamic configurations are generated. According to the configuration, the distribution system would trigger the voltage supply to various micro grids to feed voltage to the selected units. The proposed method improves the performance of power distribution with least voltage loss.
Production of Green Hydrogen through Renewable Energy Sources based Microgrid
Authors: Sony Anubhav , Acharjya Kalyan , Sharma Kunal , N Beemkumar
Published: June 2024

The world is focusing on generation and utilization of green hydrogen which is generated from renewable energy power sources (REPS). In order to reduce global warming at many places, the green hydrogen can give a viable solution as well as can able to solve many problems. At the same time, hybrid wind-PV based standalone systems are becoming popular and it will be a better idea to include hydrogen energy into such hybrid standalone systems. Unfortunately both speed of wind as well as irradiance are randomly changing, hence, a battery bank unit (BBU) must be integrated into system for maintain power quality. Nevertheless, batteries necessitate extensive upkeep and are prone to selfdischarge, resulting in a gradual decline in storage capacity over time. Therefore, the storage of hydrogen could present a financially viable alternative to the utilization of batteries in high power range applications. Typically, an aqua electrolyzer (AE) has the ability to effortlessly transform water into hydrogen and oxygen by means of electricity. A rapid and optimal analysis is necessary in order to enhance the production of hydrogen from REPS in general. Hence, it is imperative to develop a new method of control that can enhance the quality of hydrogen production amidst unpredictable fluctuations in solar irradiance and wind speed. The incorporation of the whale optimization technique into the corresponding controllers of each converter ensures a consistent and reliable production of hydrogen, even in the face of sudden fluctuations in irradiance and wind speed. The Whale Optimization Algorithm (WOA) method is contrasted with the Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) in order to demonstrate the advantages of monitoring the system’s response in enhancing the production of hydrogen from hybrid REPS in a Microgrid. Results are included in this paper with the help of MATLAB Simulink.
Whale Optimization for MPPT of Single Stage Grid Integrated PV Plant with H∞ and TSK-Fuzzy Controllers
Authors: Tyagi Rajneesh Kumar , Singh Vaishali , Sharma Vishal , Kulhar Kuldeep Singh
Published: June 2024

Numerous techniques for maximum power point tracking (MPPT) are extensively employed to extract the utmost energy from Photovoltaic (PV) panels. The P&O method is a highly effective technique among those who perturb and observe. Nevertheless, in the presence of partial shading conditions, it becomes imperative to employ an optimization algorithm that can accurately pinpoint the global maximum location (GML) amidst the numerous local maximum locations. The Whale-inspired algorithm is a renowned optimization method for efficiently identifying GML in comparison to numerous other optimization algorithms. An inverter is required to interfacing between PV and grid in single stage grid connected system as it will also work as a MPPT device by controlling voltage corresponding to GML. In this situation, the utilization of a whale-inspired optimization technique can enhance energy harvesting through an appropriate inverter controller. The combination of TS-Fuzzy and H∞ controllers through hybridization can lead to rapid response times and increased system stability. Hence, this paper introduces a new controller designed for the inverter to function effectively in a single-stage grid-connected PV system. The proposed controller can also provide the grid with high-quality power. This work showcases comprehensive findings derived from Hardware-in-the-Loop (HIL) testing conducted using OPAL-RT technologies.
Mayfly Optimization Algorithm for MPPT of PV System under Partial Shading Conditions
Authors: Pratap Singh Akhilendra , Srivastava Manish , Soumya K. , Kulhar Kuldeep Singh
Published: June 2024

Maximum power point tracking (MPPT)methods are most popular to harvest maximum energy from renewable energy sources for generating electric power. Among many renewable energy sources, solar energy is a primary and mostly available on the earth. Generally photovoltaic (PV) panel are arranging in a proper sequence to produce required electric power. Many conventional MPPT algorithms are available on PV system but working on uniform irradiances. Partial shading is a common phenomenon on PV systems. During partial shading conditions, conventional MPPT algorithms will fail to exhibits its best performance due to occurrence of many local peak powers. Hence, an efficient optimization method must be incorporated to identify global maximum power point among all possible points. Mayfly optimization is one of the best in existing optimization methods. However, perturb and observe (P&O) method must be incorporate with Mayfly technique to produce the best performance under both uniform irradiances and partial shading condition. A boost converter is used as a MPPT device in this paper due to its merits over other converters. Extensive results are carried out on OPAL-RT platform by establishing Hardware – in the – Loop (HIL) to validate the proposed method.
MPPT of PV System under Partial Shading Conditions by Novel Control with Modified Grey Wolf Optimization Technique
Authors: Singh B.P. , Meena Mansingh , S S Neetha , Kulhar Kuldeep Singh
Published: June 2024

Solar energy is a primary source on earth and photovoltaic (PV) cells are using to produce electricity through solar energy. Due to nonlinear behavior of power vs voltage curves of PV system, a maximum power point tracking (MPPT) circuits must be employed to harvest maximum energy. Among many MPPT techniques, a Perturbed and Observed (P&O) algorithm is one of the best methods. Generally partial shading conditions (PSCs) are commonly appearing on the PV systems. Under these PSCs, the P-V curve exhibits multiple peaks where one can be a global maximum and other are local maximum points. The global maximum location needs to be identifying to harvest more energy during PSCs. Hence, modified grey wolf optimization method is integrated with P&O algorithm to make a hybrid control method of a boost converter to operate PV system at its best utilization point. Hardware – in the – Loop (HIL) is designed with the help of two OPAL-RT modules to present various results under different operating conditions.
Hybrid GWO-ANN Associated SMC based Boost Converter for MPPT of PV System under Partial Shading Conditions
Authors: Gupta Rakhi , Singh Vaishali , Kumar Pandey Vijay , Singh Kulhar Kuldeep
Published: June 2024

Power generation from Photovoltaic (PV) modules is the best way to utilize the renewable energy in many places. The output of the PV modules will depends on many factors including changes in solar irradiance, temperature, partial shading conditions, operating voltage across terminals of module etc. Apart from operating voltage of PV modules, remaining all factors are depending on nature which we cannot change easily. Hence obtaining an effective voltage regulation of terminals to achieve maximum power from PV module is very important for best utilization. There are many existing maximum power point tracking (MPPT) mechanisms on PV systems but an effective integrating mechanism is required to achieve the best operation under all possible conditions. Artificial neural network (ANN) can able to produce best reference signal corresponding to maximum power location under various random changes very quickly. In other hand, sliding mode controller (SMC) is able to force the boost converter to deliver the best output voltage with fewer ripples under variable input voltage. Fastest response with fewer ripples in dc voltage can help to harvest maximum energy from PV panels. Therefore, integration of ANN with SMC can prepare the MPPT converter to deliver more energy under random changes in irradiance and temperature. The performance of ANN associated SMC for boost converter based MPPT of the PV system is presented in this paper. In order to obtain more realistic response, Hardware-in the-Loop (HIL) is established for proposed system with the help of two OPAL-RT modules. Extensive results by using HIL are presented and discussed under various case studies to enhance the proposed method.
Smart Shoe Electricity Generation via Piezo-electric Transducers
Authors: Towseef Ahmed S. , Swamy K.C.T. , Siva Reddy O. , Mohammed Saqlain M. , Mohammad Talha S. , Manohar B. , Prem Kumar Y.
Published: June 2024

Smart Shoe, also referred to as intelligent wearable footwear, has become increasingly popular. In recent times, the primary concern has been the challenges associated with generating electricity, largely stemming from the excessive use of existing resources. The need for power is growing more and more with each passing day. With the continuous advancements in technology and the utilization of sophisticated methods, wearable devices have been developed to generate electricity. Our research aims to produce renewable energy from the environment without any associated costs or negative impact on the environment. This objective can be accomplished through the utilization of Smart Shoes. The intelligent footwear has the capability to generate electricity, making it a highly effective solution. Our proposed system utilizes piezoelectric transducers to convert the mechanical foot pressure exerted by humans into electrical energy. This energy can then be harnessed to charge various devices that are commonly used in our daily commercial activities, such as smartphones. This type of technology is widely used for fall detections, posture monitoring, foot progression, angle monitoring, human activity recognition and these are all rely on the intellectual ability of shoe. Wearing a ‘Smart Shoe’ represents a minor advancement towards a more intelligent future.
Designing a Renewable Energy System for Industrial IoT with Artificial Intelligence
Authors: Menaka C. , Awasthi Aishwary , Chandra Yadav Dhyan , Jain Sandeep Kumar
Published: June 2024

This paper reviews the integration of renewable energy systems with Industrial IoT (IIoT) through Artificial Intelligence (AI). It examines various studies focusing on the design and monitoring of solar-powered wireless sensor nodes in diverse IIoT settings, particularly outdoors. A proposed distributed network architecture, underpinned by open-source technologies, aims for efficient solar power harvesting and data acquisition on solar radiation and ambient parameters. This data aids in devising estimation techniques to predict solar panel voltage outputs, optimizing energy utilisation of solar-powered sensor nodes. The discourse extends to photovoltaic plants, emphasising continuous monitoring and fault detection for operational safety and reliability. Reviewed works advocate embedding AI and IoT for remote sensing, fault detection, and diagnosis, addressing challenges posed by undetectable faults. Furthermore, the paper explores AI’s transformative potential in the broader energy sector, impacting electricity production, distribution, energy storage, and efficiency. The synergy of AI, IIoT, and renewable energy systems is underscored as a conduit for enhancing energy management, operational transparency, and deploying cost-effective solutions for complex industrial challenges, significantly bolstering the efficiency and intelligence of industrial production and services.
Advancing Agriculture in Smart Cities: Renewable Energy and Artificial Intelligence-Powered IoT
Authors: Sikka Rishi , Pal Singh Daljeet , Sharma Mohit Kumar , Ojha Ananta
Published: June 2024

This paper reviews advancements in vertical farming and smart agriculture technologies, focusing on the integration of Artificial Intelligence (AI) and Internet of Things (IoT) in urban agricultural practices. Vertical farming, propelled by AI and IoT, addresses land and water scarcity in urban settings, offering a sustainable solution by utilizing vertical spaces for cultivation. Smart AgroTech systems in urban farming exemplify the convergence of IoT and renewable energy resources, facilitating efficient farm operations. These systems automate irrigation based on real-time data, providing remote monitoring and control to farmers, thus reducing error margins in farming operations. Furthermore, AI-powered robots and drones are revolutionizing traditional farming landscapes by automating crucial tasks like weeding, spraying, and crop monitoring, conserving resources and augmenting overall productivity and quality of produce. The reviewed studies unveil a shift towards datadriven, automated, and sustainable farming practices crucial for food security in growing urban populations, laying a foundation for future explorations in this interdisciplinary domain.
Nuclear Fusion Reactors: Challenges and Potential as a Future Energy Source
Authors: Ramesh Kumar S. , Renuka G. , Bijlwan Sheela , Abbas Ahmed H.R. , Sharma Smriti , Waghulde K.B.
Published: June 2024

This review article delves into the promising yet challenging realm of nuclear fusion reactors as a potential future energy source. The paper provides a comprehensive overview of global fusion research, highlighting its potential benefits and the technical obstacles that have hindered its widespread commercial adoption. Fusion energy, with its abundant resources, minimal waste generation, and low emissions, emerges as a long-term solution for a sustainable energy future. However, its technical complexities suggest that its widespread commercialization may not be realized until the end of the century. The article further explores the environmental compatibility, safety, and resource implications of fusion energy. A significant emphasis is placed on the paramount importance of safety in the development of fusion power reactors. The review underscores the need for robust safety cases, accident identification methods, and the establishment of internationally recognized safety standards. Additionally, the paper identifies knowledge gaps and areas necessitating further research, ensuring that fusion power stations meet rigorous safety objectives while minimizing environmental impact. Through a holistic examination of fusion’s potential and challenges, this review offers insights into its role in shaping the future energy landscape.
Legal Framework for Energy Transition: Balancing Innovation and Regulation
Authors: Bisht Yashwant Singh , Alzubaidi Laith H. , Gulbakhor Uralova , Yuvaraj S. , Saravanan T. , Senthil Kumar R. , Singh Dari Sukhvinder
Published: June 2024

The global energy landscape is undergoing a significant transformation, driven by the dual imperatives of innovation and regulation. This paper reviews the work in the areas of sustainable energy transitions, focusing on the intricate balance between demand-side innovations and governance, as seen in Germany’s energy transition journey. The study further delves into the European context, highlighting the emergence of Renewable Energy Communities (RECs) and the challenges and opportunities they present in achieving a just and democratic energy transition. The paper also introduces the World Economic Forum’s Energy Transitions Index (ETI), a comprehensive tool that tracks the performance of energy systems at the country level, offering insights into the macroeconomic, institutional, social, and geopolitical considerations that shape energy transitions globally. Collectively, these areas of study underscore the importance of a holistic approach to energy transition, one that harmonizes technological advancements with policy frameworks and societal needs.
Innovative Approaches to Harvesting and Storing Renewable Energy from Ambient Sources
Authors: Umamaheswari B. , Santhosh S. , Ulugbek Absoatov , Singh Rajesh , Abbas Ahmed H.R. , Sherje Nitin P.
Published: June 2024

This paper reviews innovative methodologies in the realm of renewable energy harvesting and storage from ambient sources. One focal area is the untapped potential of water wave energy, a globally distributed renewable energy source. Current technologies, primarily reliant on electromagnetic generators, face challenges, especially in irregular environments and at low frequencies. However, the advent of the triboelectric nanogenerator (TENG) offers a promising solution, especially for low-frequency water wave motions. TENG technology not only presents a new avenue for large-scale blue energy harvesting but also signifies a shift towards the era of the Internet of Things, where energy is derived from various sources including human motion and vibrations. Another domain delves into the history and evolution of energy harvesting, tracing back to traditional methods like water wheels and windmills. The modern era witnesses a resurgence in interest due to advancements in lowpower electronics, wireless standards, and miniaturization. Lastly, wearable biosensors, poised to redefine personalized healthcare and telemedicine, necessitate innovative power solutions. Wearable energy harvesters, capable of converting ambient energy sources into electricity, are emerging as pivotal components in self-powered wearable sensors, paving the way for real-time health monitoring and human-machine interfaces.
Traitement du diabète sucré : à part l’insuline ?
Authors: Remmel Paul , Kurtz Maxime
Published: June 2024

L’insulinothérapie ne constitue pas le seul pilier de la prise en charge du diabète sucré chez le chien ou le chat. L’alimentation, entre autres, participe grandement au contrôle de la glycémie. Une restriction en glucide améliore le contrôle de la glycémie et les chances de rémission. Malgré des prises en charge exhaustives et multimodales, certains individus continuent d’avoir un mauvais contrôle de leur diabète. Dans ces cas, une comorbidité doit être recherchée et l’obésité doit être prise en compte comme cause d’insulinorésistance secondaire à la glucotoxicité. Enfin, à l’inverse du chien, lors de diabète sucré chez le chat, une synthèse d’insuline endogène persiste souvent et permet l’utilisation d’exénatide afin de limiter la variabilité glycémique ou d’hypoglycémiants oraux lorsque l’observance du traitement injectable est mauvaise. Les hypoglycémiants oraux historiques ont une efficacité moindre, mais une nouvelle classe thérapeutique (les inhibiteurs du cotransport sodium-glucose de type 2) semble prometteuse.
La parasitologie, l’enseignement et l’écriture : trois de ses passions
Authors: Barbaray Maryvonne
Published: June 2024

Le Professeur Gilles Bourdoiseau nous a quittés le 18 mars dernier, à l’âge de 71 ans, victime d’une tumeur cérébrale dévastatrice.
Vaccine-associated axillary lymphadenopathy with a focus on COVID-19 vaccines
Authors: Wilpert Caroline , Wenkel Evelyn , Baltzer Pascal Andreas Thomas , Fallenberg Eva Maria , Preibsch Heike , Sauer Stephanie Tina , Siegmann-Luz Katja , Weigel Stefanie , Wunderlich Petra , Wessling Daniel
Published: June 2024

Axillary lymphadenopathy (LA) after COVID-19 vaccination is now known to be a common side effect. In these cases, malignancy cannot always be excluded on the basis of morphological imaging criteria.

Narrative review for decision-making regarding control and follow-up intervals for axillary LA according to currently published research. This article provides a practical overview of the management of vaccine-associated LA using image examples and a flowchart and provides recommendations for follow-up intervals. A particular focus is on patients presenting for diagnostic breast imaging. The diagnostic criteria for pathological lymph nodes (LN) are explained.

Axillary LA is a common adverse effect after COVID-19 vaccination (0.3–53%). The average duration of LA is more than 100 days. LA is also known to occur after other vaccinations, such as the seasonal influenza vaccine. Systematic studies on this topic are missing. Other causes of LA after vaccination (infections, autoimmune diseases, malignancies) should be considered for the differential diagnosis. If the LA persists for more than 3 months after COVID-19 vaccination, a primarily sonographic follow-up examination is recommended after another 3 months. A minimally invasive biopsy of the LA is recommended if a clinically suspicious LN persists or progresses. In the case of histologically confirmed breast cancer, a core biopsy without a follow-up interval is recommended regardless of the vaccination, as treatment appropriate to the stage should not be influenced by follow-up intervals. For follow-up after breast cancer, the procedure depends on the duration of the LA and the woman’s individual risk of recurrence.

Vaccination history should be well documented and taken into account when evaluating suspicious LN. Biopsy of abnormal, persistent, or progressive LNs is recommended. Preoperative staging of breast cancer should not be delayed by follow-up. The risk of false-positive findings is accepted, and the suspicious LNs are histologically examined in a minimally invasive procedure.
Ruthenium Chloride Catalyzed One-Pot Synthesis of Aldehydes Directly from Benzylic or Primary Alkyl Halides in Aqueous Medium
Authors: Muthaiah Senthilkumar , Kiran Kiran
Published: June 2024

We report the first example of an acid- and oxidant-free one-pot conversion of benzylic or primary alkyl halides into aldehydes by using simple ruthenium chloride as the catalyst. The developed synthetic strategy is pot-economical and is also cheap as it uses hexamethylenetetramine as a reagent, employs as little as 0.5 mol% of ruthenium chloride, and efficiently converts the benzylic or primary alkyl halides into aldehydes in aqueous medium. The methodology was also found to be highly selective, as it forms the aldehyde product exclusively without forming possible byproducts, namely amines or carboxylic acids. The methodology is also superior in comparison with the conventional Sommelet and Kornblum oxidation reactions as it avoids the use of excess acid or DMSO, and uses very cheap and ecofriendly hexamethylenetetramine as both the formylating agent and base. The recyclability of the developed catalyst system was also tested, and showed excellent activities for up to three cycles.
Exploring the resilience development of secondary school English teacher-researchers: a Chinese case study
Authors: Gu Haibo , Wang Siyi , Chen Xiaofeng , Wang Qian
Published: June 2024

Teacher education research has recognised the importance of teacher resilience. However, the development of language teacher-researchers resilience remains in its early stages. To address this gap, this study adopted a qualitative approach to explore the resilience development of two Chinese secondary school English teacher-researchers and its contributing factors. Data from interviews and case documents indicated that teacher-researchers resilience was dynamic and developed over time. Two different patterns of resilience development were identified: low-to-high and W-shaped recovery. Furthermore, individual and environmental factors and their interactions jointly contributed to the resilience development of the two secondary school English teacher-researchers. Their participation in classroom research provided them with diverse ways to sustain their resilience. The implications for teacher development are offered to teachers in China and other similar contexts.
Symplectic and Lagrangian polar duality; applications to quantum harmonic analysis
Authors: de Gosson Maurice , de Gosson Charlyne
Published: June 2024

Polar duality is a well-known concept from convex geometry and analysis. In the present paper we study a symplectically covariant versions of polar duality, having in mind their applications to quantum harmonic analysis. It makes use of the standard symplectic form on phase space and allows a precise study of the covariance matrix of a density operator.