The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO- λ ) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller's performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

A DC microgrid with renewable energy sources can achieve reduced current ripple, higher efficiency, faster dynamics, high voltage gain, and less operational stress by interfacing with an interleaved boost converter (IBC). The stability of an IBC linked to a DC microgrid supplying a constant power load (CPL) can be imperceptibly guaranteed by a conventional controller. A tightly regulated CPL with nonlinear and negative incremental impedance characteristics will lead to stability issues. Uncertainties such as load and line variations will further affect the stability of the system. A nonlinear passivity-based control algorithm requires more attention than a traditional controller to achieve the stability of power converters. This article explains the Brayton-Moser (BM) passivity-based controller (PBC) for a 2-level interleaved boost converter (IBC) interfaced DC microgrid with CPL. The suggested controller can achieve high signal stability by injecting a series-connected virtual impedance. The stability of the proposed controller has been assessed using the Lyapunov stability approach. A BM passivity-based controller for a 2-level IBC with CPL has been derived and investigated under various operating modes using MATLAB and Simulink. It was also observed that the proposed system achieves at least 2 % improvement in efficiency and 50 % reduction in current ripple. To evaluate the performance of BM Passivity-based controller, a comparative analysis was performed between the suggested controller and the traditional PI controller, which is also included in this paper.

Surging clean energy demand clashes with grid stability concerns, calling for creative solutions. This work explores the synergy between peer-to-peer (P2P) energy trading and grid-connected wind-solar systems. It is apparent that, in the contemporary grid scenario, renewable power injection involving integrated wind-solar power generation is proliferating, and hence grid stability and excess energy utilization become critical challenges. P2P trading emerges as a promising remedy, allowing prosumers (consumers who also produce) to directly exchange surplus energy, fostering a more decentralized and efficient energy system. Leveraging MATLAB simulations, the project assesses the technical feasibility of such systems. It then designs and implements a P2P energy transfer system with a user-friendly dashboard, empowering prosumers to actively participate and optimize their energy usage. By analyzing benefits for both prosumers and the grid, the project showcases the significant potential of P2P trading in paving the way for a sustainable and equitable energy future.

Electromyography study focuses on the classification of electromyography (EMG) signals using machine learning (ML) techniques. The classification of EMG signals with ML techniques improves the response and accuracy of myo-electric prosthetic hands and rehabilitation systems. One of the key techniques to get reliable surface EMG signals is to employ multiple sensors. Integrating multiple sensors in myoelectric hands can inflate the cost of prosthetic hands. To study the effectiveness of implementing a single sensor combined with machine learning algorithms to accurately classify the EMG signals, an experiment was conducted and reported in the paper. EMG signals were collected from five volunteers to create a comprehensive dataset. Various machine learning algorithms were then applied to classify the EMG data effectively. The proposed approach achieved an accuracy of over 80%, highlighting the potential of using single-channel EMG sensors combined with machine learning for accurate signal classification. This approach could be beneficial for various applications, including prosthetic control and rehabilitation devices.

Electrocardiogram (ECG) is one among the most common detecting techniques in the analysis and detection of cardiac arrhythmia adopted due to its cost efficiency and simplicity. In a clinical routine, ECG database is collected on daily basis and these databases are reviewed manually. Along with other conventional methods, various approaches using machine learning has been proposed in the past few years. But these would require in-depth knowledge on several parameters and pre-processing techniques in the specific domain. This study is aimed at implementing a more reliable deep learning model that has the capacity to diagnose arrhythmia from a database with 109,446 samples in 5 different categories. In our proposed work, we have used deep learning methodologies for the diagnosis and detection of cardiac arrhythmia automatically. Balancing the biasedness in the waveforms from MIT-BIH arrhythmia database, model is developed. MIT-BIH arrhythmia database with the ECG waveforms promises good accuracy. This automated prediction of the disease using CNN and ResNet-18 architectures are compared in terms of accuracy. CNN has accuracy approximately 97.86% and 98.14% for improved ResNet-18. Also, a comparative analysis is done with the proposed model and already existing techniques. Several limitations and future opportunities are also reviewed. We believe it can be used considerably for cardiac arrhythmia prediction worldwide. Based on the results obtained, ResNet-18 architecture can be used as an efficient procedure, that reduces the burden of training a deep convolutional neural network from start, resulting in a technique that is simple to use.

Prosthetic arms are an effective way to enhance the quality of life for patients with upper-limb amputations. Though there are plenty of prosthetic arms commercially available on the market, functional prosthetic hands are still unaffordable for many due to complex control and design costs. In order to tackle this challenge, this paper explores the use of a swappable end-effector based prosthetic forearm with a machine learning-based advanced control unit. In this paper, two different end-effectors, such as a simple four-bar linkage based gripper and an anthropomorphic hand, have been implemented to test the prosthetic hand functionality using quadratic SVM based classification and a dual-stage control unit. The control unit of the proposed system consists of two levels of controls: first raspberry pi-based EMG data processing and classification, and then an end-effector microcontroller for the end-effector control, which helps in choosing two different types of end-effectors for the user. The design and dataset details are explained in this paper.

World’s population is rapidly expanding, and so is the need for an uninterrupted power supply. Along with this, there is also a rapid rise in tech-savvy initiatives and industrialization. All these lead to an increase in the carbon footprint, thereby resulting in an increasingly polluted world. Most governments, regardless of their politics, are pledging to reduce their carbon emissions and shift to greener energy resources. Integrating renewable energy (RE) into the current energy system is a key step in that direction, but the existing energy infrastructure is incapable of incorporating RE sources and catering to the rising demands without technological interventions. Electricity obtained through RE is very volatile and subject to environmental conditions. In such a scenario, the RE alone would not be able to support the grid at peak demand times. Grid would need additional support in the form of demand response (DR). DR incentivizes consumers to reduce their electricity usage during peak demand, but calculating the real-time data of all the consumers and rewarding them becomes a hassle without smart, secure, and reliable communication and storage systems. Hence, to enhance participant trust and automate the entire DR process, blockchain and smart contracts (SC) are viable solutions due to their transparency and immutability. This paper works on a SC-based DR programme to incentivize users based on their choices using an ethereum test network (testnet).

Due to the benefits of metaheuristic optimization techniques, in this paper, we introduced flower pollination optimization algorithms for finding optimal bands in airborne hyperspectral images and classification using a modified wavelet Gabor filter (MGFNet) convolutional neural network. The proposed flower pollination optimization algorithm has been investigated to select optimal bands from hyperspectral images with deep wavelet features, which are nonlinear, discriminant, and invariant. These bands are effective for hyperspectral image classification, object detection. Moreover, in demand to maintain the widespread issue of imbalanced samples for the classification of Hyperspectral image, we have selected only optimized bands rather than whole bands of hyperspectral images. More importantly, we proposed a modified Gabor based wavelet filter which helps to extract exact information from the spatial and spectral features of hyperspectral imagery. The proposed approaches are carried out on three hyperspectral datasets: Indian pines, Pavia University, and Salinas scene hyperspectral images. In addition, the proposed FPO and MGFNet open up a new frame for further research.

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6-18%), and evaporation rates (1.2-11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 mg / m 3 in mid-level inflow regions (3.6 m water elevation) compared to 86 mg / m 3 in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model's accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management.

Hyperspectral (HSI) data provide vast amounts of data which contain rich spatial and spectral information about each pixel of an image, such information is useful in detecting objects and identifying materials. However, the information provided by the HSI image is challenging to process. Hence, the compression of HSI makes it easier to utilize the data without the need for any heavy computation. Traditional compressive sensing techniques are adequate for lower dimensional signals, but they fail to preserve the essential spectral information provided by HSIs while performing compression. The proposed method aims to implement a Multilinear Compressive Learning (MCL) model for ideal compression of HSIs and a CNN based U-NET architecture is used for effective reconstruction of the compressed signal. The core of the algorithm involves two key blocks: (a) A multidimensional compressive sensing block for performing compression along every dimension, preserving spectral and spatial correlations. The employed sensing matrix is learned with respect to the given data for effective compression, (b) Reconstruction of the signal using a CNN based U-NET algorithm, which consists of encoders and decoders for effective reconstruction of the signal. Band selection is also performed in this technique to optimally select the most informative bands. The proposed model was implemented on the Indian Pines, Pavia University, and Salinas scenes datasets. The compression and reconstruction results were evaluated using performance metrics.

The renewable energy sources become a larger part of the power grid, managing their intermittent nature is of utmost significance. Blockchain technology provides secure and transparent path for energy trading to individuals directly with each other (peer-to-peer) in these systems. Proposed work gives a framework to monitor blockchain-based energy trading, where it tracks the parameters like how much energy is being generated and used, along with the details of trades recorded on the blockchain. It also monitors how well the automated trading agreements (smart contracts) are working and how fast the entire system is running. By keeping an eye on these aspects, the framework can help ensure this new way of trading energy with renewables is secure, transparent, and efficient.

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

Beneath the surface lies a huge challenge and opportunity, the underground environment of coal mines. This subterranean world, where human endeavor meets the rawness of nature, is a melting pot of industry, technology, and resource extraction. Coal mining is an industry deeply rooted in history, dating back centuries to the earliest human civilizations. It represents an effort to use the Earth’s ancient energy reserves, stored as carbon-rich coal, for heating, industrial processes, and power generation. Mining in the underground environment presents a unique set of challenges and risks that require constant vigilance and innovative safety measures. This article proposes a continuous, seamless monitoring system that transmits the underground mine data to the surface station which requires advanced arrangements to emerge from the impediments of routine hardware, which falls flat to supply real-time knowledge. Next, attention is drawn to a cutting-edge system fitted with an ESP32 microcontroller, temperature, CO, methane, and airflow sensors, and LoRa for data transfer. Thresholds and buzzers have been set for alarming, which alerts workers at the right moment. A novel TinyML-based anomaly detection system that integrates with an Arduino cloud and MQTT broker. By implementing cutting-edge techniques and imposing historical constraints, this revolutionary strategy ultimately increases the safety of under- ground mines where there will be a significant improvement in safety monitoring technology.

Smart grids (SGs) are technology-powered electricity networks that support bidirectional power and data flows. This allows real-time monitoring of demand and enhances the grid’s capability to dynamically adjust the generation and reduce the gap between supply and demand. However, implementing a smart grid in the power network comes with its own set of security challenges, such as cyber-security, distrust in participants, and lack of customer engagement due to various cyber-attacks. Such cyber-attacks will create distrust among consumers/prosumers to adopt the smart grid and distributed energy resources (DER) framework. To circumvent this, a blockchain-supported hybrid authentication and handshake algorithm (BSHAHA) for smart grids is proposed in this work, which authenticates data communication between peer-to-peer, aggregators, virtual power plants, and the grid. The algorithm was developed incorporating elliptic-curve cryptography (ECC) and advanced encryption standards (AES) to enhance privacy and session security. The proposed algorithms are verified and tested using formal cyber-security tools, such as the Random oracle model and AVISPA as well as by informal security analysis. Furthermore, to simulate a real-time test environment, this paper utilized ns-3 network simulator to simulate different smart meter scenarios, and the proposed algorithms are tested for power consumption and scalability, and results are presented. Moreover, blockchain simulation was first done in the local blockchain using Ganache and Truffle IDE and later using the Holesky Ethereum test network and remix-IDE. Lastly, this paper presented a comparison analysis of power consumption for different consensus mechanisms.

Boost converters are essential for microgrid systems, facilitating the systematic integration of renewable energy systems and also ensuring efficient energy conversion and reducing voltage mismatches that may occur due to faults, leading to instability and issues in power quality. Closed loop Proportionalintegral (PI) control of the boost converter is crucial to moderate the terminal output voltage of the boost converter according to the application. The main aim of this manuscript is to optimize these PI values to have a more efficient boost converter operation for microgrids. Optimization techniques like elephant herding optimization (EHO) and grey wolf optimization (GWO) have been investigated to obtain a better value of the PI controller for boost converter control and compared with PI controller values obtained by traditional auto tuning. The step response analysis to compare the rise times, peak times, and peak values is performed to have a better comparison between these techniques. Rise time and peak time are approximately 10% and 8% less in the case of GWO compared to EHO, much higher in the case of auto tuning. Also, a comparison regarding different error indices like integral absolute error (IAE), integral of squared error (ISE), and integral time absolute error (ITAE) has been made among these optimization techniques along with the traditional autotuning method through simulation, which will pave the way to choosing a better optimization technique to have an efficient PI controller value and, in turn, an efficient operation of the boost converter for microgrid applications.

Now days the impact of climate change in India are mostly affected to the agriculture field, most of the agricultural crops are being badly affected in terms of their performance of the crop. The crop yield is used to predict in advance because of its harvest, its help the farmers for taking good decisions in marketing. The results of knowing crop yield and building prototype for the purpose of easily available to the farmer. Thus, for such kind of data analysis in the crop, there are different techniques and algorithms to predict the crop in a field. In this we have used image processing algorithm. The data used are images are taken from the drone system. Our approach starts by seeing rows inside the area (or acre) and finding all the trees in a crop yield, by this knowing the size of plant in the crop can be easily to store the data of plants. Lately there has been a wide source of spatial photogrammetry available for agriculture. Most of this data gives new information about the crops. By analysis of crop statics and methods have not been widely performed due to limitations on the images and software involved. The classification was also validated by comparing dozens of trees in the field, with excellent results. The purpose of this paper is to provide farmers with a method for quickly and automatically classifying trees by size, regardless of the presence or absence of problems, so that they can more easily monitor the health of individual trees and gain a better grasp on how those trees are distributed across a given area.

Renewable sources of energy and microgrids are advancing rapidly. Among the renewable sources of energy, solar is the most effective and widely amended. Standalone solar Photovoltaic (PV) systems generate very low DC voltage and require DC-DC converters powered by controllers to expediate their ideal characteristics. This paper aims to compare the fractional order proportional integral (FOPI) and proportional integral (PI) controllers using the particle swarm optimization (PSO) technique. From the frequency and time responses obtained, it has been found that the FOPI controller has the least rise time of 0.0231 s, while the highest was found to be $F O(P I)^{n}$, with a rise time of 0.0343 s.

With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system. Here, we propose a novel hybrid methodology for wind speed and solar irradiance forecasting. The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. By integrating a non-linear convergence parameter and the dimension learning hunting approach, the performance of WOA can be drastically enhanced, leading to premature convergence, enhanced population variety, and effective global search. The final prediction outcome is obtained by summing the individual reconstructed sub-series prediction outcomes. To evaluate its efficacy, the proposed model is compared to five well-established models. The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and stability, thus confirming that a hybrid forecasting model approach combining an efficient decomposition method with a simplified but efficient parameter-optimized neural network can enhance its accuracy and stability.

Generating Power through renewable sources of energy is an alternate prospective resolution in our day-to-day life. System combining of two or more energy sources are often called hybrid systems. In this paper Design and simulation of battery interface wind-solar hybrid system is implemented. This can be achieved by making use of different renewable energy sources such as wind turbines, solar PV Array, and batteries. here with different control algorithm Grid side Load power is maintained efficiently in spite of variation in level of irradiation, speed of wind, SOC level of battery, Temperature. This hybrid system implementation is done in MATLAB/Simulink for system have specification of solar voltage of 230VDC, Battery of 90V 60Ah, and wind voltage of 230VAC at 12m/s with Grid side AC load to maintain as 1kW.