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Advances and development of wind–solar hybrid renewable energy technologies for energy transition and sustainable future in IndiaWhile solar power projects are built on a continuous ground, wind power projects require scattered land, raising transmission costs and increasing the risk of land-related complications. Wind–solar hybrid (WSH) projects have been proposed to address these issues and accelerate installation. WSH power projects will create a well-defined area with sufficient infrastructure, including evacuation facilities, where the project’s risks can be reduced. The extensive coastline of India is endowed with high wind flow speed and plentiful solar power resources, creating an ideal environment for WSH projects to prosper while simultaneously improving grid stability and reliability. WSH plants guarantee higher transmission efficiency and cost-effectiveness than their stand-alone counterparts. As of 30.11.2021, 3.75 GW of WSH projects have been granted, with 0.148 GW of operational capacity and 1.7 GW of WSH projects in various bidding phases. In this paper, we discussed state-wise WSH potential, the key players in the WSH project, the National WSH, and the State WSH policy and amendments. Also, the WSH project’s physical progress and commercial details are covered. A feasibility study of the WSH plant is performed, and the primary design strategy for deploying WSH power facilities in India is discussed. It covers every step of this process, from design technique to choosing and evaluating potential locations for such hybrid projects, optimally placing wind turbines and solar panels, overall capacity mix for hybrid plants, and ultimately power evacuation optimization. Additionally, a brief study of the savings from these hybrid plants and the environmental, social, and governance standards which are necessary to implement these projects are provided. The potential challenges connected with WSH technologies are examined in depth, and potential solutions and mitigations for the challenges are provided. Designing a WSH for small-scale irrigation is provided along with the size and choice of wind and solar systems. Degradation of PV systems and carbon savings are included, along with some policy measures to boost the proportion of WSH in the entire power mix. In India, the development of large-scale WSH projects is still in its early stages, and more research is required to explore technical, commercial, and policy elements that influence project design. The policy suggestions for improvement of the WSH project are provided. The WSH project developers, potential investors, stakeholders, innovators, policymakers, manufacturers, designers, and researchers will benefit from the recommendations based on the review’s findings.
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Advances in electric vehicles for a self-reliant energy ecosystem and powering a sustainable future in IndiaElectric vehicles (EVs) are essential for solving various mobility, environmental sustainability, and energy security issues. They help reduce greenhouse gas emissions, improve air quality, and promote economic development by creating jobs and developing novel innovations. Despite the numerous benefits of EVs, several difficulties persist, including range anxiety, limited infrastructure for charging, and high starting prices. However, continued breakthroughs in battery innovation, charging infrastructure development, and supporting government regulations steadily reduce these obstacles and increase global EV adoption. Significant EV adoption will necessitate ongoing coordination among governments, stakeholders in the industry, and communities to meet infrastructure requirements, incentivize consumers, and encourage sustainable mobility behaviors. The full potential of EVs can be realized to build a more environmentally friendly, healthier, and more sustainable future for future generations by encouraging innovation, investing in facilities, and raising the public's consciousness. This research aims to analyze current technological advancements in EVs, problems, opportunities, and potential solutions that can serve as the foundation for effective strategies and assist policymakers in formulating plans for target adaptation and achievement in India.
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Enabling Trust in Automotive IoT: Lightweight Mutual Authentication Scheme for Electronic Connected Devices in Internet of ThingsThe emergence of smart and embedded devices and the adaptation of new technologies with the Internet of Things have entirely changed online shopping with a new ecommerce paradigm. However, while the accessibility of IoT services has many advantages, it also creates hazards to customerrelated data. Due to pervasive e-commerce services, anyone can intercept and be compromised, creating great security and privacy concerns. To address these security challenges and to provide lightweight authentication for all entities, we introduce a “Lightweight Mutual Authentication (LMA) Scheme for Connected Devices in IoT”. The proposed scheme uses automatic and mutual authentication for all entities, employing a distributed approach within a server-based architecture. It is lightweight because it provides a secure way of using e-services with fewer steps, and it is automotive because the entities automatically authenticate each other. The LMA is formally validated in TCL, and the experimental results show that it decreases computation cost by about 56%, increases throughput by about 33.3%, and communication cost remains the same as the average of the other three schemes. In evaluation, the results demonstrate that the presence of the LMA leads to a 20-millisecond increase in delay and a 2% decrease in PDR for 100 devices.
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Appliances Load Pattern Reconstruction from Adaptive Delta-Driven Sampled Smart Meter DataIn recent days the interest in the usage of smart meters is raising. It is evident from the widespread use of smart meters in contemporary society. The stakeholders in the smart grid must profit from the gathering and processing of fine-grained metering data. Time invariance characterizes the classical data sampling method. As a result, a significant volume of unnecessary data is gathered, sent, and analyzed. A method of adaptive delta-driven sampling (ADDS) of the smart meter data is proposed. It compensates the aforementioned shortfall and can lead towards a significant real-time compression without losing pertinent information. Subsequently, the compressed form of data can be efficiently processed, analyzed, stored and transmitted. It promises a significant transmission and computational effectiveness with a diminished latency. It is shown that the devised form of compressed data can be effectively reconstructed using a low complexity reconstruction algorithm. The reconstruction error is measured in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The applicability is tested using the power consumption patterns of coffee machines, computer stations, fridges and freezers. The proposed solution attains an overall compression gain of 1.84-times, 2.49-times, 7.55-times respectively for the coffee machine, computer stations, and fridges and freezers. Moreover, the obtained values of RMSE and MAE confirm an appropriate reconstruction using the devised method.
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Electrooculogram Compression based on Wavelet Packet DecompositionThe electrooculogram (EOG) represents the electrical activity of the eye. Since most EOG-based applications need a large amount of data to be stored and transmitted, compression is required. A study on the use of discrete wavelet packet decomposition (DWPD) for compressing EOG signals is presented in this paper. The EOG signals are recorded using a commercial bioamplifier. The compression algorithm is implemented using the MATLAB software. The performance of the compression was evaluated using three parameters: compression ratio (CR), energy retention, and percent root-mean-square difference (PRD). The experimental results show that the DWPD allows for CRs of 82% while retaining almost 100% of the signal energy with a PRD of only 7%.
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Enhanced the Hosting Capacity of a Photovoltaic Solar System Through the Utilization of a Model Predictive ControllerThe global expansion of solar-powered within distribution networks with Low Voltage (LV) is experiencing substantial expansion. Despite the various advantages offered by solar photovoltaic generation, surpassing the constraints on Hosting Capacity (HC) within these networks persist a significant technical problem in system operation, especially in relation to voltage operation. This research delves into the effectiveness of improving the Hosting Capacity (HC) of a photovoltaic (PV) system within an LV distribution system. It utilizes a Model Predictive Controller (MPC) to achieve this enhancement and contrasts its performance with reactive power control. The study examines scenarios encompassing both linear and non-linear loads to assess the impact of these control strategies on the PV system’s harmonic current in the LV distribution network. Through detailed analysis, the MPC controller demonstrates superior adaptability and responsiveness, maintaining stable active power at 95.5 kW before accommodating a 100% PV system penetration and experiencing a substantial increase to 192 kW. The hosting capacity, thereby, sees a notable 101.05% improvement under MPC control. Additionally, the study reveals that MPC optimizes reactive power utilization, resulting in a 17.9% reduction in reactive power and an 18.3% enhancement in bus voltage compared to reactive power control. Notably, MPC exhibits superior adaptability to both linear and non-linear loads, emphasizing its potential as an effective solution for optimizing the performance of PV systems within LV distribution grids. This research underscores the significance of advanced control strategies in facilitating the integration of renewable energy systems while ensuring grid stability and reliability.
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Energy-efficient architecture for high-performance FIR adaptive filter using hybridizing CSDTCSE-CRABRA based distributed arithmetic design: Noise removal application in IoT-based WSNAn energy-efficient architecture of high-performance FIR adaptive filter design using approximate distributed arithmetic (DA), which is integrated with canonic signed digit-based triangular common sub expression elimination (CSDTCSE) and carry-resist adder based Booth recorder adder (CRABRA) is proposed for noise removal in sensor nodes. Distributed arithmetic is coupled with two signed 32-bit, 16-bit radix-8 Booth algorithms and approximate computation under 2-bit adder to design FIR adaptive filter for decreasing partial products (PP) together with accumulation circuits. The truncation of LSB in the PP is presented to approximate the PP to reduce memory complexity and hardware overhead. An approximation recoding adder decreases the energy usage, area, and critical path. Approximate Wallace trees are applied to the PP accumulation to lessen the latency. The canonic signed digit-based triangular common sub-expressions elimination framework is proposed, which significantly reduces a count of logic operators and logic depth in implementing the FIR filter. The proposed algorithm is activated in Verilog coding and synthesized using Xilinx 14.5 ISE simulation software. The proposed design successfully reduces delay, area, and power by maintaining better accuracy with performance.
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Physics‐based and data‐driven approaches for lifetime estimation under variable conditions: Application to organic light‐emitting diodesThe prognosis of organic light-emitting diodes (OLEDs) not only requires early detection of a bearing defect, but also the capability to predict their life data under all operational scenarios. The use of sophisticated machine learning (ML) algorithms is undoubtedly becoming an increasingly exciting research direction, as these algorithms can yield high predictive models with minimal domain expertise. The central question of this perspective is: how well can ML models advance our ability to forecast the lifetime of OLEDs compared to the physics-based models? In this paper, data-driven methods, feed-forward neural networks (FFNN), support vector machines (SVMs), k-nearest neighbors (KNNs), partial least squares regression (PLSR), and decision trees (DTs), are used to predict the lifetime and reliability of OLEDs through analyzing the lumen degradation data collected from the accelerated lifetime test. The final predicted results indicate that both the data-driven and our physics-based OLED lifetime models fit well the experimental data. The main drawback of the former method is that their efficacy is highly contingent on the quantity and quality of the operational dataset. Among all these methods, much more reliability information (time to failure) and the highest prediction accuracy can be achieved by FFNN.The prognosis of organic light-emitting diodes (OLEDs) not only requires early detection of a bearing defect, but also the capability to predict their life data under all operational scenarios. The use of sophisticated machine learning (ML) algorithms is undoubtedly becoming an increasingly exciting research direction, as these algorithms can yield high predictive models with minimal domain expertise. The central question of this perspective is: how well can ML models advance our ability to forecast the lifetime of OLEDs compared to the physics-based models? In this paper, data-driven methods, feed-forward neural networks (FFNN), support vector machines (SVMs), k-nearest neighbors (KNNs), partial least squares regression (PLSR), and decision trees (DTs), are used to predict the lifetime and reliability of OLEDs through analyzing the lumen degradation data collected from the accelerated lifetime test. The final predicted results indicate that both the data-driven and our physics-based OLED lifetime models fit well the experimental data. The main drawback of the former method is that their efficacy is highly contingent on the quantity and quality of the operational dataset. Among all these methods, much more reliability information (time to failure) and the highest prediction accuracy can be achieved by FFNN.
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Model Predictive Control of Consensus-based Energy Management System for DC MicrogridThe increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1μsec and 5% overshoot.
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A Survey on Energy Storage: Techniques and ChallengesIntermittent renewable energy is becoming increasingly popular, as storing stationary and mobile energy remains a critical focus of attention. Although electricity cannot be stored on any scale, it can be converted to other kinds of energies that can be stored and then reconverted to electricity on demand. Such energy storage systems can be based on batteries, supercapacitors, flywheels, thermal modules, compressed air, and hydro storage. This survey article explores several aspects of energy storage. First, we define the primary difficulties and goals associated with energy storage. Second, we discuss several strategies employed for energy storage and the criteria used to identify the most appropriate technology. In addition, we address the current issues and limitations of energy storage approaches. Third, we shed light on the battery technologies, which are most frequently used in a wide range of applications for energy storage. The usage and types of batteries are described alongside their market shares and social and environmental aspects. Moreover, the recent advances in battery state estimation and cell-balancing mechanisms are reviewed.
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Cancelable template generation based on quantization conceptsThe idea of cancelable biometrics is widely used nowadays for user authentication. It is based on encrypted or intentionally-distorted templates. These templates can be used for user verification, while keeping the original user biometrics safe. Multiple biometric traits can be used to enhance the security level. These traits can be merged together for cancelable template generation. In this paper, a new system for cancelable template generation is presented depending on discrete cosine transform (DCT) merging and joint photographic experts group (JPEG) compression concepts. The DCT has an energy compaction property. The low-frequency quartile in the DCT domain maintains most of the image energy. Hence, the first quartile from each of the four biometrics for the same user is kept and other quartiles are removed. All kept coefficients from the four biometric images are concatenated to formulate a single template. The JPEG compression of this single template with a high compression ratio induces some intended distortion in the template. Hence, it can be used as a cancelable template for the user acquired from his four biometric traits. It can be changed according to the arrangement of biometric quartiles and the compression ratio used. The proposed system has been tested through merging of face, palmprint, iris, and fingerprint images. It achieves a high user verification accuracy of up to 100%. It is also robust in the presence of noise.
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Design and implementation of a low-cost circuit for medium-speed flash analog to digital conversionsDespite the considerable advancements in analog-to-digital conversion (ADC) circuits, many papers neglect several crucial considerations: Firstly, it does not ensure that ADCs work well in the software or hardware. Secondly, it is not certain that ADCs have a wide range of amplitude responses for the input voltages to be convenient in many applications, especially in electronics, communications, computer vision, CubeSat circuits, and subsystems. Finally, many of these ADCs need to look at the suitability of the proposed circuit to the most extensive range of frequencies. In this paper, a design of a low-cost circuit is proposed for medium-speed flash ADCs. The proposed circuit is simulated based on a set of electronic components with specific values to achieve high stability operation for a wide range of frequencies and voltages, whether in software or hardware. This circuit is practically implemented and experimentally tested. The proposed design aims to achieve high efficiency in the sampling process over a range of amplitudes from 10 mV to 10 V. The proposed circuit operates at a bandwidth of frequencies from 0 Hz to greater than 10 kHz in the simulation and hardware implementation.
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Efficient implementation of double random phase encoding and empirical mode decomposition for cancelable biometricsBiometric-based systems for secure access to different services have gained a significant attention in recent years. To ensure the protection of biometric data from potential hackers, it is crucial to store them in the form of secure templates. Cancelable templates offer an effective solution through allowing template replacement in case of security breaches. In this paper, we propose a novel unimodal cancelable biometric system that works on bio-signals such as voiceprint, electroencephalography (EEG), and electrocardiography (ECG) signals. The key feature of our proposed system is the utilization of Empirical Mode Decomposition (EMD) to decompose the bio-signals into different Intrinsic Mode Functions (IMFs). Among these IMFs, the first IMF, which carries the majority of the signal energy and distinguishes the bio-signal, plays a pivotal role in our system. To ensure the security of the cancelable biometric template, an encryption algorithm is employed. We use the Double Random Phase Encoding (DRPE) algorithm along with its random phase masks to encrypt the first IMF after converting it into 2D format. The use of DRPE and its random masks ensures a non-invertible transformation, which enhances the security of the encrypted data. To generate the cancelable template, we replace the first IMF of a reference signal with the encrypted first IMF obtained from the bio-signal. The resulting template retains the essential distinguishing characteristics of the bio-signal, while safeguarding its security. The verification process in our system involves matching of the encrypted first IMF of the stored templates with the encrypted first IMF of a new input signal. Extensive simulation analysis has been conducted to evaluate the performance of the proposed system. Various metrics, including Equal Error Rate (EER) and Area under Receiver Operating Characteristic curve (AROC), have been considered. The results demonstrate the high performance and stability of our system, even in the presence of different levels of white Gaussian noise, with an EER close to 0 and an AROC close to 1. In conclusion, our work presents an efficient implementation of the DRPE and EMD for the development of a robust and secure cancelable biometric system. The proposed system shows promising results and holds great potential for enhancing the security and reliability of biometric-based access control.
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Enhancing Leukemia Detection: An Automated Approach Using Deep Learning and Ensemble TechniquesAs leukemia ranks high among the global causes of death, it's crucial to identify it early to enhance the prognosis for patients. The majority of diagnostic processes used today rely on medical experts inspecting samples manually. This is a laborious process that lacks an automated detection mechanism and takes a lot of time. With a focus on acute lymphoblastic leukemia (ALL), this work suggests an automated diagnostic method that uses Deep Learning (DL)-based ensembles to improve leukemia detection and prediction. We propose to utilize a combination of ten DL techniques (ResNet, ResNeXt, SE-ResNet, Inception V3, VGG, and its variants) and three ensemble techniques (Max voting, Averaging, and Stacking) to constitute the leukemia detection models and observe their performances. The ALL IDB benchmark leukemia dataset was evaluated using these techniques, with performances measured across several metrics namely: classification accuracy, F1 score, precision, recall (sensitivity), Kappa index, and ROC-AUC score. The findings from the experiments demonstrate a notable enhancement in leukemia detection performance when utilizing the proposed techniques. In particular, the proposed Ensemble Max Voting technique surpasses all other stateof-the-art detection models in the literature with an accuracy of 100.0% and an F1 score of 0.997. The main achievement of this study is the identification of the most effective method among various models and techniques for detecting leukemia.
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Satellite Imagery-Based Cloud Classification Using Deep LearningA significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network’s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.
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A computational study of a laminar methane–air flame assisted by nanosecond repetitively pulsed dischargesNanosecond repetitively pulsed (NRP) discharges have been considered a promising technique for enhancing combustion efficiency and control. For successful implementation, it is necessary to understand the complex plasma–combustion interactions involving chemical, thermal, and hydrodynamic pathways. This paper aims to investigate the mechanisms enhancing a laminar methane–air flame assisted by NRP discharges by high fidelity simulations of the jet-wall burner employed in a previous experimental study. A phenomenological plasma model is used to represent the plasma energy deposition in two channels: (1) the ultrafast heating and dissociation of $\mathrm{O_2}$ resulting from the relaxation of electronically excited $\mathrm{N_2}$, and (2) slow gas heating stemming from the relaxation of $\mathrm{N_2}$ vibrational states. The flame displacement, key radical distribution and flame response under plasma actuation are compared with experimental results in good agreement. The computational model allows a systematic investigation of the dominant physical mechanism by isolating different pathways. It is found that the kinetic effect from atomic O production dominates the flame dynamics, while the thermal effect plays a minor role. Hydrodynamic perturbations arising from weak shock wave propagation appear to be sensitive to burner geometry and is found to be less significant in the case under study.
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An Efficient Approach for the Detection and Prevention of Gray-Hole Attacks in VANETsVehicular Ad-Hoc Networks (VANETs) deliver a wide range of commercial as well as safety applications and further motivate the advancements of Internet of Vehicles (IoV), Intelligent Transportation Systems (ITS), and Vehicles to Everything (V2X) communication. Despite their potential benefits, VANETs are susceptible to a variety of security attacks due to their open, distributed, and dynamic nature, which includes intrinsic protocol design issues. One such an infamous security attack is the Gray-Hole Attack (GHA), typically has two variants: Smart GHA and Sequence Number-based GHA. In Smart GHA, the malicious node behaves normally during the route discovery process, while in Sequence Number-based GHA, the malicious node starts misbehaving during the route discovery process. In either case, once the route is successfully established, it starts dropping the packets. In this paper, a novel security approach called ‘‘Detection and Prevention of GHA’’ (DPGHA) is proposed to detect and prevent both variants of GHA in Ad Hoc On-Demand Distance Vector (AODV) based VANETs. The approach is based on the generation of dynamic threshold values of abnormal differences of received, forwarded, and generated control or data packets among nodes and their sequence numbers. The proposed DPGHA is implemented and tested in NS-2 and SUMO simulators and its various performances are compared with the most relevant benchmark approaches. The results showed that the proposed DPGHA performed better than the benchmark approaches in terms of reduced routing overhead by 10.85% and end-to-end delay by 3.85%, increased Packet Delivery Ratio (PDR) by 4.67% and throughput by 6.58%, and achieved a maximum detection rate of 2.3%.
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Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER ProThe cement industry is a major energy consumer, with most of its costs associated with fuel and energy requirements. While traditional thermal power plants generate electricity, they are both harmful and inefficient. In this study, battery depth of discharge (DOD) is evaluated for four different battery technologies in the context of the cement industry. The battery technologies evaluated are lead-acid (LA), lithium-ion (Li-ion), vanadium redox (VR), and nickel-iron (Ni-Fe). Five cement plants in Pakistan are considered, including Askari Cement Plant, Wah (ACPW), Bestway Cement Plant, Kalar Kahar (BCPKK), Bestway Cement Plant, Farooqia (BCPF), Bestway Cement Plant, Hattar (BCPH), and DG Cement Plant, Chakwal (DGCPC). Four hybrid energy generation models (HEGMs) were proposed using the HOMER pro software. HEGM-1 combines a diesel generator, photovoltaic system, converter, and battery system, while HEGM-2 consists of a photovoltaic system, converter, and battery system. HEGM-3 is a grid-connected version of HEGM-1 and HEGM-4 is the grid-connected version of HEGM-2. A reference base model using only grid connection is also considered. A multi-criteria decision analysis (MCDA) was performed using a cumulative objective function (COF) that includes net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions. The main objective was to maximize COF while minimizing NPC, LCOE, and GHG emissions using optimal battery technology and DOD. The results indicate that VR is the most optimal battery technology, with a DOD of 10% achieved in DGCPC using HEGM-3. This results in a 61.49% reduction in NPC, 78.62% reduction in LCOE, and 84.00% reduction in GHG emissions compared to the base model.
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ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networksSleep Apnea (SA) can cause health complications including heart stroke and neurological disorders. The Polysomnography (PSG) test can detect the severity of sleep disturbance. However, it is expensive and requires a dedicated sleep laboratory and expertise to examine the patients. Therefore, it is not available to a large population in developing countries. This leads to the development of cost-effective and automated patient examination methods for the detection of sleep apnea. This study suggests an approach of using the ECG signals to categorize sleep apnea. In this work, we have devised an original technique of feature space designing by intelligently hybridizing the multirate processing, a mix of wavelet-empirical mode decomposition (W-EMD), modes-based Hjorth features extraction, and Adam-based optimized Multilayer perceptron neural network (MLPNN) for automated categorization of apnea. A publicly available ECG dataset is used for evaluating the performance of the suggested approach. Experiments are performed for four different sub-bands of the considered ECG signals. For each selected sub-band, five "Intrinsic Mode Functions" (IMFs) are extracted. Onward, three Hjorth features: complexity, activity, and mobility are mined from each IMF. In this way, four feature sets are formed based on wavelet-driven selected sub-bands. The performance of optimized MLPNN, for the apnea categorization, is compared for each feature set. Five different evaluation parameters are used to assess the performance. For the same dataset, a systematic comparison with current state-of-the-artwork has been done. Results have shown a classification accuracy of 98.12%.
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A Novel Integration Technique for Optimal Location & Sizing of DG Units With Reconfiguration in Radial Distribution Networks Considering ReliabilityThis paper introduces an advanced approach for optimizing the distribution network reconfiguration (DNR) with the placement and sizing of multiple types of distributed generators (DGs). The method employs the ant colony optimization algorithm (ACOA), which is an innovative adaptive optimization algorithm, while also considering the system’s reliability. The primary objectives of the optimization problem are to minimize active power loss (APL), reduce voltage drop ( $V_{D}$ ) on buses, enhance system stability (SS), and improve overall reliability by reducing energy not supplied (ENS) to end-users. The optimization process involves determining the optimal location and size of DGs in the radial distribution network (RDN) using the ACOA meta-heuristic. The method maintains the radial structure of the system by selectively opening lines during the DNR process. The proposed technique is evaluated through simulations carried out on the IEEE-33 & -69 bus RDNs under various scenarios. The optimal solution is achieved by combining DG Type-1 with integration of DNR to reduce the APL and amplify the $V_{p}$ of buses in both RDNs. In this scenario APL is reduced to 87.97% (IEEE-33) and 92.83% (IEEE-69), respectively. Similarly, the $V_{p}$ of the buses significantly improved to 0.9776 p.u. (IEEE-33) and 0.9888 p.u. (IEEE-69), respectively. The results demonstrate the superiority of the presented ACOA-based approach over other techniques, such as fireworks algorithm (FWA) and adaptive shuffled frogs leaping algorithm (ASFLA). Combining the DNR and DGs placement in a simultaneous manner yields the best performance for the distribution network, resulting in lower APL, reduced $V_{D}$ , improved SS, and enhanced reliability. Furthermore, considering reliability in the optimization process significantly reduces ENS for customers and enables meeting their maximum load demand. Overall, the concurrent consideration of DNR and DGs placement using ACOA proves to be more effective than alternative algorithms.
