65 research outputs found
Design and development of non-isolated modified SEPIC DC-DC converter topology for high-step-up applications: Investigation and hardware implementation
No full textA new non-isolated modified SEPIC front-end dc-dc converter for the low power system is proposed in this paper, and this converter is the next level of the traditional SEPIC converter with additional devices, such as two diodes and splitting of the output capacitor into two equal parts. The circuit topology proposed in this paper is formulated by combining the boost structure with the traditional SEPIC converter. Therefore, the proposed converter has the benefit of the SEPIC converter, such as continuous input current. The proposed circuit structure also improves the features, such as high voltage gain and high conversion efficiency. The converter comprises one MOSFET switch, one coupled inductor, three diodes, and two capacitors, including the output capacitor. The converter effectively recovers the leakage energy of the coupled inductor through the passive clamp circuit. The operation of the proposed converter is explained in continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The required voltage gain of the converter can be acquired by adjusting the coupled inductor turn’s ratio along with the additional devices at less duty cycle of the switch. The simulation of the proposed converter under CCM is carried out, and an experimental prototype of 100 W, 25 V/200 V is made, and the experimental outcomes are presented to validate the theoretical discussions of the proposed converter. The operating performance of the proposed converter is compared with the converters discussed in the literature. The proposed converter can be extended by connecting voltage multiplier (VM) cell circuits to get the ultra-high voltage gain
MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems
No full textThis paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biological interaction between predator and prey. The proposed MOMPA can address multiple and conflicting objectives when solving optimization problems. The MOMPA is formulated using elitist non-dominated sorting and crowding distance mechanisms. The proposed method is tested on various multi-objective case studies, including 32 unconstrained, constraint, and engineering design problems with different linear, nonlinear, continuous, and discrete characteristics-based Pareto front problems. The results of the proposed MOMPA are compared with several well-regarded Multi-Objective Water-Cycle Algorithm, Multi-Objective Symbiotic-Organism Search, Multi-Objective Moth-Flame Optimizer algorithms qualitatively and quantitatively using several performance indicators. The experimental results demonstrate the merits of the proposed method.No Full Tex
A study of institutional, contextual and socioeconomic factors affecting county e-government:
No full textGovernments at all levels in the United States are rapidly transforming to Internet to provide public services and public administrators are increasingly implementing various strategies to enable this transformation. Scholars and academicians have researched the growth of this phenomenon in recent decades, including the factors associated with the adoption of e-government at the state and municipals levels. E-Government literature however provides little information related specifically to counties' adoption of e-government in the United States. Research on county e-government has tended to focus primarily on socioeconomic factors. Although some researchers have studied the effect of institutional and contextual factors on county e-government in particular states, none have studied their influence on counties across the United States.Based on a survey of county administrators who are primarily responsible for e-government services, this research attempts to capture the role played by institutional, contextual and socioeconomic factors on e-government adoption at the county level all over the United States. The institutional variables consist of size and structure of the county government, budget resources, technical capacity, stakeholder support, contracting and presence of an IT champion. The contextual variables consist of the measure of the county's professional networking, external collaboration, regional pressure and business demand in the county. Additionally, certain socio-economic variables are considered, such as population, education and income level of the county residents. These factors are tested based on an evaluation of county websites using a conceptual framework consisting of three e-government dimensions: e-information, e-transaction and e-participation. These dimensions are operationalized based on the Rutgers E-Governance Index and validated by an expert review process. Literature also suggests an evolutionary approach to e-government growth - in terms of stages ranging from webpage development to full service integration and the involvement of all sections of society. Accordingly, the research also tests the stages of development of e-government among counties by assessing their status in each dimension and determining if the proposed dimensions follow a staged pattern.Ph.D.Includes bibliographical references (p. 137-143)by Aroon P. Manohara
Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions
No full textThe photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed
Epidural Needle Insertion Simulator: A device for training resident anaesthesiologists
No full textEpidural anaesthesia is a pain relief technique commonly performed on patients in labor. It requires insertion of a needle into patients back into the epidural space of the spine and inject anaesthetic. It is a blind procedure and anaesthesiologists rely only on the forces felt during needle insertion to determine the position of the needle tip. It is a complex procedure that requires training. However, in a majority of hospitals the residents are trained on patiets. In this thesis development and validation of a new epidural needle insertion simulator with haptic feedback is discussed. . The simulator has 1DOF for needle insertion and 1DOF for needle orientation. The simulator uses a cable-pulley mechanism to transmit forces to the needle. The simulator provides active force-feedback by means of a motor in the needle insertion direction and passive force-feedback by means of a brake in the needle orientation direction. The simulator simulates needle-tissue and needle-bone interaction forces. It also incorporates simulation of different virtual patients, which can be selected from the graphical user interface. The real-time position of the needle can be seen in the graphical user interface. The simulator is validated through experiments by expert anaesthesiologists and novices. The simulator is validated for face and constuct validity. The results were promising showing high acceptance rate with addition of some features. Moreover, difference in performance between experts and novices was found and thus evaluating similarity to a real scenario.BioMechanical EngineeringMechanical, Maritime and Materials Engineerin
de-VAP - (Decentralized desiccant enhanced evaporative cooling integrated facade)
No full textThe objective of the thesis is to reduce the cooling load of the offices in Delhi, India by integrating passive strategies and low-ex cooling (evaporative) technology through facades as a decentralized ventilation system. The cooling demand in India is going to increase up to 8 folds by 2030. Almost 50% of the energy is spent on space cooling for offices in Indian climatic conditions. In order to reduce the cooling demand alternative low ex cooling technologies are being researched and implemented. In this thesis, various types of evaporative cooling and their various properties and its application on Delhi’s climatic scenario and its limitations were studied as one of the low-ex techs. The study concludes by choosing Dew-point indirect evaporative cooler because of its high wet bulb effectiveness with no addition of humidity. For continuous operation, the humidity in the air needs to removed before supplying it to the cooler. So, the design involves a combination of Dew-point Indirect evaporative cooler (D-IEC) coupled with Desiccant coated heat exchanger (DCHE). The system also requires a source for heating and cooling down the water for Regeneration cycle and Dehumidification cycle, evaporative cooler respectively. The cooling demand of the building needs to be addressed by multiple devices in a decentralized ventilation system. The total number of devices required determines the cost of installation and ease of maintenance over the years and it depends on the cooling load. So, it is necessary to reduce the cooling demand of the building using passive strategies before integrating the evaporative cooler. The building’s cooling load has been reduced to 50W/m2 by adapting suitable passive strategies like shading systems, reducing U values of walls, glazing and roof, reducing infiltration and the internal heat gain. The above-mentioned strategies result in 146.41kWh/m2/yr with a water-based chiller (CENTRALIZED), and it is efficient when compared to the recommended national figure of 180 kWh/m2/yr. But using the De-VAP systems (DECENTRALIZED) cuts down the energy by even much further to up to 40% (92.26kWh/m2) in which almost 1/3rd of the energy can be generated by installing PV on the roof. This design takes us further one step closer to the NET ZERO building.COOL FacadeArchitecture, Urbanism and Building Sciences | Building Technology | Sustainable Desig
Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems
No full textAbstract The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems
Optimizing residential flexibility for sustainable energy management in distribution networks
No full textIn search of a sustainable and green economy, many initiatives have been undertaken to promote clean energy and enhance local flexibility. Residential flexibility, achieved through home appliances capable of adjusting their consumption profiles, offers a feasible solution for operators to address challenges such as congestion and balancing in distribution systems. This paper considered an improved approach for aggregators to provide flexibility in distribution systems. By leveraging load flexibility resources, the model facilitates the rescheduling of real-time and shifting appliances to meet the demands of Balance Responsible Parties (BRPs) or Distribution System Operators (DSOs). This study uses a number of approaches to solve the recommended model effectively despite the problem's inherent complexity. An extensive test case with twenty residential houses equipped with seven types of appliances each is run in order to confirm and compare the optimization algorithms' performance. The results show that by rescheduling home appliance loads across 24 hours, the aggregator may effectively accommodate flexibility requests from DSOs/BRPs while optimizing the expenses associated with user compensation. To further improve the optimization process, this study uses a new Reinforced Learning Quantum Inspired Grey Wolf Optimization (RLQIGWO). Through the integration of reinforcement learning and quantum mechanics principles into the original grey wolf optimizer, RLQIGWO achieves better performance in load balancing, resource utilization, and execution of tasks. The findings demonstrate that the proposed RLQIGWO improves the efficacy and competence of flexibility options in distribution networks, paving the way to a more adaptable and strong energy management strategy
MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems
No full textIn the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners
Multi-objective Approach for Dynamic Economic Emission Dispatch Problem Considering Power System Reliability and Transmission Loss Prediction Using Cascaded Forward Neural Network
No full textAbstract This study addresses the significant problem of Dynamic Economic Emission Dispatch (DEED), a critical consideration in power systems from both economic and environmental protection viewpoints. Reliability stands as another vital facet, impacting maintenance and operation perspectives. The integration of Artificial Neural Network (ANN)-based transmission loss prediction into the DEED model is also essential to address specific limitations and enhance the overall performance of the dispatch process. Traditionally, the DEED model relies on a single B-loss coefficient to estimate transmission losses. While this approach simplifies calculations, it fails to account for the significant variations in demand that occur throughout the dispatch period and it leads to inaccuracies in loss prediction, especially in dynamic environments. Using a single coefficient, the model cannot adequately capture the complex, non-linear relationships between power generation, load, and transmission losses under different operating conditions. To overcome this limitation, this study introduces an ANN-based loss prediction method integrated into the DEED model and uses trained ANN to replace the process of finding B-loss coefficients during each dispatch period. This paper also introduces a strategy leveraging the multi-objective northern goshawk optimizer algorithm, characterized by a non-dominated sorting and crowding distance mechanism, to enhance DEED considerations incorporating reliability (DEEDR). This novel algorithm improves the solution space effectively, maintains high population diversity and enables an even distribution of individuals sharing the same rank in the objective space. The fundamental objective of this study is to balance fuel cost, emission, and system reliability in power system operations. Compared with a few existing multi-objective optimization algorithms, this study demonstrates superior performance in generating a series of non-dominated solutions. The experimental results highlight its competitive and potential as an efficient tool in the DEED and DEEDR problems, promising a synergistic coordination of economy, environmental protection, and system reliability benefits in power system management
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