The deep integration of renewable energy resources, including solar photovoltaic (PV) and wind turbine (WT) energy, mainly depend on the inexpensive technological improvement of global emissions and ...the precise techniques for power quality. Grid-connected inverters act as key components in distributed generation systems for cutting-edge technology. The inverter connects the renewable energy sources and power distribution network systems for the conversion of power. In grid-connected systems, several current and voltage harmonics affect the system performances. Likewise, highly unstable devices coupled with the growing demand for nonlinear loads and renewable energy resources influence the power networks and systems performance in terms of power quality. The effective solutions to these problems are passive filters (PFs), static var generators, and active power filters (APFs). However, the use of PFs in a high-power system increases its cost, size, and weight. This study aims to assess the most advanced APFs by reducing the number of power switches and focus on the reduction of cost, size, and weight of grid-connected inverters. Several studies compared and evaluated reduced-switch-count APF inverter topologies, such as AC–AC, back-to-back, and common leg, under the single-phase and three-phase systems. Recently, cost-effective solutions to reduce the number of components, transformerless inverters, multilevel and multifunctional inverters based on the APF in PV, and wind energy conversion systems have been greatly explored. The current techniques and their limitations for developing advanced inverter-based devices for renewable energy systems are discussed with justifications. Therefore, this review would potentially help industrial researchers improve power quality in PV and WT energies and power distribution network systems.
The combined trends of urban heat island (UHI) intensification and global warming led to an increased tendency towards on the greening of cities as a tool for UHI mitigation. Our study examines the ...range of research approaches and findings regarding the role of green roofs in mitigating urban heat and enhancing human comfort. This review provides an overview of 89 studies conducted in three main climate types (hot–humid, temperate, and dry), from 2000 till 2020. All of the reviewed studies confirm the cooling effect of green roofs and its contribution to reduced heat island intensity regardless of the background climatic condition. However, dry climate has the highest (3 °C) median cooling effect of green roofs among all the climates investigated. Hot–humid climate presents the lowest cooling potential (median = 1 °C) of green roofs among all the climate types. Moreover, green roofs contribute a median surface temperature reduction of 30 °C in hot–humid cities. This value is relatively low for temperate climates (28 °C). Notably, no study has examined the impact of green roofs on surface temperature reduction in dry climates. This review can benefit urban planners and various stakeholders.
Using Green Roofs to Reduce Heat Islands in Different Climates. Display omitted
•Dry climates had the highest median cooling effect caused by green roofs (3 º C) amongst all the investigated climates.•Hot–humid climates showed the lowest value of cooling effect (median=1 °C) caused by green roofs amongst all the investigated climates.•Green roofs contributed to 30 °C, and 28 °C reduction in median surface temperature in hot–humid, and temperate climate cities.•There is a lack of research on the impact of green roofs on surface temperature reduction in hot-dry climates.
To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable ...energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.
Expeditious urbanization, population growth, and technological advancements in the past decade have significantly impacted the rise of energy demand across the world. Mitigation of environmental ...impacts and socio-economic benefits associated with the renewable energy systems advocate the higher integration of the distributed energy systems into the conventional electricity grids. However, the rise of renewable energy generation increases the intermittent and stochastic nature of the energy management problem significantly. Therefore, an optimal energy management technique is required to achieve a high level of system reliability and operational efficiency. A state-of-the-art systematic review of the different optimization techniques used to address the energy management problems in microgrids is presented in this article. The different optimization techniques used in energy management problems, particularly focusing on forecasting, demand management, economic dispatch, and unit commitment, are identified and critically analyzed in this review. The inferences from the review indicated that the mixed integer programming techniques were widely used, considering their simplicity and performance in solving the energy management problem in microgrids. The multi-agent-based techniques and meta-heuristics algorithms outperformed the other conventional techniques in terms of the efficiency of the system due to the decentralized nature of the EMS problem in microgrids and the capability of these techniques to act effectively in such scenarios. In addition, it was also evident that the use of advanced optimization techniques was limited in the scope of forecasting and demand management. Advocating the need for more accurate scheduling and forecasting algorithms to address the energy management problem in microgrids. Finally, the need for an end-to-end energy management solution for a microgrid system and a transactive/collaborative energy sharing functionality in a community microgrid is presented.
•Review of optimization techniques used in microgrid energy management systems.•Mixed integer linear program is the most used optimization technique.•Multi-agent systems are most ideal for solving unit commitment and demand management.•State-of-the-art machine learning algorithms are used for forecasting applications.•The meta-heuristic algorithms are commonly used in economic dispatch application.
Mandibular and craniofacial bone defects can be caused by trauma, inflammatory disease, and benign or malignant tumors. Patients with bone defects suffer from problems with aesthetics, speech, and ...mastication, resulting in the need for implants. Conventional methods do not always provide satisfactory results. Most of the techniques proposed by researchers in the field of biomedical engineering use reverse engineering, computer-aided design (CAD), and additive manufacturing (AM), whose implementation can improve the outcomes of reconstructive surgeries. Several literature reviews on this particular topic have been conducted. However, they provide mostly overviews of AM technologies for general biomedical devices. This paper summarizes the use of existing medical AM techniques for the design and fabrication of mandibular and craniofacial implants, and then discusses their advantages and disadvantages in terms of accuracy, costs, energy consumption, and production rate. The aim of this study is to present a comparative review of the most commonly used AM technologies to aid researchers in selecting the best possible AM technologies for medical use. Studies included in this review contain CAD designs of mandibular or cranial implants, as well as their fabrication using AM technologies. Special attention is paid to PolyJet technology, because of its high accuracy, and economical efficiency.
Medium-voltage (MV) multilevel converters are considered a promising solution for large scale photovoltaic (PV) systems to meet the rapid energy demand. This article focuses on reviewing the ...different structures and the technical challenges of modular multilevel topologies and their submodule circuit design for PV applications. The unique structure of the converter's submodule provides modularity, independent control of maximum power point tracking (MPPT), galvanic isolation, etc. Different submodule circuits and MPPT methods to efficiently extract the PV power are reviewed. The integration of the multilevel converters to PV systems suffers unbalanced power generation during partial PV shading conditions. Several balancing strategies to solve this problem are presented and compared to give a better understanding of the balancing ranges and capabilities of each strategy. In addition, the paper discusses recent research advancements, and possible future directions of MV converters-based large-scale PV systems for grid integration.
Modular multilevel converters (MMC)s are promising candidates for large‐scale grid‐connected photovoltaic (PV) systems. Due to their modular structure, MMCs provide a direct connection of the PV ...arrays to the converter submodules. They also offer scalability, independent maximum power point tracking, and enhanced power quality with internal power flow capabilities. However, the intermittent nature of PV arrays introduces a power unbalance inside the converter, which affects its operation. This paper addresses the issue and proposes an energy balancing strategy for the grid‐connected MMC‐based PV system. It uses the internally generated leg currents to control the power flow inside the converter and inject a three‐phase balanced current to the grid with low total harmonic distortion. Compared to the existing strategies, the proposed strategy can overcome any condition of the power unbalance with minimal submodule voltage fluctuations. A 162‐kW, 9‐kV PV grid‐connected system is modelled and simulated in MATLAB Simulink environment. The corresponding results are presented to demonstrate the effectiveness of the proposed control strategy for grid‐connected PV systems.
Software services communicate with different requisite services over the computer network to accomplish their tasks. The requisite services may not be readily available to test a specific service. ...Thus, service virtualisation has been proposed as an industry solution to ensure availability of the interactive behaviour of the requisite services. However, the existing techniques of virtualisation cannot satisfy the required accuracy or time constraints to keep up with the competitive business world. These constraints sacrifices quality and testing coverage, thereby delaying the delivery of software. We proposed a novel technique to improve the accuracy of the existing service virtualisation solutions without sacrificing time. This method generates the service response and predicts categorical fields in virtualised responses, extending existing research with lower complexity and higher accuracy. The proposed service virtualisation approach uses conditional entropy to identify the fields that can be used to drive the value of each categorical field based on the historical messages. Then, it uses joint probability distribution to find the best values for the categorical fields. The experimental evaluation illustrates that the proposed approach can generate responses with the required fields and accurate values for categorical fields over four data sets with stateful nature.
In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential ...evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Advancements in power conversion efficiency and the growing prevalence of DC loads worldwide have underscored the importance of DC microgrids in modern energy systems. Addressing the challenges of ...power-sharing and voltage stability in these DC microgrids has been a prominent research focus. Sliding mode control (SMC) has demonstrated remarkable performance in various power electronic converter applications. This paper proposes the integration of universal droop control (UDC) with SMC to facilitate distributed energy resource interfacing and power-sharing control in DC microgrids. Compared to traditional Proportional-Integral (PI) control, the proposed control approach exhibits superior dynamic response characteristics. The UDC is strategically incorporated prior to the SMC and establishes limits on voltage variation and maximum power drawn from the DC–DC converters within the microgrid. A dynamic model of the DC–DC converter is developed as the initial stage, focusing on voltage regulation at the DC link through nonlinear control laws tailored for Distributed Generation (DG)-based converters. The UDC ensures voltage stability in the DC microgrid by imposing predetermined power constraints on the DGs. Comparative evaluations, involving different load scenarios, have been conducted to assess the performance of the proposed UDC-based SMC control in comparison to the PI control-based system. The results demonstrate the superior efficiency of the UDC-based SMC control in handling dynamic load changes. Furthermore, a practical test of the proposed controller has been conducted using a hardware prototype of a DC microgrid.
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