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Vector Control And Dynamics Of Ac Drives

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. 

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

We use an X-ray stacking technique to investigate the evolution of the mean X-ray luminosity of ∼21,000 red galaxies at 0.3 ≤ z < 0.9 as a function of their stellar mass and redshift. The red galaxies are selected from the 9.3 deg 2 Boötes field of the NOAO Deep Wide-Field Survey (NDWFS). The mean X-ray luminosity is an order of magnitude larger than we would expect from stellar sources alone and therefore must be primarily due to AGN emission. The X-ray luminosities (L x ≈ 10 41 ergs s −1) imply that these must be sources with relatively low accretion rates and/or accretion efficiencies onto their central super-massive black hole (SMBH). The mean X-ray luminosity increases significantly as a function of optical luminosity and stellar mass, implying that more massive galaxies have higher nuclear accretion rates than lower mass galaxies. We calculate that the mean X-ray luminosity evolves as (1 + z) 3.3±1.5. This suggests a global decline in the mean AGN activity of normal early-type galaxies from z ∼ 1 to the present. If accreting at typical AGN efficiencies, SMBHs within red galaxies accreted an insignificant proportion of their mass in this time.

National Optical Astronomy Observatory

The shift from centralized to distributed generation and the need to address energy shortage and achieve the sustainability goals are among the important factors that drive increasing interests of governments, planners, and other relevant stakeholders in microgrid systems. Apart from the distributed renewable energy resources, fuel cells (FCs) are a clean, pollution-free, highly efficient, flexible, and promising energy resource for microgrid applications that need more attention in research and development terms. Furthermore, they can offer continuous operation and do not require recharging. This paper examines the exciting potential of FCs and their utilization in microgrid systems. It presents a comprehensive review of FCs, with emphasis on the developmental status of the different technologies, comparison of operational characteristics, and the prevailing techno-economic barriers to their progress and the future outlook. Furthermore, particular attention is paid to the applications of the FC technologies in microgrid systems such as grid-integrated, grid-parallel, stand-alone, backup or emergency power, and direct current systems, including the FC control mechanisms and hybrid designs, and the technical challenges faced when employing FCs in microgrids based on recent developments. Microgrids can help to strengthen the existing power grid and are also suitable for mitigating the problem of energy poverty in remote locations. The paper is expected to provide useful insights into advancing research and developments in clean energy generation through microgrid systems based on FCs.

Review of Fuel Cell Technologies and Applications for Sustainable Microgrid Systems

A literature review and statistical analysis of photovoltaic-wind hybrid renewable system research by considering the most relevant 550 articles: An upgradable matrix literature database

Battery Charge Management for Hybrid PV/Wind/Fuel Cell with Storage Battery

In the last 15 years, Double Fed Induction Generator (DFIG) had been widely used as a wind turbine generator, due its various advantages especially low generation cost so it becomes the most important and promising sources of renewable energy. This work focuses on studying of using DFIG as a wind turbine connected to a grid subjected to various types of fault. Crowbar is a kind of protection used for wind turbine generator protection. ANFIS controller is used for protection of DFIG during faults. The fault current under symmetric and asymmetric fault is presented as well as a way to control the increase in rotor current which leads to voltage increase in DC link between wind generator and the grid. ANFIS is used for solving such problem as it is one of the most commonly AI used techniques. Also the current response of DFIG during fault is improved by adapting the parameters of PI controllers of the voltage regulator using fuzzy logics. ANFIS also in this paper is used for detecting and clearing the short circuit on the DC capacitor link during the operation. A simulation study is illustrated using MATLAB/Simulink depending on currents and voltages measurement only for online detection of the faults. The proposed technique shows promising results using the simulation model.

https://cyberleninka.org/article/n/592906.pdf

Protection of DFIG wind turbine using fuzzy logic control

One of the main interested subjects in electric power systems is the integration of electric vehicles to the existing power network as well as the influence of this integration with the other types of renewable sources connected to the multi area power system networks. Load Frequency Control (LFC) are used to regulate and control the output frequency signal of the electric generated power within an area in response to changes in system loads and power in tie line changed with other area. This paper presents a new design of various types of load frequency PI controllers based on different types of Artificial Intelligent (AI) optimization techniques such as Fuzzy logic, FOPID tuned by fuzzy and Model Predictive Control (MPC) for a four area power system. The performance of the controller under study shows an enhancement in the frequency deviation signal as well as the peak overshoot and Settling time for the frequency output signal. The performance of the proposed scheme is validated using MATLAB/SIMULINK tools.

Load Frequency Control for Multi Area Smart Grid based on Advanced Control Techniques

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

https://www.mdpi.com/1424-8220/21/7/2269/pdf

Ahmed F. Bendary

Papers

Battery Charge Management for Hybrid PV/Wind/Fuel Cell with Storage Battery

Protection of DFIG wind turbine using fuzzy logic control

Load Frequency Control for Multi Area Smart Grid based on Advanced Control Techniques

Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System.

Elephant herding algorithm-based optimal PI controller for LVRT enhancement of wind energy conversion systems