AC microgrid protection – A review: Current and future prospective

http://dspace.uniten.edu.my/jspui/bitstream/123456789/15172/1/Optimized%20controller%20for%20renewable%20energy%20sources.pdf

Optimized controller for renewable energy sources integration into microgrid: Functions, constraints and suggestions

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.

MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

https://www.mdpi.com/1996-1073/12/8/1457/pdf

An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

/pdf/combined-economic-emission-dispatch-of-microgrid-with-the-2p9m4xuu.pdf

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

A plug-in hybrid electric vehicles (PHEV) charger adapter consists of an AC/DC power factor correction (PFC) circuit accompanied by a full-bridge isolated DC/DC converter. This paper introduces an efficient two-stage charger topology with an improved PFC rectifier as front-end and a high-frequency zero voltage switching (ZVS). Current switching (ZCS) DC/DC converter is the second part. The front-end converter is chosen as bridgeless interleaved (BLIL) boost converter, as it provides the advantages like lessened input current ripple, capacitor voltage ripple, and electromagnetic interference. Resettable integrator (RI) control technique is employed for PFC and DC voltage regulation. The controller achieves nonlinear switching converter control and makes it more resilient with the faster transient response and input noise rejection. The second stage incorporates a resonant circuit, which helps in achieving ZVS/ZCS for inverter switches and rectifier diodes. PI controller with phase shift modulator is used for second-stage converter. It improves the overall efficacy of the charger by lowering the switching losses, lowering the voltage stress on the power semiconductor devices, and reversing recovery losses of the diodes. The simulations and experimental results infer that the overall charging efficiency increases to 96.5%, which is 3% higher than the conventional two-stage approach using the interleaved converter.

An Effective Charger for Plug-In Hybrid Electric Vehicles (PHEV) with an Enhanced PFC Rectifier and ZVS-ZCS DC/DC High-Frequency Converter

Single-walled carbon nanotube (SWCNT) plays a major role in electromagnetic absorption and shielding. Their applications as semiconductors make a breakthrough in communication by miniaturizing the communication devices. The main drawback of the SWCNT is found to 
 
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 noise. Because of this limitation, high attenuation at the low-frequency band cannot be achieved, limiting its application in terms of selectivity. The spectral density study shows that the noise’s amplitude is directly proportional to the temperature and inversely proportional to the number of carriers in the nanotube. The SWCNT is mainly synthesized using hydrocarbons which contains carbonaceous impurities. On the removal of impurities, more surface oxygen functional groups are formed. On the other hand, the diameter of the carbon nanotube is very small, increasing the resistance of carrier flow. In this research work, gas adsorption was used in SWCNT by treating the carbon nanotube using nitric acid. Isotherms determine porous size. The adsorbate-adsorbent interaction on carbon nanotube reduces the microporosity in the surface by treating with nitric acid. Therefore, the density of the surface increases and the CNT bundle separation will be reduced, increasing the carbon nanotube’s resistivity. This increase in resistivity reduces the excess carrier flow; therefore, the temperature will reduce the 
 
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 noise. The proposed system is cost-effective and has shown 11% improvement by reducing the noise amplitude by increasing carbon nanotube resistance. This proposed method has less complexity compared with existing models.

Reduction of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1">
                     <mn>1</mn>
                     <mo>/</mo>
                     <mi>f</mi>
                  </math> Noise in Single-Walled Carbon Nanotubes (SWCNTs) Using Gas Adsorption Technique

S. Angalaeswari

Papers

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

An Effective Charger for Plug-In Hybrid Electric Vehicles (PHEV) with an Enhanced PFC Rectifier and ZVS-ZCS DC/DC High-Frequency Converter

Reduction of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>1</mn> <mo>/</mo> <mi>f</mi> </math> Noise in Single-Walled Carbon Nanotubes (SWCNTs) Using Gas Adsorption Technique

Efficient Power Management of Grid operated MicroGrid Using Fuzzy Logic Controller (FLC)

Design and implementation of a robust iterative learning controller for voltage and frequency stabilization of hybrid microgrids