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Power Electronics Converters Applications And Design

Microgrids energy management systems: A critical review on methods, solutions, and prospects

This fifth edition continues the tradition of excellence with thorough coverage of recent trends and changes in the clinical diagnosis and treatment of CNS diseases, detailed relevant neuropathologic, genetic, and clinical findings, and how those changes relate to MRI findings. It remains a comprehensive, internationally acclaimed, state-of-the-art reference for all who have an interest in neuroradiology – trainees to experts in the field, basic science researchers, and clinicians.

Magnetic Resonance Imaging of the Brain and Spine

Fundamentals Of Neural Networks Architectures Algorithms And Applications

The increase in energy demand and reduction in resources for conventional energy production along with various environmental impacts, promote the use of renewable energy for electricity generation and other energy-need applications around the world. Wind power has emerged as the biggest renewable energy source in the world, whose potential, when employed properly, serves to provide the best power output. In order to achieve self-sustenance in energy supply and to match the critical needs of impoverished and developing regions, wind power has proven to be the best solution. However, wind power is intermittent and unstable in nature and hence creates lot of grid integration and power fluctuation issues, which ultimately disturb the stability of the grid. In such cases, energy storage technologies are highly essential and researchers turned their attention to find efficient ways of storing energy to achieve maximum utilization. The use of batteries to store wind energy is very expensive and not practical for wind applications. Compressed Air Energy Storage (CAES) is found to be a viable solution to store energy generated from wind and other renewable energy systems. A detailed review on various aspects of a CAES system has been made and presented in this paper which includes the thermodynamic analysis, modeling and simulation analysis, experimental investigation, various control strategies, some case studies and economic evaluation with the role of energy storage towards smart grid and poly-generation.

/pdf/a-review-on-compressed-air-energy-storage-a-pathway-for-3c23hr48tu.pdf

A review on compressed air energy storage – A pathway for smart grid and polygeneration

This work presents a new adaptive scheme for energy management in an independent microgrid. The proposed energy management system has been developed to manage the utilization of power among the hybrid resources and energy storage system in order to supply the load requirement based on multi-agent system (MAS) concept and predicted renewable powers and load powers. Auto regressive moving average models have been developed for predicting the wind speed, atmospheric temperature, irradiation, and connected loads. The structure proposed in this paper includes renewable sources as primary source and storage system as secondary source. A wind generator and solar PV array system together acts as primary source, which supplies power to the local load most of the time in this energy management strategy. When they fail to meet the load demand, the secondary source present in the system will assist the primary source and help to attain the goal of satisfying load demand without interruption. If the primary source and secondary source together are not able to meet the load demand then load shedding will be executed according to the priority set. Thus the developed MAS algorithm co-ordinates the hybrid system components and achieves energy management among renewable energy sources, storage units, and load under varying environmental conditions and varying loads. STATCOM based compensation has been implemented to balance the reactive power demand and to mitigate the voltage fluctuations and harmonics on the AC bus. The proposed microgrid has been simulated with MAS concept in Matlab/Simulink environment. The results presented in this paper show cases the effectiveness of the proposed energy management controller.

/pdf/intelligent-energy-management-control-for-independent-1pe5axvoz4.pdf

Intelligent energy management control for independent microgrid

Magnetic resonance imaging is a noninvasive technique that has been developed for its excellent depiction of soft tissue contrasts. Instruments capable of ultra-high field strengths, ≥7 Tesla, were recently engineered and have resulted in higher signal-to-noise and higher resolution images. This paper presents various subsystems of the MR imaging systems like the magnet subsystem, gradient subsystem, and also various issues which arise due to the magnet. Further, it also portrays finer details about the RF coils and transceiver and also various limitations of the RF coils and transceiver. Moreover, the concept behind the data processing system and the challenges related to it were also depicted. Finally, the various artifacts associated with the MR imaging were clearly pointed out. It also presents a brief overview about all the challenges related to MR imaging systems.

/pdf/a-review-on-potential-issues-and-challenges-in-mr-imaging-47g0q01sxg.pdf

A Review on Potential Issues and Challenges in MR Imaging

The electrical power system operates under variable load condition, so the load tracking problem will arise which can cause voltage and frequency instabilities. Energy storage devices are able to balance the fluctuation of power generation and consumption. In this article the use of Compressed Air Energy Storage (CAES) system for microgrid is proposed. Under variable load conditions, the stored compressed air from the CAES system will be utilized to meet the demand. During power generation an excess power is used to compress the air. An air flow controller for CAES is designed to control the air flow from the CAES system, so that the microgrid follows the various load demands to maintain the stable frequency. The performance of the proposed system is validated through simulation study for various load conditions is carried out using MATLAB/Simulink.

Frequency Control of Microgrid Based on Compressed Air Energy Storage System

Frequent load fluctuation and set-point variation may affect the stability of grid-connected heavy-duty gas turbine power plants. To overcome such problems, a novel neuro-fuzzy controller is proposed in this paper for singleshaft heavy-duty gas turbines ranging from 18.2 MW to 106.7 MW. A neuro-fuzzy controller was developed using a hybrid learning algorithm and the effectiveness of the controller for all heavy-duty gas turbine plants (5, 6, 7, and 9 series) is demonstrated against load disturbance and set-point variation in a grid-connected environment. Various time domain specifications and performance index criteria of the neuro-fuzzy controller are compared with that of a fuzzy logic controller and an artificial neural network controller. The simulation results indicate that the neuro-fuzzy controller yields optimal transient and steady-state responses and tracks set-point variation faster than a fuzzy logic or artificial neural network controller. Hence, the neuro-fuzzy controller is identified as an optimal controller for heavy-duty gas turbine plants. The neuro-fuzzy controller proposed in this paper is also applicable to the latest derivative Speedtronic controller.

https://journals.tubitak.gov.tr/elektrik/issues/elk-17-25-3/elk-25-3-60-1511-242.pdf

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

Many parts of remote locations in the world are not electrified even in this Advanced Technology Era. To provide electricity in such remote places renewable hybrid energy systems are very much suitable. In this paper PV/Wind/Battery Hybrid Power System (HPS) is considered to provide an economical and sustainable power to a remote load. HPS can supply the maximum power to the load at a particular operating point which is generally called as Maximum Power Point (MPP). Fuzzy Logic based MPPT (FLMPPT) control method has been implemented for both Solar and Wind Power Systems. FLMPPT control technique is implemented to generate the optimal reference voltage for the first stage of DC-DC Boost converter in both the PV and Wind energy system. The HPS is tested with variable solar irradiation, temperature, and wind speed. The FLMPPT method is compared with P&O MPPT method. The proposed method provides a good maximum power operation of the hybrid system at all operating conditions. In order to combine both sources, the DC bus voltage is made constant by employing PI Controllers for the second stage of DC-DC Buck-Boost converter in both Solar and Wind Power Systems. Battery Bank is used to store excess power from Renewable Energy Sources (RES) and to provide continuous power to load when the RES power is less than load power. A SPWM inverter is designed to convert DC power into AC to supply three phase load. An LC filter is also used at the output of inverter to get sinusoidal current from the PWM inverter. The entire system was modeled and simulated in Matlab/Simulink Environment. The results presented show the validation of the HPS design.

http://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.oai-journals-pan-pl-85128/c/oai-journals-pan-pl-85128_full-text_11_paper.pdf

J. Kanakaraj

Papers

Intelligent energy management control for independent microgrid

A Review on Potential Issues and Challenges in MR Imaging

Frequency Control of Microgrid Based on Compressed Air Energy Storage System

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

Modeling and simulation of stand-alone hybrid power system with fuzzy MPPT for remote load application