Improving the Performance of Doubly Fed Induction Generator Using Fault Tolerant Control—A Hierarchical Approach

Nazir, Muhammad Shahzad; Wang, Yeqin; Mahdi, Ali Jafer; Sun, Xinhao; Zhang, Chu; Abdalla, Ahmed N.

doi:10.3390/app10030924

Cited by 20 publications

(11 citation statements)

References 35 publications

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“…To assess the exact potential of a wind project, determining the basic economic factors and configuration of an energy conversion system is important. When small wind turbines are installed, the generated and transmitted power are set to a direct current by a rectifier [24,25]. A simplified structure of a WECS is shown in Figure 3 [26].…”

Section: Types Of Wecssmentioning

confidence: 99%

Comparison of Small-Scale Wind Energy Conversion Systems: Economic Indexes

et al. 2020

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Wind energy is considered as one of the most prominent sources of energy for sustainable development. This technology is of interest owing to its capability to produce clean, eco-friendly, and cost-effective energy for small-scale users and rural areas where grid power availability is insufficient. Wind power generation has developed rapidly in the past decade and is expected to play a vital role in the economic development of countries. Therefore, studying dominant economic factors is crucial to properly approach public and private financing for this emerging technology, as industrial growth and energy demands may outpace further economic studies earlier than expected. In this study, a strategy-focused method for performing economic analysis on wind energy based on financial net present value, levelized cost of energy, internal rate of return, and investment recovery period is presented. Numerical and simulation results depict the most optimal and economical system from a 3 and a 10 kW wind energy conversion system (WECS). Moreover, the aforementioned criteria are used to determine which WECS range is the most suitable investment with the shortest payback period. Finally, an economically viable and profitable wind energy system is recommended.

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Section: Types Of Wecssmentioning

confidence: 99%

Comparison of Small-Scale Wind Energy Conversion Systems: Economic Indexes

et al. 2020

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“…The hourly solar irradiance is obtained, the power output of the photovoltaic power generation system can be obtained according to the performance parameters of the photovoltaic panel, as follows (Nazir et al., 2020b; Temiz and Javani, 2020): …”

Section: Related Workmentioning

confidence: 99%

“…With the deepening of research on the use of renewable energy worldwide and the reduction of power generation costs, more and more different types of renewable energy power generation are connected to the power system. Since, obviously, various energy sources complement one another in time and location (Nazir et al., 2020b; Zhang et al., 2020) Combined generation of renewable energy forms will offset intermittent energy losses and increase the resilience of the grid to intermittent renewable energies (Ahmed et al., 2019; Cai et al., 2020). It is necessary to evaluate the utilization value of complementary power generation using multiple energy forms.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

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Jiang

et al. 2020

Energy Exploration & Exploitation

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Generating systems are known as adequately reliable when satisfying the load demand. Meanwhile, the efficiency of electrical systems is currently being more influenced by the growing adoption of the Wind/Solar energy in power systems compared to other conventional power sources. This paper proposed a new optimization approach called Metaheuristic Scanning Genetic Algorithm (MSGA) for the evaluation of the efficiency of power generating systems. The MSGA is based on a combination of metaheuristic scanning and Genetic Algorithm. The MSGA technique is used for evaluating the reliability and adequacy of generation systems integrated with wind/Solar energy is developed. The usefulness of the proposed algorithm was tested using Reliability Test System ‘IEEE-RTS-79’ which include both of wind power and solar power generation. The result approve the effectiveness of the proposed algorithm in improving the computation time by 85% and 2% in comparison with the particle swarm optimization (PSO) and differential evolution optimization algorithm (DEOA) respectively. In addition, the proposed model can be used to test the power capacity forecasting scheme of the hybrid power generation system with the wind, solar and storage.

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“…e government and legislative authorities incentives to use new energies, concerns about the high and rising price of fossil fuels including its scarcity, and environmental issues are the most important motivations for the integration of renewable energy resources into conventional power systems [1,2]. In these circumstances, new technologies such as the combined cooling, heating, and power (CCHP) technology can organically combine heating, cooling, and power supply to realize the cascade utilization of energy, help to improve the utilization rate of energy, and reduce the emission of pollutant gases [3,4]. In the context of promoting the rapid development of clean energy and the energy coupling with the microgrid (MG) the CCHP system and MG technology are combined [5,6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm‐Random Forest Regression

et al. 2021

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The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production (wind-solar/photovoltaic (PV), heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power (CCHP) is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and sustainability in the industry. This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic (PV), CCHP system, fuel cell, and energy storage devices (batteries, cold storage, hot water tanks, and so on) are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model.

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Improving the Performance of Doubly Fed Induction Generator Using Fault Tolerant Control—A Hierarchical Approach

Cited by 20 publications

References 35 publications

Comparison of Small-Scale Wind Energy Conversion Systems: Economic Indexes

Comparison of Small-Scale Wind Energy Conversion Systems: Economic Indexes

Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm‐Random Forest Regression

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