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dc.contributor.authorMAKHIJA, KULDEEP KAUR-
dc.date.accessioned2019-09-24T07:03:29Z-
dc.date.available2019-09-24T07:03:29Z-
dc.date.issued2018-05-
dc.identifier.urihttp://dspace.dtu.ac.in:8080/jspui/handle/repository/16496-
dc.description.abstractFor the social and economic growth and in order to enhance the living standards of human among the invention of energy, it is observed that nearly all energy is applied. In order to advance the human life and assure routine necessities of the human such as lightning, food processing, comfort capacity and communication, are met correctly by energy, as energy is much significant factor in helping the productive procedures. In the environment, a significant characteristic of global warming is carbon content, which may be decreased to a large extent by utilizing renewable sources of energy. Sunlight is the major significant renewable sources of energy which is utilized through the solar PV mechanism. Since sunlight is freely available, this mechanism is much desired. The Maximum Power Point Tracking Controller (MPPT) is the mechanism that constructs the photovoltaic modules to run at optimum rate and extort more energy out of it. Using MPPT in order to attain the maximum effectiveness, several paradigms are proposed from time to time. MPPT paradigms should be efficient as well as must have a fast convergence. This report proposes an artificial- intelligence-based solution to interface photovoltaic (PV) array with a resistive load and to deliver maximum power to the load. The maximum power delivery to the load is achieved by MPPT controller which employs adaptive neurofuzzy inference system (ANFIS). The proposed ANFIS-based MPPT offers an extremely fast dynamic response with great accuracy. The system consists of photovoltaic module, boost converter and ANFIS controller to control the duty cycle of boost converter switch. Later, the solar system is hybridized with the wind energy system as the hybrid renewable energy system gives a very versatile scope of renewable energy system. The entire proposed system has been modeled and simulated using MATLAB/simulink software. The simulation results show that the proposed ANFIS MPPT controller is very efficient, very simple and low cost. The conventional tracking MPPT mechanisms have a major demerit of being slow.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTD-4329;-
dc.subjectHYBRID RENEWABLE ENERGY SYSTEMen_US
dc.subjectNEURO-FUZZYen_US
dc.subjectMPPTen_US
dc.subjectANFISen_US
dc.titleMODELLING AND SIMULATION OF HYBRID RENEWABLE ENERGY SYSTEM USING NEURO-FUZZY BASED MPPTen_US
dc.typeThesisen_US
Appears in Collections:M.E./M.Tech. Electrical Engineering

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