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Modeling Of Grid Connected Hybrid System With Fuzzy Logic Controller For Voltage Regulation

P.Loganthurai, G.Manikandan
Department of Electrical and Electronics Engineering, K.L.N College of Engineering, Madurai, India.
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This paper presents the voltage regulation of hybrid power system with the inter connection of PV system, wind energy conversion system and battery system. The voltage regulation is done with the help of fuzzy logic controller through simulations using MATLAB / SIMULINK. In the proposed system the solar energy and wind energy is combined to produce electrical energy and a battery source is used for charging and discharging of energy. Then power electronic converters are used for the conversion of ac to dc and dc to ac. A PWM inverter is used for the conversion of dc to ac supply. In which a control circuit is designed using fuzzy logic controller for gate signals for the voltage regulation of the hybrid energy system. Then the hybrid energy system is connected to the grid from which the energy is supplied to the loads. The voltage regulation is done for both AC and DC loads.


PV, Wind, Battery, PWM Inverter, Fuzzy logic controller.


The effective utilization of renewable energy systems is discussed by many authors. The optimal sizing for windsolar- battery hybrid power system according to the system working ingrid-connected and stand-alone modes is carried out in the system [1]. The renewable energy sources are time dependent and its availability has a daily and seasonal pattern which leadsto difficulties in the regulation of the output power to match-up with the demand [2]. In the distribution system the weakest branches which are found to go to the instability state are selected for DG allocation to maintain voltage stability in the system [3]. The system cost involves investments, replacements and operation and maintenance as well as loss of load costs [4].The whole power distribution system is designed as a system with controllable converters with the overall system cost and reliability that actually improve the system performance [5].The developed methodology helps to obtain the optimal number of wind turbines, PV panels and storage units ensuring that the system total cost is minimized while guaranteeing a highly reliable source of load power to the system [6].The Power-flow calculations are carried out in the system to assess the impact of fluctuation of solar irradiance on the grid voltage has to be analyzed in the system [7]. The impacts of a large-scale wind generation on the voltage profile, system operation, and system security have been investigated and studied in the system [8]. The system plan was developed traditionally to achieve a minimum cost objective (MCO) in the system while satisfying the reliability, energy demand, stability and battery constraints in the system. The minimum emissions objective (MEO) is also an important objective to achieve the result to the above mentioned constraints. The above problems can be solved using linear programming which minimizes the two objectives at the same time of a multi-objective problem in the system [9].The ability of energy storage system to increase the penetration of renewable generation on weak electricity grids is predicted and to improve the generation of electricity in the system [10].The generation and storage units for each system are properly sized in order to meet the load demand and minimize the total annual cost in the system [11].A decision support technique will help the decision maker’s to study the factors in the design of a hybrid power system for grid connected applications that relates mainly to social and political conditions, and to the technical advances and economics in the system[12].The isolation and the demand in load are modeled as stochastic variables with the help of historical data and experimentation results respectively [13].The optimal design of a hybrid wind-solar power system for either autonomous or grid-linked applications in which the analysis employs linear programming techniques to minimize the average production cost of electricity while meeting the load requirements in reliability, and takes the environmental factors into consideration in the design and operation phases [14]. For a given load and a desired loss of power supply probability is based on optimum number of PV modules and batteries were calculated based on the minimum cost of the power system [15].


In hybrid power systems, a number of renewable energy generators and storage components are combined to meet the energy demand of the power system. It mainly includes PV generators and wind generators, the others sources of electrical energy can also be added to meet the energy demand. It is essential to know the energy demand and the resources available at that site before developing a hybrid power system. The energy planners must study the availability of solar energy, wind, and other potential resources at that site. This will help them to design what kind of hybrid power system will be suitable to meet the demand. In this paper, a brief technical description of some of the different hybrid power system configurations is considered. It mainly includesPV system, wind energy and battery system to form a hybrid power system as shown in fig. 2.1.
The fuzzy logic controller (FLC) is used in the fuzzy inference system which consists of a formulation of the mapping from a given input set to an output set. The process of mapping denotes the basis from which the inference or conclusion can be made. The structure of fuzzy inference system comprises of three components namely a rule base, a data base, and a reasoning mechanism. The first component is the rule base that contains a selection of fuzzy rules then the second component is the data base, that defines the membership functions used in the fuzzy rules and the third component is a reasoning mechanism that performs the inference procedure according to the rules to derive a desired output.
The FLC consists of four components, they are fuzzification interface, knowledge base, decision making logic, and defuzzification interface as shown in fig 3.1.
Fuzzification: In the process of fuzzification, the input variables are fuzzified i.e. it is the conversion of the input data into suitable linguistic values.
Knowledge base: The knowledge base consists of linguistic control rule base anda database. The database provides the necessary definitions, which are used to specify the fuzzy data manipulation andlinguistic control rules in an FLC. By means of set of linguistic control rules the rule base determines the control policy of domain experts.
Decision making logic: It is the capability in which human intelligence is stimulated using human decision making based on fuzzy concepts.
Defuzzification: The defuzzification is the process in which it converts the range of output variables into corresponding universe of discourse, the system is said to be a non-fuzzy logic decision system if the output from the defuzzifier is a control action for a process. There are different techniques for defuzzification such ascentroid method, maximum method, height method etc.


In the proposed system the hybrid power system includes the PV system, the wind energy system, and the battery system
A. PV system:
The equivalent circuit of PV cell is shown in fig 4.1 in which a bypass diode is connected across the circuit.


The modeling and simulation of hybrid power system with fuzzy logic controller has been demonstrated for voltage regulation of the system in which the issues in varying nature of the renewable energy sources are considered in the system. The fuzzy logic controller is designed with the help of MATLAB / SIMULINK in which the d axis and q axis voltage is given as input the fuzzy logic controller and the output dq axis voltage is used for generating pulses for the gate signal of PWM inverter for regulation of voltage in the system. The voltage regulation is verified for AC and DC loads in the hybrid power system


The authors are grateful to the principal and management of K.L.N college of Engineering, Madurai for providing all facilities for the research work.


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