عنوان

Controller parameter optimization for photovoltaic system using metaheuristic algorithm

Number

TL48670

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Controller parameter optimization for photovoltaic system using metaheuristic algorithm

General Material Designation

[Thesis]

First Statement of Responsibility

Rahila Naseeha Abul Kalaam

Subsequent Statement of Responsibility

Muyeen, S. M.

Name of Publisher, Distributor, etc.

The Petroleum Institute (United Arab Emirates)

Date of Publication, Distribution, etc.

2015

Specific Material Designation and Extent of Item

133

Text of Note

Committee members: Al Durra, Ahmed; Al Sayari, Naji; Beig, Abdul Rahiman; Harid, Noureddine

Text of Note

Place of publication: United States, Ann Arbor; ISBN=978-1-339-52784-0

Dissertation or thesis details and type of degree

M.S.

Discipline of degree

Electrical Engineering

Body granting the degree

The Petroleum Institute (United Arab Emirates)

Text preceding or following the note

2015

Text of Note

In recent years, the demand of renewable energy is rising rapidly, especially on photovoltaic (PV) power. The grid integration issues of photovoltaic power should be handled carefully. In grid-connected PV system, grid-tied inverter plays an important role in normal and fault ride-through operations. The cascaded control is widely used for controlling the grid tied-inverter. The tuning of four PI controllers in cascaded control of grid-tied PV inverter along with another PI controller in maximizing output power is very cumbersome when the system is difficult to express in terms of a mathematical model due to the system nonlinearity. In this thesis, an attempt is made to design the parameters of all PI controllers of a grid-tied PV system which works well in different types of grid-fault conditions. Utilizing two metaheuristic algorithms namely Bacteria Foraging Optimization (BFO) and Cuckoo Search (CS), the complete optimization process is splitted into two phases: the first part is designing the cost function and for this purpose initially Response Surface Methodology (RSM) is used. RSM is a statistical tool which helps to give a response between variables and factors. The cost function from RSM is the response recovery time after a fault. As RSM has its own limitations of high computational time and statistical behavior in this study RSM is replaced later by Artificial Neural Network (ANN) to derive the cost function. Another reason of using ANN is to reduce the number of design experiments required to develop the fitness function. The second part of the study is to apply BFO & CS to the cost function to minimize the fault recovery time. An extensive comparative study is done for RSM-BFO, ANN-BFO, RSM-CS, and ANN-CS. The optimization results obtained from proposed methods are also compared with standard genetic algorithm. The PV panel, boost converter, inverter, and distribution system along with the controllers are modeled using PSCAD/EMTDC. Transient performance of the PI controllers with optimum design values is evaluated under different symmetrical and unsymmetrical grid fault conditions. Finally, the best optimization method is recommended for grid-tied PV system.

Alternative Energy; Electrical engineering

Subject Term

Applied sciences;Bacteria foraging optimization algorithm;Controller parameter optimization;Cuckoo search algorithm;Photovoltaic system

Al Jahwari, Dawood Sulaiman

Muyeen, S. M.

Subdivision

Electrical Engineering

The Petroleum Institute (United Arab Emirates)

Call Number

1771907949; 10027586

Electronic name

p

[Thesis]

276903

a

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