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Paper Title. Optimization of Electrical System for a Large DC Offshore Wind Farm by Genetic Algorithm. M. Zhao, Z. Chen, F. Blaabjerg Institute of Energy Technology, Aalborg University. Contents. Introduction Optimization Model Genetic Optimization Application Example Summary. Background.

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Optimization of Electrical System for a Large DC Offshore Wind Farm by Genetic Algorithm

M. Zhao, Z. Chen, F. Blaabjerg

Institute of Energy Technology, Aalborg University

• Introduction

• Optimization Model

• Genetic Optimization

• Application Example

• Summary

• Going to sea

• Large investment

• High cost in Electrical system

• Challenge in optimization of Electrical System

Minimize Cost

Subject to

Objective Obj_Value = Cost - α(Rsys - Rmin)

Function

• αis the penalty coefficient

Combined

• Cost:

• System Reliability Rsys

• Reliability Threshold Rmin

• Reliability Calculation Modeling

• Viewed as a graph

• Stochastic network

• Component in two states

• Multiple terminals

• Component Reliability

λ: Failure rate

r: Repair duration

• Reliability Definition:

1. >= 1 Operative paths

2. N Operative paths (√)N = Number of WT

3. >=M Operative paths (+) M < N

• Step 1: Find an operative path L_i from all the wind turbines to PCC

• Step 2: Repeat Step 1 to Find all the possible operative paths

• Deal with complex, multi-variables optimization problems

• Capable to find global optimum solution

• Flow chart of GA

• Encoding

• The design of system is represent by some variables, which are encoded into binary string.

• Decoding

• Local grid topology – X1

• DC-DC converter location – X2

• Selection: Rank-based selection

• Chromosomes are ranked according to fitness values

• Selection operator:

• Less fitness value -> higher probability to be selected

• Crossover: Single-Point crossover.

• Mutation: Full bits mutation with variable probability

• Pm=Pm-ΔPm

• Feasibility Check

• G=0.4+C((FAVG(t-1)-FAVG(t))/FAVG(t)) FAVG(t-1)>FAVG(t)

• G=0.4 FAVG(t-1)<FAVG(t)

C is a constant which determines how the improvement of fitness will influence G

• 2 MW wind turbines

• 200 MW offshore wind farm

• 150 km DC transmission

N Population size 20

MAX_G Maximum generation 70

Pc Probability of crossover 0.6

Pm,init Initial probability of mutation 0.1

Pm,step Step value of Pm. 0.0018

Rmin Reliability threshold 0.5

αPenalty coefficient 40

C Replacement Ratio 5

Bias Bias coefficient in selection 2.0

• Electrical system of an offshore wind farmcan be modeled as:

‘Network Data’ and ‘Component Parameters’

• Via defining variables to present a system design, Genetic Algorithm can be applied to optimize the electrical system.

• Objective: Minimum cost with required reliability .

• More factors shall be considered in the future.