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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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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
• Going to sea
• Large investment
• High cost in Electrical system
• Challenge in optimization of Electrical System
Optimization Model

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 Introduction
• 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

Reliability Calculation
• 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
Genetic Algorithm
• Deal with complex, multi-variables optimization problems
• Capable to find global optimum solution
• Flow chart of GA
Optimization Variables and Coding
• Encoding
• The design of system is represent by some variables, which are encoded into binary string.
• Decoding
Variable examples
• Local grid topology – X1
• DC-DC converter location – X2
GA Implementation
• 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
Generation Updating
• 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

Application Example
• 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

Summary
• 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.
Thank You