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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
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
Contents

  • Introduction

  • Optimization Model

  • Genetic Optimization

  • Application Example

  • Summary


Background
Background

  • Going to sea

  • Large investment

  • High cost in Electrical system

  • Challenge in optimization of Electrical System


Optimization model
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 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
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
Genetic Algorithm

  • Deal with complex, multi-variables optimization problems

  • Capable to find global optimum solution

  • Flow chart of GA



Optimization variables and coding
Optimization Variables and Coding

  • Encoding

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

  • Decoding


Variable examples
Variable examples

  • Local grid topology – X1

  • DC-DC converter location – X2


Ga implementation
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
Generation Updating

  • Adaptive Generation Gap

    • 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
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
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

For Your Attention!


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