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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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  1. 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

  2. Contents • Introduction • Optimization Model • Genetic Optimization • Application Example • Summary

  3. Background • Going to sea • Large investment • High cost in Electrical system • Challenge in optimization of Electrical System

  4. 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

  5. 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

  6. 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

  7. Genetic Algorithm • Deal with complex, multi-variables optimization problems • Capable to find global optimum solution • Flow chart of GA

  8. Optimization Structure

  9. Optimization Variables and Coding • Encoding • The design of system is represent by some variables, which are encoded into binary string. • Decoding

  10. Variable examples • Local grid topology – X1 • DC-DC converter location – X2

  11. 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

  12. 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

  13. 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

  14. Optimization Results

  15. Best 5 solutions

  16. 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.

  17. Thank You For Your Attention!

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