Download Artificial Intelligence in Power System Optimization by Weerakorn Ongsakul, Dieu Ngoc Vo PDF

By Weerakorn Ongsakul, Dieu Ngoc Vo

With the massive bring up of AI purposes, AI is being more and more used to resolve optimization difficulties in engineering. some time past 20 years, the purposes of man-made intelligence in energy structures have attracted a lot study. This publication covers the present point of purposes of synthetic intelligence to the optimization difficulties in energy structures. This publication serves as a textbook for graduate scholars in electrical energy method administration and can be worthwhile if you have an interest in utilizing synthetic intelligence in strength method optimization.

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39). Step 10: If n < Nmax or Err(n)max > İ, n = n + 1 and return to Step 6. Step 11: Calculate total cost using the objective function. where Nmax is the maximum number of iterations and İ is the threshold of the maximum error of ALHN. 00741PG23 $/h F3 ( PG 3 ) 45 d PG 3 d 180 MW These units supply a load demand of 210 MW. 005 Nmax = 2500; İ = 10–4. 7089. 4628. 5269. 57/h The convergence characteristic of ALHN for the problem based on the maximum error is shown in Fig. 13. min = 6. 8517e-005, max = 9.

32). 39). Step 10: If n < Nmax or Err(n)max > İ, n = n + 1 and return to Step 6. Step 11: Calculate total cost using the objective function. where Nmax is the maximum number of iterations and İ is the threshold of the maximum error of ALHN. 00741PG23 $/h F3 ( PG 3 ) 45 d PG 3 d 180 MW These units supply a load demand of 210 MW. 005 Nmax = 2500; İ = 10–4. 7089. 4628. 5269. 57/h The convergence characteristic of ALHN for the problem based on the maximum error is shown in Fig. 13. min = 6. 8517e-005, max = 9.

120) Ô+ 2 bg Í FC (ai + bV i Î i =1 ˚ ˛ Ó 1 - sk 2 k =1 M +Â Ê (Vgk )2 ˆ Á - 2b ˜ Ë g ¯ 2 È k Ê k NG ˘ 1 Ê k NG k ˆ k ˆ +  ÍVl ,h Á Vl , p -  DliVi , p ˜ + bh Á Vl , p -  DliVi , p ˜ ˙ ¯ 2 Ë ¯ ˙˚ k =1 l =1 Í i =1 i =1 Î Ë k k ˆ M Ê NG Vi , p NL Vl , p -1 -1 +  Á Â Ú g (V )dV + Â Ú g (V )dV ˜ ˜¯ k =1 Á l =1 0 Ë i =1 0 M NL where Vik,p output of continuous neuron p(i,k) representing power output of unit i in subinterval k, PGik, Vlk,p output of continuous neuron p(l,k) representing power flow on line l in subinterval k, Plk,p, k VȜ output of multiplier neuron Ȝ(k) representing Lagrangian multiplier associated with power balance constraint Ȝk, VȖk output of multiplier neuron Ȗ(k) representing Lagrangian multiplier for emission constraint Ȗk, k Vl ,Ș output of multiplier neuron Ș(l,k) representing Lagrangian multiplier for transmission constraint Șlk.

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