Research Article
Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning
Algorithm 1
Proposed learning algorithm.
| % Initialization | | Xmin ⟵ PerrMin | | Xmax ⟵ PerrMax | | Ymin ⟵ dotPerrMin | | Ymax ⟵ dotPerrMax | | IncX ⟵ (Xmax − Xmin)/(Nx − 1) | | IncY ⟵ (Ymax − Ymin)/(Ny − 1) | | M ⟵ Nx Ny | | for i = 0 toM − 1 | | cNetX ⟵ (i DIV Ny) IncX + XMin | | cNetY ⟵ (i MOD Ny) IncY + YMin | | cNet (i) ⟵ (cNetX, cNetY) | | W (i) ⟵ 1 | | Fold (i) ⟵ 0 | | end for | | F ⟵ Fold | | tconOld ⟵ 0 | | pitchCon ⟵ 0 | | % Execute algorithm | | (errPow, derrPow, errPowSum) ⟵ MODEL (0) | | for t = 0 to tEnd | | if t ≥ tconOld + Tc then | | errPowSat ⟵ MIN (Xmax, MAX (Xmin, errPow)) | | derrPowSat = MIN (Ymax, MAX (Ymin, derrPow)) | | [RBFout, F] = RBF(cNet, W, errPowSat, derrPowSat) | | Fold ⟵ F | | pitchCon ⟵ (pi/4) − RBFout | | if ABS (errPowSat) < minErr then | | Winc ⟵ 0 | | else | | errM ⟵ errPowSat KP + derrPowSat KD + errPowSum KI | | Winc ⟵ Fold errM mu | | end if | | W ⟵ W + Winc | | tconOld ⟵ t | | end if | | (errPow, derrPow, errPowSum) ⟵ MODEL (pitchCon) | | end for |
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