Optimal capacitor value calculation for self excited induction generators using hybrid grey wolf differential...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Optimization Algorithms

Background:

• Self-excited induction generators (SEIGs) are crucial for isolated micro-hydro and wind power.
• Optimal excitation capacitance determination is a complex nonlinear challenge affecting SEIG performance.
• Capacitance impacts voltage buildup, regulation, frequency, and power quality under varying conditions.

Purpose of the Study:

• To develop and validate a hybrid Grey Wolf Optimizer-Differential Evolution (GWO-DE) algorithm for optimal excitation capacitor calculation in three-phase SEIGs.
• To formulate capacitor selection as a constrained single-objective optimization problem integrating voltage regulation, speed range, and cost.
• To enhance SEIG performance and power quality through optimized capacitance.

Main Methods:

• A hybrid GWO-DE algorithm was developed, incorporating an adaptive switching parameter for exploration-exploitation balance.
• The optimization problem utilized a steady-state per-phase equivalent circuit with core losses and a fifth-order magnetizing characteristic.
• The GWO-DE algorithm was benchmarked against PSO, GWO, GA, and Nelder-Mead across 24 operating conditions.
• An elite archive and stagnation-triggered reinitialization were employed to prevent premature convergence.

Main Results:

• The GWO-DE algorithm demonstrated superior performance, achieving significantly better mean fitness than comparative algorithms (e.g., 3.07x better than PSO).
• GWO-DE exhibited faster convergence (38.7% faster than PSO) and maintained terminal voltage within ±5% of rated value up to 85% load.
• Optimized capacitance ensured voltage total harmonic distortion (THD) compliance with IEEE 519 standards.
• Experimental validation on a 3.7 kW SEIG prototype confirmed GWO-DE predictions with a low average error of 2.32%.

Conclusions:

• The proposed GWO-DE algorithm effectively solves the optimal excitation capacitance problem for SEIGs.
• The optimized capacitance significantly improves voltage regulation, extends the operating speed range, and ensures power quality.
• A capacitor value contour map is provided as a practical design tool for field applications of SEIGs.