Fuel Saving Optimization for Diesel Generator Set Based on Grey Wolf-Particle Swarm Algorithm
Aiming at minimize fuel consumption,utilizing a neural network model of the diesel engine's indicated thermal efficiency as the basis for fuel consumption rate calculation.The optimization problem is formulated to determine the optimal speed for fuel economy under various load conditions,considering the upper and lower limits of engine speed and power constraints.A hybrid algorithm,combining particle swarm optimization and grey wolf optimization(GWPSO),is proposed,demonstrating its superiority through benchmark functions.The GWPSO algorithm exhibits balanced global and local optimization characteristics,making it more suitable for minimizing fuel consumption rates.Optimization results indicate that lower electrical loads result in more significant fuel savings,with a fuel savings rate exceeding 30％at 10％load and an overall fuel savings rate of 11.9％under comprehensive conditions.Optimizing the speed effectively reduces the fuel consumption rate of the variable-speed diesel generator set.
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