Optimization of closing law of turbine guide vanes based on improved artificial ecosystem algorithm

• Published in: Journal of hydrodynamics. Series B Vol. 35; no. 3; pp. 582 - 593
• Main Authors: Wang, Li-ying, Zhang, Jia-jie, Fan, Hong-gang
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2023
• Subjects: Engineering; Engineering Fluid Dynamics; Hydrology/Water Resources; Numerical and Computational Physics; Simulation; optimization calculation; the closing law of guide vane; Hydraulic transient process; improved artificial ecosystem-based optimization (IAEO)
• ISSN: 1001-6058; 1878-0342
• DOI: 10.1007/s42241-023-0034-y

Optimization of the closing law of the guide vane is the most economical and efficient way to reduce the risk incurred by pressure and speed excursions, thus guaranteeing the security of the hydro-turbine and the whole hydraulic network. In order to optimize the closing law of the guide vane of hydraulic turbine, an improved artificial ecosystem optimization algorithm was proposed (IAEO). The reverse learning was used to initialize the population, multi-strategy bound handing schemes was used to improve the algorithm convergence speed. Twenty-three mathematical benchmark functions were used to test the IAEO. Results showed an improvement in the IAEO algorithm convergence speed and a stronger exploration than other algorithms. IAEO algorithm was used to optimize the closing law of the guide vane of hydraulic turbine based on the hydraulic transient calculation. The results showed that the maximum pressure in the spiral casing inlet, the minimum pressure in the draft tube inlet and the maximum speed all meet the design requirements by use of the closing law of the guide vane optimized by IAEO. Compared with other algorithms such as particle swarm optimization (PSO), artificial ecosystem-based optimization (AEO) and grey wolf optimizer (GWO), the closing law of the guide vane optimized by IAEO algorithm was proved to be of great advantages in distribution of safety margin of each optimization goal.