Application of immune algorithm-based particle swarm optimization for optimized load distribution among cascade hydropower stations.

*(English)*Zbl 1186.90134Summary: The immune algorithm-based particle swarm optimization (IA-PSO), which is proposed by involving the immune information processing mechanism into the original particle swarm optimal algorithm, improves the ability to find the globally excellent result and the convergence speed with its special concentration selection mechanism and immune vaccination. Based on analyzing the model of load distribution among cascade hydropower stations and the traits of IA-PSO, the corresponding mathematical description and the solution procedure made with IA-PSO are given in detail. The result demonstrates that IA-PSO can achieve both a superior load distribution scheme and a higher convergence precision as compared to PSO, and will hopefully be applied to solving more extensive optimization problems.

##### MSC:

90C59 | Approximation methods and heuristics in mathematical programming |

PDF
BibTeX
XML
Cite

\textit{A. Li} et al., Comput. Math. Appl. 57, No. 11--12, 1785--1791 (2009; Zbl 1186.90134)

Full Text:
DOI

##### References:

[1] | Zhang, Y., Comment and analysis on the methods of solving daily economic operation in-plant or among cascaded hydropower stations, Water power, 7, 49-50, (2000) |

[2] | Zhang, Z.; Zhou, J.; Yang, J., A priority dynamic programming method for hydropower plants, Hydropower automation and dam monitoring, 29, 1-4, (2005) |

[3] | Cai, X.; Lin, S.; Ma, P.; Sun, F.; Liu, Z., Study on optimal operation of cascaded hydropower plants in electricity market, Power system technology, 27, 6-9, (2003) |

[4] | Zhang, R.; Han, G.; Bai, J.; Wang, Q., The application of a genetic algorithm in in-plant economical operation of hydropower station, Journal of north China institute of water conservancy and hydroelectric power, 27, 61-64, (2006) |

[5] | Li, C.; Ji, C.; Li, W., Study on application of particle swarm optimization to in-plant economic operation of hydropower station, Water resource and hydropower engineering, 37, 88-91, (2006) |

[6] | J.-B. Park, K.-S. Lee, J.-R. Shin, Economic load dispatch for non-smooth cost functions using particle swarm optimization, in: IEEE Power Engineering Society General Meeting, Ontario, Canada, 2003, pp. 938-943 |

[7] | Gaing, Z.-L., Particle swarm optimization to solve the economic dispatch considering the generator constraints, IEEE transactions on power systems, 18, 1187-1195, (2003) |

[8] | Li, H.; Li, C.; Zhou, Y., A practical unit commitment and load distribution model for hydropower plants, Hydropower automation and dam monitoring, 27, 1-4, (2003) |

[9] | Yang, L.; Xu, H., The daily load optimal distribution study on cascaded hydroelectric stations, Journal of henan education institute (natural science), 10, 30-33, (2001) |

[10] | Xie, X.; Zhang, W.; Yang, Z., Overview of particle swarm optimization, Control and decision, 18, 129-134, (2003) |

[11] | Wang, X.; Zhang, M., Short-term scheduling optimization of hydro-thermal power systems based on refined particle swarm algorithm, Power system technology, 18, 129-134, (2003) |

[12] | Tang, J.; Xiong, X.; Wu, Y.; Jiang, X., Power system reactive power optimization based on modified particle swarm optimization algorithm, Electric power automation equipment, 24, 81-84, (2004) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.