基于最优权重融合的火电机组智能燃烧优化方案评价

目的  随着人工智能技术的发展，基于智能优化算法的燃烧优化方案层出不穷，如GA、PSO、FPA等，这些方案各有优缺点。依托智慧电厂平台开发实时燃烧优化系统时，需要权衡选择最佳技术方案。 方法  针对传统TOPSIS法的权重赋值主观性较强的问题，提出了最优权重融合法对其进行改进，并利用改进TOPSIS法建立了火电机组智能燃烧优化方案评价体系。从优化效果、优化周期、可靠性三个方面对GA、AGA、PSO、FPA、CSO和GSA六种方案进行综合评价。 结果  结果表明：经过MCD指标分析体系和与传统方法对比的双重验证，改进TOPSIS法的赋权与MCD指标重要性排序一致，相比于传统TOPSIS法更能辨别出各方案的优劣，其结果更符合实际生产过程的要求，具有客观性强、准确性好的优点。 结论  文章研究成果可以为火电机组燃烧优化方案的抉择提供有价值的参考。

Evaluation of Intelligent Combustion Optimization Scheme for Thermal Power Unit Based on Optimal Weight Fusion

  Introduction  With the development of artificial intelligence technology, combustion optimization schemes based on intelligent optimization algorithms emerge endlessly, such as GA, PSO, FPA, and so on. When developing the real-time combustion optimization system based on the intelligent power plant platform, it is necessary to weigh and choose the best technical scheme.   Method  Aiming at the problem that the weight assignment of the traditional TOPSIS method is highly subjective, an optimal weight fusion method was proposed to improve it, and the improved TOPSIS method was used to establish an evaluation system for the intelligent combustion optimization scheme of thermal power units. The six schemes of GA, AGA, PSO, FPA, CSO, and GSA were evaluated comprehensively from three aspects: optimization effect, optimization period, and reliability.   Result  The results show that through the double verification of the MCD index analysis system and the comparison with the traditional method, the weight of the improved TOPSIS method is consistent with the importance order of the MCD index, and compared with the traditional TOPSIS method, the pros and cons of each scheme can be distinguished better. The results are more in line with the requirements of the actual production process and have the advantages of strong objectivity and good accuracy.   Conclusion  The research can provide a valuable reference for the selection of combustion optimization schemes for thermal power units.