混流式水轮机内特性模型改进及在外特性曲线拓展中的应用
混流式水轮机综合特性曲线反映了水轮机高效率区域的特性,但不满足水轮机大范围内过渡过程仿真需求,在仿真前需要对水轮机低效率及负效率区域的特性进行拓展。目前常用的拓展方法其原理均是根据水轮机综合特性曲线中各参数的变化趋势并结合飞逸特性曲线等约束进行的拓展,没能充分考虑水轮机内在规律,拓展结果过度依赖个人经验,具有较大的随意性。该文通过分析水轮机各部件的能量损失建立了水轮机能量平衡关系式,结合流量调节方程对水轮机内特性模型进行了改进。针对改进后的内特性模型特点设计了一种遗传算法与最小二乘法相结合的参数辨识方法,采用水轮机综合特性曲线及飞逸特性曲线对模型参数进行了辨识,采用辨识后的水轮机模型绘制了较大...
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| Published in | 农业工程学报 Vol. 33; no. 7; pp. 58 - 66 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
西安理工大学水利水电学院,西安,710048
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2017.07.008 |
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| Abstract | 混流式水轮机综合特性曲线反映了水轮机高效率区域的特性,但不满足水轮机大范围内过渡过程仿真需求,在仿真前需要对水轮机低效率及负效率区域的特性进行拓展。目前常用的拓展方法其原理均是根据水轮机综合特性曲线中各参数的变化趋势并结合飞逸特性曲线等约束进行的拓展,没能充分考虑水轮机内在规律,拓展结果过度依赖个人经验,具有较大的随意性。该文通过分析水轮机各部件的能量损失建立了水轮机能量平衡关系式,结合流量调节方程对水轮机内特性模型进行了改进。针对改进后的内特性模型特点设计了一种遗传算法与最小二乘法相结合的参数辨识方法,采用水轮机综合特性曲线及飞逸特性曲线对模型参数进行了辨识,采用辨识后的水轮机模型绘制了较大范围的水轮机特性曲线并与实测特性曲线进行了比对,并结合实测结果对误差来源及误差对过渡过程影响进行了分析。结果表明改进后的混流式水轮机内特性模型能够正确描述水轮机特性,采用最小二乘法与遗传算法相结合的方法能够辨识模型中的参数,将该模型应用在水轮机外特性曲线拓展及过渡过程仿真中,机组过渡过程中最大转速上升率相对误差从2.11%降低到0.54%,最大压力上升率相对误差从10.70%降低到9.52%,说明该模型能够减小仿真误差、减小传统方法中对个人经验的依赖,对过渡过程计算提供了参考。 |
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| AbstractList | 混流式水轮机综合特性曲线反映了水轮机高效率区域的特性,但不满足水轮机大范围内过渡过程仿真需求,在仿真前需要对水轮机低效率及负效率区域的特性进行拓展。目前常用的拓展方法其原理均是根据水轮机综合特性曲线中各参数的变化趋势并结合飞逸特性曲线等约束进行的拓展,没能充分考虑水轮机内在规律,拓展结果过度依赖个人经验,具有较大的随意性。该文通过分析水轮机各部件的能量损失建立了水轮机能量平衡关系式,结合流量调节方程对水轮机内特性模型进行了改进。针对改进后的内特性模型特点设计了一种遗传算法与最小二乘法相结合的参数辨识方法,采用水轮机综合特性曲线及飞逸特性曲线对模型参数进行了辨识,采用辨识后的水轮机模型绘制了较大范围的水轮机特性曲线并与实测特性曲线进行了比对,并结合实测结果对误差来源及误差对过渡过程影响进行了分析。结果表明改进后的混流式水轮机内特性模型能够正确描述水轮机特性,采用最小二乘法与遗传算法相结合的方法能够辨识模型中的参数,将该模型应用在水轮机外特性曲线拓展及过渡过程仿真中,机组过渡过程中最大转速上升率相对误差从2.11%降低到0.54%,最大压力上升率相对误差从10.70%降低到9.52%,说明该模型能够减小仿真误差、减小传统方法中对个人经验的依赖,对过渡过程计算提供了参考。 TK73; 混流式水轮机综合特性曲线反映了水轮机高效率区域的特性,但不满足水轮机大范围内过渡过程仿真需求,在仿真前需要对水轮机低效率及负效率区域的特性进行拓展.目前常用的拓展方法其原理均是根据水轮机综合特性曲线中各参数的变化趋势并结合飞逸特性曲线等约束进行的拓展,没能充分考虑水轮机内在规律,拓展结果过度依赖个人经验,具有较大的随意性.该文通过分析水轮机各部件的能量损失建立了水轮机能量平衡关系式,结合流量调节方程对水轮机内特性模型进行了改进.针对改进后的内特性模型特点设计了一种遗传算法与最小二乘法相结合的参数辨识方法,采用水轮机综合特性曲线及飞逸特性曲线对模型参数进行了辨识,采用辨识后的水轮机模型绘制了较大范围的水轮机特性曲线并与实测特性曲线进行了比对,并结合实测结果对误差来源及误差对过渡过程影响进行了分析.结果表明改进后的混流式水轮机内特性模型能够正确描述水轮机特性,采用最小二乘法与遗传算法相结合的方法能够辨识模型中的参数,将该模型应用在水轮机外特性曲线拓展及过渡过程仿真中,机组过渡过程中最大转速上升率相对误差从2.11%降低到0.54%,最大压力上升率相对误差从10.70%降低到9.52%,说明该模型能够减小仿真误差、减小传统方法中对个人经验的依赖,对过渡过程计算提供了参考. |
| Abstract_FL | The combined characteristic curve of Francis turbine shows the performance of turbine working in the high efficiency area. But it isn't sufficient for simulating the system transmit process in a large area, such as turbine working in the low efficiency area and negative area in the rejection transient process. Therefore, expanding the combined characteristic curve of turbine to low efficiency and negative efficiency areas is necessary before the simulation. In general, the combined characteristic curve expanding methods, such as frequently used methods of back propagation of artificial neural network method and radial basis function neural network method, are based on the trend of each parameter in the high efficiency area. But the inherent laws in the turbine are not considered in those methods, and the expanding results are relying largely on personal experience. In this paper, the energy loss formulas on each component of turbine, such as guide vane inlet, blade inlet, blade outlet and so on, were obtained by velocity triangle analysis. According to the turbine flow regulation equation combined with the energy balance equation, the Francis turbine internal characteristic model was obtained. For the complex style and more parameters features of the model, a parameter identification method which combined the genetic algorithm and the least square algorithm was designed to avoid the remaining local optimum only by genetic algorithm or can't be solved only by the least square algorithm. It was proved that the algorithm was effective through contrast of the measurements and the simulation of turbine HLN574 in the case. The Francis turbine internal characteristic model agreed well with measurements in most area, except the area of large unit speed area. The cause of error in the large unit speed area was analyzed for complex flow state in the large unit speed area and the assumed conditions can't be satisfied. For obtaining the effect of model error on transient process simulation result, a rejection transient was simulated each time by Francis turbine internal characteristic model and measurement curve and the simulated result showed that this effect was small. Therefore, we concluded: 1) Energy loss as conditions charge should be considered in the Francis internal characteristic model and the energy balance equation and flow regulation equation should be also considered; 2) The designed parameter identification method was effective in the internal characteristic model parameters ensure; 3) The model error would increase in large unit speed area but it can be ignored in the simulation of transient process. The application of this model in the combined characteristic curve expanding could reduce the randomness of traditional methods. The model has important value in the calculation of transient process. |
| Author | 门闯社 南海鹏 |
| AuthorAffiliation | 西安理工大学水利水电学院,西安710048 |
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| Author_FL | Nan Haipeng Men Chuangshe |
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| Author_xml | – sequence: 1 fullname: 门闯社 南海鹏 |
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| Keywords | models algorithms 特性曲线 模型 参数辨识 transient process 算法 characteristic curve 过渡过程 混流式水轮机 computer simulation Francis turbine 计算机仿真 parameter identification |
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| Notes | The combined characteristic curve of Francis turbine shows the performance of turbine working in the high efficiency area. But it isn＇t sufficient for simulating the system transmit process in a large area, such as turbine working in the low efficiency area and negative area in the rejection transient process. Therefore, expanding the combined characteristic curve of turbine to low efficiency and negative efficiency areas is necessary before the simulation. In general, the combined characteristic curve expanding methods, such as frequently used methods of back propagation of artificial neural network method and radial basis function neural network method, are based on the trend of each parameter in the high efficiency area. But the inherent laws in the turbine are not considered in those methods, and the expanding results are relying largely on personal experience. In this paper, the energy loss formulas on each component of turbine, such as guide vane inlet, blade inlet, blade outlet and so on, were obtained |
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| Snippet | 混流式水轮机综合特性曲线反映了水轮机高效率区域的特性,但不满足水轮机大范围内过渡过程仿真需求,在仿真前需要对水轮机低效率及负效率区域的特性进行拓展。目前常用的拓展... TK73; 混流式水轮机综合特性曲线反映了水轮机高效率区域的特性,但不满足水轮机大范围内过渡过程仿真需求,在仿真前需要对水轮机低效率及负效率区域的特性进行拓展.目前常用... |
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| SubjectTerms | 参数辨识 模型 混流式水轮机 特性曲线 算法 计算机仿真 过渡过程 |
| Title | 混流式水轮机内特性模型改进及在外特性曲线拓展中的应用 |
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