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水电站工程安全监测仪器服役时变模型
Time-varying Service Models of Safety Monitoring Instruments for Hydropower Projects
水电站工程安全监测仪器服役时长和时变特性,是影响工程安全监测设计、实施和运行管理的核心因素之一,然而国内外却鲜有对于安全监测仪器服役时变规律的研究。收集了我国10个水电站工程安全监测仪器服役表现的实际大样本数据,总结其重要时间节点,统计其监测仪器总失效率,揭示了安全监测仪器失效率的时变规律。结果表明:①施工期,7个工程监测仪器失效率增量模型符合正态函数,6个工程监测仪器失效率增量先增后减,拐点出现在施工期的51%~84%处;3个工程失效率持续上升。②运行期,工程监测仪器失效率均呈平稳上升趋势,符合线性模型。提出的安全监测仪器服役时变模型均能有效预估各工程安全监测仪器失效率变化情况。
The service time and time-varying characteristics of safety monitoring instruments in hydropower projects are critical factors affecting the design, implementation, and operational management of engineering safety monitoring. However, there is limited research on the time-varying behavior of these instruments both in China and abroad. In this study we collected actual large-sample data on the service performance of safety monitoring instruments from ten hydropower projects in China. Important time nodes for the ten projects were summarized, and the total failure rates of monitoring instruments in each project were calculated, revealing the time-varying patterns of failure rates. Results indicate that: 1)During the construction phase, the failure rate increment model of monitoring instruments in seven projects follows a normal distribution, with the failure rate increment in six projects initially increasing and then decreasing. The inflection point occurs at 51%-84% of the construction period. The failure rate of monitoring instruments in three projects consistently increased throughout the construction phase. 2)During the operational phase, the failure rates of monitoring instruments exhibited a steady upward trend, conforming to a linear model. The time-varying models proposed in this paper effectively predict the changing failure rates of safety monitoring instruments in hydropower projects.
水电站工程 / 安全监测仪器 / 仪器完好率 / 仪器服役时变特性 / 仪器服役时变模型
hydropower projects / safety monitoring instruments / intact rate of instrument / time-varying service characteristics of instruments / time-varying service models of instruments
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水库大坝安全监测是工程安全的重要保障措施,亟需结合新一代信息技术,提升大坝安全监测能力。系统总结了raybet体育在线
近年来在大坝安全监测智能感知与智慧管理技术方面的研究及应用工作,通过研发系列化智能传感器、智能采集单元和物联网感知平台,建设统一的大坝安全监测数据资源池,开发通用化安全监测云服务系统,搭建专业数据挖掘平台和综合可视化应用,实现了大坝安全监测数据感知、传输、管理、分析及展示全链路应用,形成了大坝安全监测全生命周期智慧解决方案。研究成果已在乌东德、溪洛渡、向家坝、大藤峡等100余项水利水电工程中成功应用,为保障工程建设及运行安全发挥了重要的支撑作用,具有很好的推广应用前景。
Monitoring on reservoir and dam safety is an important guarantee for the safe operation of project. It is urgent to improve the ability of dam safety monitoring by using the new generation of information technology. In this paper we systematically summarize the researches and applications made by Changjiang (Yangtze) River Research Institute in intelligent perception and intelligent management technology of dam safety monitoring in recent years. We built a unified data resource pool for dam safety monitoring by developing a series of intelligent sensors, intelligent acquisition units and Internet of things sensing platform, and developed a generalized safety monitoring cloud service system. We also built professional data mining platform and comprehensive visualization application to realize the full link application of data perception, transmission, management, analysis and display for dam safety monitoring, forming a full life cycle intelligent solution for dam safety monitoring. The research achievements have been applied in more than 100 water conservancy and hydropower projects, such as Wudongde, Xiluodu, Xiangjiaba and Datengxia hydropower stations, playing an important supporting role in ensuring the safety of project construction and operation.
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高坝泄洪伴随着能量的传递和释放,时至今日泄水建筑物遭受破坏的实例仍屡见不鲜,高坝泄洪消能是坝工建设与运行的关键技术之一。以2000年以来国内新建的11座特高拱坝(坝高≥200 m)为主要考察对象,其泄洪消能布置和水力指标均居世界前列。在系统总结探讨水垫塘消能评价指标、孔口体型、泄洪消能布置和水力特性的基础上,重点阐明坝身消能的3种典型创新模式:水舌碰撞的二滩模式、水舌不碰撞的锦屏一级模式、水舌碰撞与不碰撞结合的旭龙模式。研究思路和工程应用逐渐从“泄洪消能工程安全”向“泄洪消能工程安全与减轻岸坡泄洪雾化并重”转变,且水垫塘冲击动水压力均<15×9.81 kPa(标准限值)。近20 a特高拱坝建设快速发展极大地推动了该领域的技术进步和泄洪消能模式创新。建议开展复杂边界数值模拟研发、高坝泄洪TDG(总溶解气体)生成与释放过程研究等。
The flood discharge of high dam is accompanied by the transmission and release of energy. Up to now, examples of damage to discharge structures are still common. The flood discharge and energy dissipation of high dam is one of the key technologies for dam construction and operation. Eleven newly-built super-high arch dams with heights larger than 200 m in China since 2000 are taken as the main research objects. The layout of flood discharge and energy dissipation and hydraulic indexes of these super-high dams are among the top in the world. We systematically summarized their energy dissipation evaluation indexes, orifice shapes, flood discharge and energy dissipation layouts and hydraulic characteristics of plunge pool. On this basis, we further expounded three typical innovative modes of energy dissipation of dam body: Ertan mode with jets collided, Jinping mode with jets not collided, and Xulong mode with a combination of jets both collided and not collided. The research priority route and engineering application gradually changed from flood discharge and energy dissipation safety to the safety of flood discharge and the reduction of the atomization of bank slope, with the dynamic pressure in plunge pool smaller than 15×9.81 kPa(standard limit). The rapid development of super-high arch dam construction in the recent two decades has greatly promoted the technological progress and the innovation of flood discharge and energy dissipation mode in this field. We suggest further strengthen the research and development on numerical simulation of complex boundary and the research on the generation and release process of TDG for high dam flood discharge.
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位移监控模型需要对拱坝变形性态兼具良好的解释和预测能力。水压-滞后-周期性温度-时效四因子HHST(Hydraulic,Hysteretic,Seasonal and Time)模型能够合理地解释锦屏一级拱坝的黏弹性滞后变形性态。为进一步提升该模型的预测精度,使用支持向量机(SVM)建立有限元计算所得拱坝黏弹性滞后位移与其因果因子之间的隐式关系,再将其融入到HHST模型中,进而基于多元线性回归建立拱坝位移的组合监控模型。以锦屏一级拱坝为例,减少输入因子数的组合模型的预测精度明显高于直接以HHST模型中18个因子作为输入的单一模型;SVM对滞后水压位移分量的预测精度明显高于基于约束最小二乘法的线性回归模型,采用2种滞后水压分量所建组合模型对拱坝变形性态具有相近的解释能力,而采用SVM滞后水压分量建立的组合模型可有效地提高拱坝位移的预测精度,多测点均方误差(MSE)平均降低21.67%,决定系数R<sup>2</sup>整体提高0.07%。
A displacement monitoring model should well interpret and predict the deformation behavior of arch dam. HHST model could explain the viscoelastic hysteretic deformation behavior of Jinping-I arch dam. To further improve the prediction accuracy of the HHST model, the nonlinear relationship between the finite element method (FEM)-calculated viscoelastic hysteretic displacement of arch dam and its causal factors is modeled by the support vector machine (SVM) and is used as a whole variable in the HHST model. In subsequence, a combinatorial monitoring model is established for the displacement of arch dam based on multiple linear regression (MLR). Case study of the Jinping-I arch dam shows that the prediction accuracy of the combinatorial monitoring model, which has a reduced number of input factors, is significantly higher than that of simple models directly established with all the 18 causal factors of the HHST model. SVM has a better prediction accuracy for the hysteretic hydraulic displacement than that of constrained least square method-based linear regression model. The two combined monitoring models, respectively using the SVM and linear regression-based hysteretic hydraulic displacement component, have similar interpretation ability for the measured deformation behavior of arch dams, while the former can effectively improve the prediction accuracy of dam displacement, with the average mean square error(MSE) of multiple monitoring points dropping by 21.67% and the average determination coefficient <i>R</i><sup>2</sup> rising by 0.07%.
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对于寒冷地区的混凝土大坝,由于表层保温层的影响,其内部温度往往滞后于气温变化。当内部温度计缺失时,使用水力-季节-时间(HST)模型进行大坝预测时存在较大的误差,且即使利用内部温度计进行多元回归(MR)模型的建模也无法反映温度与变形的非线性关系。因此,针对现阶段对高寒区变形预测精度低的问题,提出利用反向学习后的鲸群(OWOA)算法对RReliefF因子加权支持向量机(RFWSVR)与分布滞后线性模型(DLM)的温度因子的超参数进行寻优,以构建缺乏内部温度计的寒区混凝土大坝变形预测模型。结果表明:通过对所建立的变形预测模型与传统统计模型和其余常用机器学习算法的性能比较,证明所建立模型具有较高的预测精度,能更好地反映保温混凝土大坝的工作特点。
Due to the protection of surface thermal insulation layer, the internal temperature of concrete dam in cold region lags behind the air temperature. When internal thermometers are lacking, HST (hydrostatic-seasonal-time) model generates large error in predictions; even when internal thermometers are present, MR (multiple regression) model can not refelct the nonlinear relation between temperature and deformation. To address the problem of inaccurate deformation prediction in alpine regions, we propose to use a support vector regression (SVR) weighted by the RReliefF algorithm and DLM(Distribution Lag Model) temperature factors to predict dam displacement. The necessary hyperparameters are optimized using an improved whale swarm approach with OBL (Opposition-based Learning). By comparing the performance of the proposed model with MR and other machine learning algorithms, we found that the proposed model has higher prediction accuracy and better reflects the working characteristics of an insulated concrete dam.
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致谢:感谢中国长江三峡集团有限公司流域枢纽运行管理中心、乌东德建设部、白鹤滩建设部、向家坝电厂、清江梯调管理中心等为本研究收集工程监测仪器基本信息资料提供的支持和帮助。
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