From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing

Zhaohui Xu; Anyu Yang

doi:doi:10.11648/j.sr.20261406.18

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From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing

Zhaohui Xu^*, Anyu Yang

Published in Science Research (Volume 14, Issue 6)

Received: 24 September 2026 Accepted: 22 October 2026 Published: 10 November 2026

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Abstract

The operation and maintenance (O&M) of small hydropower stations has long relied on personal experience, facing widespread dilemmas such as difficulty in inheriting expertise, challenges in accident review due to missing decision data, and obstacles in quantifying optimization. To address these issues, this paper proposes a novel O&M model for small hydropower stations based on operation decision tracing. By utilizing an intelligent operation agent to automatically record the complete chain of "operating conditions—decisions—risks—outcomes," this model transforms the implicit experience of operators into structured data assets. The article systematically elaborates on the implementation mechanism of decision tracing and analyzes its data value from three perspectives: operation review, experience inheritance, and continuous evolution. Furthermore, it explores the significance of this model in promoting the transformation of small hydropower stations towards reduced staffing or unattended operation. Centered on the capitalization of data assets, this model reconstructs the O&M system of small hydropower stations, establishing a closed-loop feedback mechanism for self-iteration. Ultimately, this research provides new theoretical insights and practical pathways for enhancing the management level, intrinsic safety, and sustainable development of existing small hydropower stations in the era of intelligent transformation.

Published in	Science Research (Volume 14, Issue 6)
DOI	10.11648/j.sr.20261406.18
Page(s)	445-448
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Small Hydropower Station, Operation and Maintenance Model, Decision Tracing, Data Asset, Experience Inheritance, Unattended Operation

1．引言

我国小水电站分布广泛、数量众多，广泛分布于1500多个县，2016年达到最高峰47529座

[1]

。与大中型水电站相比，小水电站单站规模小、收益有限、地处偏远，难以配置充足的专业运行人员，运维模式长期停留在“老师傅带徒弟”的经验传承阶段。

小水电运维长期依赖个人经验，面临经验传承难、事故复盘缺数据、优化量化难三大困境。

（1）经验依附于人。运行判断、故障处置、调节时机等关键知识存储在资深运行人员的头脑中，人员流动即意味着经验流失

[2]

。小水电站运行人员老龄化问题突出，经验断层风险日益加剧。

（2）复盘缺乏数据。现有研究多聚焦设备状态监测，缺乏对操作决策全过程的记录与分析，复盘往往依赖当事人回忆，难以还原真实的“工况—决策”链条，事故教训无法有效转化为制度改进

[3, 4]

。

（3）优化无从量化。水能利用效率、调节策略优劣等缺乏可对比的历史数据支撑，运行优化只能依赖模糊的定性判断。

2026年水利部等七部门联合印发的《关于加快推动小水电绿色转型高质量发展的指导意见》明确提出，到2035年基本实现电站智能化、集约化、标准化。智能化不仅是设备与算法的升级，更是运维模式的重构

[5]

。本文提出基于运行决策留痕的运维新模式，将隐性经验转化为可传承、可复盘、可优化的数据资产，为小水电站向少人值守、无人值守迈进提供支撑。

2．决策留痕机制

2.1．机制定位

决策留痕是运行智能体六步构建流程（数据接入与语义建模、工况识别、安全边界判定、决策生成、解释与留痕、分级受控输出）中的关键一环。其核心思想是：智能体生成每一条运行建议时，同步生成结构化的决策解释信息

[6]

，并将运行工况、运行建议、执行结果与解释信息结构化存储，形成可追溯的运行决策记录

[7]

。智能运检代理自动记录‘工况-决策-风险-结果’全链条”。

与传统SCADA系统的历史数据存储相比，决策留痕的本质区别在于：历史库存储的是“发生了什么”（测点数值与报警事件），而决策留痕存储的是“为什么这样做”（触发工况、采用规则、风险评估与执行结果），两者共同构成完整的运行认知链条。

2.2．留痕数据结构

每条决策记录包含以下要素：

1）触发工况：决策生成时刻的工况标签（如“并网发电—来水平稳—水位正常”），以及触发该工况判定的关键测点数值与状态信号；

2）采用规则：策略库中被激活的规则编号、适用条件描述与实际匹配情况；

3）风险说明：安全边界判定结果（安全区/临界区/禁止区）及对应的风险等级；

4）建议内容与执行方式：建议的具体操作内容、是自动下发还是经人工确认执行；

5）执行结果与反馈：执行后的状态变化、运行人员是否干预及干预原因。

上述要素按统一的结构化模式存储，支持按时间、工况、规则、风险等级等多维度检索，构成电站运行知识的基础数据层

[7, 8]

。

2.3．闭环反馈

决策留痕并非单向记录，而是构成“决策—留痕—复盘—策略优化”的闭环：复盘结论可以转化为策略库规则的修订或新增，使运行策略库随运行时间推移而持续完善

[9, 10]

。这种机制使电站的运行知识体系具备了自我迭代能力。

3．数据驱动的价值实现

3.1．运行复盘：从记忆还原到事故反演

传统复盘依赖当事人回忆，信息损耗大、主观性强。基于决策留痕，复盘模式发生根本变化：

1）全过程回放。以时间为轴回放事故前后的工况演变、系统建议、人工干预与执行结果，完整还原决策链条；

2）对照分析。将实际处置过程与策略库的标准处置路径对照，定位偏差环节——是工况识别遗漏、规则缺失，还是人工干预不当

[10]

；

3）规律挖掘。对同类工况下的历史处置记录进行聚类分析，识别高频问题模式与薄弱环节，为预防性措施提供依据。

这种数据驱动的复盘模式，使事故教训从“口头警示”转化为“结构化案例”

[8,11]

，从记忆回溯转向数据驱动的事故重演，显著提升了复盘的客观性与深度。

3.2．经验传承：从个人经验到组织资产

小水电站经验传承的核心难题在于隐性知识难以显性化

[12]

。决策留痕机制为此提供了三条路径：

1）规则化沉淀。运行专家对留痕记录中的优秀处置案例进行审核，将其中的判断逻辑提炼为策略库规则，使个人经验固化为可复用的组织知识

[13]

；

2）情境化培训。新入职人员通过检索特定工况下的历史决策记录，学习“在什么情况下、依据什么规则、采取什么操作”，培训从抽象规程学习转变为情境化案例学习

[9]

；

3）跨站共享。同一管理主体旗下多座电站的留痕数据可以脱敏汇聚，相似水头、相似机型电站的运行经验可以横向迁移，缓解单站数据量不足的问题

[14]

。将个人隐性知识转化为组织规则与情景化培训素材

[2, 13]

。

3.3．持续进化：为高阶智能化奠基

留痕数据中的工况标签、安全区判定与执行反馈天然构成监督学习所需的标注信息，为机器学习提供标注数据，驱动策略迭代优化奠定基础

[15]

：

1）工况识别优化。以人工确认过的工况标签为样本，训练更精细的工况识别模型，提升复杂工况下的识别准确率

[16]

；

2）安全边界自适应。基于历史越限与故障样本，对动态安全边界参数进行数据驱动整定，使边界更贴合电站实际特性；

3）策略效果评估。以执行后的状态变化与发电效益为反馈，评估各策略的实际效果，支持策略库的优胜劣汰

[15, 17]

。

这一演进路径保持了“规则为纲、数据为用”的可信架构，避免了一步跨入黑盒模型带来的信任风险

[6]

。

4．对运维模式变革的意义

4.1．支撑少人值守与无人值守

少人值守、无人值守的前提是为远程监控中心提供带上下文的运行态势感知

[18, 19]

。决策留痕与工况识别的结合，使远程监控中心接收到的不再是孤立的报警信号，而是带工况语境、带建议依据的结构化运行态势，远程值班人员的决策负荷显著降低。云南省已建成省级小水电智慧运维平台，对85座水电站实施统一远程智能监控运维；甘肃科源电力集团明珠公司实现9座电站集控管理。这类集约化模式的深化，必然要求站端具备结构化的决策表达能力，决策留痕机制正填补了这一环节。

4.2．提升本质安全水平

决策留痕使每一次操作都有据可查，强化了运行人员的责任意识；使每一次异常处置都可追溯，支撑安全监管的精准化；使每一次经验教训都可沉淀，形成安全水平的螺旋式上升。这种以数据为纽带的管理闭环，是小水电站本质安全建设的长效机制

[20]

。

4.3．适应人员结构变化

面对运行人员老龄化与年轻人员招聘难的双重压力，决策留痕将资深人员的经验在流失前完成数字化固化，使电站运行能力不再依赖特定个人。这一特性对地处偏远、人员流动性大的小水电站尤为重要。

5．结论

本文针对小水电站经验依赖型运维模式的结构性缺陷，提出了基于运行决策留痕的运维新模式，主要结论如下：

1）决策留痕机制通过自动记录“工况—决策—风险—结果”完整链条，将隐性运行经验转化为结构化数据资产，重构了小水电站的知识管理方式；

2）留痕数据在运行复盘、经验传承与持续进化三个层面释放价值，形成“决策—留痕—复盘—优化”的自我迭代闭环；

3）该模式为小水电站少人值守、无人值守转型提供了认知基础，对提升电站本质安全水平与管理水平具有长远意义。

后续工作将聚焦留痕数据的标准化模式设计、跨电站经验迁移机制以及基于留痕数据的机器学习应用验证。

基金项目

本文为浙江省科技计划项目（2025C04006）的阶段性成果之一。

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Xu, Z., Yang, A. (2026). From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing. Science Research, 14(6), 445-448. https://doi.org/10.11648/j.sr.20261406.18

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Xu, Z.; Yang, A. From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing. Sci. Res. 2026, 14(6), 445-448. doi: 10.11648/j.sr.20261406.18

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AMA Style

Xu Z, Yang A. From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing. Sci Res. 2026;14(6):445-448. doi: 10.11648/j.sr.20261406.18

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@article{10.11648/j.sr.20261406.18,
  author = {Zhaohui Xu and Anyu Yang},
  title = {From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing},
  journal = {Science Research},
  volume = {14},
  number = {6},
  pages = {445-448},
  doi = {10.11648/j.sr.20261406.18},
  url = {https://doi.org/10.11648/j.sr.20261406.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20261406.18},
  abstract = {The operation and maintenance (O&M) of small hydropower stations has long relied on personal experience, facing widespread dilemmas such as difficulty in inheriting expertise, challenges in accident review due to missing decision data, and obstacles in quantifying optimization. To address these issues, this paper proposes a novel O&M model for small hydropower stations based on operation decision tracing. By utilizing an intelligent operation agent to automatically record the complete chain of "operating conditions—decisions—risks—outcomes," this model transforms the implicit experience of operators into structured data assets. The article systematically elaborates on the implementation mechanism of decision tracing and analyzes its data value from three perspectives: operation review, experience inheritance, and continuous evolution. Furthermore, it explores the significance of this model in promoting the transformation of small hydropower stations towards reduced staffing or unattended operation. Centered on the capitalization of data assets, this model reconstructs the O&M system of small hydropower stations, establishing a closed-loop feedback mechanism for self-iteration. Ultimately, this research provides new theoretical insights and practical pathways for enhancing the management level, intrinsic safety, and sustainable development of existing small hydropower stations in the era of intelligent transformation.},
 year = {2026}
}

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TY  - JOUR
T1  - From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing
AU  - Zhaohui Xu
AU  - Anyu Yang
Y1  - 2026/11/10
PY  - 2026
N1  - https://doi.org/10.11648/j.sr.20261406.18
DO  - 10.11648/j.sr.20261406.18
T2  - Science Research
JF  - Science Research
JO  - Science Research
SP  - 445
EP  - 448
PB  - Science Publishing Group
SN  - 2329-0927
UR  - https://doi.org/10.11648/j.sr.20261406.18
AB  - The operation and maintenance (O&M) of small hydropower stations has long relied on personal experience, facing widespread dilemmas such as difficulty in inheriting expertise, challenges in accident review due to missing decision data, and obstacles in quantifying optimization. To address these issues, this paper proposes a novel O&M model for small hydropower stations based on operation decision tracing. By utilizing an intelligent operation agent to automatically record the complete chain of "operating conditions—decisions—risks—outcomes," this model transforms the implicit experience of operators into structured data assets. The article systematically elaborates on the implementation mechanism of decision tracing and analyzes its data value from three perspectives: operation review, experience inheritance, and continuous evolution. Furthermore, it explores the significance of this model in promoting the transformation of small hydropower stations towards reduced staffing or unattended operation. Centered on the capitalization of data assets, this model reconstructs the O&M system of small hydropower stations, establishing a closed-loop feedback mechanism for self-iteration. Ultimately, this research provides new theoretical insights and practical pathways for enhancing the management level, intrinsic safety, and sustainable development of existing small hydropower stations in the era of intelligent transformation.
VL  - 14
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Zhaohui Xu

Rural Electrification Institute, Ministry of Water Resources, Hangzhou, China

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Anyu Yang

Rural Electrification Institute, Ministry of Water Resources, Hangzhou, China

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Xu, Z., Yang, A. (2026). From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing. Science Research, 14(6), 445-448. https://doi.org/10.11648/j.sr.20261406.18

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Xu, Z.; Yang, A. From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing. Sci. Res. 2026, 14(6), 445-448. doi: 10.11648/j.sr.20261406.18

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Xu Z, Yang A. From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing. Sci Res. 2026;14(6):445-448. doi: 10.11648/j.sr.20261406.18

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@article{10.11648/j.sr.20261406.18,
  author = {Zhaohui Xu and Anyu Yang},
  title = {From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing},
  journal = {Science Research},
  volume = {14},
  number = {6},
  pages = {445-448},
  doi = {10.11648/j.sr.20261406.18},
  url = {https://doi.org/10.11648/j.sr.20261406.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20261406.18},
  abstract = {The operation and maintenance (O&M) of small hydropower stations has long relied on personal experience, facing widespread dilemmas such as difficulty in inheriting expertise, challenges in accident review due to missing decision data, and obstacles in quantifying optimization. To address these issues, this paper proposes a novel O&M model for small hydropower stations based on operation decision tracing. By utilizing an intelligent operation agent to automatically record the complete chain of "operating conditions—decisions—risks—outcomes," this model transforms the implicit experience of operators into structured data assets. The article systematically elaborates on the implementation mechanism of decision tracing and analyzes its data value from three perspectives: operation review, experience inheritance, and continuous evolution. Furthermore, it explores the significance of this model in promoting the transformation of small hydropower stations towards reduced staffing or unattended operation. Centered on the capitalization of data assets, this model reconstructs the O&M system of small hydropower stations, establishing a closed-loop feedback mechanism for self-iteration. Ultimately, this research provides new theoretical insights and practical pathways for enhancing the management level, intrinsic safety, and sustainable development of existing small hydropower stations in the era of intelligent transformation.},
 year = {2026}
}

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TY  - JOUR
T1  - From Automation to Intelligence: A New Operation and Maintenance Model for Small Hydropower Stations Based on Operation Decision Tracing
AU  - Zhaohui Xu
AU  - Anyu Yang
Y1  - 2026/11/10
PY  - 2026
N1  - https://doi.org/10.11648/j.sr.20261406.18
DO  - 10.11648/j.sr.20261406.18
T2  - Science Research
JF  - Science Research
JO  - Science Research
SP  - 445
EP  - 448
PB  - Science Publishing Group
SN  - 2329-0927
UR  - https://doi.org/10.11648/j.sr.20261406.18
AB  - The operation and maintenance (O&M) of small hydropower stations has long relied on personal experience, facing widespread dilemmas such as difficulty in inheriting expertise, challenges in accident review due to missing decision data, and obstacles in quantifying optimization. To address these issues, this paper proposes a novel O&M model for small hydropower stations based on operation decision tracing. By utilizing an intelligent operation agent to automatically record the complete chain of "operating conditions—decisions—risks—outcomes," this model transforms the implicit experience of operators into structured data assets. The article systematically elaborates on the implementation mechanism of decision tracing and analyzes its data value from three perspectives: operation review, experience inheritance, and continuous evolution. Furthermore, it explores the significance of this model in promoting the transformation of small hydropower stations towards reduced staffing or unattended operation. Centered on the capitalization of data assets, this model reconstructs the O&M system of small hydropower stations, establishing a closed-loop feedback mechanism for self-iteration. Ultimately, this research provides new theoretical insights and practical pathways for enhancing the management level, intrinsic safety, and sustainable development of existing small hydropower stations in the era of intelligent transformation.
VL  - 14
IS  - 6
ER  -

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