Journal of Hydroelectric Engineering

水力发电学报

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The journal is included in the following databases:
(i)Scopus,
(ii)The Chinese Science Citation Database Source Journals,
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(vi)Chinese Academic Journals (CD).


2026 Vol. 45, No. 4 Published: 2026-04-25

	1	Evaluation method for intelligence degree of intelligent dams
		LU Liehuan, HU Yu, CHEN Li, SHENG Jinbao, GONG Shilin, SUN Zhengdong, MA Rui, LIU Qingliang, LIAN Xinjun, WANG Haojun, OUYANG Jun, LIU Xianliang, LEI Ting, LIU Xiaming, HAN Zhi, XIAO Peng, LI Qingbin
		DOI: 10.11660/slfdxb.20260401
		Presently, there exists no method or even no unified scientific standard available yet to evaluate the intelligence degree of a dam, whereas this is crucial to promote intelligent dam construction. Based on the definition and fundamental characteristics of intelligent dams, this paper develops an evaluation system for assessing the intelligence degree, across the four dimensions of Perception, Analysis, Decision-Making, and Control. This system classifies intelligent dams into four major grades: Digital Intelligent Dams, Simulated Intelligent Dams, Twinned Intelligent Dams, and Intelligent-Control Intelligent Dams. Each grade focuses on one core performance, which is further divided into three levels of maturity (primary, intermediate, and advanced, respectively), totaling 12 intelligent grades. An evaluation method for intelligent dams is formulated following a certain evaluation logic, i.e. determining grades within dimensions and progressing grade by grade. Through case studies of four reservoirs, this paper explores the practicality and effectiveness of our new evaluation system and identifies directions for further improvement.
		2026 Vol. 45 (4): 1-11 [Abstract] ( 58 ) PDF (3252 KB) ( 91 )

	12	Large language model-driven automated construction by knowledge graphs for intelligent construction in hydraulic engineering
		WANG Xudong, MA Gang, ZHANG Dongliang, QU Tongming, ZHOU Wei
		DOI: 10.11660/slfdxb.20260402
		Knowledge graphs can efficiently integrate the knowledge of a hydraulic project and advance digitalization significantly. However, traditional methods face challenges in cross-domain ontology construction, high annotation cost, and limited transferability. This study constructs an automated knowledge graph framework that leverages large language models (LLMs) for cross-domain intelligent hydraulic construction. The method has two parts: (1) constructing a shared ontology through terminology discovery, co-occurrence networks, and LLM reasoning to resolve cross-domain semantic inconsistencies; (2) extracting enhanced knowledge, combining prior knowledge, hybrid retrieval, dynamic prompting, and chain-of-thought reasoning to reduce LLM hallucinations. Numerical experiments show the shared ontology achieves structural consistency, with cross-domain knowledge extraction reaching an average F1 score of 84.5, outperforming conventional models. This validates the method's effectiveness in multi-subdomain knowledge integration with reduced annotation requirements.
		2026 Vol. 45 (4): 12-26 [Abstract] ( 104 ) PDF (5537 KB) ( 75 )

	27	Deformation-coupled prediction model based on spatial feature fusion and multi-point collaboration
		JIN Shenle, YANG Pingrong, HU Chao, TAN Liwei, LIU Congcong, GAN Xiaoqing
		DOI: 10.11660/slfdxb.20260403
		This study develops a collaborative multi point ensemble forecasting framework to address the limitations of conventional dam deformation prediction models—relying on isolated single point data and thereby failing to simultaneously account for multi source environmental factors and spatial synergistic effects. First, we use a correlation variation-based CA KMeans clustering algorithm to partition the dam’s monitoring stations into several sub clusters by their temporal deformation characteristics and spatial positions, so as to enhance inter point synergy. And, we construct a high dimensional spatiotemporal feature matrix (HST M) by integrating multi source influencing factors－such as water pressure, temperature, time dependent effects, and spatial coordinates－to characterize the dam’s overall deformation behavior comprehensively. Then, an autoencoder is adopted to perform dimensionality reduction and feature refinement on these high dimensional inputs, automatically extracting critical nonlinear correlations while suppressing redundancy. Finally, ridge regression serves as the predictor, leveraging its L2 regularization to deliver stable, well generalized deformation forecasts even under high dimensional, multicollinear conditions. Case studies demonstrate our new framework not only enhances predictive accuracy and robustness but offers high applicability and computational efficiency.
		2026 Vol. 45 (4): 27-42 [Abstract] ( 40 ) PDF (1649 KB) ( 38 )

	43	Game-based clearing method for variable-speed pumped storage participating in integrated electricity, frequency regulation, and ramping markets
		LI Fei, HUANG Kangjie, SU Kai, YAN Xinyue, LI Xianshan, WANG Qiujie
		DOI: 10.11660/slfdxb.20260404
		As a future trend in developing pumped storage, its variable speed operation features fast two-way responses and betters its regulation performance. However, under the current market arrangements, fragmented pricing signals and decoupled bidding decisions across the power markets constrain the revenue potential of a variable-speed pumped storage plant (VSPS). This paper describes a game-based approach for the clearing of a VSPS plant that participates in the coupled electricity-frequency regulation-ramping markets. A Stackelberg bi-level model is formulated, where, at the upper level, the VSPS plant acts as the leader and maximizes its profit by submitting its price–quantity offers to each market, while at the lower level, the joint market operator minimizes the total system operating cost and clears market prices and awarded quantities. The bi-level model is reformulated as a single-level optimization model via the Karush-Kuhn-Tucker conditions, using the McCormick envelopes to linearize the bilinear revenue terms. Case studies show that, in multi-product trading settings, our new approach reduces system operating costs effectively, and reveals and quantifies the flexibility value of VSPS in ancillary services, increasing the plant revenues significantly.
		2026 Vol. 45 (4): 43-58 [Abstract] ( 50 ) PDF (819 KB) ( 25 )

	59	Deep reinforcement learning-driven optimal scheduling for hydro-photovoltaic-storage complementary systems
		XIANG Cong, HUANG Xianfeng, LI Junchen, ZHOU Shihao, FANG Guohua, ZHOU Lun
		DOI: 10.11660/slfdxb.20260405
		The independent operation of hydropower, photovoltaic (PV) and energy storage stations is constrained by transmission channel capacity, leading to frequent curtailment of water and PV power, which limits the clean energy absorption capacity of power grids. To address such an issue, this paper describes an optimal scheduling method for hydro-PV-storage complementary systems based on the Asynchronous Advantage Actor-Critic (A3C) algorithm, which is applicable to large-scale hydro-PV-storage coordinated operation scenarios. First, an operational scenario of hydro-PV-storage stations is constructed, and an optimal scheduling model is built based on the short-term versus medium- and long-term complementary guidance mechanism. Then, for a hydro-PV-storage complementary system, we transform its optimal scheduling problem into a Markov decision process, and achieve efficient strategy exploration and learning via deep reinforcement learning algorithms. Finally, we validate the method through application to such a system in the Yarkant River basin, Xinjiang. Results show the A3C algorithm stably converges to high reward values and improves system-absorbed electricity significantly with its computational cost notably lower than other algorithms, demonstrating its promising practical application value.
		2026 Vol. 45 (4): 59-72 [Abstract] ( 53 ) PDF (3905 KB) ( 44 )

	73	Method of generating extreme runoff scenarios for river basins coupled with meteorological and hydrological elements
		CHEN Gang, WANG Yongcan, DU Chengrui, LUO Bin, WANG Liang, YANG Junwen, NIE Zhuang
		DOI: 10.11660/slfdxb.20260406
		Extreme meteorological events have been occurring frequently worldwide, posing dual challenges to renewable energy integration and secure power supply for the grid systems with a high hydropower proportion. To enhance the systems’ dynamic responding capability to such extreme events, this study describes a new methodology for generating and selecting the extreme streamflow scenarios of a river basin by coupling its key meteorological and hydrological elements. First, we use sensitivity analysis based on the Shapley additive explanations (SHAP) theory to reveal the critical influence of precipitation, soil moisture content, and air temperature over the basin on its streamflow. Then, an adaptive machine learning framework by coupling meteorological-hydrological elements with the streamflow, is constructed. It uses a Markov Chain Monte Carlo (MCMC) approach to simulate the extreme meteorological-hydrological events, and integrates the historical observational data as inputs to generate an ensemble of the streamflow scenarios. Finally, the scenarios are integrated using an enhanced K-means clustering algorithm, and the dissimilarity between individual scenarios within each cluster to the cluster centroid is calculated by combining with a modified Dynamic Time Warping (DTW) algorithm to select the optimized extreme streamflow scenarios based on the principle of maximum dissimilarity. Our method proves effective and applicable through validation using the streamflow data (1952-2006) from the Wujiang River basin in Southwest China and the corresponding meteorological-hydrological records.
		2026 Vol. 45 (4): 73-85 [Abstract] ( 71 ) PDF (6115 KB) ( 36 )

	86	Reconstruction and scenario analysis of extreme drought in Chongzhen era of late Ming Dynasty in middle Yellow River Basin
		QIN Feidi, WENG Baisha, PENG Hui, YAN Denghua
		DOI: 10.11660/slfdxb.20260407
		Facing the challenges in reconstructing historical extreme low runoffs and quantifying current drought defense capabilities, this study constructs a framework integrating the Transformer model with an implicit encoding of current defense conditions strategy. By using self-attention mechanisms, we develop a new model that captures the hydrological memory and embeds the modern land surface and engineering regulation patterns via training on the data of the present time. This model is validated against the extreme drought in the Chongzhen era of the late Ming Dynasty (1637-1643) in the middle Yellow River Basin, which achieves a high accuracy in modern testing (NSE = 0.82) and reproduces the historical drought events effectively. Scenario analysis indicates that a recurrence of such a drought event today, despite the existing current drought defense system, would cause an annual grain yield loss of greater than 40% and an economic loss above 1.5% of the existing GDP. These findings reveal the vulnerability boundaries of the current defense systems under extreme climate conditions, helping enhance the resilience of a river basin.
		2026 Vol. 45 (4): 86-103 [Abstract] ( 56 ) PDF (8539 KB) ( 43 )

	104	Study on procurement management of clean energy projects from perspective of partnering
		MAO Nianze, WANG Qi, WANG Can, TANG Wenzhe
		DOI: 10.11660/slfdxb.20260408
		China’s dual carbon goals have accelerated the development of its clean energy projects, promoting investment to grow rapidly. Among various factors, procurement management poses a substantial impact on the implementation of clean energy projects. However, previous studies lack a systematic understanding of the key factors and their interactive relationships in the procurement management. This study develops and validates a procurement management model for clean energy projects, revealing the action paths of partnership and procurement digital level and their significant positive effects on project performance. Through comprehensive surveys, we examine the status quo of procurement management, and identify the primary challenges and critical influencing factors. A case is analyzed with corresponding suggestions for procurement management. The findings would be useful for understanding the key factors in the procurement management in clean energy projects.
		2026 Vol. 45 (4): 104-113 [Abstract] ( 45 ) PDF (844 KB) ( 33 )

	114	Effect of foils on hydrodynamic performance of backward bent duct buoy as oscillating water column wave energy converter
		XIANG Yixin, LUO Ping, ZHANG Yongliang
		DOI: 10.11660/slfdxb.20260409
		This study examines the backward bent duct buoy (BBDB) as an oscillating water column (OWC) wave energy converter, and develops a BBDB-OWC device integrated with certain foils to optimize the bottom-mounted appendages. A three-dimensional computational fluid dynamics (CFD) model is constructed in the framework of STAR-CCM+ to couple wave propagation, air chamber compression, and structural response, and focus on comprehensive analysis of the effects of foil parameters－including chord length, width, and spatial positioning－on the hydrodynamic performance of this device under regular wave conditions. The simulation results indicate the foils can alter the local flow field structure and turbulence distribution, imposing a certain influence on the vorticity field around the device. However, its contribution to the overall energy conversion efficiency is limited, with the capture width ratio varying by no more than 5%. This study demonstrates the stability of the device’s hydrodynamic performance under the effect of foils, and helps further studies on parametric design and optimization of the appendage structures for wave energy converters.
		2026 Vol. 45 (4): 114-124 [Abstract] ( 37 ) PDF (5473 KB) ( 60 )