Hydroelectric Unit Research Articles

Trend Prediction of Vibration Signals for Pumped-Storage Units Based on BA-VMD and LSTM

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

A new strategy for reducing pressure fluctuation of Francis turbine by bionic modification of local components

Long term operation of hydroelectric units in low load conditions can induce large-scale blade vortices, swirling vortex ropes in the draft tube, and low-frequency high amplitude pressure fluctuation. These phenomena will cause adverse consequences such as excessive vibration of the unit and blade breakage of the runner. Taking inspiration from the protuberances of the leading edge of a humped whale flipper, the present study firstly proposes bionic modifications of guide vanes and draft tube to suppress high-amplitude pressure fluctuations for Francis turbine. Numerical simulations of transient flow characteristics of the prototype unit (PU), the bionic guide vane unit (BG), and the bionic draft tube unit (BDG) under two low load conditions are conducted. Results indicated that the bionic draft tube has a good effect on suppressing pressure fluctuations in the vaneless area and draft tube. Under the Q/QBEF = 0.41 working condition, BDG causes the main frequency amplitude of pressure fluctuation at the center point of the draft tube inlet to change from 8000.2 Pa to 390.9 Pa, a decrease of 95.11 %. Under the Q/QBEF = 0.57 working condition, BDG causes the maximum decrease rate of the main frequency amplitude in the draft tube to be 60.5 %. The reducing effect in BDG of monitoring points in the guide vane area has reached over 43 %. Under low load conditions, the vortices near the wall in the draft tube of BDG are intercepted by bionic structures, reducing the vortex scale and helping to prevent the generation of large swirling vortex ropes. The bionic guide vanes have a significant control effect on pressure pulsation in the the guide vane and vaneless regions, although perform poorly in the draft tube.

Using bionic tubercles to control swirling flow instabilities of a hydraulic turbine during the load rejection process

With the development of renewable energy sources, operations of hydroelectric units under off-designed conditions and frequently transition processes have gradually become normalized, which is accompanied by threats from swirling vortex ropes, high amplitude pressure fluctuations, and strong hydraulic vibrations. This study introduces bionic tubercles into the Francis turbine's draft tube and guide vane design to suppress pressure fluctuation and stabilize swirling vortex rope during the load rejection process. Results reveal that the bionic Francis turbine (BFT) reaches its maximum speed of 686.4 rpm at 3.804s, marking a 6.08 % reduction from the prototype Francis turbine's maximum speed during load rejection. Moreover, it significantly diminishes the principal frequency amplitude of low-frequency pressure fluctuations caused by swirling flow instability from the draft tube cone, achieving a reduction by one to two magnitude orders. It is interesting that the scale of the circumferential vortex in the vaneless region and blade vortices within the runner channels is verified to decrease. More importantly, it is attributed that the bionic tubercles effectively disrupt and disintegrate the walled circumferential vortex structures in the draft tube.

Research on Optimal Control Strategy for Hydroelectric Units Based on Generalized Smith Predictor and Shortest Time Control

Abstract At present, most hydroelectric unit controllers use PID speed controllers. The principle of PID control is simple and the technology is mature, but there is less consideration for the need for deep participation of the unit in power grid regulation, resulting in a decrease in regulation quality and even instability of the regulation process when the unit deviates from the design operating conditions, which is difficult to meet the requirements of fast response and high-precision operation of the unit in the new power grid. This article proposes a new control strategy based on the generalized Smith predictor and the shortest time control. Through simulation comparison of the dynamic characteristics of the new control strategy and the conventional PID controller, the results show that the proposed new control strategy has better dynamic characteristics and control accuracy.

MILP model for short-term peak shaving of multi-grids using cascade hydropower considering discrete HVDC constraints

The thriving of high-voltage direct current transmission for hydropower tackles supply-demand imbalances but complicates grids safety, especially concerning the related challenges of varying numbers of hydroelectric units operating, including the effects of discrete rectifier station altering current and vibration zones with minimum numbers of hydroelectric units operating. Here, a mixed integer linear programming model considering high-voltage direct current safety constraints for daily peak-shaving of cascade hydropower plants serving multi-grids is proposed to address these challenges. First, the model integrates the altering current constraint across multiple intervals to ensure stable operation following the passive transition of the direct current terminal into islanded system state. Second, a power distribution strategy considering load differences is adopted to compensate for shortcomings of traditional peak shaving treatments for multiple grids to achieve effective results. Finally, a method for linearizing the segmented vibration zones of hydropower station with minimum numbers of units operating is adopted during model solving to ensure that the model meets safe specifications. The case study indicates that besides guaranteeing the proposed security constraints, the model reduces the average distance by 30.4 % and 30.3 % during the dry and flood seasons, respectively, with better calculation time and variance of the residual load.

Fault diagnosis of hydroelectric units based on GASF and parallel CNN-GRU-MSA

Abstract Diagnosing the vibration signals of hydropower units is crucial for safe and stable operation. This paper proposes a fault diagnosis method for hydropower units based on Gramian Angular Summation Fields (GASF) and parallel convolutional neural networks-gated recurrent unit-multi-headed self-attention (CNN-GRU-MSA). The original data forms a double branch, and the first branch selects the original timing signal for feature extraction using GRU. The second branch converts the timing signal into a 2D image using GASF for feature extraction using CNN, and the merged signal is enhanced with MSA for feature values. The experimental results show that the accuracy of the method reaches 97.2%. In order to explore the generalization and practicability of the proposed model, the public dataset of Jiangnan University is introduced for re-analysis. The diagnostic result of 600 rpm is 98.5%, and the diagnostic result of 800 rpm and 1000 rpm is 100%, significantly better than the other comparative models. This study can be valuable to the hydropower unit’s fault diagnosis methods.

Hydroelectric Unit Vibration Signal Feature Extraction Based on IMF Energy Moment and SDAE

Hydroelectric Unit Vibration Signal Feature Extraction Based on IMF Energy Moment and SDAE

Operational risk assessment of hydropower units based on PSSCA-VMD-CNN-GBiLSTM and multi-feature fusion

Hydroelectric units are an essential part of hydropower plants nowadays, and assessing and predicting the operating conditions of hydroelectric units is of great significance for the reliable operation of hydropower plants. Firstly, an optimization method based on polynomial regression with Pearson and Spearman fusion and Sine Cosine Algorithm (SCA) algorithm (PSSCA) is proposed to optimize the parameters in the variational modal decomposition (VMD), so as to suppress the influence of noise on the vibration signals of hydroelectric units. Then, the sample entropy and root-mean-square fusion of the VMD decomposed modal functions are reconstructed into a hybrid feature vector set so that this vector set can maximally contain the feature information of the vibration signals of the hydroelectric units. Finally, the CNN-GBiLSTM assessment model is constructed by combining the Convolutional Neural Network (CNN) model, the Bi-directional Long Short-Term Memory (BiLSTM) model, and the global average pooling layer, which is used to predict the upper and lower limits of the healthy vibration of the hydropower unit, and to construct the risk indicator of the hydropower unit operation by combining the values of the upper and lower limits. The results of simulation and comparative experiments show that the method proposed in this study can visually represent the three operational risk scenarios of a hydropower unit: low risk, medium risk, and high risk. It can effectively accomplish the operational risk assessment of hydropower units and improve the assessment accuracy and assessment speed, thus proving the effectiveness and superiority of the method.

Fault Diagnosis of Hydropower Units Based on Gramian Angular Summation Field and Parallel CNN

To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model’s accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units.

Nonlinear modelling and design of synergetic controllers for single penstock multi-machine hydropower system

The existence of a shared pipeline in a multi-machine system can lead to hydraulic coupling effects between units. It increases the difficulty of controlling hydroelectric units and affects the stability of the power system. This paper refines the model of the hydroelectric generator unit and builds a nonlinear model of a hydro-generator unit with the single penstock multi-machine system. The synergetic control theory was introduced, and the governor and excitation system controllers were designed separately. A joint synergistic controller is obtained by analyzing the two systems' synergistic factors and controlled characteristics. This controller realizes the joint control of the excitation and governor system. The simulation selected a proportion-integral-derivative (PID) controller with optimized parameters, synergistic governor controller (SEC), and synergistic excitation controller (SGC) for comparison to verify the effectiveness of the proposed method. The results indicate that the proposed control method exhibits competent control effects in the single penstock multi-machine system. It can reduce the impact of hydraulic coupling, shorten the adjusting time, and ensure the fast and stable operation of the system. In addition, the values of system time parameters and synergetic control parameters are analyzed separately to provide a reference for achieving optimal control.

Multivariate iterative nonlinear chirp mode decomposition: principles and applications

Abstract The rotor-bearing system (RBS) of a hydroelectric unit includes multiple components such as the rotor, rotor shaft, coupling, turbine, and main shaft. There is a strong coupling relationship between the vibration signals of multiple bearing sections. At the same time, the vibration signal of the RBS contains rich frequency components, which can effectively reflect the operating state of the shaft system of the unit. Therefore, synchronous signal analysis across multiple rotor bearing sections is required to completely grasp the operating status of the unit. In this study, the Multivariate Iterative Nonlinear Chirp Mode Decomposition (MINCMD) is proposed to analyze signals on multiple sections simultaneously. MINCMD follows the step-by-step extraction idea of Adaptive Multivariate Chirp Mode Decomposition (AMCMD), but it adopts a new algorithm framework by modifying the objective function. Furthermore, AMCMD does not converge well without a good initial instantaneous frequency (IF). Therefore, a method based on parameterized demodulation (PD) is introduced in MINCMD to solve the multi-channel frequency initialization problem. The effectiveness of the proposed algorithm is verified through a simulation test and real data from hydroelectric units.

Calculation method for natural frequency and damping ratio of hydropower units based on motion model separation

Abstract In recent years, the research on characteristic extraction methods for non-stationary and non-linear vibration signals of hydroelectric units has been a hot topic in the fault diagnosis of hydroelectric units. The effectiveness of feature extraction directly affects the accuracy of fault diagnosis. This article proposes a vibration signal feature extraction method for hydroelectric units based on motion model separation and logarithmic attenuation rate method. The under-damped decay oscillation motion trajectory is obtained from the signal after motion model separation. And then, the logarithmic attenuation rate method in the time domain analysis is used to calculate the system damping ratio and natural frequency. In this process, the principle of selecting the envelope interpolation function is proposed, and the effectiveness of the cubic Hermite interpolation function is determined. Based on the calculation effectiveness and accuracy requirements, the effective step length of the vibration signal is determined to be Δt = 0.001, and the effective calculation interval for extracting the decay oscillation characteristics is [ts , tend ]. The results show that the proposed method is more straightforward, efficient, has higher computational accuracy and better signal-to-noise ratio adaptability, and can provide a theoretical basis for practical engineering applications.

МОДЕЛЬ ОПТИМАЛЬНОГО РОЗПОДІЛУ ГІДРОРЕСУРСІВ ГЕС З РЕЗЕРВУВАННЯМ ПОТУЖНОСТЕЙ ДЛЯ НАДАННЯ ДОПОМІЖНИХ ПОСЛУГ АВТОМАТИЧНОГО ВІДНОВЛЕННЯ ЧАСТОТИ

The publication is devoted to the development of a mathematical model for the optimal allocation of hydro resources among hydroelectric units of HPPs for the production of electric energy and the provision of automatic frequency res-toration reserves with an asymmetric adjustment range. The concept of building a mathematical model for the optimiza-tion problem is presented. The main goal of the optimization is to maximize the benefit of the power generation com-pany from the use of available hydro resources for the production of electricity and the provision of services for the regulation of IPS of Ukraine modes. Areas of determining the main characteristics of the operation mode of hydro-power units of HPPs were studied and a system of restrictions on the load capacity of hydrounits was formed. A system of restrictions to control the balance of water resources in the reservoir and a system of restrictions to control the val-ues of water runoff for hydroelectric power stations have been defined. For the level of the power generating company, a system of restrictions on the control of the minimum values of the volumes of electricity and ancillary services for the fulfillment of already concluded agreements, as well as a system of restrictions on the control of the volumes of electric-ity and ancillary services within the predicted volumes of market demand, has been formed. The structure is substanti-ated and the components of the target function are described. Approaches to solving the problem of forecasting the amount of electrical energy imbalances on the balancing market of Ukraine are defined. Peculiarities of the process of finding optimal solutions regarding the loading levels of hydropower units with available hydro resources have been studied. The presence of the effects of "economic impracticality of the full use of available water resources" was noted, and the main consequences of the manifestations of this effect were investigated. References 12, figures 3, tables 2.

Optimization technology of hydroelectric power plant unit speed control based on the constriction coefficient-based particle swarm gravitational search algorithm fusion model

In recent years, the integration of wind power into hydropower networks has become a trend. But after wind power is connected to the grid, its unstable power will have an impact on the stability of the power grid. In order to improve the impact of wind power grid connection on the stability of the power supply network, a water motor speed control fusion model considering wind power interference is proposed. Firstly, analyze the impact of wind power grid connection and construct a speed regulation model for grid connected units. Secondly, the gravity search algorithm is introduced to optimize the unit speed control parameters. Considering that the algorithm is prone to local convergence, the convergence factor particle swarm optimization algorithm is adopted to optimize the gravity search algorithm. In addition, considering the impact of wind power interference on unit regulation, the Hopf bifurcation theory is introduced to analyze the dynamic regulation parameter range of hydropower units, in order to meet the requirements of stable operation of the power grid. The research contribution is mainly reflected in suppressing wind power grid connection interference through the speed regulation of hydroelectric units. At the same time, considering the impact of multiple interferences on grid connection, an intelligent model is introduced to optimize the speed control parameters of the unit, thereby improving the stable impact of wind power grid connection on the water conservancy grid. In the simulation analysis of hydroelectric unit speed regulation, after adding 5 % disturbance signal, the proposed model under rigid water hammer disturbance could stabilize the unit's stable control within 15 s, which is superior to other models. In the simulation analysis of speed regulation under wind power disturbance, the proposed model could achieve the fastest and most stable speed regulation under rigid water hammer step wind power disturbance, with a duration of 60 s. The proposed model could respond quickly to small overshoot and overshoot in elastic water hammer slope type wind power disturbances. Therefore, the proposed speed regulation technology for hydropower units has excellent application effects in practical scenarios, which will provide effective technical references for wind power grid connection and hydropower unit regulation.

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. The proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

Effect of Water-added-mass on Modal Behavior of Shaft-line of Large Hydroelectric Generator Unit

The water-added-mass to a hydro turbine-shaft-line can significantly affect its modal behavior including the vibration modes and their natural frequencies. So it is of great significance to consider the effect of added water mass on the modal behavior of a hydro turbine to assure the safe and efficient operation of the hydro turbine-shaft-line system. In this investigation, the effect of water-added-mass on the shaft-line of one large prototype hydroelectric generator unit has been investigated in detail via the finite element tool. First, a CAD model of the shaft-line was created based on actual dimensions. Then a finite element model of the entire shaft-line was generated with high-quality hexahedral and tetrahedral cells. A first round of finite element simulation of the shaft-line in the air was performed without considering the effect of surrounding water, but the results were not realistic. On this basis, a finite element-based water-structure coupling simulation was carried out to analyze the effect of water-added-mass on the modal behavior of the shaft-line of the large hydroelectric generator unit. The result is more realistic to reality than that in the air. The results of this study show that when the water surrounding the turbine runner is added to the hydro-turbine shaft-line, it increases the overall mass of the shaft-line system, thereby reducing the natural frequencies of the system. The decrease in natural frequencies can cause the shaft-line to approach its critical speed during operation, vibrate at higher amplitudes than usual, and even lead to potential damage to the shaft-line system. According to the results calculated with the finite element water-structure coupling method, the countermeasures to improve the evaluation of the water-added-mass effect on the structural modal behavior of the shaft-line for the hydroelectric generator units have been provided. The conclusion can also apply to other hydraulic turbine and pump units.

Study of the Hydrological Mode of the Volga-Akhtuba Canal Depending on the Hydrograph of the Volzhskaya HPP Operation

The flow rates and water flow rates in the Volga-Akhtuba Canal, which is of critical importance in the watering of Akhtuba river and Volga-Akhtuba bottom land territory, have been determined in various hydrologic phases. Relationships between discharge flows through the Volgograd hydroelectric unit and flows through the Volga-Akhtuba Canal have been established. Graphs of the relationship between the water level in the lower canal pound and water flows through the Volgograd hydroelectric unit in various years are constructed. Recommendations are given for additional watering of the Volga-Akhtuba bottom land.

Standalone hybrid power-hydrogen system incorporating daily-seasonal green hydrogen storage and hydrogen refueling station

In light of the recent developments in the green hydrogen industry, it is essential to investigate its compatibility and integration with existing electrical grids. This task becomes more challenging when considering its implementation in off-grid sites that solely rely on renewable energy. In cases where off-grid sites should provide electricity and hydrogen to a variety of consumers, it is compulsory to have electrical and hydrogen storage devices with long-term storage capabilities. Consequently, a precise programming technique is necessary to manage the storage and consumption of hydrogen and electricity. To investigate such a model, this paper presents a connected electricity-hydrogen grid in which all the required energy is supplied by solar and hydroelectric units. This off-grid system supplies the necessary energy for industrial and residential loads, electric vehicle charging stations, fuelcell car refueling stations, and natural gas pipelines. The power-to-hydrogen (P2H) and hydrogen-to-power (H2P) systems are used to facilitate energy flow between the electrical and hydrogen sectors. A daily-seasonal hydrogen storage is integrated as well. The proposed method contributes by integrating and improving the modeling, management, optimization, and operation of the daily-seasonal hydrogen storage and electrical storage. Its primary objective is to supply electricity and hydrogen to various consumers using solely renewable energy, even in the face of renewable energy fluctuations and outages. The model is mathematically expressed as a mixed integer linear optimization, implemented in GAMS software, and solved by the CPLEX solver. It aims at maximizing profit from selling electricity and hydrogen to the users. Results demonstrate that P2H and H2P units have a complementary operation with the solar system. When there is abundant solar energy, the P2H converts excess clean energy to green hydrogen and H2P operates when solar energy is not available. Seasonal storage also stores hydrogen from spring to autumn and discharges it in winter because of higher hydrogen prices in this season. Such an operation increases the annual profit by about 9%. When the hydroelectricity unit faces an outage in hours 8 to 13, the fuelcell uses the stored hydrogen and comes into operation to compensate hydropower outage. The net present value of profit amounts to $275,179 per year. The total hydrogen required is approximately 49,870 kg per year, with a total electricity requirement of 1190 MWh per year. The levelized revenue from hydrogen is achieved at around $4 per kilogram, while the levelized revenue from electricity is about $0.058 per kilowatt-hour. It is concluded that the integration of seasonal storage effectively reduces costs, boosts profits, and efficiently manages renewable energy fluctuations across different seasons.

Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management

This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system. The DSM program not only enhances power grid security but also lowers operational costs. The research addresses the DED problem with and without DSM implementation to analyze its impact. Demonstrating effectiveness on two test systems, the suggested method's efficacy is showcased. The recommended method's simulation results have been compared to those obtained using Cheetah Optimizer Algorithm (COA) and Grey Wolf Optimizer. The optimization results indicate that, for both the 10-unit and 20-unit systems, the proposed ECOA algorithm achieves savings of 0.24% and 0.43%, respectively, in operation costs when Dynamic Economic Dispatch is conducted with Demand-Side Management (DSM). This underscores the advantageous capability of DSM in minimizing costs and enhancing the economic efficiency of the power systems. Our ECOA has greater adaptability and reliability, making it a promising solution for addressing multi-objective energy management difficulties within microgrids, particularly when demand response mechanisms are incorporated. Furthermore, the suggested ECOA has the ability to elucidate the multi-objective dynamic optimal power flow problem in IEEE standard test systems, particularly when electric vehicles and renewable energy sources are integrated.

Research on Ultra-Low-Frequency Oscillation Suppression Method of High-Head, Large-Capacity Hydropower Units

Aiming at the problem of ultra-low-frequency oscillation (ULFO) caused by an irrational setting of governor parameters of high-head hydraulic turbine units, this paper proposes a method to optimize the additional damping control parameters of the governor to suppress ULFO. Firstly, the mechanism of ULFO is elaborated and the effect of system damping torque on ULFO is analyzed. Secondly, Hamilton’s theory is used to rank the magnitude of each unit’s contribution to the ULFO. Then, the parameters of the damping controller are optimized using the variational modal decomposition–Hilbert transform method and the Grey Wolf Optimization algorithm. It realizes the phase compensation of the hydroelectric unit governor and suppresses the ultra-low-frequency oscillation. Finally, the proposed method is validated using the simulation conducted in CloudPSS. The test results indicate that the proposed method can effectively suppress the ULFO caused by the high-head hydroelectric unit.