Transient safety assessment and risk mitigation of a hydroelectric generation system

Li, Huanhuan; Xu, Beibei; Arzaghi, Ehsan; Chen, Diyi; Aggidis, George A.; Zhang, Jingjing; Patelli, Edoardo

doi:10.1016/j.energy.2020.117135

Cited by 20 publications

(10 citation statements)

References 65 publications

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“…The guide vane opening is 10mm, the rated speed of the turbine is 68.67r/min, the water head is 25m, and the average flow rate is 0.31m 3 It can be seen from the figure that the time domain waveforms of three cavitation noise signals with different intensities are basically the same, and the amplitudes are between -0.2 ~0.4; It can be seen from the spectrum that its frequency range is widely distributed in the range of 0 ~20kHz. The amplitude is relatively strong when the frequency is lower than 2KHz and relatively weak when it is higher than 2KHz.It can be seen that it is really difficult to find the difference between different cavitation noise signals by using traditional signal processing methods, so it is imperative to use deep learning method.…”

Section: Training Data Acquisitionmentioning

confidence: 99%

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Analysis of Optimal Parameters for Discriminating Cavitation Types by SSAE-RF

Kang¹,

Liu

2022

J. Phys.: Conf. Ser.

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Hydro power has many advantages, such as pollution-free, relatively mature technology and high security. Hydro turbine is the core component of hydro power station. Cavitation has always been one of the main threats to the safe operation of hydraulic turbine units. In order to improve the overall classification and recognition accuracy of cavitation noise signal features of hydro turbine, a deep learning algorithm model based on stack sparse coding combined with random forest is proposed, and the optimal parameter selection of the algorithm model is analyzed in detail, so as to enhance the efficiency of deep learning algorithm. A group of cavitation noise signals with different intensities are selected. On the premise that other parameters of the algorithm remain unchanged, and choose a different parameter each time of training, and the optimal parameters of the algorithm are finally found; The default parameter algorithm model and support vector machine algorithm are used for comparison. The results show that the deep learning algorithm is superior to the traditional classification and recognition algorithm, and the overall classification and recognition accuracy of the deep learning algorithm using the optimal parameters is effectively improved. The research results can be used as an important reference for the deep learning algorithm to extract, classify and identify the cavitation noise intensity characteristics of hydro turbines.

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Section: Training Data Acquisitionmentioning

confidence: 99%

“…Water turbine is the core equipment of hydropower station. It is a complex and huge mechanical system, and its operation stability is affected by many factors [3]. Cavitation is the most common failure mode of hydraulic mechanical system [4], which is often caused by cavitation and other modes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Optimal Parameters for Discriminating Cavitation Types by SSAE-RF

Kang¹,

Liu

2022

J. Phys.: Conf. Ser.

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“…Its primary task is to regulate the active power output of the hydro-generator set according to the constant change of the load of the power system, minimize the influence of environmental disturbance and load disturbance, and maintain the frequency of the set within the specified range, which plays an indispensable role in maintaining the safe, stable, and economic operation of the hydropower plant. However, the inertia of the flowing water in the penstock, the nonlinear characteristics of the hydro-generator set, and the load disturbance of the power system that occurs at any time make the control of the HTGS very difficult [6,7].…”

Section: Introductionmentioning

confidence: 99%

Optimized Takagi–Sugeno Fuzzy Mixed H2/H∞ Robust Controller Design Based on CPSOGSA Optimization Algorithm for Hydraulic Turbine Governing System

Qian

Zou

et al. 2022

Energies

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The hydraulic turbine governing system (HTGS) is a complex nonlinear system that regulates the rotational speed and power of a hydro-generator set. In this work, an incremental form of an HTGS nonlinear model was established and the Takagi–Sugeno (T-S) fuzzy linearization and mixed H2/H∞ robust control theory was applied to the design of an HTGS controller. A T-S fuzzy H2/H∞ controller for an HTGS based on modified hybrid particle swarm optimization and gravitational search algorithm integrated with chaotic maps (CPSOGSA) is proposed in this paper. The T-S fuzzy model of an HTGS that integrates multiple-state space equations was established by linearizing numerous equilibrium points. The linear matrix inequality (LMI) toolbox in MATLAB was used to solve the mixed H2/H∞ feedback coefficients using the CPSOGSA intelligent algorithm to optimize the weighting matrix in the process so that each mixed H2/H∞ feedback coefficients in the fuzzy control were optimized under the constraints to improve the performance of the controller. The simulation results show that this method allows the HTGS to perform well in suppressing system frequency deviations. In addition, the robustness of the method to system parameter variations is also verified.

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“…The global energy industry is confronted with increasing pressures from population growth and environmental protection, which propels the developments in renewable energy sources (RESs) (Li et al, 2020). Wind energy is one of the fastest-growing, mature, and clean RESs, and its global installed capacity exceeded 651 GW in 2019 (de Carvalho et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Design of a novel hybrid control for permanent magnet synchronous generator–based wind energy conversion system

Feng

Deng

et al. 2021

Journal of Vibration and Control

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With the ever-increasing permeation of wind energy into electrical grids, the low operating efficiency and poor stability seem to become major challenges for the power industry because of the stochastic characteristic of wind speeds. Aiming at these problems, this article designs a novel hybrid control method for a permanent magnet synchronous generator–based wind energy conversion system. A port-controlled Hamiltonian with dissipation model is proposed to apply in the models of the machine and grid-side converters of the permanent magnet synchronous generator–based wind energy conversion system. Then, this port-controlled Hamiltonian with dissipation model is used to the energy shaping method of interconnection and damping assignment. The hybrid control strategy, containing the outer-loop proportional–integral control and inner-loop passivity-based control, is finally designed in this article. To achieve the analysis, a Simulink model of the 1.5 MW permanent magnet synchronous generator–based wind energy conversion system is established under various types of wind speeds. A comparative analysis between the proposed hybrid control strategy and conventional proportional–integral vector control is conducted through the electrical behaviors and the harmonic distortion rates. The simulation results verify that the hybrid control strategy has better regulation performance than that of conventional proportional–integral control strategy in terms of maintaining at optimal state and improving system stability. It is also demonstrated that the proposed control strategy optimizes the quality of supplied power, which is of significance to reduce the harmonic pollution of wind energy.

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Transient safety assessment and risk mitigation of a hydroelectric generation system

Cited by 20 publications

References 65 publications

Analysis of Optimal Parameters for Discriminating Cavitation Types by SSAE-RF

Analysis of Optimal Parameters for Discriminating Cavitation Types by SSAE-RF

Optimized Takagi–Sugeno Fuzzy Mixed H2/H∞ Robust Controller Design Based on CPSOGSA Optimization Algorithm for Hydraulic Turbine Governing System

Design of a novel hybrid control for permanent magnet synchronous generator–based wind energy conversion system

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