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Aiming at the problem that the vibration signals of the hydrogenerator unit are nonlinear and nonstationary and it is difficult to extract the signal features due to strong background noise and complex electromagnetic interference, this paper proposes a dual noise reduction method based on intrinsic time-scale decomposition (ITD) and permutation entropy (PE) combined with singular value decomposition (SVD). Firstly, the vibration signals are decomposed by ITD to obtain a series of PRC components, and the permutation entropy of each component is calculated. Secondly, according to the set permutation entropy threshold, the PRC components are selected for reconstruction to achieve a noise reduction effect. On this basis, SVD is carried out, and the appropriate reconstruction order is selected according to the position of the singular value difference spectrum mutation point for reconstruction, so as to achieve the secondary noise reduction effect. The proposed method is compared with the LMD-PE-SVD and EMD-PE-SVD dual noise reduction method by simulation, taking the correlation coefficient and signal-to-noise ratio to evaluate the noise reduction performance and finding that the ITD-PE-SVD noise reduction has good noise reduction and pulse effect. Furthermore, this method is applied to the analysis of the upper guide swing data in the X-direction and Y-direction of a unit in a hydropower station in China, and it is found that this method can effectively reduce noise and accurately extract signal features, thus determining the vibration cause, which is helpful to improve the turbine fault recognition rate.

Aiming at the problem that it is difficult to extract the characteristics of the draft tube pressure fluctuation signal under the background of strong noise, this study proposes a dual noise reduction method based on adaptive local iterative filtering (ALIF) and singular value decomposition (SVD). First, perform ALIF decomposition of the signal to be decomposed to obtain a series of IMF components, calculate the sample entropy of each component, select some IMF components to reconstruct according to the set sample entropy threshold, and then perform SVD decomposition on the reconstructed signal, and according to the location of the singular value difference spectrum mutation point, the appropriate number of reconstructions is selected for reconstruction, so as to achieve the double noise reduction effect. The ALIF-SVD dual noise reduction method proposed in this study is compared with the single ALIF, EMD, and EMD-SVD dual noise reduction method through simulation, and the correlation coefficient, signal-to-noise ratio, and mean square error are used to evaluate the noise reduction. It is found that the ALIF-SVD dual noise reduction method avoids the phenomenon of modal aliasing in the decomposition process, effectively removes the noise, and can retain the useful information of the original signal, and the noise reduction effect is better. A unit of a hydropower station in China is further selected as the research object, and its draft tube pressure fluctuation data were analyzed for noise reduction. It was found that this method can accurately extract the signal characteristics under strong noise background, so as to determine the type of pressure fluctuation of the unit, which is helpful to improve the fault recognition rate of hydraulic turbines. And it provides some technical support for the safe and stable operation of hydropower units and the promotion of condition-based maintenance strategy and improves the intelligent level of hydropower station operation management.

Photovoltaic and wind power is uncontrollable, while a hydro–pumped storage–photovoltaic–wind complementary clean energy base can ensure stable power transmission in the whole system through power quantity regulation by the hydropower station and the pumped storage station. Reasonable allocation of installed capacities of various power sources in the system can improve the reliability and economy of systematic power supply. A system model was built generalizing the hydropower station and the pumped storage station as an energy storage unit, compensating and regulating the natural output process to match the system output and the load and to establish a correlation between the installed capacity of the base and the output index. Installed capacity allocation optimization was studied through an optimization model built with an initial investment of the base as the objective function and with power supply guarantee rate, power abandonment rate, and installed capacity as restraints and solved using improved artificial sheep algorithm (IASA) based on the shepherd dog supervision mechanism. A Yellow River clean energy base was selected for a case study analyzing the influence of power supply guarantee rate and power abandonment rate on installed capacity allocation and investment. For the two most important parameters in the optimization process, that is, the power supply guarantee rate and the power abandonment rate, after qualitative and quantitative analysis, it is found that the power supply guarantee rate has a greater impact on the initial investment. In this study, a combination of the power abandonment rate of 18% and the guaranteed rate of 90% is finally selected for the optimization calculation. The case study indicates that sole increase of installed photovoltaic or wind capacity resulted in the increase of both power supply guarantee rate and power abandonment rate; an appropriate increase in the installed capacity of the pumped storage station raised the power supply guarantee rate and lowered the power abandonment rate; and the optimal installed capacity allocation of the photovoltaic, wind, pumped storage, and hydropower under a specific load condition of the case project is 4.6:1.4:1.7:1.

The unbalanced forces generated by pumped storage units operating under non-ideal operating conditions can cause pressure pulsations. Due to the noise interference, the feature information reflecting the operating state of the unit in the pressure pulsation is difficult to extract. Therefore, this paper proposes a noise reduction method based on sparrow search algorithm (SSA) optimized variational mode decomposition (VMD) combined with singular value decomposition (SVD). Firstly, SSA is used to realize the adaptive optimization of VMD parameters for ideal decomposition of the signal. Then, the noise reduction of the decomposed signal is performed by using the sensitivity of the Permutation Entropy (PE) for small mutations. The noise reduction and reconstruction of the decomposed signal are carried out again by using SVD. The experimental and comparison results show that the mean square error of the signal after VMD-SVD feature extraction is reduced from 1.0068 to 0.0732 and the correlation coefficient is increased from 0.2428 to 0.9614. It is proved that the method achieves better results in the pressure pulsation signal of pumped storage units and has some application significance for the fault diagnosis of pumped storage units.