Due to the inherent non-stationary and nonlinear characteristics of original streamflow and the complicated relationship between multi-scale predictors and streamflow, accurate and reliable monthly streamflow forecasting is quite difficult. In this paper, a multi-scale-variables-driven streamflow forecasting (MVDSF) framework was proposed to improve the runoff forecasting accuracy and provide more information for decision-making. This framework was realized by integrating random forest (RF) and Gaussian process regression (GPR) with multi-scale variables (hydrometeorological and climate predictors) as inputs and is referred to as RF-GPR-MV. To validate the effectiveness and superiority of the RF-GPR-MV model, it was implemented for multi-step-ahead monthly streamflow forecasts with horizons of 1 to 12 months for two key hydrological stations in the Jinsha River basin, Southwest China. Other MVDSF models based on the Pearson correlation coefficient (PCC) and GPR with/without multi-scale variables or the PCC and a backpropagation neural network (BP) or general regression neural network (GRNN), with only previous streamflow and precipitation, namely, PCC-GPR-MV, PCC-GPR-QP, PCC-BP-QP, and PCC-GRNN-QP, respectively, were selected as benchmarks. Experimental results indicated that the proposed model was superior to the other benchmark models in terms of the Nash–Sutcliffe efficiency (NSE) for almost all forecasting scenarios, especially for forecasting with longer lead times. Additionally, the results also confirmed that the addition of large-scale climate and circulation factors was beneficial for promoting the streamflow forecasting ability, with an average contribution rate of about 15%. The RF in the MVDSF framework improved the forecasting performance, with an average contribution rate of about 25%. This improvement was more pronounced when the lead time exceeded 3 months. Moreover, the proposed model could also provide prediction intervals (PIs) to characterize forecast uncertainty, as supplementary information to further help decision makers in relevant departments to avoid risks in water resources management.

Accurate health evolution trend forecasting of aero-engine is essential for operation reliability and maintenance costs of aeronautical equipment. In this study, an intelligent deep learning method, systematically blending the dispersion entropy-based multi-scale series aggregation scheme and long short term memory (LSTM) neural network, is proposed for forecasting the health evolution trend of aero-engine. Firstly, a comprehensive measurement of health levels, namely, integrated health state index (IHSI), is developed with high-dimensional dataset. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is exploited to decompose the IHSI sequence into several multi-scale series to further capture the internal characteristics of original sequence. Subsequently, multi-scale series aggregation assisted with dispersion entropy analysis theory is conducted for obtaining the aggregated sub-series (ASS). Finally, the ASS are served as the inputs of LSTM network to complete the health evolution trend forecasting of aero-engine. To demonstrate the effectiveness of the proposed method, six approaches are present for the comparisons of forecasting performance. The experimental results indicate that the proposed method can effectively measure the health evolution process of aero-engine and further obtain more accurate trend forecasting results.

In order to improve the control performance and suppress the “S” characteristics area instability of the pumped-storage unit (PSU), this paper proposes an adaptive Takagi-Sugeno fuzzy model-based generalized predictive controller (ATS-GPC) for the PSU. First, the T-S fuzzy model is used to obtain the controlled autoregressive integrated moving average (CARIMA) model, in which the fuzzy C-means (FCM) clustering algorithm is used for the identification of antecedent parameters and the least square method (LSM) is used to obtain the consequent parameters. Meanwhile, the T-S fuzzy model can be online adjusted according to the real-time tracking error feedback to decrease the influence of the initial offline trained fuzzy model. Then, the generalized predictive controller is designed for the PSU based on the CARIMA. Finally, some numerical simulation experiments including the start-up process, frequency disturbance process, frequency tracking experiments, and robustness analyses have been conducted to verify the proposed method. The experiments results have shown that the proposed ATS-GPC can significantly improve the control performance of the PSU and effectively suppress the unstable operation in “S” characteristics area. In addition, the strong robustness of the proposed controller is verified.

This paper investigates the nonlinear modeling and stability of a doubly-fed variable speed pumped storage power station (DFVSPSPS). Firstly, the mathematical model of DFVSPSPS with surge tank considering nonlinear pump turbine characteristics was derived and established. Then, Hopf bifurcation analysis of DFVSPSPS was performed. The stable region was identified and verified by example analysis. Moreover, the effect mechanism of nonlinear pump turbine characteristics on the stability of DFVSPSPS was explored. Finally, the influence of factors on the stability and dynamic response of DFVSPSPS was studied. The results indicate that the emerged Hopf bifurcation of DFVSPSPS is supercritical and the region on the low side of the bifurcation line is the stable region. Nonlinear head characteristics have a significant influence on the stability and dynamic response of DFVSPSPS. Nonlinear speed characteristics have an obvious effect on the stability and dynamic response of DFVSPSPS only under positive load disturbance and unstable surge tank. Nonlinear head characteristics are unfavorable for the stability of DFVSPSPS under positive load disturbance and favorable under negative load disturbance. A smaller flow inertia of penstock, a smaller head loss of penstock and a greater unit inertia time constant are favorable for the stability of DFVSPSPS. The stable region under the positive disturbance of active power is larger than that under the negative disturbance of active power. The time constant of the surge tank presents a saturation characteristic on the stability of DFVSPSPS.

The Takagi–Sugeno (T–S) fuzzy model identification is a very powerful tool for modelling of complicated nonlinear system. However, the traditional T-S fuzzy model typically uses the L2-norm loss function, which is very sensitive to outliers or noises. So an unreliable model may be obtained due to the presence of outliers or noises. In this paper, the outliers and noises robust T-S fuzzy model identification method based on the fuzzy c-regression model (FCRM) clustering and the L1-norm loss function is proposed. The hyper-plane-shaped clustering algorithm has been proved to be more effective than hyper-sphere-shaped clustering algorithm in T-S fuzzy model identification. Therefore the FCRM clustering algorithm is used in T-S fuzzy model identification for structure identification in the antecedent part. A mass of relevant researches have pointed out that the L1-norm loss function is more robust to outliers and noises than L2-norm loss function. In order to reduce the negative influence of outliers and noises, the L1-norm loss function is employed to enhance the robustness of T-S fuzzy model instead of the L2-norm loss function in the consequent part. Regression and classification applications have been used to demonstrate the validity of the proposed method. The experimental results show that the proposed method has significantly improved the modelling accuracy in dealing with data contaminated by outliers and noises compared with other algorithms.