Multi-model modeling is an important method for solving the modeling problem of complex non-linear systems. However, there is no such good classification principle of its sub-models' time span, and the division of sub-windows affects the accuracy of the model, as well as the cost of computing. Through analysis of the characteristics of the bed temperature object, the principal component analysis was introduced into the method of designing the time span division of the sub-windows. The dividing points of the time span of the sub-windows were determined by piecewise analysis, rolling merging, and cyclic validation. The problem with this is that the sub-models in different windows may be different. Aiming at the problems of modeling in the sub-windows, the contribution rates of variables to the principal components were analyzed, the independent variables that play a major role in the dependent variables in the sub-windows were determined with the results of PCA, and the regression models in the the sub-windows were identified by multivariate linear regression. Compared to the sub-window model established by the principal component regression method, the former method had higher accuracy and could better reflect the actual operation of the bed temperature object. INDEX TERMS Multi-model, principal component analysis, sub-window span, cumulative contribution rate, principal component regression, bed temperature.

The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Aiming at the problem that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for high-pressure heater system. Firstly, an improved particle swarm optimisation algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimise the particle position and velocity update process. The problem that particle swarm optimisation (PSO) tends to fall into local optimum at the late stage of iterative optimisation search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimisation process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid KPCA-IDEPSO-PNN model is proposed. Non-linear features are extracted using kernel principal component analysis (KPCA) for fault monitoring. The IDEPSO algorithm is used to iteratively find the best probabilistic neural network (PNN) to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.

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The paper is devoted to a theoretical study of nonlinear wave on a free-surface thin film down an inclined uneven plane. The problem is quite different from that of a viscous films flow along a smooth surface. Thus nondimensional variables are introduced in two ways according to the different relationship of the shallow water parameter and the topography parameter. Further, the zero-order and first-order stream function are derivated on the basis of perturbation method. Finally, the equations which govern the surface height of surface wave on a viscous fluid film down an inclined uneven wall are obtained.

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated.