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Accurate prediction of photovoltaic (PV) power generation is the key to daily dispatch management and safe and stable grid operation. In order to improve the accuracy of the prediction, a finite iterative PV power prediction model with long range dependence (LRD) characteristics was developed using fractional Lévy stable motion (fLsm) and applied to a real dataset collected in the DKASC photovoltaic system in Alice Springs, Australia. The LRD prediction model considers the influence of current and past trends in the stochastic series on the future trends. Firstly, the calculation of the maximum steps prediction was introduced based on the maximum Lyapunov. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the order stochastic differential equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The PV power forecasting model with the LRD characteristics was obtained by discretization of FSDE. By comparing statistical performance indicators such as root max error, mean square error with Conv-LSTM, BiLSTM, and GA-LSTM models, the performance of the proposed fLsm model has been demonstrated. The proposed methods can provide better theoretical support for the stable and safe operation of PV grid connection. They have high reference value for grid dispatching department. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation. [ABSTRACT FROM AUTHOR]

To expand the information of nest individuals, the idea of complex-valued encoding is used in cuckoo search (PCS); the gene of individuals is denoted by plurality, so a diploid swarm is structured by a sequence plurality. The value of independent variables for objective function is determined by modules, and a sign of them is determined by angles. The position of nest is divided into two parts, namely, real part gene and imaginary gene. The updating relation of complex-valued swarm is presented. Six typical functions are tested. The results are compared with cuckoo search based on real-valued encoding; the usefulness of the proposed algorithm is verified.

This study investigated the prevalence of 16S rRNA methylase genes in 267 Enterobacteriaceae isolates collected from pets. The rmtB gene was detected in 69 isolates, most of which were clonally unrelated. The coexistence of the rmtB gene with the bla(CTX-M-9) group genes and/or qepA within the same IncFII replicons was commonly detected. The two dominant types of IncF plasmids, F2:A-:B-, carrying rmtB-qepA, and F33:A-:B-, carrying the rmtB-bla(CTX-M-9) group genes (and especially bla(CTX-M-65)), shared restriction patterns within each incompatibility group.