Taking the G4-73№8D centrifugal fan as research object and utilizing the software of NUMECA to simulate flow fields of volute with different widths, the parameters of width are optimized through contrastive analysis of the effects on internal flow characteristics and performance. Results show that the optimized scheme can improve the uniformity of internal flow field, full pressure and efficiency seperatly increase by 0.52% and 0.48% under the design flow. For the needs of variable fan load operation, this paper puts forward the optimization principle of width parameter.

A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) algorithm for load prediction. Using measured load data from a specific user-level IES at the Tempe campus of Arizona State University, USA, as a case study, a comparative analysis between the VTDS method and other approaches is conducted. The results demonstrate that the method proposed in this study achieved higher accuracy in short-term forecasting of the IES’s multiple loads.

When a single-phase grounding fault occurs in a power distribution system, the fault characteristics are not prominent and are easily drowned out by noise, making fault line selection difficult. A fault line selection method based on improved empirical wavelet transform (EWT) and GIN network is proposed to solve this problem. Firstly, EWT is optimized using kurtosis as the basis and N-point search method. EWT decomposes the electrical signal into a series of modal components, and noise is filtered out by weighted permutation entropy to reconstruct the signal, obtaining a denoised electrical signal. Then, according to the topology of the power distribution network, a corresponding graph structure is constructed. The Mahalanobis distance between each point and the overall structure in the denoised electrical signal is calculated and used as the input to each node in the GIN network. Finally, the GIN network autonomously mines the characteristics of each graph structure, performs graph classification, and realizes fault line selection. Experimental results show that the proposed method has a solid anti-noise ability and an accuracy of up to 99.95%, effectively completing fault line selection in power distribution networks.