A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

STACKING SEQUENCE OPTIMIZA-TION OF LAMINATED COMPOSITE CYLINDER SHELL FOR MAXIMAL BUCKLING LOAD

A new optimization method for the optimization of stacking of composite glass fiber laminates is developed. The fiber orientation and angle of the layers of the cylindrical shells are sought considering the buckling load. The proposed optimization algorithm applies both finite element analysis and the mode-pursuing sampling （MPS）method. The algorithms suggest the optimal stacking sequence for achieving the maximal buckling load. The procedure is implemented by integrating ANSYS and MATLAB. The stacking sequence designing for the symmetric angle-ply three-layered and five-layered composite cylinder shells is presented to illustrate the optimization process, respectively. Compared with the genetic algorithms, the proposed optimization method is much faster and efficient for composite staking sequence plan.

Aerodynamic Simulation of A Containership to Evaluate Cargo Configuration Effect on Frontal Wind Loads

Fuel consumption has always been a matter of concern for ships propulsion. In this research we aim to develop computer models of several containership cargo stacking configurations and discuss an optimal configuration at a constant front wind speed. The paper presents the simulation results by using ANSYS CFX for a 1:4 scale PostPanamax 9000 TEU containership. The ship is modelled in a cubic domain that contains unstructured mesh with details, in such a way that can demonstrate the influence of the container configuration on wind force. Also the numerical results are verified versus wind tunnel test data. An optimal stack configuration led to about 25%reduction in air resistance. It is proposed that in order to reduce the wind drag force and consequently reduce the fuel consumption and pollutant emissions, empty spaces between the cargo containers and unbalanced cargo distribution over the deck should be inhibited. Also, it is advised to make the cargo distribution on the most forward and aftward deck areas more streamlined.