Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.
Insulator is an important part of transmission line. Defective insulators will cause potential safety hazard to transmission lines. Image detection technology can improve the efficiency of insulator defect detection and greatly reduce the maintenance cost. However, the existing insulator defect detection technology has the disadvantages of low accuracy and long detection time. An insulator defect detection method based on improved ResNeSt and Region Proposal Network (RPN) was proposed. First, this method builds a new network based on ResNeSt. Secondly, we added the improved RPN to the improved ResNeSt for feature extraction, to better detect minor defects on insulators. Finally, we enhanced the data processing and labeled the open insulator data set. On this data set, the proposed model is tested and a large number of controlled experiments are done. The results show that the proposed network is more accurate and faster than the control group. Moreover, the proposed network has an accuracy rate of 98.38% for insulator defect detection, which can detect 12.8 pictures per second. The proposed method has good efficiency and practicability in aerial photo insulator defect detection.
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