Effect of CF and RPP on the Mechanical and Electrical Properties of Smart Aggregate

By using redispersible polymer powder（RPP） and carbon fiber（CF） to adjust the flexibility and electrical properties of the smart aggregate, a new kind of smart aggregate with Z type structure was proposed. The study shows that Z type aggregate is more sensitive to the feedback of external force than the prism aggregate in the same loading environment, and it indicates that Z type aggregate is more suitable for the research and application of concrete health monitoring. Although the incorporation of RPP would cause the compressive strength of the aggregates and the elastic modulus of hardened cement mortar to reduce slightly within the dosage of RPP by 2.25% because of the polymer film formed in the internal system, this would improve the deformability of the aggregates. In the early loading stage（in the first 60 seconds）, the intelligent concrete specimens implanted with Z type smart aggregate do not show higher sensitivity as expected, although the resistance change rate changes a little bit more, the overall of it is still in balance. Adding RPP could improve the flexibility of smart aggregates exactly, and it plays an active role in prolonging the life of the smart aggregates. By implanting Z type aggregates the damage and failure of the concrete structure could be predicted accurately in this study. The results of this paper will help to promote further research and application of intelligent concrete.

Event-Oriented Dynamic Security Service for Demand Response in Smart Grid Employing Mobile Networks

Equipped with millions of sensors and smart meters in smart gird,a reliable and resilient wireless communication technology is badly needed.Mobile networks are among the major energy communication networks which contribute to global energy consumption increase rapidly.As one of core technologies of smart grid employing mobile networks,Demand Response(DR) helps improving efficiency,reliability and security for electric power grid infrastructure.Security of DR events is one of the most important issues in DR.However,the security requirements of different DR events are dynamic for variousactual demands.To address this,an event-oriented dynamic security service mechanism is proposed for DR.Three kinds of security services including security access service,security communication service and security analysis service for DR event are composited dynamically by the fine-grained sub services.An experiment prototype of the network of State Grid Corporation of China(SGCC) is established.Experiment and evaluations shows the feasibility and effectiveness of the proposed scheme in smart grid employing mobile network.

Consistency analysis of accelerated degradation mechanism based on gray theory

A fundamental premise of an accelerated testing is that the failure mechanism under elevated and normal stress levels should remain the same. Thus, verification of the consistency of failure mechanisms is essential during an accelerated testing. A new consistency analysis method based on the gray theory is pro- posed for complex products. First of all, existing consistency ana- lysis methods are reviewed with a focus on the comparison of the differences among them. Then, the proposed consistency ana- lysis method is introduced. Two effective gray prediction models, gray dynamic model and new information and equal dimensional （NIED） model, are adapted in the proposed method. The process to determine the dimension of NIED model is also discussed, and a decision rule is expanded. Based on that, the procedure of ap- plying the new consistent analysis method is developed. Finally, a case study of the consistency analysis of a reliability enhancement testing is conducted to demonstrate and validate the proposed method.

Development of a Module for Measuring Electrical Variables in Power Transformers Based in IoT,to Manage and Monitoring by Telemetry Mechanism

This work shows the development of a module that performs measurements of electrical variables in a low voltage power transformer.These variables are sent by means of the IEEE802.11 standard,connecting to a database stored in the cloud;associating with the meter IoT concepts,this to allow a client to perform an analysis,monitoring and management of their electrical network.For the construction of this module,non-invasive current sensors connected to a three-phase meter are used and a communication card is used that allows data to be extracted from the meter and sent to the cloud database.This module,to convert a conventional electrical power transformer into a smart electrical power transformer,making it a Smart Object and thus extract information from it telemetrically in real time.

A novel forecasting approach to schedule aggregated electric vehicle charging

To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.