In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data.
Since the smart charging piles are generally deployed in complex environments and prone to failure, it is significant to perform efficient fault diagnosis and timely maintenance for them.
Are smart charging piles an important part of the smart grid?
Abstract: With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an important part of the smart grid.
Can cost-sensitive logistic regression predict smart charging pile faults?
In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data.
Can CS-LR predict smart charging pile faults based on classified data?
CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles.
These include EV charging installation and maintenance of hardware, uptime maintenance, energy distribution across the networks, among others, in the ambit of CPOs for the day-to-day operation of charging stations. As such, CPOs guarantee a seamless charging experience for EV drivers, full reliability within the network.
Technology has a very important role to play in energy storage and has been instrumental in getting the industry to where it is now. That said, we're still learning and solving complex problems each day. This means the industry needs software developers and data scientists, along with machine learning and optimisation experts.