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Matrix containing the binary charging decisions of all charging poles for the remaining time steps. j ω Vector containing the binary charging decisions of all charging poles at the current time step. top s Vector containing the non-zero elements.

An automatic diagnosis system is proposed by this paper for a more and more important issue, preventive maintenance. Every year, various workplace accidents happen due to undesirable maintenance. No matter how stringent the rules governing the maintenance of electrical equipment may be, it is always a challenge for the power industry due to the large number of electrical equipment and the shortage of manpower. In this paper, an automatic diagnosis system for testing electrical equipment for defects is proposed. Based on nondestructive inspection, infrared thermography is used to automate the diagnosis process. Thermal image processing based on statistical methods and morphological image processing technique are used to identify hotspots and the reference temperature. Qualitative and quantitative analyses are carried out on the gathered information and inspection results are presented after being processed by the diagnosis. The thermal diagnosis system proposed by this paper can be used at the various power facilities to improve inspection efficiency as illustrated in the experiment results.

Abstract:The economic and environmental benefits brought by electric vehicles (EVs) cannot be fully delivered unless these vehicles are fully or partially charged by renewable energy sources (RES) such as photovoltaic system (PVS). Nevertheless, the EV charging management problem of a parking station integrated with RES is challenging due to the uncertain nature of local RES generation. This paper aims to address these difficulties by deploying an energy storage system (ESS) in parking stations and exploiting the charging and discharging scheduling of EVs to achieve better utilization of intermittent PVS for EV charging. A real-time charging optimization scheme is also formulated, using mixed-integer linear programming (MILP) to coordinate the charging or discharging power of EVs along with the power dispatches of power grid and ESS based on the vehicles' charging or discharging priorities and electricity price preferences. Extensive simulations show that the proposed approach not only maximizes the satisfaction of EV owners in terms of fulfilling all charging and discharging requests, but also minimizes the overall operational cost of the parking station by prioritizing the utilization of energy from PVS, ESS, and scheduling of every EV's charging and discharging.

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