Abstract-A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.Index Terms-Smart grid, communication infrastructure, intelligent network, interconnected power system, monitoring, sensing, cyber security I. BACKGROUND S MART grid is a term referring to the next generation power grid in which the electricity distribution and management is upgraded by incorporating advanced two-way communications and pervasive computing capabilities for improved control, efficiency, reliability and safety. A smart grid delivers electricity between suppliers and consumers using two-way digital technologies. It controls intelligent appliances at consumers' home or building to save energy, reduce cost and increase reliability, efficiency and transparency [1]. A smart grid is expected to be a modernization of the legacy electricity network. It provides monitoring, protecting and optimizing automatically to operation of the interconnected elements. It covers from traditional central generator and/or emerging renewal distributed generator through transmission network and distribution system to industrial consumer and/or home users with their thermostats, electric vehicles, intelligent appliances [2]. A smart grid is characterized by the bidirectional connection of electricity and information flows to create an automated, widely distributed delivery network. It incorporates the legacy electricity grid the benefits of modern communications to deliver real-time information and enable the near-instantaneous balance of supply and demand management [3]. Many technologies to be adopted by smart grid have alread...

Abstract-A smart grid is a new form of electricity network with high fidelity power-flow control, self-healing, and energy reliability and energy security using digital communications and control technology. To upgrade an existing power grid into a smart grid, it requires significant dependence on intelligent and secure communication infrastructures. It requires security frameworks for distributed communications, pervasive computing and sensing technologies in smart grid. However, as many of the communication technologies currently recommended to use by a smart grid is vulnerable in cyber security, it could lead to unreliable system operations, causing unnecessary expenditure, even consequential disaster to both utilities and consumers. In this paper, we summarize the cyber security requirements and the possible vulnerabilities in smart grid communications and survey the current solutions on cyber security for smart grid communications.

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Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings’ health condition. However, though these methods generally work well, they still have two potential drawbacks when facing massive fault data: (1) the feature extraction process needs prior domain knowledge, and therefore lacks a universal extraction method for various diagnosis issues, and (2) much training time is generally needed by the traditional intelligent diagnosis methods and by the newly presented deep learning methods. In this research, inspired by the feature extraction capability of auto-encoders and the high training speed of extreme learning machines (ELMs), an auto-encoder-ELM-based diagnosis method is proposed for diagnosing faults in bearings to overcome the aforementioned deficiencies. This paper performs a comparative analysis of the proposed method and some state-of-the-art methods, and the experimental results on the rolling element bearings data set show the effectiveness of the proposed method not only with adaptive mining of the discriminative fault characteristic but also at high diagnosis speed.