The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentivebased, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.
Aiming to suppress the influence of uncertain disturbances in the drive control of permanent magnet synchronous machines (PMSM), such as the parameter uncertainties and load disturbance, a robust anti-interference control for the angular position tracking control of a PMSM servo system has been proposed in this paper. During the position tracking, uncertain system disturbances being regarded as a lumped unknown term will be online observed by a nonlinear disturbance observer (NDOB), of which the influence will consequently be counteracted by a robust backstepping compensator (RBC). The asymptotical stability of proposed control scheme is analyzed and designed according to the Lyapunov stability criterion, and its convergence against the system uncertain disturbance is verified on a prototype PMSM servo platform and shows good performance in rotor angular position tracking and anti-interference.
Authentication and authorization (A & A) mechanisms are critical to the security of Internet of Things (IoT) applications. Smart grid system processing and exchanging data without human intervention, known as smart grids, are well-known as IoT scenarios. Entities in such smart grid systems need to identify and validate one another and ensure the integrity of data exchange mechanisms. However, at present, most commonly used A & A protocols are centralized, resulting in security risks such as information leaks, illegal access, and identity theft. In this study, we propose a new distributed A & A protocol for smart grid networks based on blockchain technology to address with these risks. The proposed protocol integrates the decentralized authentication and immutable ledger characteristics of blockchain architectures suitable for power systems with a novel blockchain technique to realize both identity authentication and resource authorization for smart grid systems. We discuss the security of and threat models for prior A & A protocols and demonstrate how our protocol protects against these threats. We further demonstrate an approach to a real deployment of our A & A protocol using the FISCO consortium platform, applying algorithms from smart contract systems. Finally, we present the results of experimental simulations showing the efficacy and efficiency of our proposed protocol.
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