In this paper, we consider the islanded microgrid restoration with communication link faults, where faults refer to certain classes of data manipulation attacks. To compensate for the impact of faults in communication links on microgrid stability, a distributed adaptive event-triggered compensator with adaptive parameters has been designed. This avoids the use of global information and reduces communication redundancy. To achieve voltage and frequency recovery, an distributed secondary control strategy is proposed. Using Lyapunov stability theorem, it is proved that the proposed method is effective in counteracting the primary control bias that exists in islanded microgrids in the presence of communication link faults and limited computational resources. The main advantage of our method is the scalability and resilience. Finally, the paper presents the results of a simulation test of an islanded microgrid consisting of four DGs with faults in communication links. These results show that the proposed secondary control strategy is effective.

In this paper, an inductive power transfer (IPT) system for distribution insulator based on reconfigurable autotransformer magnetic coupler (ATMC) is proposed. A three-stage IPT system architecture is proposed based on the form of a 35kV insulator structure. A novel multi-tap magnetic coupler in the form of autotransformer is proposed. The voltage conversion ratio and output characteristics of the IPT system can be reconstructed by changing the winding taps connected to the IPT system. An ATMC-based IPT system circuit model is established. The parameters of ATMC are optimized. The effect of system parameter reconstruction by changing the winding taps of ATMC is analyzed. An IPT system based on a reconfigurable ATMC with three winding taps on both the primary and secondary sides is designed, an experimental prototype is built.$3\times 3$sets of experiments are completed, and the solution to reconstruct the system state by changing the winding taps of ATMC is verified. The maximum efficiency of the system is close to 90%.

The optimal tracking problem for continuous-time boiler turbine systems (BTSs) with asymmetric input constraints is considered in this paper. Considering that continuous updating of control input will reduce the service life of engineering oriented system, a novel dynamic event-based triggering mechanism is proposed to reduce the number of controller updates, where a positive internal dynamic variable is introduced to expand the threshold. The objective is to find the optimal event-triggered control strategy for a given performance index function so that the system can track the ideal signal while minimizing the cost function. Firstly, by introducing tracking error variable, the optimal tracking problem is transformed into the optimal stability problem with symmetric input constraints. Then, three neural networks are designed to approximate the system model, cost function and control strategy respectively, and the feasibility of the proposed optimization algorithm is strictly proved by Lyapunov method, and the system will not exhibit Zeno behavior. Finally, simulation results effectively indicate the feasibility of the developed method in the industrial oriented system.Note to Practitioners—With the development of the national economy, China’s demand for electricity is also increasing. The BTSs in thermal power generation unit should not only realize load tracking, but also consider minimizing fuel consumption, minimizing pollutant emissions, maximizing the service life of BTSs. In this paper, adaptive dynamic programming (ADP) method and dynamic event-based mechanism are used to solve the optimal tracking problem of BTSs. The dynamic event-based mechanism not only affects the real performance of the BTSs, but also reduces the number of controller updates and saves resources of the BTSs.

Fault feature extraction based on prior knowledge and raw data is increasingly becoming more challenging in energy Internet fault diagnosis due to complicated network topology and coupling disturbances introduced into the systems. Deep learning methods that have emerged in recent years, such as the convolutional neural networks (CNNs), have shown a number of advantages and great potentials in the field of feature extraction and image recognition. However, CNNs does not work well in fault diagnosis for industrial systems, due to the totally different data representations between images used in recognition and signals obtained from industrial processes. This paper tackles this problem by introducing a novel and generic fault diagnosis method for complicated system, namely, the Spearman rank correlation-based CNNs (SR-CNNs). By imposing the Spearman rank correlation image layer on the typical CNNs, the multiple time-series signals measured by the phasor measurement units (PMUs) is converted to appropriate data images, which are then fed to the CNNs. With the aid of this novel design, different fault features can be comprehensively extracted while the fault can be identified more quickly and precisely than other conventional approaches. To validate the efficacy of the proposed approach, an IEEE defined power gird with many new energy resources are used as the test platform. The experimental results confirm the effectiveness and superiority of the proposed method in energy Internet fault diagnosis over conventional methods.

Selection of electricity fee payment site (EFPS) is a complex multi-objective optimization problem. Proper site selection of EFPS can not only facilitate the users to pay the electricity payments, but also save costs in running processes of the power enterprises (PEs). Firstly, two-dimensional genetic code is improved and the multi-object optimization model of EFPS is established. Secondly, considering a model city as an example, the NSGA-II algorithm is applied to solve the optimization model. Finally, the Pareto optimal solution set and several locating schemes based on different preferences are obtained by simulation. In the follow-up of the site selection work, experts can choose the method of adaptive weights and other methods to determine which scheme to apply. Example analysis shows that the results of the study has strong applicability, and it has practical significance to the site selection of the EFPS or other business halls.