The extensive integration of distributed energy resources transforms the conventional power distribution network into a complex, tightly-coupled cyber-physical system denoted as active distribution network (ADN). In this paper, a data-driven anomaly identification method based on the cloud-edge computing architecture is proposed to reduce the complicated risk of ADN operations. Especially, the proposed method would serve as a widely applicable and efficient line of defense against either cyber or physical anomalies. On one hand, a hierarchical data flow scheme is designed to balance the timeliness and economical requirements of anomaly identification by taking advantage of coordinated cloud-edge computing. On the other hand, an integrated algorithm is proposed on the basis of the proposed data flow architecture in order to identify anomalies with satisfactory robustness and accuracy, even under complicated operating conditions. Results of numerical experiments have validated the computational superiority of the proposed method over the state-of-the-art by simulating a variety of physical faults and cyber attacks.

The direct-drive wave energy conversion (WEC) system adopts direct-drive power takeoff technique and the linear permanent magnet generate (LPMG) is coupled directly to the floater, which leads to simple configuration and high efficiency. This type of device usually has common features in term of structure, model, optimal control strategy, and power conditioning. Taking the AWS as the example, the models of the direct-drive WEC are first presented, and recommendations are also proposed for their applications. To extract maximum power from the wave, the AWS has to be regulated in resonance with the wave. Different types of control strategy using mechanical and electrical devices have been developed. Their features and applications are compared and discussed. An optimal control strategy, which enables AWS extracting maximum power from the irregular wave, is proposed. Since the floater reciprocates, not only the magnitude but also the direction of the translator motion speed changes. In this condition, the frequency, terminal voltage, and output active power of the LPMG vary. These features may not satisfy the requirements of the grid code for the integration of wave generation into power grid. Hence, the power conditioner is very important for the integration of the AWS into the power grid. The power conditioners are developed to integrate the AWS into the power grid, and the application of the energy storage in smoothing the output power of the AWS is also studied.