In this article, a new type of physics package with high signal to noise ratio for a rubidium atomic frequency standard is reported. To enhance the clock transition signal, a slotted tube microwave cavity with a field orientation factor of 0.93 and an absorption cell with the diameter of 30 mm were utilized in design of the cavity-cell assembly. Based on the spectral analysis of the three commonly used rubidium spectral lamps, the spectral lamp filled with Xe gas was chosen as the optical pumping source for its small line shape distortion. To suppress the shot noise of the signal, a band pass interference filter was used to filter out Xe spectral lines from the pumping light. A desk system of the rubidium frequency standard with the physics package was realized, and the short-term stability of the system was predicted and tested. The measured result is 2.4 × 10 τ up to 100 s averaging time, in good agreement with the predicted one.

The microgrid central controller (MGCC) integrates the functions of control, monitoring, and communication in microgrid system, and has powerful capabilities of information collection and data processing. However, with the development of microgrid system worldwide, the information security management capabilities of the MGCC are poor, If information / network attacks cannot be actively detected and identified, it will easily reduce the reliability of the microgird system operation. Attackers can use abnormal information or use the MGCC as a springboard to further attack the upper-layer system. Aiming at the above problems, this paper presents an attack detection method based on convolutional neural network, and a detailed design process of attack detection model of the MGCC is proposed. In the attack detection method, the important data streams in the MGCC are used as the input of the convolutional neural network model, then the convolutional neural network model detects or classifies these data streams, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.

Photovoltaic grid-connected interface devices are an important class of smart devices in microgrids. The authenticity and reliability of the data they acquire, as well as the safety and stability of operation, are related to the safe and reliable operation of the entire microgrid system. However, in the context of microgrid intelligence and informatization, information / network attacks will become the norm, making network-dependent information interaction methods subject to various security risks. The photovoltaic grid-connected interface device involves an open operating environment and is extremely vulnerable to network attacks. The attack information will occupy the space or resources of the photovoltaic grid-connected interface device, making the photovoltaic grid-connected interface device unable to respond to other important requests or instructions in a timely manner, and in severe cases, will cause the device to be paralyzed and affect the normal system operation. Aiming at the above problems, this paper presents an attack detection method based on the gradient-upgraded decision tree model, and gives a detailed design process of attack detection model of the photovoltaic grid-connected interface device. That is, the important data flow in the photovoltaic grid-connected interface device is used as the input of the gradient-upgraded decision tree model, and then the gradient-upgraded decision tree model detect or classify flows, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.

With the continuous improvement of the level of economic development and the increasingly serious environmental problems, countries around the world are focusing more on renewable energy. Wind energy is an important category of renewable energy because of its advantages. However, wind power is very dependent on the climate environment. It operates in an open operating environment, and its communication depends on the network interaction method. With the proposal of the Internet of Everything, the power grid is developing in the direction of information and intelligence. There are more and more attacks, and the security and stability of wind power interface devices have been threatened. As the power grid involves many areas and households, once the power outage occurs, the economic losses will be huge and even cause major security accidents. This paper proposes a deep belief network research of wind power control management unit based on attack recognition to improve the safety and operational reliability of wind power generation.