In AC microgrids (MGs), the required high control gain for dealing with control faults would reduce the robustness of the secondary control to communication constraints. To tackle with this problem, the subarea physical infrastructure of AC MGs and the two-layer secondary control framework are designed. For the physical infrastructure, the whole AC MG is divided into several areas, where each area is composed of a distributed generator (DG) and several geographical-close loads. Furthermore, by utilizing the advanced metering infrastructure (AMI), loads transmit the local load change information to the DG in the same area. Based on this physical infrastructure, a distributed event-triggered fixed-time fault-tolerant (ET-FTFT) secondary control framework is proposed in this paper to improve the resilience of the secondary control to sensor and actuator faults/attacks, and communication constraints, simultaneously. The adopted two-layer control framework is to decouple the communication constraints and control faults. The event-based strategy is adopted in the upper layer to reduce the communication burden and Zeno behavior can be evaded. Real-time simulations based on the NI-PXI real-time simulator validate the advantages of the distributed ET-FTFT secondary control framework, which are the better robustness to control faults, and the fixed-time convergence to improve the power quality.

Recently, many distributed fault-tolerant (Di-FT) secondary controls have been designed to improve the robustness of the islanded AC microgrids (MGs) to actuator faults/attacks. However, some problems still exist among the existing research: (a) the convergence speed of the faulty MG's states has not been discussed, which is important for reducing the inter-harmonic component around the fundamental frequency thereby improving the power quality; (b) large computing and sampling resources are required by the relatively high control gain for dealing with the possible serious actuator faults. Therefore, a distributed event-triggered fixed-time (Di-ET-FT) fault-tolerant secondary control of the faulty islanded AC MG is designed in this paper to release the communication burden, save the computing resources, and achieve the fixed-time convergence speed for improving power quality simultaneously. The Di-ET-FT fault-tolerant secondary control has a two-layer control framework, which are the observer layer as the upper layer and the fault-tolerant control layer as the lower layer. Meanwhile, two event-triggered strategies are designed in these two layers to decrease sample/control updating numbers and communication numbers, respectively. Zeno behavior of the Di-ET-FT fault-tolerant secondary control can be evaded effectively by proving that the interevent time is lower bounded by a positive value. The abovementioned advantages of the designed secondary control are validated by the real-time simulation based on NI-PXI simulator.

As an effective method to improve the reliability, the advanced communication infrastructure also exposes the islanded AC microgrid (MG) to potential privacy threats. Although the existing differential-privacy based secondary controls provide the privacy protection through adding noise, the noise would bring about large harmonic components thereby degrading the power quality. To tackle with this problem, a novel integrated privacy-preserving distributed fixed-time (PDF) secondary control of the islanded AC MG is proposed for the accurate frequency restoration and active power sharing. The state decomposition strategy and the additional virtual proportional (AVP) coefficient are innovatively designed to achieve the privacy preservation, where the active power ratio of each DG is decomposed into several sub-states. The integrated PDF secondary control only requires each DG to transmit the first sub-state of active power ratio through communication network, which would not increase the communication burden. The designed privacy-preserving mechanism can protect the initial value and real-time running values simultaneously when the AVP coefficient is not disclosed to the external eavesdropper. The real-time simulations based on the NI-PXI real-time simulator are conducted to validate the effectiveness and advantages of the integrated PDF secondary control.

Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain adaptive methods require a large amount of data and training time. To overcome the aforementioned challenges, firstly, sample rejection and pseudo label correction using K-means (SRPLC-K-means) were developed and explored to filter the noisy samples and correct the pseudo labels to obtain pseudo labels with higher confidence. Furthermore, a bearing fault diagnosis method based on the improved transfer component analysis and deep belief network is proposed, which can achieve subdomain adaptation and improve the compactness of the samples, leading to a complete bearing fault diagnosis under variable working conditions that is faster and more accurate. Finally, the results of the comparative tests confirmed that the proposed method could boost the average accuracy of 0.73%, 0.99%, and 5.55% in the three tests than the state-of-the-art methods, respectively. Moreover, the comparison of the time required for a fault diagnosis using different methods shows that compared to the end-to-end models, the proposed method reduces the time required by 594.9 s and 1431.6 s, respectively.

Disruptive technologies have been employed in various fields with a strategic priority in several countries as the core driving force of the fourth industrial revolution, significantly impacting the development of new power systems. It is a kernel to effectively identify the future potential of disruptive technologies. To overcome the subjectivity and limitations of existing disruptive technology identification methods, we propose a disruptive technology identification method based on patent evolution analysis. Firstly, the evolution matrix of the patent data is calculated. Afterward, we dig into the characteristics of disruptive technologies to build a more targeted identification index system. Finally, the fields of electric power communication and energy generation are selected as typical cases to build the patent data sets. The future development of the identified technologies, including the identified quantum technologies and controlled fusion, is analyzed. The results demonstrate that the proposed model can identify the key technologies of new power systems accurately and contribute to completing the industrial upgrading and transformation more rapidly.