Machine learning-based approaches for fault diagnosis on transmission lines have attracted increasing attention in recent years, yet concerns over their robustness and explainability hamper their practical applications. Moreover, separate algorithms are normally used to solve the fault classification and location tasks. In this work, we focus on these tasks and propose an integrated model based on the convolutional neural network, whose improved performance over separate models is demonstrated by numerical results. Besides, explainability of the proposed model is demonstrated and analyzed. Specifically, the class activation maps and attention maps illustrate that the proposed structure can reduce the degradation of the model performance due to data pollution, enhancing the credibility of the proposed method in practical applications. Moreover, the proposed model outperforms existing machine learning-based models in terms of accuracy and robustness. Such a structure has been proven effective on field data and can be applied to many other tasks of fault diagnosis.

In AC–DC parallel systems, line‐commutated‐converter (LCC)‐based high‐voltage direct‐current (HVDC) emergency control strategy can improve the stability of the power system. This study proposes an LCC HVDC emergency control strategy that utilises the global information measured by wide‐area measurement systems. The design of the proposed LCC HVDC emergency control strategy takes advantage of dynamic surface control and adaptive control. The Lyapunov stability analysis is used to prove that the AC–DC parallel system is uniformly ultimately bounded in the presence of the uncertainties. A two‐area four‐generator AC–DC parallel system is developed in PSCAD/EMTDC to verify the effectiveness and correctness of the proposed strategy. Simulation results show that the proposed LCC HVDC emergency control strategy can improve the stability of an AC–DC parallel system and is robust. Its superiority is demonstrated by comparing the proposed method with controllers designed using backstepping technique and pole placement technique.

Existing machine-learning research on power grids relies on online measurements without missing data. We propose a missing data reconstruction model based on generative adversarial networks to supplement existing methods. This model fits better spatio-temporal data with several improvements over previous approaches. First, the loss function considers both distribution and value differences, leveraging all available information to minimize differences between original and generated data. Then, a deep-learning architecture incorporating convolutional neural layers and nonlocal blocks is developed to extract the spatial-temporal information in electrical feature maps. The proposed method exhibits enhanced credibility by neglecting invalid consecutive data under phasor measurement unit (PMU) failures (proven by attention maps generated in nonlocal blocks), and higher accuracy than existing models for recovering data under random data missing/PMU failure conditions (proven by numerical results). Finally, the proposed data reconstruction model is effectively applied to an online framework for transient stability assessment.

Online transient stability assessment (TSA) is crucial for ensuring the security of modern grids. However, problems with limited sample sizes and data imbalance hinder the performance of data-driven TSA classifiers. Addressing this challenge, this article presents a generative adversarial network (GAN)-based model to generate instability samples. Unlike existing methods, our approach moderately alters the long-tailed distribution of instability moments within the raw dataset, producing a more diverse database for TSA tasks. A convolutional neural network-based supervised model, mapping the relationship between fault-clearance electrical characteristics and instability moments of the power system, is incorporated to drive the GAN model to generate realistic yet rare instability cases. Numerical results have proven the superiority of the proposed model over existing case generation methods in terms of realistic and diverse sample generation. In addition, the effectiveness of the proposed data augmentation scheme is demonstrated for online TSA applications.

Wind turbines with direct-driven permanent magnet synchronous generators (PMSGs) are widely used in wind power generation. According to the dynamic characteristics of PMSGs, an impact analysis of PMSG-based wind power penetration on the transient stability of multi-machine power systems is carried out in this paper based on the theory of extended equal area criterion (EEAC). Considering the most severe PMSG integration situation, the changes in the system’s equivalent power-angle relationships after integrating PMSGs are studied in detail. The system’s equivalent mechanical input power and the fault period electrical output power curves are found to be mainly affected. The analysis demonstrates that the integration of PMSGs can cause either detrimental or beneficial effects on the system transient stability. It is determined by several factors, including the selection of the synchronous generators used to balance wind power, the reactive power control mode of PMSGs and the wind power penetration level. Two different simulation systems are also adopted to verify the analysis results.

With the increase in a number of online high‐voltage direct current (HVDC) systems in China, a big question for their use as black‐start and restoration of power system outages arises. Line‐commutated converters (LCC)–HVDC systems can significantly reduce recovery time. The black‐start‐up and recovery conditions for LCC–HVDC systems and the connected AC systems are discussed in this study. The appropriate start‐up and recovery modes are identified and the results of the control characteristics of LCC–HVDC systems are analysed in detail. The stability requirements of AC systems in terms of LCC–HVDC system effects are evaluated. Furthermore, the restoration using LCC–HVDC systems, including the priority sequence index of the multi‐HVDC links and the optimisation model of the mentioned problem, is discussed in detail. Compared to the conventional black‐start strategy, the applicability and effectiveness of the proposed method are illustrated using a part of the actual Guangzhou power system as a test system.