In this paper, a novel integrate protection for VSC-HVDC transmission line is proposed. The protection scheme includes a main protection and a backup protection. The main protection is based on current limiting reactor power (CLRP), while the backup protection is based on variation tendency of current. The changing characteristics of CLRP and current are theoretically analyzed under different fault conditions. Based on the characteristics, internal and external faults of DC transmission line can be identified. Then criteria of the novel integrate protection can be built. The main protection can detect the faults quickly without communication, while the backup protection is used to identify the high resistance faults and provide a backup if main protection fails. The scheme can protect the whole transmission line without dead zone, simplify the algorithm and improve the speed of protection. A typical VSC-HVDC system is modeled using PSCAD/EMTDC. Comprehensive simulations is done to verify the performance of proposed scheme. Furthermore, the protection does not require a high sample frequency and does not require data synchronization. These make it valuable for application in practical VSC-HVDC transmission system.

It is still a significant challenge to accurately locate faults of grounding electrode lines in high voltage direct current (HVDC) transmission systems, especially high resistance faults and remote faults. In this paper, a novel fault location approach based on characteristic-harmonic measured impedance (CHMI) is proposed. This method works well under high resistance faults and remote faults. Considering that there are abundant and steady characteristic harmonics in electrical signals of grounding electrode lines, the CHMI is defined first according to the equivalent circuit of the grounding electrode system. Then, the CHMIs of the two grounding electrode lines under faults are analytically derived. On the basis of that, the fault branch is identified by the amplitude ratio of the two CHMIs. Furthermore, since both of the CHMIs of the two lines are relevant to fault distance and fault resistance, the precise fault distance is obtained by solving two fault location functions simultaneously and eliminating fault resistance. The proposed algorithm is finally validated through numerous simulations covering various fault scenarios carried out in PSCAD/EMTDC.

High frequency current injection-based protection is widely used for fault detection of grounding electrode line in high-voltage direct current (HVDC) system. Since the grounding electrode line is several times longer than the wavelength of the injected current, the relationship between the measured impedance and fault distance is very complex. Due to the complex impedance characteristic, the current injection-based protection has dead zone under some circumstances. To eliminate the protection dead zone, an improved approach to high frequency current injection-based protection was proposed. First, the characteristics of the high frequency impedance of the grounding electrode line under different fault conditions were investigated. Analysis results showed that when the line length was close to the integer multiple of the half wavelength of the injected current, the measured impedance of the grounding electrode line may be the same as normal operation impedance. Thus the current injection-based protection will be unable to recognize the fault. Second, a two-layer optimum method was proposed to select the frequency of the injected signal. With the new frequency, the current injection-based protection was able to eliminate the dead zone of the traditional protection. Finally, a ±800 kV HVDC simulation model was constructed, and the simulation results verified the accuracy of our conclusions and the effectiveness of our proposed protection method.

Currently, the clustering of dynamic multi-machine equivalent modeling of wind farm (WF) is based on the deterministic data. To serve the early warning calculation of power system dispatch better, a dynamic multi-machine equivalent modeling method of WF considering the uncertainty of wind power prediction is first proposed in this paper. First, the probability density functions of wind speed and wind power prediction errors are described and the multi-scenario sampling method is adopted to reflect the uncertainty. Second, the probabilistic clustering method is used to obtain the probability of each wind turbine generator (WTG) belonging to each cluster. The cluster with the biggest probability is selected as the clustering result of the WTGs in the spirit of the Bayesian classification. Third, the WTGs and collector system are simplified into a single-machine equivalent model in each cluster. Finally, the WF output response curve is compared among the detailed WF models, the conventional and the proposed equivalent models in the accurate and inaccurate scenarios of wind power prediction, respectively. The results show that the proposed equivalent method considers the uncertainty in reality and has a better accuracy in the inaccurate scenario of wind power prediction.

In order to improve the fault monitoring performance of grounding electrode lines in ultra-high voltage DC (UHVDC) transmission systems, a novel fault monitoring approach based on the high-frequency voltage standing-wave ratio (VSWR) is proposed in this paper. The VSWR is defined considering a lossless transmission line, and the characteristics of the VSWR under different conditions are analyzed. It is shown that the VSWR equals 1 when the terminal resistance completely matches the characteristic impedance of the line, and when a short circuit fault occurs on the grounding electrode line, the VSWR will be greater than 1. The VSWR will approach positive infinity under metallic earth fault conditions, whereas the VSWR in non-metallic earth faults will be smaller. Based on these analytical results, a fault supervision criterion is formulated. The effectiveness of the proposed VSWR-based fault supervision technique is verified with a typical UHVDC project established in Power Systems Computer Aided Design/Electromagnetic Transients including DC(PSCAD/EMTDC). Simulation results indicate that the proposed strategy can reliably identify the grounding electrode line fault and has strong anti-fault resistance capability.

Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation environments. To address these limitations, this paper proposes the Language-Guided Enhanced Anomaly Power Equipment Detection Network (LEAD-Net), a novel framework that leverages text-guided learning during training to significantly improve defect detection performance. Unlike traditional methods, LEAD-Net integrates textual descriptions of defects, such as historical maintenance records or inspection reports, as auxiliary guidance during training. A key innovation is the Language-Guided Anomaly Feature Enhancement Module (LAFEM), which refines channel attention using these text features. Crucially, LEAD-Net operates solely on image data during inference, ensuring practical applicability. Experiments on a real-world substation dataset, comprising 8307 image–text pairs and encompassing a diverse range of defect categories encountered in operational substation environments, demonstrate that LEAD-Net significantly outperforms state-of-the-art object detection methods (Faster R-CNN, YOLOv9, DETR, and Deformable DETR), achieving a mean Average Precision (mAP) of 79.51%. Ablation studies confirm the contributions of both LAFEM and the training-time text guidance. The results highlight the effectiveness and novelty of using training-time defect descriptions to enhance visual anomaly detection without requiring text input at inference.