Voltage source converter based multi-terminal HVDC (VSC-MTDC) technology has shown many advantages. However, following a large disturbance, the DC voltage and the output power of the VSC station with fixed droop control may hit their limits, thereby reducing the DC voltage control ability of the whole MTDC system. Here an adaptive droop control scheme is proposed on the basis of both the DC voltage deviation factor and the power sharing factor. It contributes to ensure the DC voltage and the power loading rate of each converter within their limits during large disturbances, and the power sharing capability of the whole MTDC system remain high. The working principle of the proposed adaptive droop control is discussed in details. Case study and simulations are performed based on the PSCAD/EMTDC model of a $\pm$320kV four-terminal VSC-HVDC system to validate the effectiveness of the proposed adaptive droop method.

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

Accurate and rapid fault diagnosis is paramount to stabilizing proton exchange membrane fuel cells (PEMFC). To achieve this, this study proposes a novel fault diagnosis method that integrates a convolutional neural network (CNN), a bi-directional long short-term memory network (BiLSTM), and a waveform fault attention (WFA) mechanism. In the proposed framework, data are classified into five distinct categories utilizing a hierarchical clustering algorithm. Additionally, data augmentation techniques are implemented to bolster model performance. The introduction of amplitude attention and temporal difference attention, in conjunction with the construction of WFA, enables the accurate extraction of temporal-spatial features, significantly improving the distinguishability of fault diagnosis. Furthermore, feature contribution is evaluated using permutation feature importance (PFI) to identify key features, enhancing the interpretability of the model. Experimental findings verify that the proposed method enables high-precision fault identification, with precision values spanning 97–100% and an average stability of 98.3%, demonstrating robust performance even when the volume of original sample data is limited. This performance markedly surpasses that of extant methodologies. The comprehensive approach augments the accuracy, reliability, and interpretability of PEMFC fault diagnosis, and introduces a novel research paradigm for feature extraction, thereby possessing significant theoretical and practical application value.

With the ongoing energy transition, large-scale integration of inverter-based renewable generation at DC sending ends has significantly weakened grid strength and increased vulnerability to disturbances from the DC receiving end. These disturbances may trigger severe transient voltage variations and synchronization instability of renewable energy converters, especially under weak-grid conditions where conventional fault ride-through schemes become ineffective. To address this challenge, this paper establishes a mathematical model of a high-renewable-penetrated sending-end system with DC transmission and analytically derives the converter stability boundaries under different grid strengths and fault severities. Based on the identified stability region, a virtual power-angle increment feedback control strategy is proposed to suppress transient instability and mitigate voltage impacts. The effectiveness and practical feasibility of the proposed method are validated through Simulink simulations and RT-LAB hardware-in-the-loop experiments. The results demonstrate that the proposed approach enhances synchronization robustness and provides an effective solution for secure power delivery in future renewable-dominated systems.

Accurate multi-horizon solar power forecasting is vital for modern power distribution networks, yet standard deep learning models face limitations due to weather stochasticity and prohibitive computational requirements on resource-constrained edge devices. To address these issues, this paper proposes a Physics-Informed Similar-Day Data Augmentation (PI-SDDA) framework coupled with a Pruning-Optimized Temporal Convolutional Network (POTCN). The PI-SDDA strategy integrates multi-variable meteorological dynamics with candidate cluster size optimization to capture diurnal thermal periodicity and transient weather variations. To enable lightweight deployment, POTCN incorporates Batch Normalization scaling factors for channel-level sparsification within dilated causal residual blocks, significantly reducing parameter redundancy while preserving temporal receptive fields. Experimental results demonstrate superior forecasting performance across fifteen-minute, one-hour, and two-hour prediction horizons under standard statistical metrics compared with existing benchmarks. Edge hardware deployment tests on an embedded edge computing platform further verify low inference latency and minimal power consumption. The proposed framework provides an accurate, resource-efficient solution for real-time edge intelligence in power systems.

The large-scale and distributed integration of photovoltaics (PVs) into distribution networks has significantly intensified voltage fluctuations and complicated grid operation. The aggregative-based control can effectively reduce control complexity while exploiting the grid operation support potential of PV systems. To address this issue, this paper proposes a hierarchical coordinated optimization method for reactive power dispatch and droop control of PV aggregators. By considering the interaction between optimization and local control, a fully distributed two-level hierarchical coordination framework is construct. At the upper level, the hierarchical coordinated optimization model for reactive power dispatch and droop control is formulated by embedding droop control parameters as decision variables. In this way, the global optimality of dynamic control is enhanced, and the adaptability of control decisions to real-time operating conditions is improved. At the lower level, each PV aggregator controller with the optimized droop settings, coordinates the internal PV inverters via a consensus algorithm and dynamically optimizes their reactive power outputs in response to real-time disturbances. To solve the resulting hierarchical model efficiently, the improved alternating direction method of multipliers based on Nesterov’s accelerated gradient is developed through better utilization of historical iteration information. Case studies on the modified IEEE 123-bus test system demonstrate the effectiveness of the proposed method in reducing power losses and voltage quality improvement.