Abstract:The reliability of All-DC wind farms is of great significance for enhancing the consumption capacity of renewable energy and promoting the construction and development of new power systems. This paper conducts a detailed analysis of the topological structure of All-DC wind farms. Based on the sequential Monte Carlo method, a reliability assessment model is constructed. From dimensions such as space and objects, reliability assessment indicators of hierarchical classes, object classes, time limit classes, and degree classes are defined, and a multi-level and multi-link reliability index system is established. At the same time, the cloud droplet influence value integrated cloud model method is introduced. By using the normal cloud generator, the randomness of index data are effectively processed, and a comprehensive assessment of the multi-dimensional reliability of All-DC wind farms is achieved. Finally, a 100 MW wind farm in Northwest China is taken as an example for simulation verification. The results fully demonstrate the effectiveness and superiority of the proposed method, when compared to conventional approaches. The proposed method can provide key technical support for the planning, operation, and maintenance of All-DC wind farms and the integration of new energy into the grid.

Abstract:Protection challenges are common issues in low-voltage direct current systems, particularly in scenarios involving high-impedance faults and arc faults. These faults may escalate into flashovers, accompanied by current distortion, thereby causing severe damage to equipment. To address these issues, this paper proposes a backup protection scheme based on the discrete wavelet transform to reliably detect and classify such faults in distribution lines. Distinguishing fault conditions from resistive load is challenging due to their similar current magnitudes, the proposed method introduces a technique for square pulse injection into the power lines. This active approach effectively differentiates these cases, thereby preventing erroneous tripping. The proposed standalone approach can be easily integrated into existing intelligent electronic devices to enhance conventional current-based protection methods. The algorithm generates binary signals representing the magnitude of current waveform distortion at both positive and negative poles. By analyzing these signals through decoders, the system state is accurately identified, triggering protective actions as necessary. This ensures robust protection and continuous power delivery despite challenging fault conditions.

Abstract:Detecting inter-turn short circuits in ultra-high voltage shunt reactors is challenging due to weak fault signatures. This paper establishes an accurate three-segment fault model and derives, for the first time, explicit analytical expressions for the short-circuit phase equivalent resistance (Req) as a function of both short-circuit turn ratio (α) and transition resistance (Rk). In particular, this model reveals specific Req variation laws: monotonic decrease with α for metal faults and non-monotonic behavior for transition resistance faults. Leveraging these insights, an innovative identification method is proposed. Its core innovation is a theory-driven, flexible threshold (Rset) calculation based solely on the derived Req=f(α,Rk) function and user-defined pa-rameters (minimum detectable αmin and maximum tolerable Rk_max), thereby reducing reliance on empirical values. Simulation results validate the proposed model and method, demonstrating superior sensitivity and ac-curacy, especially for small-turn inter-turn short circuits with transition resistance, compared to conventional ze-ro-sequence methods. This provides a robust theoretical foundation for practical fault detection.

Abstract:The mass access of a power electronics-based source/load resource to a distribution network brings about complex power-quality issues, which aggravate loss problems. This paper proposes a hierarchical loss-optimization design method for the multi-winding inductive filtering transformer applied in a distribution system with the function of harmonic elimination. The transformer parameters are classified hierarchically by means of a Sobol algorithm-based sensitivity analysis, which contributes to reducing the optimization dimension and improving the optimization accuracy. The load harmonic transfer relationship in the inductive filtering transformer is established, and the impedance constraint is obtained. According to the design specification of an inductive filtering transformer, the constraint and optimization parameters of transformer loss optimization are determined. Furthermore, the hierarchical architecture is established through a Sobol sensitivity analysis of the optimized parameters. At last, a case study and simulation are carried out, which show that the transformer loss can be effectively reduced and that optimization algorithms can be applied to further improve the optimization ability.

Abstract:Although carbon flow is a powerful tool for assigning emission responsibility to consumers, it has not been explored in hybrid AC-DC systems. This paper presents a novel probabilistic carbon-flow model to quantify the distribution of carbon intensity in such systems. To further characterize the importance of uncertain inputs, such as renewable energy and loads, on probabilistic carbon flow, a global sensitivity analysis (GSA) strategy is introduced. To alleviate the computational burden of traditional Monte Carlo methods in quantifying these metrics, it incorporates an adaptive polynomial chaos expansion (PCE)-based surrogate model. This significantly reduces the computing burden while maintaining high statistical accuracy. Simulations validate the proposed carbon flow model in the hybrid AC-DC system and reveal the excellent performance of the PCE-based GSA method.

Abstract:With the increasing penetration of renewable energy, ensuring reliability and security of power supply has become a significant challenge. In this paper, a novel photovoltaic (PV) intraday power supply guarantee capability forecasting method is proposed. Different from the conventional PV power forecasting methods, it can provide diverse forecasting information, including guarantee power, low output power period, and power supply guarantee probability. PV guarantee power which represents a conservative prediction of PV power is forecasted based on convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) model with a compound loss function. PV low output power period represents the period when the power gap between the theoretical maximum PV power and the actual PV power is higher than a specific threshold. It is forecasted using hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model based on an improved spatiotemporal feature encoding method. The power supply guarantee probability which represents the risk of PV power shortage is obtained using quantile regression model based on gradient boosted regression tree (GBRT) considering different PV power supply demands. Furthermore, new indexes, including guarantee rate, guarantee energy ratio, success index, forecasting gain index, and probability forecasting accuracy are proposed to evaluate the forecasting performance. The effectiveness of the proposed method is verified based on actual operation data in a province in Northwest China.

Abstract:To improve the state-of-charge (SOC) balancing ability and reduce the power loss, this paper proposes a discontinuous pulsewidth modulation (DPWM)-based battery power management method for cascaded H-bridge converter-based battery energy storage systems (CHB-BESS). First, two types of voltage-clamping principles are designed for each submodule (SM) of the CHB-BESS. Second, to address the limitations of conventional DPWM with fixed low power adjustment in CHB converter applications, the proposed method determines the maximum number of voltage-clamping SMs according to their SOC values and the output voltage references of the CHB-BESS. As a result, the SOC balancing ability can be fully exploited under both active and reactive power operation conditions, and the total power loss of the CHB-BESS can be substantially reduced. Finally, voltage-clamping SMs are selected according to their SOC sorting results, and the output voltage references of remaining non-voltage-clamping SMs are recalculated for modulation. Simulation and experimental results indicate that under varying active and reactive power operation conditions, the proposed method can improve the SOC balancing speed and reduce the power loss of the CHB-BESS.

Abstract:Non-intrusive load monitoring (NILM) has gained widespread attention for improving residential energy efficiency by analyzing appliance-level energy consumption. Although machine learning-based NILM methods have demonstrated excellent performance, their effectiveness heavily relies on the availability of sufficient training data. Federated learning (FL) has emerged as a promising approach for collaboratively training NILM models by aggregating distributed knowledge across clients, effectively harnessing decentralized data reserves. However, due to significant data distribution discrepancies among clients, the global model aggregated through FL often deviates from the client-specific optimum. To address this challenge, this paper proposes a directed knowledge transfer-based personalized model learning method. In this method, clients acquire eligible models through peer-to-peer communication and perform cross-architecture knowledge transfer via knowledge distillation. Furthermore, a data-driven model trust evaluation mechanism is designed to pre-screen candidate models and guide directed knowledge transfer, thereby reducing communication overhead and improving transfer efficiency. Additionally, consistency learning is introduced to mitigate potential overfitting during personalized model training. Extensive experiments on three public datasets, PLAID, WHITED and HOUIDI, demonstrate that the proposed method achieves superior training efficiency and performance compared to existing methods.

Abstract:When single-phase grounding (SPG) faults occur in lines with inverter-based resources (IBRs) at both ends, traditional non-unit protections may malfunction due to the limited amplitude and controlled phase of zero-sequence current. To address this issue, this paper proposes an integrated sequence component-based non-unit protection scheme. By calculating the remote zero-sequence current using only local components, the method eliminates the need for real-time communication. The resulting fault distance calculation is highly resilient to fault resistance and is independent of positive-sequence current injections by IBRs. The proposed method seamlessly meets the reactive power support requirements of various grid codes. Simulation results confirm the effectiveness of the proposed method for SPG fault protection for lines with IBRs at both ends.

Abstract:With the rapid growth of distributed energy resources (DERs), electrical vehicles (EVs), and energy storage (ES), electricity consumers are transitioning into prosumers. This paper proposes a distributed coordination and value allocation framework for multi-owner heterogeneous resources, addressing key challenges of conflicting interests, poor interoperability, and low utilization. A sharing economy-based mechanism is introduced for peer-to-peer surplus energy exchange without dedicated infrastructure. The mechanism operates through three stages: bidding, winner determination, and settlement. To ensure truthfulness, winner determination incorporates flexible “AND/OR” bid combinations across multiple periods. The Vickrey-Clarke-Groves (VCG) mechanism is further applied in non-trading periods to evaluate individual contributions. This integrated design maximizes social welfare and promotes a growing energy-sharing ecosystem. Numerical experiments based on the IEEE15-bus system and comprehensive performance for funding settlement, individual rationality, budget balance comparison are conducted. The proposed method effectively addresses the dynamic continuity constraints of multi-entity systems, which are neglected by traditional methods. Evaluations show that “OR” and “AND” bidding enhance social welfare/winning rate by 22.3%/5% and the total transferred funds by 146%, respectively.

Abstract:With the rapid adoption of electric vehicles (EVs), the inadequate deployment of urban charging infrastructure has intensified the mismatch between charging demand and power supply capacity. This paper proposes a multi-objective joint optimization planning method for electric vehicle charging stations (EVCSs) and electric vehicle swapping stations (EVSSs). First, a dynamic path optimization method based on real road network topology is presented. Then, considering EV travel characteristics and power consumption models, a spatial-temporal charging demand forecasting model is proposed. Furthermore, electric-traffic coupling principles are integrated into the planning framework to meet the refined requirements of power grid and road network coupling. Finally, the proposed approach is validated using the road network of a city in southern China, which is divided into multiple subregions based on points of interest, together with the IEEE 118-bus test system. The forecasting results reveal a bimodal temporal pattern of charging demand, with peak loads concentrated in public, workplace, and commercial areas during the daytime and shifting predominantly to residential areas at night. The planning result shows that 8 EVCS and 5 EVSS should be deployed within the study area.

Abstract:With the continuous expansion of wind power integration, dual-sequence synchronization stability has become a critical concern for wind farms under asymmetrical grid faults. In particular, in multi-parallel systems, stability analysis is further complicated by various coupling effects. To address this challenge, a dual-sequence nonlinear dynamic model is developed to identify and characterize three types of coupling interactions. By incorporating the equal area criterion, the impact of coupling effects on dual-sequence synchronization stability under different scenarios is analyzed. On this basis, a method for calculating the coupling critical point is developed under accordance with grid code requirements, which can be utilized to determine appropriate current injection levels for wind farms. Finally, simulation results are presented to validate the effectiveness of the theoretical analysis and the proposed calculation method.