Grid synchronization has always been an important challenge for grid-connected converters under extremely distorted grid conditions. Moreover, how to quickly and accurately extract multiple required harmonics is also essential for control systems. In this paper, two types of generalized delayed signal superposition (GDSS) operators capable of extracting any arbitrary harmonic component out of an input signal are derived. In order to show the benefits of GDSS operators, a new grid information estimation concept based on multiple GDSS operators (referred to as MGDSS-PLL) is proposed to track the fundamental and multiple targeted harmonics under single-phase and three-phase adverse grid conditions. MGDSS-PLL can be flexibly tuned to extract any harmonic components according to specific requirements within one fundamental period, and it also exhibits great robustness to grid disturbances. Simulations and experimental results are presented for verifying the performance of the MGDSS-PLL.

The complex-coefficient filter-based phase-locked loop is widely used in state estimation for the constant frequency grid system. However, its performance under power systems with a variable frequency is generally unacceptable and related applications are consequently rare. This letter proposes an adaptive enhanced complex-coefficient filter-based phase-locked loop (AECCF-PLL) that accurately estimates the operational state of variable frequency grids. By deriving the relationship between model parameters and frequency, a frequency adaptive module is established to dynamically correct the model parameters in real time. Furthermore, a stability analysis of the system is provided to meet the requirements of stability and fast response for state estimation in more electric aircraft variable frequency grids and the computational workload is relatively small. Experimental tests show strong evidence for the effectiveness and validity of the proposed AECCF-PLL method.

The vertical integration of ‘Generation-Grid-Load-Storage’ in microgrids for port areas is a prevailing trend. To comprehensively and accurately assess the operational efficiency of microgrids and develop an effective means for promoting the sustainable and scalable development of microgrids in port areas, an applicable evaluation index system and comprehensive evaluation method are essential. Addressing the issues of subjectivity in quantitative evaluation decision-making for different configuration and allocation schemes in port areas and the lack of a comprehensive evaluation system for microgrids in port areas, this study aims to scientifically and reasonably evaluate and select the most optimal configuration and energy dispatching schemes for microgrids in port areas. In this paper, the performance indicators of microgrids in port areas are hierarchically structured and classified into five dimensions: economic, energy efficiency, environmental, system reliability, and safety. A comprehensive evaluation index system for microgrid systems in port areas is constructed. Furthermore, an evaluation model for microgrid operational efficiency based on improved CRITIC-TOPSIS is proposed. The coefficient of variation is introduced to measure the relative strength and relative variation of indicators among dimensions, reducing the impact of correlation between indicators and improving the accuracy and objectivity of the evaluation results. Then, the TOPSIS method is used to calculate the comprehensive evaluation values and rank the optimal microgrid schemes in port areas. Finally, the feasibility of the evaluation index system and evaluation method for the operational efficiency of microgrids in port areas is verified through case analysis. The results indicate that the evaluation method based on improved CRITIC-TOPSIS can objectively and quantitatively evaluate the operational efficiency of microgrids in different port areas. The proposed method reasonably avoids the strong subjectivity in weight calculation by traditional expert judgment during the planning phase of microgrids in port areas, providing a new and scientifically effective engineering evaluation and analysis method for the evaluation of microgrid operational efficiency in port areas.

To address the limitations of traditional harmonic detection methods for active power filters in variable-frequency-grids of the More Electric Aircraft (MEA), including inadequate filtering performance and poor adaptability to frequency variations, this paper proposed a harmonic detection algorithm tailored for active power filters in variable-frequency-grids of the MEA. Firstly, the filtering principle of the proposed operator is derived in detail, and its filtering characteristics and frequency adaptability are analyzed. Secondly, based on the proposed operator, a comprehensive harmonic current detection algorithm is designed to accommodate the wide frequency variation of variable-frequency-grids. Then, the complexity of the operator was analyzed. Due to the symmetry of the coefficients, the operation time of the operators can be reduced. Finally, experimental validation and comparative analysis with traditional harmonic detection methods are conducted. Experimental results demonstrated that the proposed harmonic current detection algorithm effectively detects harmonics in complex distorted current signals of aerospace power systems, confirming the algorithm’s accuracy, superior performance, strong robustness and practical applicability.

High penetration of renewable energy and dynamic variations in network topologies impose stringent requirements on the speed, accuracy, and topological adaptability of power flow (PF) prediction. Traditional iterative PF methods suffer from low computational efficiency and poor convergence stability when repeatedly applied across multiple scenarios or under frequent topological changes. Conversely, purely data-driven models struggle to explicitly capture topological heterogeneity and physical constraints, resulting in limited generalization capability and a lack of physical consistency. To address these challenges, this study develops a physics-informed heterogeneous graph neural network (PI-HeteroGNN) framework guided by modularity topological priors for PF prediction. By modeling the power system as a heterogeneous graph, PI-HeteroGNN distinguishes the inherent differences among loads, generators, composite nodes, and slack buses, thereby capturing structural couplings among diverse components. Furthermore, modularity Q is integrated to characterize the community structures and regional coupling features of the grid, while branch conductance and susceptance are encoded as edge features to enhance the model's sensitivity to topological variations and electrical coupling. Additionally, a physical residual loss is formulated based on AC PF equations to enforce active and reactive power balances at each node, thereby ensuring the physical consistency of the predictions. Comprehensive experiments conducted on the IEEE 118-bus system and its subgraphs demonstrate that PI-HeteroGNN achieves lower prediction errors than several baseline models while maintaining strong robustness against line-outage topological perturbations. Overall, the integration of heterogeneous graph learning, topological priors, and physics-driven constraints effectively enhances the accuracy, physical viability, and topological generalization of PF prediction in modern power systems.