Accurate identification of load voltage exponent and frequency characteristic parameters under steady-state operating conditions is essential for the stability assessment and control of power systems. However, since internal load disturbances are often correlated with system-side voltage and frequency fluctuations, the conventional ordinary least squares （OLS） method may yield significant bias in such scenarios. To address this issue, this paper introduces the instrumental variable （IV） method and under the assumption that internal disturbances follow short-range correlated colored noise, establishes a statistical estimation framework for load characteristic parameters identification to resolve the endogeneity problem. Firstly, the formation mechanism of endogeneity in closed-loop systems and the resulting estimation bias are analyzed. Secondly, an IV-based identification procedure is established, which consists of steps such as correlation analysis, instrumental variable construction with multi-port information, and parameter estimation using the two-stage least squares method. The unbiasedness and variability characteristics of IV estimation are systematically analyzed, and criteria for selecting the lag order L are proposed. Finally, simulation studies are carried out to investigate the influence of different IV parameter settings on the estimation results, and the proposed method is validated from multiple perspectives, including endogeneity intensity and the proportion of static versus dynamic load components. The results demonstrate that the proposed method can markedly enhance the accuracy of load characteristic parameters identification under steady-state conditions with strong internal disturbances.

The high proportion of power electronics in new power systems introduces the "dimensionality disaster" challenge in system modeling and analysis. Among existing model order reduction methods, theoretical analysis approaches are highly targeted but suffer from insufficient universality. The state variable selection principle of the singular perturbation method is tied to linearized models, making it difficult to accurately capture comprehensive dynamic characteristics. To address this, the state variable selection principle is improved by leveraging data-driven techniques to obtain full dynamic characteristics. The least absolute shrinkage and selection operator (LASSO) is employed for state variable selection, supplemented with critical state variables based on physical information. Perturbation methods are then applied to achieve system dimensionality reduction. Finally, the approach is tested in a typical grid-connected converter system, demonstrating that the reduced-order system can reflect the small-disturbance stability characteristics of the original system, validating the method′s effectiveness.

To address the issue of simulation accuracy degradation caused by virtual power losses in traditional associated discrete circuit(ADC) models for voltage-source converters (VSCs) in real-time simulation, a dual-mode constant admittance method is proposed. Firstly, by analyzing the mechanism of equivalent energy storage mutation during switching state transitions, the accumulation law of virtual losses under high-frequency PWM modulation is revealed. Secondly, based on the characteristics of switching actions during unlocking and blocking states, a constant admittance model for the converter in the unlocked state is constructed using the proposed semi-implicit decoupling method, eliminating virtual losses while maintaining a constant admittance matrix. For the blocked state, where switching transitions are fewer, the traditional ADC model is retained. Simulation results demonstrate that the proposed converter modeling exhibits minimal virtual power dissipation and high accuracy in both unlocked and blocked conditions, with no numerical oscillations during state transitions.

In response to the challenges in stability analysis posed by the multiple-input multiple-output (MIMO) characteristics in power systems, this paper innovatively introduces input-output stability theory and the small-gain theorem into the calculation of system stability margins. A feedback interconnection framework of "system-disturbance" is established, along with a quantitative method for assessing stability margins. The proposed approach comprises two key contributions: first, the computation of the maximum singular value of the system to establish a robust stability criterion for MIMO systems, which effectively characterizes the coupling characteristics among variables; second, the application of the small-gain theorem to develop an explicitly quantifiable gain margin index, thereby enabling the computation of stability margins that systematically account for system uncertainties. Simulation results demonstrate that the proposed index accurately reflects the system's disturbance rejection capability and the influence of control parameters on stability, even under complex variations in system dynamics. This method provides a unified analytical tool for evaluating the stability characteristics of diverse devices in power systems, offering significant theoretical and practical value.

As a crucial component of power systems, AC transmission lines play a pivotal role in the energy exchange process during system oscillations, especially in prevalent scenarios where the time-scale dynamics of internal potentials in new energy generation equipment are comparable to those of transmission lines. Under such conditions, the dynamic characteristics of AC transmission lines will profoundly participate in the energy exchange process of system oscillations. Therefore, investigating the energy storage mechanism of AC transmission lines and their fundamental modes of involvement in the energy exchange of system oscillations hold foundational significance for clarifying the physical mechanisms behind these oscillations. However, existing researches have predominantly focused on mathematical characterizations of AC transmission lines based on instantaneous voltage and current values or harmonic relationships, lacking a physical understanding of how transmission lines participate in the energy exchange process of system oscillations. Consequently, the oscillation mechanisms in new energy power systems remain unclear. Firstly, a typical converter-interfaced AC system is taken as an example, the basic operational principles of AC transmission lines is elucidated under the objective of stable power transmission. Secondly, the analytical relationships characterizing the dynamics of AC transmission lines is established. Thirdly, their dynamic characteristics and energy storage implications are analyzed. Finally, a detailed examination of the energy exchange behavior in closed-loop system dynamics is conducted, deepening the understanding of how AC transmission lines intrinsically participate in system dynamic processes and laying a vital cognitive foundation for revealing the general energy exchange mechanisms underlying current system oscillations.

A particle swarm optimization algorithm based demodulation method for amplitude frequency modulation characteristics of power system oscillation signals is proposed to address the problem of power system oscillation caused by the continuous increase in the proportion of new energy grid connected. Firstly, starting from the physical mechanisms of formation and evolution of AC electrical quantities, the constraint relationships of oscillation signals under disturbances are clarified, revealing the modulation patterns of their amplitudes and frequencies over time. Unlike traditional harmonic superposition methods based on Fourier decomposition or wavelet analysis, this study emphasizes that oscillation signals are not formed by static harmonic superposition but rather result from a continuous modulation process shaped by the system’s dynamic closed-loop action, which carries greater physical significance. On this basis, a PSO-based amplitude⁃frequency modulation feature extraction method is designed. By minimizing the deviation between the fitted waveform and the actual waveform, the amplitude⁃frequency modulation parameters that determine the oscillation process are extracted. Simulation results show that the proposed method can accurately reflect the oscillation patterns of typical converter-interfaced power systems, with the fitted waveforms highly consistent with the original signals. Dynamic model experiments further validate the applicability and effectiveness of the proposed algorithm in practical systems.

Due to the negative resistance characteristics of power electronic devices, high-penetration renewable energy power systems face significant resonance stability issues. Limited by heavy high-order determinant computations, the conventional s-domain node admittance matrix method becomes inefficient for large systems. To address this problem, the s-domain node admittance matrix method is optimized by impedance aggregation, enabling fast resonance stability analysis for high-penetration renewable energy power systems. Firstly, the s-domain impedance models of typical components in high-penetration renewable energy power systems are presented, together with the procedures of the conventional s-domain node admittance matrix method. Next, a general impedance aggregation method independent of system topology and scale is proposed. The partial inheritance of resonance modes and node voltage mode shapes in impedance-aggregated systems is revealed, and regional observability and regional linkage are introduced to distinguish global modes and local modes. Then, node numbering rules and analysis procedures for the optimized method are developed, transforming the resonance stability analysis for the complete system into a collection of subsystem analyses, and the efficiency improvement is verified in terms of asymptotic computational complexity. Finally, resonance stability analysis is conducted on an offshore wind power system in Zhejiang Province, validating the effectiveness of the proposed method.

The operation strategy of an integrated energy system is crucial to achieve complementary advantages among various energy sources. Therefore, a day-ahead multi-objective optimal operation strategy for low carbon industrial integrated energy system based on knee point-driven is proposed. Firstly, the mathematical model of an electric-gas-heat industrial integrated energy system with an automatic assembly line production system is developed. Then, the multi-objective optimal operation model with the goal of minimizing the energy consumption cost and carbon emissions for the industrial integrated energy system is formulated. Finally, a knee point-driven approach is developed to solve the multi-objective optimization problem. Experimental results show that the knee point-driven multi-objective optimal operation strategy can make full use of time-of-use electricity price and the dispatchable loads to reduce energy cost and carbon emission.

With the continuous increase in the proportion of new energy in the power system, the market-oriented trading mode of new energy is gradually forming a system. Different proportions of new energy and different clearing modes will affect the clearing results of spot electric energy and frequency regulation ancillary service markets. To address this, the trading models of new energy participation in the market are summarized and a comparative analysis of the clearing rules of typical frequency regulation markets is conducted such as PJM (Pennsylvania-New Jersey-Maryland interconnection)and CAISO(California independent system operator)in the United States. On this basis, in order to simplify the complex market clearing process caused by separately accounting for opportunity costs, a frequency regulation market clearing model is constructed, where generating units voluntarily consider opportunity costs during capacity bidding, and the clearing sequence is determined based on the units′ declared capacity bids and mileage bids. Furthermore, based on this model, sequential and joint clearing models for the spot electric energy and frequency regulation ancillary service markets are proposed. To study the cost-effectiveness of market clearing under different mechanisms with high and low proportions of new energy participation, simulation cases are constructed for comparative analysis, which demonstrate that the joint clearing mode has certain advantages in the context of high proportions of new energy.

With the construction of the southern regional power market, the scale of cross-provincial and cross-regional power transactions is gradually expanding. In the future, the co-existence of the dual-track system of plan and market is expected to exist for a long time, in which the decomposition of the contracted power is of great significance to the market operation and the interests of the market entities. To better promote the implementation of the scheduling plan of the power generations and ensure the interests of the market entities, a two-stage decomposition method of the preferential power generation plan electricity based on multi-band uncertainty sets of inflow water is proposed. In the first stage of power decomposition, the annual power decomposition model for the wet season and the dry season is established by taking into account the characteristics of inflow water during the wet season and the dry season, so as to realize the seasonal decomposition of the annual power to the month. And then, with the goal of consistency of the power generation progress, a monthly power decomposition to daily model based on the consistency of progress coefficients is established. In the second stage of power decomposition, the uncertainty of inflow water is considered and described as a multi-band uncertainty set, and a two-layer model is used to solve the conservative power decomposition curve under the power decomposition curve of the first stage, starting from the time scale of the monthly power decomposition to the day. Finally, the planned power decomposition curves and conservative power decomposition curves of each station are obtained by decomposing the hydropower stations in Yunnan Province as an example, and the effectiveness of the method proposed is comparatively analyzed.

Defective insulator detection is one of the critical tasks in the operation and maintenance of smart grids. To address the challenges of multi-target and multi-scale detection in aerial insulator images, a defective insulator detection method is proposed based on an improved CenterNet architecture. The method adopts an anchor-free detector as the foundational framework and innovatively integrates three key technologies. Firstly, an expanded feature enhancement module is designed, which effectively enlarges the feature receptive field through dilated convolutions, significantly improving the model's ability to capture multi-scale target features. Secondly, a convolutional block attention mechanism is embedded into the network to dynamically optimize the weight distribution of feature channels, enhancing both detection accuracy and computational efficiency. Finally, a multi-scale feature pyramid structure is employed to achieve the fusion and complementarity of multi-level features. Experimental validation demonstrates that this method excels in defective insulator detection under complex scenarios, achieving an average precision of 95.17 %, with all metrics significantly outperforming existing mainstream algorithms, fully proving its advantages in practical applications for power line inspection.

In current simulation studies on the impact of wildfires on the electric field of transmission line gaps, the relationship between burning particles and conductor spacing, as well as the electric field distortion effect, remains unclear. Additionally, the influence of meteorological factors and vegetation characteristics on the spatial distribution of burning particles has not been considered. To address this, a highly coupled multi-physics optimization simulation model integrating thermal, flow field, electric, and particle motion is developed based on simulated experiments and real-scenario parameters. This model combines micro- and macro-scale perspectives to analyze the intrinsic mechanisms of electric field distortion in transmission lines caused by wildfires. After verifying the model's accuracy, the proposed model can simulate flame combustion at different stages of wildfires and particle motion in transmission line gaps at a microscopic level, while also providing theoretical support for macro-scale analysis of the influence of vegetation characteristics and meteorological factors on gap insulation performance. The results show that the electric field on the conductor surface during a wildfire is approximately 7.14 times that under normal conditions. The spacing between burning particles and transmission conductors affects the electric field distortion effect, with the maximum distortion ratio and range beginning to rise significantly and gradually stabilizing when the spacing is between 0.2 and 0.15 meters. Vegetation characteristics and meteorological factors influence the insulation performance of the gap by altering the spatial distribution of burning particles in wildfire gaps. The research findings contribute to evaluating the safety of transmission lines during wildfire disasters and provide a theoretical basis for emergency response strategies.

Due to the complex backgrounds, small defect targets, and significant size variations among different detection objects of insulator defects in drone aerial inspection images, current object detection algorithms often lose defect features when identifying such targets, resulting in low accuracy and high misidentification rates. To address this, an improved YOLOv8n-based method for detecting insulator defects in transmission lines is proposed. Firstly, to tackle the feature loss issue of small insulator defects, a PCFocalNeXt-C2f module is designed and used to optimize the Backbone of YOLOv8n. Secondly, to mitigate feature information loss or degradation during multi-level transmission, an asymptotic feature pyramid network （AFPN） supporting direct interaction across non-adjacent layers is employed to enhance the Neck of YOLOv8n. Finally, the Sigmoid-CIoU loss function is introduced to optimize the loss function in YOLOv8n, and Sigmoid-CIoU NMS is adopted to obtain detection results, improving the model's robustness. Experimental results show that compared to the original YOLOv8n, the proposed algorithm achieves a 3.3 % increase in F1-score, with mAP@0.5 and mAP@0.5-0.95 improving by 3.8 % and 6.1%, respectively, while reducing the parameter count by 23.9 %, demonstrating superior detection performance.

The E1 phase of the high-altitude electromagnetic pulse （HEMP） has a high field strength, a fast rise time, and a short duration. It will damage the end of the intelligent equipment through the substation internal control cable coupling. Therefore, it is of particular importance to analyze the HEMP coupling response characteristics of the substation internal control cable. Through conducting research on the actual cable conditions of typical substations, the end coupling response level of switching and analog transmission cables in a substation under HEMP environment are simulated and investigated. The study reveals that when the shield is not grounded, the end of the cable induces a high voltage of approximately 70 kV. Additionally, when the core wire is grounded, the short-circuit current is approximately 4 A. The amplitude of the shield's grounding current is approximately 60 A. Finally, a biconical-linear-grid antenna is constructed as a horizontally polarised HEMP simulator. The control cable is tested in the field and compared with the simulation results to verify the validity of the simulation method.