Abbreviation (ISO4): South Power Sys Technol
Editor in chief:
In order to improve the seismic performance of the supported flexible DC converter valve, some equipment suppliers have added diagonal rod insulators between the support insulators. It is necessary to study the impact of the layout scheme and pre-tension force of the diagonal rod insulators on the overall seismic performance of the converter valve. Combined with a converter valve, finite element numerical simulation technology is used in a certain engineering project. Firstly, modal analysis is carried out to study the arrangement scheme of diagonal rod insulators and the influence of pre-tension force value on the dynamic characteristics of the valve tower. Then, the vibration mode decomposition response spectrum method and nonlinear time history analysis method are respectively used to conduct dynamic analysis of the valve tower. The results show that the addition of diagonal rod insulators can significantly increase the fundamental frequency of the converter valve tower, but the magnitude of the pre-tension force of the diagonal rod insulator has a relatively small impact on the overall frequency of the valve tower structure. The results obtained by the time history method are generally greater than those obtained by the response spectrum method. Because it can accurately simulate the nonlinear characteristic of the diagonal rod insulator which can be pulled but not compressed, it is recommended to use the time history method to study the seismic performance of pillar type electrical equipment with diagonal rod insulators.
Aiming at the lack of accuracy of traveling wave head calibration and the performance of intelligent location model affected by parameters, a short-circuit fault location method based on the beluga whale algorithm (BWO) is proposed to optimize variable mode decomposition (VMD) and kernel extreme learning machine (KELM) for MMC-HVDC transmission lines. Firstly, the BWO is used to optimize the parameters of VMD， combined with wavelet soft threshold denoising method for noise reduction and decomposition of the collected fault signals. Then the arrival moment of the initial traveling wave is calibrated by combining the Hilbert transform (HT). Next, the arrival moments of traveling waves are used as eigenvalues to construct the feature dataset. The KELM localization model is optimized using BWO. Finally, the dataset is substituted into the optimized localization model to achieve fault localization. The results show that the localization model of the method fits 99.4% with high localization accuracy and good robustness. The proposed method is highly tolerant to noise and transition resistance, and the localization error is within 500m.
In weak grid environments, the larger grid impedance enhances the coupling between the grid and each inverter in a multi-inverter parallel system, resulting in decreased system stability or even instability. To address this, a strategy for improving the stability of multi-inverter grid-connected systems under weak grid conditions is proposed. Firstly, Norton equivalent models for single and multiple grid-connected inverters in weak grids are established. Secondly, from the perspective of grid-connected current stability, the stability criterion for the multi-inverter system is decoupled into that of a single-inverter system based on the concept of equivalent grid impedance, clarifying the mechanism behind the reduced system stability in weak grids. Subsequently, an improved control strategy to enhance the stability of the multi-inverter system is proposed, which increases the stability margin of grid-connected inverters by introducing an all-pass filter into the grid voltage feedforward loop and combines a harmonic controller to improve the system's ability to suppress background harmonics. Finally, simulation analysis verifies that the proposed strategy effectively enhances the stability of multi-inverter grid-connected systems under weak grid conditions.
The coupling characteristics of shielded multi-core cables in high-altitude electromagnetic pulse（HEMP） irradiation environments are investigated through simulation and experiments. Firstly, the coupling characteristics of shielded multi-core cables under different conditions are studied via simulation, revealing the effects of cable wiring methods and parameters on coupling characteristics. A horizontally polarized radiating wave electromagnetic pulse simulator is then constructed for irradiation testing. The results show that an open-circuit shielding layer leads to high voltages coupled onto the core wires, matching the shield voltage, while the grounding shielding layer significantly reduces the coupled voltage on the core wires. Additionally, increases in cable height and length result in higher levels of coupled current. The test results verify the validity of the simulation model and the accuracy of simulation results.
From the perspective of aerial photography of UAVs(unmanned aerial vehicles) during power inspections, taking into account the particularity of the image characteristics of power equipment, a visual detection model EDR-YOLOv7 is proposed suitable for the edge of UAVs to address the common problems of the ubiquitous small target detection, target point occlusion, and the increase in model missed detection rate caused by the variable scale of aerial images, as well as increased calculation amount caused by dense detection. Firstly, a display visual center module is introduced into the neck network to capture the implicit relationship of pixels and solve the problem of missing small target features. Secondly, the dynamic sampling module is used to replace the transposed convolution to achieve flexible sampling of feature points and reduce the complexity of model calculation. Finally, in order to solve the problem of variable viewing angle scale of UAVs and the problem of accidental deletion and deviation of prediction frames caused by partial occlusion, the Inner-SIoU (inner-scylla intersection over union) loss term and the repulsion factor are added to the loss function, continuously reducing the prediction error during training iterations. After experimental verification, EDR-YOLOv7 compared to the original model increases mAP@0.5 and the detection frame rate by 3.89 % and 5.2 frames/s respectively. The model is finally deployed on the Jetson XAVIER NX edge computer and accelerated by TensorRT reasoning, which performs well in video stream detection tasks.
In recent years, affected by extreme weather, frequent wildfires have led to a significant increase in wildfire tripping incidents in the Southern Power Grid. To investigate the main influencing factors and their mechanisms behind wildfire tripping of transmission lines, data on wildfire warnings and tripping incidents along transmission corridors in Southern Power Grid region over the past five years are collected. Statistical analysis is conducted from both temporal and spatial dimensions, incorporating factors such as climate, terrain, and surface vegetation. Additionally, the concept of "transmission line wildfire tripping sensitivity" is proposed to represent the probability of wildfire-induced tripping per unit of wildfire warnings, effectively avoiding the logical fallacy of "correlation implies causation" in statistical analysis. Finally, by integrating flame spread models and air breakdown models, the underlying physical mechanisms of environmental factor influences are thoroughly analyzed. The results indicate that although wildfire warnings and tripping incidents in Southern Power Grid have increased year by year, thanks to diversified wildfire monitoring methods and timely firefighting measures, the overall wildfire tripping sensitivity has shown a declining trend annually. The findings of this study will assist grid operators in developing more targeted and differentiated wildfire management strategies based on field conditions.
Hydrogen energy is an important carrier for China's low-carbon transformation, with zero emissions and renewable characteristics. The carbon emissions and economic benefits of hydrogen storage optimization configuration are poor in existing park integrated energy systems based on photovoltaic hydrogen production. Therefore, a hydrogen storage optimization configuration method for park integrated energy systems is proposed, which takes into account photovoltaic photothermal proton exchange membrane electrolysis for hydrogen production (PV/T-PEMWE) and stepped carbon trading. Firstly, considering the comprehensive energy supply characteristics of the park, an integrated energy system framework containing PV/T-PEMWE is constructed for the park. Secondly, the working principles of photovoltaic photothermal technology (PV/T) and proton exchange membrane electrolysis for hydrogen production (PEMWE) are analyzed and PV/T and PEMWE are coupled. And a PV/T-PEMWE model is established. Finally, by introducing a stepped carbon trading mechanism and aiming at the economic and low-carbon aspects of the system, a hydrogen storage optimization configuration model for the integrated energy system in the park is constructed. Taking a integrated energy system in a certain park in the northwest region as an example for simulation, the advantages of the proposed model in terms of economy and low-carbon are verified.
As the global society is increasingly concerned about the transition to sustainable energy practices, building integrated energy systems optimization is significant in improving low-carbon and economic energy consumption. Therefore,research is conducted on the scheduling and pricing strategies of building energy operators. Firstly, the information interaction characteristics of both the supply side and the demand side are considered. A two-side optimization model of the building integrated energy system based on the Stackelberg game framework is established with the supply side as the leader and the demand side as the follower. Secondly, a deep deterministic strategy gradient algorithm is proposed based on the adaptive action exploration mechanism to solve the constructed model efficiently given the multiple information interactions between the two sides of the Stackelberg game framework. The adaptive action exploration mechanism constructs the action selection strategy of the adaptive exploration coefficient improvement algorithm based on the variance of the cumulative rewards and the average loss value of the critic network, ensuring the algorithm's accuracy and stability. Finally, the effectiveness of the proposed algorithm is verified by examples. The experimental results show that compared with other deep reinforcement learning algorithms, the proposed algorithm can improve the convergence accuracy and stability of the algorithm, as well as the total revenue of the energy operator, thus assisting the energy supply side in making better decisions.
Promoting the planning and construction of the hydrogen refueling and production network （HRPN） for hydrogen fuel cell vehicle （HFCV） is beneficial for enhancing the application of new and clean energy such as electricity and hydrogen fuel in the transportation sector. Firstly, the basic architecture of HRPN is explained. The different forms of HRPN are described including on-site hydrogen refueling stations, centralized hydrogen production pipeline transportation hydrogen refueling networks, and centralized hydrogen production logistics transportation hydrogen refueling networks. Then, an overview of the current research status of HRPN both domestically and internationally is summarized. The operation and configuration models of various hydrogen production equipment, hydrogen storage equipment, and vehicle hydrogenation equipment in HRPN are described. The operation and configuration models of the hydrogen transportation network are constructed, and the cross regional hydrogen energy interaction mode of the HRPN hydrogen transportation network is introduced. Under the above research background, the unified model of HRPN planning is constructed. Finally, the current bottlenecks faced by HRPN planning and development are summarized, and future research directions are discussed.
Under the background of dual carbon strategy, in order to promote the construction of a new round of clean, low-carbon, safe and efficient new energy system in China, a virtual power plant optimal scheduling strategy method is proposed considering electric vehicle charging and discharging technology under the green certificate carbon trading interaction mechanism. Firstly, the mathematical model of virtual power plant and load side response model are established, and the adjustable upper and lower limits of cluster electric vehicles are predicted. Secondly, considering the carbon quotas and green certificate quotas of electric vehicles, the ladder type carbon trading model and green certificate trading model are established. Based on the conversion coefficients of the two mechanisms, the green certificate carbon trading interaction mechanism is designed, and the optimal scheduling model is constructed with the goal of minimizing the comprehensive cost of virtual power plants. Finally, calculation results show that the proposed model can realize the economy and low-carbon operation of virtual power plant. And the impact of electric vehicle charging and discharging mode and penetration rate, carbon trading price and green certificate trading price on virtual power plant operation are verified and analyzed.
With the widespread integration of high proportion photovoltaics into the distribution network, the problem of node voltage and line flow exceeding limits is becoming increasingly prominent. And there is a possibility of "PV curtailment" phenomenon. Therefore, a two-stage distributionally robust optimization model is established for energy storage configuration in the distribution networks to enhance the photovoltaic consumption capacity and safety of distribution networks. The first stage of the model determines the installation location and capacity of energy storage with the goal of minimizing the annual investment cost of energy storage. The second stage simulates the optimal operation of the distribution network on typical days throughout the year, with the goal of minimizing the annual operating cost of the distribution network. Multiple uncertainty sets based on norm-2 distance are used to describe the uncertainty of the output of PVs and loads in the typical days respectively, and the four-quadrant operating characteristics of energy storage are considered. The column and constraint generation algorithm and max-min sub-problem decomposition algorithm are used to solve the model. And simulation analysis is conducted on an actual 10 kV distribution network to verify the feasibility and effectiveness of the proposed method.
With the increasing proportion of new energy in China's energy supply, its inherent volatility will bring severe challenges to the power system, and the system needs more flexible resources to meet the demand of system balance. As a kind of high-quality flexible regulating resource, conventional thermal power unit is crucial for integrating a high proportion of renewable energy. In view of the decline in the guaranteed income of thermal power units, China currently implements a two-part electricity price policy for coal-fired power units to ensure the basic income of conventional thermal power units, but it cannot effectively encourage thermal power units to further improve their regulation performance. Based on the comparative analysis of the applicability of typical capacity compensation methods at home and abroad in China's market environment, a double-differentiated capacity compensation method considering cost recovery and flexible adjustment is proposed, including two compensation methods: guaranteed cost recovery capacity compensation and incentive flexible adjustment capacity compensation, and the corresponding compensation intensity calculation model is established. This method uses universal evaluation index to help thermal power units recover costs more accurately, and can also motivate them to further improve their flexible regulation performance. Finally, simulations using the IEEE 30-node system validate the effectiveness of this compensation method in fixed cost recovery, flexible regulation capability incentives, and supporting system stability.
To address the issue of inconsistency between the actual primary traveling wave signal and the measured secondary traveling wave signal, an accurate detection method for traveling waves based on complementary ensemble empirical mode decomposition (CEEMD) and least square QR decomposition (LSQR) is proposed. Firstly, the nonlinear amplitude-frequency and phase-frequency response characteristics of the specialized traveling-wave sensor are examined, revealing the distinction between the primary and secondary traveling waves. Secondly, CEEMD is used to decompose the secondary traveling wave into inherent modal function components of different frequency bands. Then the least square method is used to construct the traveling wave inversion model, and the LSQR algorithm is used to solve the inversion components of each inherent modal function component iteratively. Finally, the inversion primary traveling wave signal is synthesized by linear superposition of each inversion component. Simulation and experimental results show that the proposed method is not affected by noise and mode aliasing effects, and the similarity between the frequency-division inversion traveling wave and the real traveling wave can reach 0.99, which realizes the accurate detection of fault traveling wave.
In order to improve the efficiency and accuracy of electricity market settlement, an anomaly detection method is proposed for power settlement electricity data based on graph theory and hybrid convolutional neural network. Firstly, the input data is preprocessed using a hybrid resampling technique to solve the class imbalance problem in the data sets. Secondly, based on graph theory, the electricity data is transformed from a one-dimensional sequence structure to a two-dimensional graph structure, and the periodicity and temporal correlation characteristics of the graph structure are mined through graph convolutional network and convolutional neural network to improve the detection accuracy of abnormal electricity. Furthermore, a spatial attention mechanism is introduced into convolutional neural network to improve the detection performance of the model. Finally, abnormal data detection is performed on the actual power data sets. And the results show that the proposed method is superior in comprehensive performance such as accuracy and area under curve (AUC) value.
ISSN 1674-0629 (Print)
Started from China Southern Power Grid Corporation
Published by: China Southern Power Grid Corporation
