Hunan Electric Power

Low-Carbon Economic Dispatch of Integrated Elec‍tric‍ity‍-‍Heat-Gas-Hydrogen-Cooling Energy Systems Considering Stepped Carbon Trading

LONG Chuanyu, YU Jiantao, TIAN Yuyu, ZHANG Zhenyi

2026, 46(3): 1-8. doi:10.3969/j.issn.1008-0198.2026.03.001

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The integration of carbon capture and storage(CCS) technology with power-to-gas(P2G) is a critical pathway for achieving low-carbon operation in integrated energy systems(IES). To address the challenges of insufficient renewable energy utilization and the limited regulation capability and suboptimal carbon reduction effects of traditional flue gas diversion CCS, this study proposes a low-carbon economic dispatch model for IES, coupling liquid-storage-based CCS with a dual-stage, multi-hydrogen-use P2G system. Firstly, a liquid storage tank and a multi-hydrogen-use structure comprising hydrogen fuel cells (HFC) and hydrogen-blended microturbines(MT) are introduced, forming the coupling structure and model of liquid-storage-based CCS and the dual-stage P2G system. Secondly, a chance-constrained algorithm is employed to manage the uncertainties in wind and solar power output. Finally, a low-carbon economic dispatch model for electricity-heat-gas-hydrogen-cooling IES, incorporating a tiered carbon trading mechanism, is proposed to quantitatively evaluate the operational performance of the integrated system. Results indicate integrating liquid-storage-based CCS with the dual-stage P2G system significantly reduces carbon emissions. Compared to a system without CCS and P2G, operating costs are reduced by 47.55%, and carbon emissions are reduced by 29.72%. Compared to an IES with flue gas diversion CCS and traditional P2G, operating costs decrease are reduced by 19.19%, and carbon emissions by 24.31%. Compared to an IES with liquid-storage-based CCS and traditional P2G, incorporating the dual-stage P2G reduces operating costs by 4.59% and carbon emissions by 13.69%, achieving diversified hydrogen utilization and full renewable energy consumption.

MPC-Based Output Impedance Shaping Method for Grid-Side MMC of Offshore Wind Turbines

LIU Yuanzhi, WEI Juan, CHEN Daojun, HUANG Sheng, XU Yuancan, HUANG Guohang, WANG Yuwei

2026, 46(3): 9-17. doi:10.3969/j.issn.1008-0198.2026.03.002

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Under the demand of high-proportion renewable energy grid integration and weak power grid support, the modular multilevel converter(MMC) based on virtual synchronous generator(VSG) exhibits a large peak value of AC-side impedance resonance due to fixed control parameters and insufficient parameter coordinated regulation. This easily triggers system oscillation, restricting the grid-connected stability and dynamic support performance. To address the above issues, this paper constructs a discrete state-space model of VSG based on model predictive control, and proposes a multi-objective optimization control method for grid-side MMC of offshore wind turbines that balances active power tracking speed, virtual speed response, and overshoot suppression. A Pareto weight allocation mechanism is introduced, and the adaptive reshaping of the amplitude-phase characteristics of MMC equivalent output impedance at the grid connection point is realized through online coordinated predictive optimization of VSG inertia J, damping D, and reactive loop integral coefficient K_q. Meanwhile, impedance frequency sweep analysis of VSG-MMC is carried out to extract positive-sequence impedance and complete frequency-domain comparison, so as to quantitatively evaluate the change of impedance robustness before and after the proposed method. Simulation results show that compared with the fixed-parameter control method, the proposed method significantly suppresses resonant impedance in key frequency bands, improves anti-disturbance capability and stability margin, and achieves the coordinated improvement of dynamic response and small-disturbance impedance stability. It provides theoretical support for the stable control of grid-forming wind turbines connected to MMC.

IENEMD-FastICA Combined Framework and Its Application in Nonlinear Vibration Signal Analysis

WEI Xuetong, HAN Yanguang, ZHU Guangming, ZHU Xiaoxun, QIAN Jiangbo

2026, 46(3): 18-25. doi:10.3969/j.issn.1008-0198.2026.03.003

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To solve the problem of unsteady, nonlinear and under-determined blind source separation (BSS) of single-channel vibration signals of rotating machinery in strong noise background, this paper presents a combined analysis framework based on improved ensemble noise-reconstructed empirical mode decomposition(IENEMD) and fast independent component analysis(FastICA). The method uses IENEMD to adaptively extract the intrinsic noise of the signal, avoid introducing external Gaussian white noise, effectively suppresses mode mixing and generates high-quality virtual multi-channel signals. Then, FastICA is used to realize the blind source separation of nonlinear mixed signals, and the dual criterion of the gradient and Pearson correlation coefficient(PCC) is used to screen the fault component, and finally the accurate identification of the fault frequency is realized through the envelope spectrum analysis. Based on Case Western Reserve University bearing dataset, the experiments show that this method has good adaptability, robustness and calculation efficiency in nonlinear and non-stationary signal processing, and the fault frequency identification error is less than 0.5%. Compared with CEEMDAN-FasteICA and VMD-FastICA, this method achieves a better balance between feature extraction accuracy and conputational efficiency.

Coordinated Scheduling Strategy of Integrated “Source‍-‍Load-Storage” Wastewater Treatment Plant Clusters

SHI Shiyi, JIANG Yiqiang, LING Shurong, HONG Yunfei, ZHOU Ye, LUO Xin

2026, 46(3): 26-35. doi:10.3969/j.issn.1008-0198.2026.03.004

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To address the scheduling challenges brought by high energy consumption, insufficient resource coordination, and uncertainties in photovoltaic output and influent load in wastewater treatment plant (WWTP) clusters, this paper proposes a coordinated scheduling strategy for integrated "source-load-storage" WWTP clusters led by a plant-network integrated operator. The core of this strategy is a cooperative game framework based on asymmetric Nash bargaining theory, which decomposes the coordinated scheduling problem into two sub-problems: social cost minimization (P1) and fair allocation of cooperative benefits (P2). To handle uncertainties in P1, a two-stage (day-ahead and real-time) robust optimization model is established to ensure the robustness of the scheduling scheme. Case study results demonstrate that the proposed method not only significantly reduces the total operating cost of the cluster but also incentivizes active participation of each plant through a contribution-based benefit allocation mechanism. Moreover, it effectively mitigates uncertainty risks, exhibiting superior economic efficiency, fairness, and robustness.

Research on Reinforcement Performance of Clamp-Type Double ‍Angle Steel Section Components for Trans‍mission Towers

GE Fengkai, SHAO Shuai, JIAN Mingjian, PENG Bo, WANG Xiulong, SUO Shuai

2026, 46(3): 36-45. doi:10.3969/j.issn.1008-0198.2026.03.005

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In order to study the mechanical properties and deformation characteristics of the double angle steel parallel reinforcement method connected by fixtures on towers, this paper conducts multi-sensitive parameter numerical simulation experiments on two types of double angle steel section composite components arranged in parallel axes. Based on the finite element test results and relative slip theory, a local buckling ultimate bearing capacity correction formula for double angle steel parallel composite components connected by fixtures is derived based on the plate shell elastic theory. The results show that the load-bearing performance of square section composite components is better than that of cross section components, with their ultimate bearing capacities increased by 14.1% to 21.2% and 1.64% to 12.57%, respectively. When the fixture connection parameters that control the force coordination between the main and auxiliary materials exceed the threshold, square section composite components are prone to transition from torsional instability to local buckling instability. The main instability modes of cross section composite components fall in overall bending and local buckling. The ultimate bearing capacity and stress coordination of the components increase slightly with the increase of frictional force or bolt preload force. The theoretical calculation correction formula derived based on deformation characteristics and slip characteristics can accurately predict the ultimate bearing capacity results of reinforcement measures for double angle steel sections with fixture connections that experience local instability, providing reference for tower reinforcement design.

Intelligent Fault Diagnosis Method of Live Working Ro‍bots for Distribution Networks Based on Multi-Source Heterogeneous Data

LI Shijiao, LI Haoyang, LIN Dezheng, REN Qingting, SHI Chengliang, YIN Xuewei

2026, 46(3): 46-52. doi:10.3969/j.issn.1008-0198.2026.03.006

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Aiming at the problems that distribution network live working robots cannot predict, prevent faults, and the maintenance response is delayed after faults occur, an intelligent fault diagnosis method based on multi-source heterogeneous data fusion and a three-level diagnosis model is proposed. Through clock synchronization of each component of the robot, a spatiotemporal synchronization acquisition mechanism is constructed to obtain multi-source data. A dynamically adaptive weighted fusion algorithm combining Kalman filtering and fuzzy logic is adopted to eliminate data heterogeneity and spatiotemporal deviations. By establishing the three-level diagnosis model, fault prediction and maintenance scheme generation are realized. Experimental results show that fault prediction reduces the probability of faults by 90%, automatic recovery shortens the fault recovery time to 9s, and the generation of maintenance schemes reduces the manual fault recovery time to 1min, pushing maintenance plans for potential hazard components avoids the occurrence of faults, which effectively improves the operational reliability and maintainability of the robot.

Research on Quantitative Analysis of Low Voltage Causes in Distribution Areas Based on Power Flow Cal‍culation

DUAN Xujin, WANG Xiaoyuan, MO Wenhui, TANG Haiguo, ZHU Jiran

2026, 46(3): 53-60. doi:10.3969/j.issn.1008-0198.2026.03.007

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To address the issues of multiple causes, strong coupling, and lack of quantitative basis in decision-making for low-voltage problems in distribution feeders, a quantitative analysis method for low-voltage causes based on three-phase four-wire load flow calculation is proposed. The method constructs a detailed feeder model considering load unbalance, neutral conductor, and grounding loop effects, and calculates the nodal voltage distribution using the forward-backward sweep method. A low-voltage index is introduced as a comprehensive evaluation metric. Through parametric analysis, single-variable perturbation calculations are performed on transformer capacity and tap settings, line conductor sizes, reactive power compensation, and three-phase load unbalance to achieve quantitative assessment and identification of the primary causes of low voltage. Case study results indicate that the proposed method can effectively characterize low-voltage features of the feeder, distinguish the influence degree of different mitigation factors, and provide a quantitative decision-making basis for precise low-voltage control.

Research on Vehicle-Grid Interaction Pricing Models and Multi-Mode Market Mechanisms Tailored to Hu‍nan Regional Characteristics

ZHANG Ziqin, LUO Zhikun, XIE Peiyuan, ZHUANG Hongbo, XIONG Shangfeng, HU Han

2026, 46(3): 61-68. doi:10.3969/j.issn.1008-0198.2026.03.008

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Based on a systematic analysis of the technological evolution, policy implementation, and pricing mechanisms of vehicle-to-grid(V2G) technology, this study focuses on constructing a multi-dimensional value-stacked discharge pricing model and market mechanism to guide electric vehicles as flexible resources in participating in grid optimization. V2G development follows a three-stage path: unidirectional orderly charging, bidirectional V2G discharge, and participation in ancillary services. Its pricing mechanism evolves from government-set pricing to market-based pricing, and from simple price differentials to multi-dimensional value aggregation. Addressing Hunan Province's regional characteristics-predominantly hydroelectric power, pronounced summer-winter peak loads, and distribution network congestion in the Changsha-Zhuzhou-Xiangtan area,the study constructs a multidimensional pricing model accounting for time, space, and market structure. It quantitatively analyzes the fundamental discharge costs, grid value, and environmental benefits. The study proposes that Hunan could draw inspiration from California's real-time pricing and Germany's market coupling mechanisms. It recommends designing time-of-use seasonal pricing tailored to seasonal load patterns, piloting nodal pricing at load centers, and establishing a composite pricing mechanism incorporating green power consumption and carbon value. Ultimately, the scaled development of vehicle-grid interaction requires establishing sustainable business models through measures such as clarifying battery depreciation compensation, standardizing communication protocols, and lowering market barriers. This approach will provide low-cost flexibility support for the new power system.

Photovoltaic Expansion Detection Method Based on GWO-‍PSO Optimized Robust Extreme Learning Ma‍chine

PENG Zhuo, LI Bin, PENG Yu, SU Sheng

2026, 46(3): 69-76. doi:10.3969/j.issn.1008-0198.2026.03.009

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Aiming at the security risk of distribution network caused by the private capacity increase behaviour of distributed photovoltaic(PV) users, this paper proposes a PV capacity expansion detection model based on grey wolf-particle swarm hybrid optimization discrepancy robust extreme learning machine. Firstly, the cosine similarity and dynamic time regularization are used to pre-process the PV power generation data to reduce the impact of regional meteorological differences. Secondly, the global search capability of the grey wolf algorithm and the fast convergence characteristics of the particle swarm algorithm are combined to optimize the implicit layer weights and bias of robust extreme learning machine to improve the robustness of the model to outliers. Altimately, the robustness of the model to capacity expansion is achieved by computing the violation expansion coefficient K to achieve the dynamic diagnosis of capacity increase intensity and time node. Based on actual PV data in Changsha region, the experiments results show that the probability density analysis of the violation expansion coefficient can identify the expansion behaviour as low as 10%, and the expansion time node positioning error is no more than 4%, and three violation expansion users are successfully detected in the actual case.

Lithium Iron Phosphate Battery Commercial & Industrial En‍ergy Storage Dwvices and Battery Cooling Strategy

YANG Juefei, WU Chuanping, ZHU Hongzhang, OUYANG Liangxuan, MO Ze

2026, 46(3): 77-82. doi:10.3969/j.issn.1008-0198.2026.03.010

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Lithium-ion battery energy storage systems are currently the mainstream support for mitigating fluctuations in renewable energy sources such as wind and solar power.Compared with other lithium-ion batteries, lithium iron phosphate(LFP) batteries offer comprehensive advantages in terms of safety, service life, high-temperature performance, and capacity. This paper presents the structure of an industrial and commercial energy storage system using 314 A·h LFP energy-storage cells. For industrial application scenarios, a cooling strategy based on cell temperature is proposed, which improves the efficiency of the energy storage system by 0.6% compared with a cooling strategy based on return-water temperature. Under operating conditions at different ambient temperatures, the energy storage system employing the cell-temperature-based cooling strategy can achieve a high energy conversion efficiency of more than 89% and has good application prospects in the scenarios of peak shaving and valley filling in the distribution network and smooth consumption of new energy.

Modular Multi-Level Converter Sub-Module Multi-Target Capacitor Voltage Equalization Control Strategy

LI Bei, CHEN Zhuo, SU Deqiang, YANG Zeyu, ZHU Yulin, RONG Fei

2026, 46(3): 83-90. doi:10.3969/j.issn.1008-0198.2026.03.011

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In order to solve the problems of high frequency and uneven device loss in the existing sub-module equalization control methods of module multilevel converters(MMC), a multi-objective voltage equalization control strategy combining capacitance-voltage equalization and on-time equalization is proposed, and a cost function is designed as a criterion for the selection of sub-modules in the next control cycle, so as to improve the comparability between different targets through normalization processing. Meanwhile, local adjustments are made based on the switching state of the sub-module in the previous cycle, which significantly reduces unnecessary switching actions and achieves smoother sub-module selection. For the voltage measurement delay, a capacitive voltage prediction model based on the switching state is constructed to further improve the voltage equalization accuracy. The method ensures the voltage equalization effect, effectively reduces the switching frequency of the MMC sub-module, makes the on-time of the sub-module more balanced, and optimizes the device loss distribution. Finally, an 11-level MMC model is built on MATLAB/Simulink, and the simulation results verify the effectiveness of the proposed method.

Diagnosing Weak Faults Method of Wind Turbine Gear‍boxes Under Random Op‍erating Conditions Based on WOA-VMD-ResNet

WANG Weiyu, WEI Jiada, TANG Zhiwei, ZHANG Hai, WANG Sijia, ZHANG Pei, HE Jianjun

2026, 46(3): 91-100. doi:10.3969/j.issn.1008-0198.2026.03.012

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Aiming at the problem that the characteristics of weak fault vibration signals are easily masked by noise and difficult to be effectively extracted by traditional methods under random operating conditions of the main drive system of wind turbines, this paper proposes a composite fault diagnosis method. Firstly, the whale optimization algorithm(WOA) is used for global optimization of the key parameters of variational mode decomposition(VMD), which breaks through the limitation that traditional VMD relies on empirical parameter setting and realizes accurate denoising of the original vibration signal and reconstruction of fault characteristics. Then, the short-time fourier transform is used to convert the denoise time-domain signal into a time-frequency image with high recognition, which is input into the residual network to complete the intelligent identification of fault types. The residual structure is used to enhance the deep learning ability of weak fault characteristics. Based on the MCC5-THU gearbox fault dataset, experiment results show that the diagnostic accuracy of this method reaches 100% under constant operating conditions and 88.75% under random operating conditions, both of which have excellent diagnostic performance and robustness. This method can effectively improve the identification accuracy of early weak faults in wind turbines and provide key technical support for the intelligent operation and maintenance of wind power equipment.

Combined Short-Term Power Forecasting Model Based on Bayesian Optimization Algorithm

LUO Qiang, WANG Ding, XU Min, DENG Xiaoliang, ZHU Shu

2026, 46(3): 101-107. doi:10.3969/j.issn.1008-0198.2026.03.013

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To tackle the problems of stochasticity and intermittency characteristic of wind power data, this paper introduces an innovative short-term wind power forecasting model which employs variational mode decomposition（VMD） combined with Transformer-BiGRU architecture to generate a hybrid forecast. Based on the principle of variational mode decomposition, the original data is decomposed into a series of frequency subsequences, and signal features are fully extracted. On this basis, a Transformer-BiGRU model is constructed that uses bidirectional gating units to capture bidirectional temporal dependencies. Then, four key hyperparameters are optimized through Bayesian optimization. Finally, the predicted values of the decomposed sub signals are accumulated to obtain the final prediction results, thus constructing a Transformer BiGRU model based on bidirectional gating units capturing bidirectional temporal dependencies.The experimental results show that the proposed model is significantly better than the comparative model, accurately predicting wind power and effectively enhancing the stability and accuracy of short-term wind power prediction, providing a technical optimization path for efficient wind power prediction.

X-Ray Detection Technology for EHV Six-Bundle Con‍ductor Based on MobileNetV3 Lightweight Network Architecture

MA Xiaojun, LI Bo, LIU Jiarui

2026, 46(3): 108-113. doi:10.3969/j.issn.1008-0198.2026.03.014

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Aiming at the deviation phenomenon of flaw detection results caused by the coexistence and mutual interference of multiple types of defects in six-bundle conductor, an X-ray flaw detection technology for ultra-high voltage six-bundle conductor based on MobileNetV3 lightweight network architecture is proposed. Firstly, the X-ray attenuation difference in different areas when X-ray passes through the clamp is used, and the internal X-ray image of the tension clamp is collected by combining Lambert-Beer law and multi-angle electrical signal reconstruction. Then, the depthwise separable convolution and lightweight channel attention mechanism enhancement methods are used to improve the capture ability of key defect features. Finally, the defect location and recognition are realized by adaptive pre selection box generation and classification regression network, and the output results are optimized by using joint vector and full connection layer. The experimental results show that the detection accuracy of this method is 99.6%. Compared with the traditional manual inspection, the single detection time is shortened from more than 120 min to less than 60 min, which can effectively identify the scene of multiple defects coexisting of fracture and loose strands, and has important practical significance for the safe and stable operation of transmission lines.

Rapid Generation Method for Safe Flight Corridors in UAV Inspection Over Confined Spaces

FAN Xiangyu, LIU Sanwei, DUAN Xiaoli, ZHANG Kunyi, SUN Peiyi, WANG Tiantian

2026, 46(3): 114-120. doi:10.3969/j.issn.1008-0198.2026.03.015

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To address the high-frequency unmanned inspection demands in confined spaces such as substations and cable tunnels, this paper proposes a set of key autonomous drone flight technologies of high safety, low computational power and strong real-time performance. First, a statistical directional prior is constructed, and a constrained enhanced A* algorithm is designed to implement directional pruning in three dimensions. This approach theoretically maintains optimality while reducing the number of search nodes by over 30%. Second, a spherical convex hull safety corridor framework is introduced, characterizing free space via four-dimensional vectors(center+radius). Combined with K-D Tree obstacle queries and adaptive oversampling, this enhances navigation map representation efficiency for unmanned aeriol vehicle(UAV) in confined spaces. Deployed in simulations and real cable tunnel scenarios, the research delivers a safe, efficient technical pathway for resource-constrained UAV platforms to achieve millisecond-level obstacle avoidance in GPS-denied environments.

Anomaly Detection and Classification Methods for Power Grid Data Middle Platform Based on Reinforcement Learning

JIANG Guang, XUE Jingyuan, HU Xiang, CAO Jie, FANG Bin

2026, 46(3): 121-129. doi:10.3969/j.issn.1008-0198.2026.03.016

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To improve the efficiency of power grid data platform operation and maintenance, this paper proposes a reinforcement learning model that integrates proximal policy optimization and multi-head self-attention mechanism. This model aims to perform real-time identification and classification of anomalies in the power grid data platform, assisting on-site maintenance personnel in quickly locating the causes of anomalies and carrying out targeted anomaly handling. First, a gated recurrent unit network is used to model time-series dependencies. Second, a multi-head self-attention layer is introduced to accurately capture local saliency features. Then, considering the characteristics of anomaly identification scenarios in power grid data platforms, a hybrid activation function is designed to ensure good performance in both identifying known anomalies and autonomously discovering new anomaly types. Finally, the model's effectiveness is verified using actual operational data from a provincial power grid company's data middle platform. Experimental results show that the overall anomaly classification accuracy reaches 98.2%, and the F1 score for identifying unknown new anomaly types reaches 91.5%, significantly outperforming other comparative models.

Dynamic Response Analysis of Conductor Ice-Shedding and Calculation Research of Ice Jump Height Un‍der Ice and Wind Loads

SUN Junlu, CHEN Bin, XU Zhiming, JIA Ying, DING Mengzhe, HE Guangyuan

2026, 46(3): 130-138. doi:10.3969/j.issn.1008-0198.2026.03.017

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At present, research on conductor ice-shedding jumping mainly focuses on the analysis of the abrupt effects of icing loads, which only consider single icing effects, neglecting the effect of wind loads, or merely using standard wind load values in design specifications, Thus, relevant researches on de-icing dynamic responses under ice and wind loads remain in adequate. In response to this issue, this paper conducts dynamic response analysis of conductor ice-shedding and calculation research of ice jump height under ice and wind loads. Combining the simulation methods of transient dynamics and fluid dynamics, the paper calculates the aerodynamic coefficients and wind load distribution of four typical ice cover section shapes:crescent, D-shaped, fan-shaped, and elliptical. A finite element model of the conductor-insulator of the transmission line is established. The de-icing and ice-jumping processes are simulated by the concentrated load method, and the de-icing jump height and tension of the conductor under different wind attack angles and ice-covered cross-sectional shapes are calculated. The results show that under the negative lift wind attack angle, the jump height of the conductor is higher than that in the windless state, and the initial tension and the minimum dynamic tension during de-icing are higher than those in the windless state. Under the positive lift wind attack angle, the opposite is true. The ice jumping behavior of wires has different cross-sections varies, and elliptical cross-section wires experiences the maximum jumping height due to bearing higher wind loads.

Explosion-Proof Test Scheme for UHV Flexible DC Converter Valve Submodules

LI Qi, YANG Yong, ZHANG Tao, ZHOU Huiying, SITU Liukai, QIN Liang

2026, 46(3): 139-145. doi:10.3969/j.issn.1008-0198.2026.03.018

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The explosion-proof test for submodules of UHV flexible DC transmission converter valves suffers from inadequate charging voltage, incomplete test coverage, and ambiguous criteria. This paper examines typical explosion faults in UHV flexible DC converter valve submodules, identifies key weak points under explosive loads, and defines quantified test indicators centered on these vulnerabilities. A repeatable explosion-proof test scheme is then designed for submodules, including tests for upper/lower arm through-faults, bypass switch failure, and post-fault current-carrying capability. Finally, prototype submodules from a domestic UHV flexible DC project undergo explosion-proof testing, confirming their compliance with explosion-resistance requirements.

Research on Galloping Suppression of Iced Conductors Based on Suspended Tuned Mass Damper

WANG Haibo, NIU Huawei, YANG Zheng, ZHANG Guoqiang, SHANG Xin, LI Bo, CHEN Zhengqing

2026, 46(3): 146-152. doi:10.3969/j.issn.1008-0198.2026.03.019

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To address the issue of iced conductors galloping, finite element models of both single and six-bundle conductor lines are established to analyze their galloping response characteristics. To suppress galloping, a control strategy based on a suspended tuned mass damper(TMD) is proposed. Based on the ANSYS finite element platform, refined finite element models of a single conductor and a six-bundle conductor are established respectively to simulate and analyze the entire galloping process. Key damper parameters are designed in accordance with modal analysis results and the theory of TMD parameter optimization. By studying the influence of TMD placement strategies-single-point, two-point, and three-point arrangements at the mid-span and quarter-points-as well as the mass ratio, it is found that the three-point configuration most effectively suppresses galloping across the entire line. When the mass ratio is 2%, basic galloping suppression can be achieved, and the suspension TMD scheme has a significant effect on galloping suppression performance.

Research and Application of Intelligent Inspection De‍vices for Overcoming Obstacles in Substations

ZHANG Jiahao, LI Qi, SUN Yuanhang, HAN Liang

2026, 46(3): 153-158. doi:10.3969/j.issn.1008-0198.2026.03.020

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Substation inspection is an effective measure to ensure the safe operation of power equipment. The existing fixed monitoring, manual inspection and robot intelligent inspection methods have problems such as single monitoring indicators, high inspection work intensity and low efficiency, and limited inspection angles. In response to this, this paper develops an obstacle-crossing substation inspection device based on intelligent recognition technology. Firstly,the study designs the various hardware components that make up the intelligent inspection device.Secondly, the study provides a schematic diagram of the operating principle of the intelligent inspection device and designs the visual module and motion module of the intelligent inspection device using YOLOv5 image recognition algorithm and motion system algorithm that integrates improved A*(A-star) and dynamic window approach(DMA). Then, the paper develops a software system platform that can conveniently control the movement of intelligent inspection devices, adjust the perspective and focal length of the image acquisition cloud platform, and transmit video streams. Finally, the image acquisition pan-tilt, wireless communication module and the body of the intelligent inspection device are assembled. The rationality and effectiveness of the intelligent inspection device and its control method are verified through practical application on site.

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