The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission. The tower’s tilt and severe deformation will cause the building to collapse. Many small changes caused the tower’s collapse, but the early staff often could not intuitively notice the changes in the tower’s state. In the current tower online monitoring system, terminal equipment often needs to replace batteries frequently due to premature exhaustion of power. According to the need for real-time measurement of power line tower, this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor, the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing, and then through the complementary filtering algorithm for comprehensive calculation of tilt angle, the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy, with low cost and power consumption. The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%, which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.

This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents $h\left(q\right)$ and scaling exponents $\tau \left(q\right)$ that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, $h\left(q\right)$ and $\tau \left(q\right)$ are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

This study proposes a novel image segmentation method, MF-DFA combined with the Allen–Cahn equation (MF-AC-DFA). By utilizing the Allen–Cahn equation instead of the least squares method employed in traditional MF-DFA for fitting, the accuracy and robustness of image segmentation are significantly improved. The article first conducts segmentation experiments under various conditions, including different target shapes, image backgrounds, and resolutions, to verify the feasibility of MF-AC-DFA. It then compares the proposed method with gradient segmentation methods and demonstrates the superiority of MF-AC-DFA. Finally, real-life wire diagrams and transmission tower diagrams are used for segmentation, which shows the application potential of MF-AC-DFA in complex scenes. This method is expected to be applied to the real-time state monitoring and analysis of power facilities, and it is anticipated to improve the safety and reliability of the power grid.

Ensuring the safety of transmission line towers is vital for human safety, power supply, economic development, and environmental protection. This study specifically examines how temperature affects tower inclination. Multifractal detrended cross-correlation analysis (MF-DCCA) is a combination of multifractal detrended fluctuation analysis (MF-DFA) and DCCA that reveals the multifractal features of two cross-correlated non-stationary signals. This paper adopts the MF-DCCA tool to investigate the cross-correlations between the internal temperature of an inclination sensor device and the posture of a transmission line tower. The tilt angle data in the x- and y-axes are used to measure the posture of the transmission line tower. We start by using Pearson correlation to assess the relationship between temperature and two inclination angles, followed by verifying their correlation with a p-value below $0.05$ using first-order linear fitting. We initially assess the multifractal features of three time series using MF-DFA before MF-DCCA analysis. All exhibit multifractal traits with $H\left(2\right)<0.5$, indicating negative persistence, especially notable in the temperature series. Finally, we adopt the MF-DCCA approach to examine the multifractal cross-correlation between tilt-angle time series and temperature time series, and the results indicate the negative persistence of the cross-correlation between the time series. Furthermore, the multifractal cross-correlation of temperature and inclination data on the y-axis was also found to be stronger than on the x-axis based on features of the scaling exponent and symmetry exponent.

The risk assessment of transmission towers is a critical approach to ensuring the safe and stable operation of power grids. In response to the increasingly frequent and severe impacts of seasonal permafrost on transmission towers in permafrost regions, this paper proposes a novel risk assessment method based on Variable Weight Theory and the Matter-Element Extension Model. By incorporating key influencing factors—such as the number of freeze–thaw cycles, soil moisture content, temperature fluctuations, soil dry density, melting settlement coefficient, and porosity ratio—an indicator system tailored to risk assessment in seasonal permafrost regions is established. Utilizing the Matter-Element Extension Model, this approach applies Variable Weight Theory to integrate both the best-worst method and the entropy weight method for calculating subjective and objective indicator weights. The risk level of the towers is then determined by their proximity to risk thresholds. Finally, a case study involving transmission towers in a specific region of a province in China is presented, where the assessment results are compared with the actual conditions to validate the rationality and effectiveness of the proposed method.

Frost heave of transmission tower foundations in seasonally frozen regions endangers cold-region power infrastructure and grid security. This study establishes a coupled model to simulate foundation freeze-thaw and frost heave processes, validated via laboratory tests. The results demonstrate that the high thermal conductivity of the tower foundation alters the temperature field distribution, inducing preferential development of the freezing front along the foundation interface. During freezing, temperature gradients drive unfrozen water migration toward the freezing front, resulting in locally high-saturation zones in shallow soil after freeze-thaw. After freeze–thaw, plastic deformation of the soil and compression and rearrangement of soil particles degrade the uplift capacity of the tower foundation and prevent full recovery of its initial position, thereby producing residual displacement. Increases in the number of freeze–thaw cycles and initial water content both intensify the cumulative frost jacking effect on the tower foundation; moreover, soil with higher water content is more susceptible to consolidation settlement after thawing. The experimental data confirmed the reliability of the coupling model. These findings offer a theoretical foundation and numerical analysis methods for the long-term sustainable operation and maintenance of infrastructure in seasonally frozen regions.