Strong ground movement induced by mining activities and its effect on power transmission structures

Surface mining activities may introduce damages to nearby infrastructure. Concerns are put forward by the power company about structural integrity of electric power transmission structures in areas where coal mining activities cause strong ground vibrations. Common practice in the power industry is to limit ground motion by specifying maximum Peak Particle Velocity. So far, there is a lack of industry-wide recognized guidelines on how ground vibration limits should be set for the transmission structures. In order to develop a defense strategy to protect power transmission lines against strong ground motions in mining areas, a systematic research work was conducted to establish strong ground vibration characteristics and to study impacts of ground excitations on transmission pole structures. Ground movements were recorded using geophones and wireless tri-axial sensing units. The process of generating ground motion response spectra via analyzing actual ground motion measurements is described in the paper. These spectra developed based on peak particle velocities were used as a basis for spectral analysis performed using validated Finite Element models to obtain structural displacements, reactions and stress states of the transmission pole structures in the mining sites. A quantitative ground motion limit was established by comparing structural responses with the corresponding design requirements.

Damage Assessment of Existing Transmission Structures Using ANFIS （Adaptive Neuro-Fuzzy Inference） Model

This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neural networks and fuzzy systems. It is a learning expert system that finds the parameters of the fuzzy sets and fuzzy rules by exploiting approximation techniques from neural networks. The condition ratings of the structural components are determined based on visually observed deterioration-symptoms and the severity of those symptoms. A supervised learning process using training data and expert opinions is used to develop the expert system rules and determine the ratings of the structural components. For the learning from training data, the model uses a combination of least-square estimator and gradient descent method. A sequential least square algorithm is used to determine the weighting factors that minimized the errors. A test case is given to illustrate the power of the proposed fuzzy-neural system. It is concluded that the Sugeno model＇s ability to tune the parameters based on the training data makes it superior to the rules produced by an expert in the conventional fuzzy logic systems.

Analysis of the harmful effects to buried oil pipeline from power line short-circuit fault

Based on the transmission line theory of the buried metallic structures, the concerned harmful effects to the buried oil and natural gas pipelines caused by the power line short-circuit fault are further discussed. A closed-form expression of the induced voltage caused by the short-circuit fault of the ultra-high voltage power （UHVAC） transmission line is given. The transmission line model of the buried pipeline is set up and a set of formulas for calculating induced voltage on the pipeline and the parameters of the buried pipeline in actual environment condition are given. At last, the characteristic of the harm^hl effects on the buried pipeline from the power line short-circuit fault are discussed.