Coastal grounding electrodes are currently an important means to alleviate land grounding electrode land constraints. In order to better invert the terrestrial geodesic resistivity in the coastal region, this paper proposes a complete set of inversion technology schemes. First, this paper proposes a layered land model for the coastal region, and a composite geodetic model is modeled by the fold junction of the land model and the ocean. Based on this, an adaptive subdivision boundary element method is proposed for solving the composite soil grounding calculation problem, and the accuracy and advantages of the method are demonstrated by examples. Finally, the paper uses the differential evolutionary algorithm to invert the exploration data of the four-point method in the coastal area, and obtains the parameters of the terrestrial layered geodetic model that meet the engineering requirements. The comparison with the grounding software CDEGS illustrates the effectiveness of the method. This paper carries out the research on the modeling and inversion methods of composite layered soil model, combining advanced numerical calculation methods and artificial intelligence algorithms to provide the support of computational tools for coastal resistivity inversion.
Currently, there are considerable challenges associated with the theoretical derivation and numerical calculation of Green’s function for spherical layered earths. In order to study the coupling effect between wide-area interconnected systems and the earth, this earth model is widely used. Artificial intelligence methods have been applied in order to overcome these challenges. First, Green’s function in the form of infinite Legendre series is derived using intelligent symbolic operations, and the weight function of Legendre series is recursively computed by means of a recursive algorithm. It is on this basis that the intelligent complex image method has been developed for solving spherical layered Green’s functions, which transforms the summation of infinite series of Green’s functions into the superposition of complex image potentials. Additionally, the upper error limit of the algorithm has been determined, and examples have been provided to demonstrate the accuracy of the algorithm. A solution based on multiple precision algorithms has been proposed to resolve the numerical singularity problem and the extremely slow convergence of the spherical layered Green’s function at the earth’s scale. It has been demonstrated that the intelligent complex image method has advantages in terms of computing speed and accuracy. A spherical layered Green’s function at the earth scale is presented in this paper as an effective and intelligent solution.
With the wide application of high voltage/ultra-high voltage (HV/UHV) DC transmission technology, the impact of DC grounding electrode location selection on the surrounding power grid has become increasingly prominent, especially the problem of DC bias hazard caused by DC grounding electrodes at provincial grid boundaries needs to be solved urgently. This paper studies the assessment and prevention of trans-regional DC bias risk, and proposes an inversion method of earth resistivity model based on the measured data of neutral current in the provincial boundary area. Firstly, the DC bias risk of provincial boundary power grid is simulated and calculated, and the influence of reasonable selection of earth model on the accuracy of risk assessment results is explained. Based on the classical Fletcher-Reeves conjugate gradient method and the measured neutral current data, a reduced-order inversion method for the earth resistivity parameters of the provincial boundary power grid is proposed. On this basis, a set of DC bias risk control scheme is formulated for the actual project of Gannan power grid. Finally, the feasibility of the scheme is verified by simulation and measured data.
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