Analysis Of Power Quality Disturbance In The Coal Mine Power System

With the steady development of the current coal industry,the scale of the power supply system in the mining area is gradually expanding,and the requirements for safe and reliable operation of the power grid in the mining area are constantly increasing.Non-linear high-power power electronic devices and sensitive equipment loads in the mining area are also widely used.More complicated power quality problems affect the safe and stable operation of the power grid in the mining area.Therefore,it is of great theoretical and practical significance to accurately and effectively detect and identify the power quality in the power grid of the mining area.In this paper,the power quality problems caused by the typical equipment load in the power supply system of the mining area are analyzed,the cause of the disturbance is analyzed,and the corresponding mathematical model is established according to its characteristics.This paper mainly analyzes the different disturbance types brought by six typical equipments in the mining area,and divides them into disturbance types such as voltage fluctuation,voltage swell,voltage sag,and power harmonics according to the characteristics of the disturbance.In view of the current problem of low detection accuracy of noisy power quality disturbances,a disturbance detection method based on wavelet noise reduction and improved generalized S-transform is proposed.Firstly,the denoising simulation of wavelet entropy is performed on the noisy rectangular wave by MATLAB software,and the single-phase voltage waveform measured from the bus node of the power supply system of the 6 k V deep well substation in the mining area is selected.The noise reduction method in this paper is used to verify and prove the selection of this paper The feasibility of wavelet entropy denoising method.Then,based on the principle of improved generalized S-transform,the four types of single simulation signals of voltage fluctuation,swell,sag,and harmonics are detected,and the amplitude and start and end times of the disturbance and the frequency of each harmonic component in the harmonic signal are analyzed.And amplitude,and compared with S transform and other methods,proved the accuracy of improved generalized S transform algorithm for disturbance detection.Based on the least squares support vector machine algorithm(LSSVM),which is insensitive to outliers and impulsive noises,this algorithm is used in the classification and identification of power quality disturbances in power systems in mining areas,and the similarity to the original fault signal is obtained through simulation High feature classification and recognition results.In this paper,the sample entropy is used to obtain the corresponding sample entropy value.Compared with the traditional RBF classification method,the sample entropy is used to analyze the disturbance signal.The result of this new classification is more accurate and can be used to obtain the characteristic value of the mine disturbance signal and combine The LSSVM classifier analyzes the typical disturbance signals in these mining areas.Finally,according to the power quality disturbance analysis method described in this paper,the simulation data is tested and identified,which verifies the effectiveness and accuracy of the proposed algorithm in various power quality analysis,and is suitable for the typical disturbance signal of the actual mining area.Analyze and research,and prospect the application innovation of power quality analysis methods in the future mine grid.The paper has 28 pictures,6 tables,and 77 references.