Series Compensation Transmission Line Fault Locator Based On Neural Networks And Differential Equations

Expanding the size of the power system, the complex structure, running status is constantly changing, especially the high-voltage transmission lines is the link between power plants and between users, an important part of the power system, undertake the task of conveying the electrical energy, but it is also the power system most prone to failure of one of the links. Most of the power system fault occurs in transmission lines, transmission lines, in the event of failure to give social and economic life have a tremendous impact, whether timely and accurate fault locate, for the power system to maintain stability and economic operation crucial. Therefore, the accurate positioning of the failure of the transmission line is an important measure to ensure network security, stability and economic operation technically.Series compensation devices in transmission lines can effectively reduce the reactance of the transmission system, improving transmission capacity, increase system stability and reduce the project cost of the transmission system, series compensation capacitors in the transmission line has been widely applications. However, due to series compensation capacitance exists to change the uniformity of the transmission line, the fault location has been a problem plaguing the industry. Fast and accurate positioning of series compensated transmission line fault on the safe and economic operation of power system has played an important significance.In this paper, the analysis of the existing series compensated transmission line fault location algorithm based on differential equations of mathematical models and neural networks will improve the integration of a new fault location method. First, by the phase-mode transformation, the phase value into the value of the modulus, and then calculate the fault distance modulus regions; Then, in accordance with the actual transmission line model for transmission line fault data samples, the use of data samples for training and testing the neural network model, and trained neural networks for series compensated transmission line fault pre-judgment in; Transmission line mathematical model of neural network pre-processing results and improvement of differential equations to solve the point of failure, assuming that the point of failure occurred in the series on both sides of the compensation capacitor, by calculating the two fault targeting solution, and then use the line at both ends to obtain the fault point voltage value, the data were calculated according to the principle of fault point voltage value should be equal to determine the true root to exclude pseudo-root, and thus determine the location of the point of failure. Finally, the use of different transition resistance, variety of different fault types or short-circuit fault simulation results show that:the method of combining neural networks and improve differential equations in series compensated transmission line fault location has a strong practical and accurate sex.