Development of an intelligent system for distance relay protection with adaptive algorithms for determining the operation setpoints

Abstract

The drastic consequences of emergencies force us to look for ways to increase the stability of the device operation at overhead power transmission lines (OHPTL). It can be achieved by developing new algorithms for determining the protection operation setpoints and detecting the damage location. Fault detection at OHPTL of 10 kV and above is mainly carried out by the devices based on the measurement of emergency mode parameters. For fault detecting one should analyze the parameters of not only current and voltage at the accident time, but also of the overhead power line. Specific active resistance, specific reactance, specific active conductivity and specific reactive conductivity are used to characterize the overhead power transmission lines. As a rule, these parameters are normalized to the unit of length of the overhead line (OHL) and linear values are used in the calculations. When analyzing power lines, tabular approximate values of longitudinal and transversal parameters in equivalent circuits are used, although solving problems in an unsimplified form leads to significant refinements of the known solutions, since OHLs are influenced by external atmospheric factors (ambient temperature, soil moisture, wind force, ice formation, etc.). The paper analyzes these characteristics and evaluates the influence of the listed factors on the linear longitudinal and transversal parameters of overhead lines. A functional dependence of external factors on the distance protection actuation setpoint was obtained. A method for automatic correction of the setpoint of the intelligent protection complex and an adaptive relay protection algorithm was developed, taking into account changes in climatic factors, enabling to reduce the “dead zone” length and increase the protection sensitivity. The use of line parameters obtained from the sensors in the calculations give rise to a more accurate fault detection based on the use of remote sensing methods.