On variational mode decomposition of wind speed for real-time thermal rating determination in power transmission system

Power transmission line capacity is restricted with the maximum temperature that can withstand without inadmissible of line sag. Regardless of the carried current, the temperature of the transmission line conductors was affected by various weather variables. However, the maximum capacity of the line current known as static thermal rating was determined based on the conservative weather conditions for safe operation, but the restriction of the line capacity may be modified which results in the additional capacity of the line. As a result, real-time thermal rating technique was applied on thermal model of the transmission line. Generally, the information about weather conditions is considered uncertainty, however, the weather variables should be dealt with and studied statistically for determining the accurate rise in the conductor temperature. The real-time thermal rating technique is evaluated using weather variables. The most important parameter is the wind speed, which greatly influence s conductor temperature and implicitly affects power transmission line capacity. Thus, in this study, the real-time thermal rating technique is developed by weather variable model based on the variational mode decomposition technique that applied only on the wind speed for adapting wind speed measurements to produce conservative evaluation of convective cooling on the conductors of power transmission system without violating the maximum operating temperature inside the core of conductors. The developed real-time thermal rating is implemented on sections of the power transmission system of western Saudi Arabia. The developed technique is compared with other techniques to investigate its applicability.

Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.