Abstract:As key electrical equipment, the operational continuity and reliability of pumped storage units are significant to the construction of new power systems. During operation, the inter-turn insulation of the excitation winding is susceptible to deterioration due to multiple factors, such as unbalanced electromagnetic force, thermal stress, and external mechanical stress. It is important to quickly and accurately extract the early weak fault characteristics of inter-turn short circuits (ITSC) and organize timely condition maintenance. The condition monitoring of ITSC faults in the excitation winding is susceptible to dynamic/static eccentricity and armature response, and early fault characteristics are hard to identify. Therefore, this study proposes a fault diagnosis method based on a time-frequency circulating current (TFMCC) map. Firstly, this study derives the equations for the same phase multi-branch circulating current in the dynamic/static eccentricity and static eccentricity composite ITSC states, and obtains the characteristic harmonics of the circulating current in each state. Secondly, a two-dimensional finite element simulation model of 334 MV·A is constructed, and the magnitude of the same phase branch circulating current under each state is calculated. Comparative experiments have verified that TFMCC can effectively distinguish between eccentricity, armature reaction disturbance, and ITSC faults. Finally, based on the salient synchronous generator simulation platform, the operating conditions of ITSC with different fault levels are simulated. Simulation results show that changes in the dynamic/static eccentricity degree cause the time domain amplitude of circulating current changes, and the armature response and dynamic eccentricity also cause the appearance of characteristic fractional harmonics. Accordingly, ITSC fault diagnosis by a single-time domain analysis or spectrum analysis can easily lead to fault misclassification. The TFMCC allows the fault to be located at the moment of occurrence. The signal intensity at the characteristic frequencies (5.5, 11, 22, 28, 39, 44.5 Hz) is significantly enhanced compared to the normal state. Meanwhile, a comparison with the dynamic/static eccentricity shows that the signal intensity of the dynamic eccentricity is mainly concentrated at 55.5 Hz, and the static eccentricity is mainly concentrated at 50 Hz. The signal intensity distribution is differentiated from that of ITSC faults. The ratio of the signal intensity difference energy (SIDER) are 7.13% and 13.96% for the no-load ITSC condition, and 6.66% and 13.44% for the load ITSC condition, much greater than zero, which can effectively identify the ITSC faults. The experiments on salient synchronous generator show that: the TFMCC of static eccentric compound ITSC 2.5%, 5%, and 7.5% under no-load conditions, the signal intensity at the intersection of time and characteristic frequency has changed significantly, and the signal intensity differences at 25 Hz before and after the fault are 13.05, 21.06, and 29.86 dB, respectively. The SIDER are 4.16%, 5.23%, and 11.11%, respectively. Meanwhile, the static eccentricity compound ITSC of 1%, 2.5%, and 4.9% under load conditions, and the signal intensity differences at 25 Hz before and after the fault are 29.64, 38.56, and 71.81 dB, respectively. The SIDER are 2.28%, 3.13%, and 15.68%, respectively, which shows that the method can effectively detect weak ITSC faults and locate the time of fault occurrence. The experimental results demonstrate that the method eliminates static eccentricity and armature response disturbances, and visualizes the operating conditions of the unit with a three-dimensional map that includes operating time, characteristic frequency, and signal intensity. At the same time, to effectively identify weak ITSC faults, this study also calculates that the SIDER of the time-frequency matrix can further quantify the magnitude of the fault and prevent false alarms, which can provide guidance for field operation and inspection personnel to carry out condition maintenance work.
齐鹏, 李永刚, 马明晗, 武玉才, 纪璇. 基于环流时频图谱的抽水蓄能机组励磁绕组匝间短路故障诊断[J]. 电工技术学报, 2023, 38(22): 6104-6120.
Qi Peng, Li Yonggang, Ma Minghan, Wu Yucai, Ji Xuan. Diagnosis of Inter-Turn Short Circuit in Excitation Winding of Pumped Storage Units Based on Time-Frequency Map of Circulating Current. Transactions of China Electrotechnical Society, 2023, 38(22): 6104-6120.
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