سیستم های تشخیصی تخلیه جزئی برای شبکه های توزیع هوشمند با استفاده از سنسورهای درجه بندی شده القایی جهت دار
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|54721||2015||15 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electric Power Systems Research, Volume 119, February 2015, Pages 447–461
Asset management and optimization is becoming increasingly adaptable for distribution utilities with the help of developments in the digital and wireless technology. Medium voltage cables, cable accessories (joints and terminations) and distribution transformers are most prone to the insulation defects. This paper presents an online partial discharge (PD) diagnostic system to detect and locate the weak insulation spots developed in the network components spread over the distribution area. The PD pulses originating and propagating from the defect site arrive at feeder joints, terminals and T-splices and are reflected from there. This makes the interpretation of the measured signals ambiguous in order to find the location of PD faults, while using conventional location techniques. An improved technique is introduced to monitor multi-section multi-branched cable network. Identification of the faulty location is carried out by using direction of arrival (DOA) of PD signals. The DOA of PD signals is obtained by using directionally calibrated Rogowski coil induction sensors. Polarity of captured PD signals with reference to supply voltage, determines their DOA. Rogowski coil along with its directional sensing feature is simulated in ATP-EMTP. The DOA technique is integrated over a medium voltage (MV) cable network and its performance is evaluated using ATP-EMTP environment. Frequency-dependent JMarti cable model is used for simulation. An intelligent algorithm is proposed for practical implementation of DOA technique for PD monitoring and automated location. Proposed scheme can be adopted in distribution automation system to improve proactive diagnostic capabilities of the network.