24 تیر 1403
رحمن دشتي

رحمن دشتی

مرتبه علمی: دانشیار
نشانی: دانشکده مهندسی سیستم های هوشمند و علوم داده - گروه مهندسی برق
تحصیلات: دکترای تخصصی / مهندسی برق
تلفن: +98-7731222752
دانشکده: دانشکده مهندسی سیستم های هوشمند و علوم داده

مشخصات پژوهش

عنوان Real Fault Location in a Distribution Network Using Smart Feeder Meter Data
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
fault location; smart feeder meter; section estimation; impedance-based method; distribution network; grounded faults
مجله Energies
شناسه DOI https://doi.org/10.3390/en14113242
پژوهشگران حمید میرشکالی (نفر اول) ، رحمن دشتی (نفر دوم) ، کارستن هندراپ (نفر سوم) ، حمید رضا شاکر (نفر چهارم)

چکیده

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.