فصلنامه تخصصی فناوری اطلاعات و ارتباطات

روشهای هوش مصنوعی برای مدلسازی و تحلیل خطا در شبکه ‏های توزیع قدرت

نوع مقاله : مقاله پژوهشی

نویسنده

موسسه آموزش عالی کار

10.22034/apj.2026.2072799.1057
چکیده
‏چکیده

پیشینه و اهداف: خطا در شبکه های توزیع فدرت بر اساس پارامترهای غیرفابل کنترل بوجود می آید. روش ‏سریعی که قادر به شناسایی و مکانیابی خطای شبکه های توزیع قدرت باشد برای نگهداری مطمئن این شبکه ‏ها حیاتی است. روشهای معمول با اندازه گیری ولتاژ انتقال دهنده ها کار می کنند اما روش پیشنهادی ‏استفاده از الگوریتمهای هوش مصنوعی برای تشخیص و تحلیل خطا در شبکه های توزیع قدرت است. ‏مجموعه ای از حسگرها برای دریافت ولتاژ و سایر داده ها تحت شرایط شبیه سازی استفاده شده است که ‏شامل خطا در مدارات کوتاه و مدارات باز است. روش پیشنهادی شامل متغیرهای اساسی مانند: نوع خطا، ‏معماری جایگزاری حسگر و فاصله است که بوسیله داده های اخذ شده کنترل می شوند. ‏

روشها: این مقاله دو الگوریتم را نمایش می دهد: یک شبکه عصبی مصنوعی و یک سیستم استنتاج انطباقی ‏فازی-عصبی. هر دوی این الگوریتمها دارای پارامترهای زیادی هستند که نیاز به تنظیم دارند. هرچند مقدار ‏هرکدام از آنها بر روی کارآیی تاثیر می گزارد ولی هدف اصلی این مقاله نحوه استفاده از الگوریتمها است. ‏مراحل الگوریتمها در مقاله توضیح داده شده است. نتایج الگوریتمها به همراه داده های استفاده شده نشان ‏داده شده است.کارآیی بدست آمده تحت متغیرهای شرایطی فراخوانده شده در فاصله 200 تا 800 متری ‏قابل اعتبار هستند. ‏

یافته ها: نتایج شبیه سازی نشان می دهد که طبقه بندی ‏ANFIS‏ دارای درستی بهتری در طبقه بندی ‏‏(97%) و خطای کمتری در تقریب فاصله (0.5%) است. هردو الگوریتم ‏ANN‏ و ‏ANFIS‏ بسیار سریع ‏آموزش می بینند (1‏‎ ms‏) بعلاوه ‏ANFIS‏ پایداری بیشتری دارد. نتایج حاصل از این یافته ها، با یکدیگر ‏مقایسه شده است.‏

نتیجه گیری: طرحی که نمایش داده شده است مقاوم، قابل محاسبه خطا در سیستمهای مدیریت بلادرنگ ‏و قابل تجمیع حسگرها در یک شبکه توزیع قدزت را دارد. ‏

کلیدواژه‌ها


عنوان مقاله English

Artificial Intelligence Approaches for Modeling and Analysis Fault in Power ‎Distribution Networks

نویسنده English

Seyed Mahmood Hashemi
KAR Higher Educational Institute
چکیده English

Background and Objectives: Fault of power distribution networks is based on the ‎uncontorable factors. Rapid approache to identidtification and localization of power ‎network fault is ctritical to maintain system riable. While traditional approaches use ‎measurements from current and voltage transformers, this study proposes an ‎artificial intelligence-driven approach for enhanced fault detection and analysis in ‎power distribution networks. A custom-designed sensing prototype captured voltage ‎and current data under simulated fault conditions, including short-circuit and open-‎circuit faults. The presented approach includes fundamental variables, such as fault ‎type, sensor placement topology, and line distance, were rigorously controlled during ‎data acquisition.‎

Methods: This paper presentes two algorithms: an Artificial Neural Network (ANN) ‎and an Adaptive Neuro-Fuzzy Inference System (ANFIS). Both used algorithms have ‎many parmeters that rquire to be tune While the values of these parameters effect ‎on the performance, but the major target of this study is using of these algorithms. ‎Steps of algorithms are described in the paper. The redults of algorithms are showed ‎with the used data. Performance was validated under variable load conditions across ‎line distances of 200–800 meters. ‎

Finding: Simulation results demonstrate that the ANFIS classifier achieved superior ‎accuracy in fault classification (99.7%) and minimal distance estimation error (0.5%). ‎Both ANN and ANFIS delivered high precision in fault detection, localisation, and ‎classification, with ANFIS exhibiting significantly faster training convergence (1 ms). ‎Indeed ANFIS has more consistency. ‎

Cnclusion: The framework presents a robust, computationally efficient solution for ‎real-time fault management (modeling), recommending (analysis) the integration of ‎dedicated sensors by power distribution network utilities to enable targeted grid ‎interventions.‎

کلیدواژه‌ها English

Fault Detection
Power Distribution Network
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