دانلود ترجمه مقاله سیستم خبره شبکه های عصبی (BPES) در عیب یابی سیستمهای قدرت – مجله الزویر

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 چکیده
۱ مقدمه
۲ سیستم هوشمند انتشار معکوس
۳ سیستم خبره یا هوشمند مولتی BP (MBPES)
۱ ۳ ساختار MBPES
۲ ۳ الگوریتم ساخت MBPES
۴ نتایج و تجزیه و تحلیل
۱ ۴ مقایسه تعداد لایه های شبکه
۲ ۴ آزمایش روی داده های ترانسفورماتور
۳ ۴ تجزیه و تحلیل داده های آزمایشی کلید قدرت خلاء یا وکیوم
۵ نتایج

بخشی از ترجمه:

۵٫ نتایج
در این مقاله، در مواجهه با داده های واقعی در ترانسفورماتور و برکر فشار قوی ، با الگوریتم گروه بندی متوالی، موازی و هیبریدی، دو شبکه جدید مولتی BP پیشنهاد کردیم. به علاوه، آنها مسئله سرعت پائین همگرایی به خاطر تعداد زیاد لایه های شبکه را با موفقیت حل کرده و سرعت شناسایی و تشخیص را نیز به میزان زیادی افزایش دادند. به علاوه، با مقایسه BPNN و BPES، می توان دید که نتایج حاصل از شبکه های مولتی BP درست تر می باشند. با استفاده از شبکه مولتی BP برای شناسایی سیستم قدرت، نه تنها می توان ترانسفورماتور را به صورت آنلاین(روخطی) و آفلاین (برون خطی) شناسایی نمود، بلکه همچنین، سیستم به سرعت توصیه هایی در جهت شناسایی مطرح می کند.

بخشی از مقاله انگلیسی:

۱٫ Introduction In modern times, power system becomes larger and more complex than before. With its fast development, higher demand for the sustainability and stability of power system is of great requirement. However, some common faults in power system have never been resolved very well and are still hindering the stability of power system, such as transmission fault, network distribution fault, power variable fault (Mizutani et al., 2007). Sometimes, even only one fault could destroy the equipments in power system, and might affect the whole power system. An even worse damage could cause conflagration and casualties, and leading to a huge pecuniary loss. Therefore, it is of great significance to do researches for preventing those faults from power system. And fault diagnosis is a powerful tool to guarantee the safety and reliability of power system. In power system, transformer is a kind of major equipment and plays an important role in power transmission. It can raise voltage so that power can be transported to the user with less loss. On the other hand, the transformer can reduce power into different voltage levels, which can satisfy variety needs of users. Because of its complex structure and function, transformer is tending to cause fault. Unfortunately, transformer fault is very difficult to predict. Moreover, if some accidents take place in transformer, the whole system has no choice but to stop to check and maintain the equipments. Therefore, it is believed that keeping the transformer running in perfect situation plays a key role in power system diagnosis (Lin and Zeng, 2009). High voltage circuit breaker (over 3 kV) is another important element in power system. It has two main functions, namely controlling and protecting. Firstly, it decides when and which parts of the power system should be started or stopped according to the requirements; secondly, when some errors occur in power networks or equipments, the high voltage circuit breaker will quickly break off the error parts from power system, so that other parts can work without influence. In other words, high voltage circuit breaker is able to control the normal current in power lines, and to deal with the overload current, short-circuit current and other abnormal current within a limited time. When a mistake happens in high voltage circuit breaker, it will usually expand to other parts of the power system and finally lead to a worse accident. There are some classical artificial intelligence technologies have been used in power system fault diagnosis, for example: the expert system (Ma et al., 2010), artificial neural networks (El-madany et al., 2011; Zhu et al., 2006; Huang et al., 2002; Karthikeyan et al., 2005), decision tree theory (Qu and Gao, 2008) etc. In recent years, some new theories have been applied in this field, such as data mining (Athanasopoulou and Chatziathanasiou, 2009), fuzzy set theory (Lee et al., 2000; Zhang et al., 2010), rough set theory (Li and Wang, 2010, Li et al., 2011), petri-network (Yang et al., 2004), support vector machine (Eristi and Demir, 2010), multi-agent systems (Zaki et al., 2007), and so on. Li and Liu had performed a comprehensive review of the above-mentioned methods (Li and Liu, 2010). They pointed out that there are some problems in the existing intelligent fault diagnosis expert system theology, such as the difficulty for knowledge gaining and managing, low on-line usage of fault diagnosis, high error rate, poor efficiency of inference process, and so on. Back propagation neural network (BPNN) expert system is an often used method in fault diagnosis. In real applications, BPNN usually has many layers. However, the training time of BPNN will grow exponentially with the layer number increasing. While more serious problem is that it is difficult to converge when BPNN has a large number of layers. Another problem is that the diagnostic accuracy Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/engappai Engineering Applications of Artificial Intelligence 0952-1976/$ – see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.engappai.2012.03.017 n Corresponding author. E-mail address: shixh@jlu.edu.cn (X. Shi). Engineering Applications of Artificial Intelligence 26 (2013) 937–۹۴۴ of BPNN is still not satisfied. To solve those problems, we propose a so called multi-BP expert system (MBPES) method. In MBPES, the whole BPNN networks are divided into many sub-BP groups within a short depth, saying about 5 layers. In this manner, the consumed training time is greatly reduced and it is easy to achieve the convergence of the training process.

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