Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach

الطاقة الشمسية هي واحدة من الطاقة المتجددة التي لا حصر لها في توليد الطاقة لبيئة خضراء ونظيفة وصحية. تمتص الألواح الشمسية المكونة من طبقة السيليكون طاقة الشمس وتتحول إلى كهرباء بواسطة عاكس خارج الشبكة. نقل الكهرباء يتم إما من هذا العاكس أو من المحول، التي تستهلكها وحدة (وحدات) الاستهلاك المتاحة للأغراض السكنية أو الاقتصادية. الشبكة العصبية الاصطناعية هي أساس الذكاء الاصطناعي وتحل العديد من المشاكل المعقدة التي يصعب من خلال الأساليب الإحصائية أو من قبل البشر. في ضوء ذلك، فإن الغرض من هذا العمل هو تقييم أداء نظام الطاقة الشمسية - المحولات - الاستهلاك (STC). قد يكون النظام في حالة انهيار كامل بسبب فشل كل من النظام الفرعي لأتمتة الطاقة الشمسية والمحول في وقت واحد أو وحدة الاستهلاك ؛ وإلا فإنه يعمل بكفاءة كاملة أو أقل. يتم النظر في حالات الفشل والإصلاحات المستقلة إحصائيًا. يتم استخدام ظاهرة الاحتمالات الأولية المدمجة مع المعادلات التفاضلية لفحص موثوقية النظام ، للنظام القابل للإصلاح وغير القابل للإصلاح، ولتحليل دالة التكلفة الخاصة به. يمكن تحسين دقة واتساق النظام من خلال نهج الشبكة العصبية للانتشار الأمامي والخلفي (FFBPNN). يمكن لآلية تعلم النسب المتدرجة أن تقوم بتحديث الأوزان العصبية وبالتالي النتائج تصل إلى الدقة المطلوبة في كل تكرار، وبغض النظر عن مشكلة تلاشي التدرج في الشبكات العصبية الأخرى، مما يزيد من كفاءة النظام في الوقت الفعلي. تم تصميم كود MATLAB لخوارزمية FFBP لتحسين قيم الموثوقية ووظيفة التكلفة من خلال تقليل الخطأ إلى الحد الأدنى حتى 0.0001. يتم النظر في الرسوم التوضيحية العددية مع جداول البيانات والرسوم البيانية الخاصة بهم، لتوضيح النتائج وتحليلها في شكل الموثوقية ووظيفة التكلفة، والتي قد تكون مفيدة لمحللي النظام.Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both solar power automation subsystem and transformer simultaneously or consumption unit; otherwise it works with fully or lesser efficiency. Statistically independent failures and repairs are considered. Using the elementary probabilities phenomenon incorporated with differential equations is employed to examine the system reliability, for repairable and non-repairable system, and to analyze its cost function. The accuracy and consistency of the system can be improved by feed forward- back propagation neural network (FFBPNN) approach. Its gradient descent learning mechanism can update the neural weights and hence the results up to the desired accuracy in each iteration, and aside the problem of vanishing gradient in other neural networks, that increasing the efficiency of the system in real time. MATLAB code for FFBP algorithm is built to improve the values of reliability and cost function by minimizing the error up to 0.0001 precision. Numerical illustrations are considered with their data tables and graphs, to demonstrate and analyze the results in the form of reliability and cost function, which may be helpful for system analyzers

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Reliability analysis for automobile engines: conditional inference trees

The reliability model with covariates for machinery parts has been extensively studied by the proportional hazards model (PHM) and its variants. However, it is not straightforward to provide business recommendations based on the results of the PHM. We use a novel method, namely the Conditional Inference Tree, to conduct the reliability analysis for the automobile engines data, provided by a UK fleet company. We find that the reliability of automobile engines is significantly related to the vehicle age, early failure, and repair history. Our tree-structured model can be easily interpreted, and tangible business recommendations are provided for the fleet management and maintenance

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time