تجدید آرایش فیدرهای شبکه توزیع یک مسئله بهینه سازی در سیستم قدرت است که با تغییر وضعیت سوئیچینگ در شبکه توزیع برای برآورده کردن توابع هدف خاصی انجام می شود. بررسی مطالعات نشان می‌دهد که اغلب تلفات توان و انحراف ولتاژ باس ها به‌عنوان توابع هدف در حل مسئله تجدید آرایش فیدرهای شبکه توزیع در نظر گرفته ‌شده است. با این ‌حال توابع هدف جدیدتر نظیر قابلیت اطمینان کمتر مورد توجه قرار گرفته‌است. در این مقاله، برای حل مسئله چندهدفه تجدید آرایش شبکه توزیع، از شاخص انرژی توزیع نشده به‌عنوان تابع قابلیت اطمینان، همراه با تلفات توان و تعداد سوئیچینگ در حضور واحدهای تولید پراکنده استفاده ‌شده است. تجدید آرایش فیدر های توزیع به طور ذاتی مساله پیچیده ای است، در نظر گرفتن تاثیر منابع تولیدپراکنده در شبکه توزیع مساله را پیچیده تر از قبل می کند، به همین منظور از یک روش تکاملی مبتنی بر ترکیب روش‌های اجتماع ذرات و بهبود یافته جهش قورباغه برای حل مسئله بهینه‌سازی غیرخطی استفاده شده است. سیستم های 33 و 70 باسه نیز برای سنجش اثربخشی الگوریتم ترکیبی پیشنهادی مورد استفاده قرار گرفته‌اند و همچنین نتایج روش پیشنهادی با نتایج سایر روش های تکاملی مقایسه می شود. Manuscript profile

Flexible AC Transmission System (FACTs) devices are used in power transmission networks to increase maximum power transmission and stability. On the one hand, they help to damp low-frequency oscillations for both local and internal areas. But on the other, the design of these devices with uncoordinated Power System Stability (PSS) may degrade the performance of the power system. In addition, the power systems are vast, complex, and nonlinear. Linear control strategies do not have satisfactory performance for these systems, especially when some disturbances occur. In this study, a nonlinear Coordinated Control Strategy based on Recurrent Neural Network (CCSRNN) is designed to control PSS and Static Synchronous Compensators (STATCOM) coordinates for a standard multi-machine power system and also using the Multi-Bound Power System Stability (MB-PSS) and Multi-Bound Recurrent Neural Network MB-RNN PSS and compare the result of each one for having better stability of the system. The results simulation proves that the controller leads to transient stability and low-frequency oscillation. Manuscript profile

Induction motors are one of the most widely used machines that are used in industrial motion control and home systems. On the other hand, the optimal determination parameters have a direct and significant effect on their efficiency, longevity, and performance. Determining the initial parameters of these machines by the classic method is time-effective and costly. Therefore in recent years, the use of computer processing such as simulation, heuristical algorithm, ANN, etc. has become common as an alternative method in this field. Cuckoo Optimization Algorithm (COA) is a relatively new algorithm and it is less used to solve this nonlinear problem. In this article, the parameter of the induction machine will be estimated using COA. COA. In the following, the results obtained from the algorithm will be compared with the results obtained from several examples of conventional algorithms. Objective functions are defined as minimizing the true values of the relative error between the measured and estimated torques of the machine in different slips. Objective functions have been used to optimize the parameters of the induction motor with an approximate equivalent circuit and the induction motor with the exact circuit. The final results show the accuracy of operation and very good convergence speed of the algorithm in solving the problem. Manuscript profile

A five-level Power Factor Correction incremental rectifier (PFC) is proposed in this paper. In this topology, the output voltage and current of the rectifier are controlled using the multilevel modulation and smart controller technologies. A multi-carrier pulse width modulation is used to create the switching pulse. In this topology, the number of semiconductor switches is reduced to 3. The smart controller is implemented using a Multilayer Perceptron (MLP) neural network and it is trained using the backpropagation algorithm. This controller is used instead of the well-known PID controller to control the input voltage and current. It should be noted that in this work, the goal is to design an intelligent controller using a neural network instead of a PID controller. The results obtained using this controller as compared to the PID controller show a decrease in the peak voltage, an increase in the rise time, and a ripple reduction in the output voltage. This study is conducted using the Simulink environment in MATLAB and the results suggest that a smart controller can be an alternative to the PID controller. Manuscript profile