Journal of Artificial Intelligence in Electrical Engineering
Volume:8 Issue: 31, Autumn 2019

Nowadays, among the renewable energy resources, wind energy is more economically viable than other new sources. To achieve the maximum power at different wind speeds, the turbine speed must be variable over a wide range. Electricity generation from wind is performed by different models of wind turbines with different generators. Due to the capabilities and advantages such as relatively high power density, low noise, high efficiency, high reliability and low maintenance costs, wind turbines equipped with permanent magnet synchronous generator (PMSG) are in increasing use. This paper presents a method for modeling and controlling the stability of PMSG-equipped power systems using static synchronous compensator (STATCOM). STATCOM is a static synchronous generator that is installed in parallel with the power grid and is used as a reactive power compensator which plays an effective role in voltage stability. The main goal of this study is to develop a reliable control strategy for wind farms and STATCOM to investigate the effect of reactive power losses under constant and sub-fault conditions. Furthermore, to increase the attenuation of the synchronous generator of single-machine system with infinite bus, STATCOM with PID controller is connected to the studied system to rise the dynamic stability of the PMSG based wind turbine.

Lung cancer is among the deadliest cancers worldwide. One of the indications of lung cancers is lung nodules which can appear individually or attach to the lung wall. Therefore, the detection of the so-called nodules is complicated. In such cases, the image processing algorithms are performed by the computer, which can aid the radiologists in locating and assessing the nodule's feature. The significant problems with the current systems are the increment of the accuracy, improvement of other criteria in the results, and optimization of the computation costs. The present paper's objective is to efficiently cope with the aforementioned problems by a shallow and light network. Convolutional Neural Networks were utilized to distinguish between benign or malignant lung nodules. In CNN's networks, the complexity increases as the number of layers increases. Accordingly, in the current paper, two scenarios are presented based on State the art and shallow CNN method in order to accurately detect lung nodules in lung CT scans. A subset of the LIDC public dataset including N=7072 CT slices of varying nodule sizes was also used for training and validation of the current approach. Training and validation steps of the network were performed approximately in five hours, and the proposed method achieved a high detection accuracy of 83.6% in Scenario1 and 91.7% in Scenario2. Due to the usage of various validated database images and comparison with previous similar studies in terms of accuracy, the proposed solution achieved a decent trade-off between criteria and saved computation costs. The present work demonstrated that the proposed network was simple and suitable for the so-called problems. Although the paper attempted to meet the existing challenges and fill up the prevailing niches in the literature, there are still further issues that requires complementary studies to shape the tapestry of the knowledge in the field.

Due to the variable and non-uniform nature of wind speed, using the variable speed wind power plants equipped with induction generators has advantages over the fixed speed wind power plants; however, it also contains some disadvantages such as consuming a large amount of reactive current in the power grid and causes voltage drop and instability. The use of static synchronous compensator or STATCOM is one of the key methods to improve the power profile in wind farms. STATCOM can control the voltage dynamics, improve transient stability, eliminate power fluctuations in the transmission network, and control the real and reactive power. Regarding to the fluctuations in electricity generated in wind farms, the presented case study proposed methods to improve the active power and compensate reactive power using the STATCOM in wind systems by modeling in Simulink environment of MATLAB software. According to the simulation results, by applying the STATCOM to the studied system, changes in active power and reactive power generated from the wind farm are significantly reduced. In addition, studies in terms of total harmonic distortion (THD) show that by employing STATCOM, the amount of output voltage distortion is vastly decreased.

Advances in wireless networking and communications technologies have led to the emergence of ‎Internet-related innovations known as the Internet of Things.‎The Internet of Things is defined as a pattern in which objects equipped with sensors and processors communicate with each other to pursue a meaningful goal.The IoT is able to integrate and transparently integrate a large number of heterogeneous and ‎diverse end systems by providing free access to select subset of data to improve a range of digital ‎services.In this paper, we survey protocols, and applications in this new emerging area ,then ‎analyze the challenges of the IoT

Artifitial neural network (ANN) is an information processing system that is formed by a large number of simple processing elements, known as artificial nerves. It is formed by a number of nodes and weights connecting the nodes. Using the trained data, the designed ANN can be adjusted in an iterative procedure to determine optimal parameters of ANN. Then for an unknown input, we can compute corresponding output using the trained ANN. There are many methods for training the network and modifications of the weights. One of the most famous and simplest methods is a back-propagation algorithm that trains the network in two stages: Feed-forward and feed-backward. In the feed-forward process, the input parameters are moved to the output layer. In this stage, the output parameters the next stage is done In this study, a 3-layer perceptron neural network was used with 28 neurons in a hidden layer for modeling the eastern component (VE) and 27 neurons in a hidden layer for modeling the northern component (VN) velocity field of the earth's crust in Iran. The minimum relative error obtained from this evaluation for the eastern component was -3.57% and for the northern component was +0.16%: also the maximum relative error for the eastern component was +38.1 % and for the northern component was +95.3%. In this study, a polynomial of degree 5 with 18 coefficients was used to model the east and north components for the evaluation of artificial neural networks in estimating the velocity rate of geodetic points. A comparison of the relative error from the polynomial model and the relative error from the neural network illustrated the superiority of the neural model with respect to the polynomial model in this region.

Deep learning has developed rapidly in recent years and has been applied in many areas that are major areas of artificial intelligence. The combination of deep learning and embedded systems has created good dimensions in the technical field. In this paper, a deep learning neural network algorithm can be designed that can be implemented on FPGA hardware. The PyTorch and CUDA were used as assistant methods. Convolution neural network (CNN) was also used for image classification. Three good CNN models such as ResNet, ResNeXt and MobileNet were reviewed in this article. Using these models in the design, an algorithm was eventually designed with the MobileNet model. Models were selected from different aspects such as floating operation point (FLOP), number of parameters and classification accuracy. In fact, the MobileNet-based algorithm was selected with a top-1 error of 5.5% in software with a 6-class data set. In addition, hardware simulation in MobileNet-based algorithms was presented. The parameters were converted from floating numbers to 8-bit integers. The output numbers of each layer were cut into integer fixed bits to fit the hardware constraint. A method based on working with numbers was designed to simulate number changes in hardware. The results of simulation show that, the top-1 error increased to 12.3%, which is acceptable.