نشریه تحقیقات نوین در سیستم های قدرت هوشمند
سال نهم شماره 4 (پیاپی 24، زمستان 1399)

Static Synchronous Compensator (STATCOM) is a FACTS device whose structure is based on a voltage source converter and is installed in parallel to control the voltage of the transmission line in the power system. In this paper, a flat adaptive control system for STATCOM output voltage control is simulated. By linearizing the nonlinear dynamics and changing the system state variables, the controller designs the rules of control input stability, and an adaptive rule is obtained for designing the controller coefficients with the least system error. In the simulation results, the adaptive controller is compared with the traditional PI controller.

After the discovery of X-rays with the increasing use of digital imaging systems, medical image processing has become more important. Medical image processing helps specialists in diagnosing diseases. In addition to major digital techniques such as computed tomography (CT) or magnetic resonance imaging (MRI), analog imaging techniques such as endoscopy or radiography are now equipped with digital sensors. By processing images using different methods, the procedure applied to patients can be improved. Algorithms play a key role in noise filtering, segmentation, extraction, and characterization that diagnose diseases. MATLAB software and image processing toolboxes provide a wide range of advanced image processing functions and interactive tools for enhancing and analyzing digital images. In this article, using several algorithms designed in MATLAB, the quality of images is examined and a more appropriate algorithm is selected.

Electric arc furnace is one of the major loads in the power grid and which, due to the fact that it is a non-linear and random load, causes many disturbances in the grid. The arc furnace causes problems such as harmonic, sub-harmonic, voltage imbalance, flicker voltage and voltage fluctuation and current on the power grid. Therefore, in order to reduce the problems caused by electric arc furnaces, it is necessary to install compensating equipment such as SVC next to them. In order to analyze the effects of electric arc furnace on the network, calculate the capacity and type of compensator required for each arc furnace, first the appropriate furnace model must be extracted and performed in the simulation software environment. After performing the simulation in the software environment and studying the furnace behavior, a suitable compensator will be designed for the arc furnace. Given that the arc furnace has a nonlinear and random behavior, it is difficult to provide a suitable and accurate model for studying the power quality phenomena caused by the furnace on the network. The purpose of this article is to obtain a suitable model for the electric arc furnace of Khuzestan Steel Company. For this purpose, the voltage and current of the furnace transformer are first taken. Then, the genetic algorithm is used to adapt the desired models and obtain the optimal model parameters and the best model is selected.

: Prior knowledge about the load data in the shape of future information plays a pivotal role in the optimal operation and planning in the electrical networks. In this paper, we design a deep learning-based network to characterize the load for the next hours. With emerging new technologies and a high growth rate of the population, short-term load forecasting (STLF) has reformed to a more complicated problem rather than in the traditional electrical networks, therefore, designing a structure that can capture spatial-temporal features is a challenging and essential task. To this end, we aim to develop a new deep learning structure, which is able to handle high volatility time series including load sequences. The designed network is composed of three different types of deep networks, convolutional neural network (CNN) as a strong spatial feature extractor, bidirectional long short-term memory unit as a suitable temporal feature learner, and encoder-decoder to enhance accuracy, which are formed in a graph-based deep network to inherently learn features of a time series and corresponding meteorological data. The proposed method is directly applicable to raw data and enhances the level of accuracy in terms of several metrics. The simulation results on actual load time series, in Shiraz, Iran, are compared with a number of well-known shallow and deep-based networks to verify the effectiveness and superiority of the designed deep network. Furthermore, the proposed STLF structure is tested in different seasons and the impact of the meteorological data is analyzed.

In this paper, optimal participation of the grid-connected energy hubs in the day-ahead energy and reserve markets is presented. The proposed scheme includes objective function that is minimized the difference between flexibility cost of hubs and their revenue in these markets. Also, this problem is constrained to power flow equations in the electrical, gas and heating networks, technical and reserve limits of these networks, and operation model of hubs including different sources and active loads. In the following, the stochastic programming is used to model the uncertainties of load, market price, networks reserve demand, renewable generation power, and energy demand of mobile active loads. Finally, by implement of the proposed scheme on the standard test network, the obtained numerical results confirms the capabilities of this scheme in the improvement of economic and flexibility situation of energy hubs, and improving the operation situation of the energy networks.

In this paper, a fuzzy rule extraction system is designed and the idea of ​​using this method in designing a fuzzy rule database is considered. First, the fuzzy backup vector machine is designed based on the clustering method. In this case, the backup vector machine will perform better in noise data or out-of-range data, and as a result, the rules extracted from it seem to be more accurate. The fuzzy backup vector machine model is simulated and then its backup vectors are used to derive fuzzy rules. The extracted rules are fuzzy rules that are obtained in the form of "if-then". The rules are extracted using two models of simple support vector machine and fuzzy support vector machine, which will show that the rules extracted from fuzzy backup vector machine in most cases have better accuracy in classification than ordinary backup vector machine. The number of extracted rules is an important parameter in the accuracy of the operation of this database, the number of rules extracted in the fuzzy method is less in most cases.