فصلنامه روشهای هوشمند در صنعت برق
پیاپی 40 (زمستان 1398)

Controlling systems in industrial processes are subject to problems such as the limitation of system signals, the uncertainties of parameters, the time delay and the failure of actuators. The design of the controller, which can satisfy the constraints, counteract and omit these effects, has attracted much attention. On the other hand, the issue of time delay is so serious and effective, which can make the system unstable and disrupt the process. Many of the devices in the systems, such as sensors and actuators, may be defective. The important thing is that any of the above or even system parameters may be uncertain. Identifying, estimating and fixing the destructive effects of the problems mentioned by the controller of the system. The proposed method of control for nonlinear systems in the presence of an uncertain parameters, delay and faults in actuators. There is no need to limit the parameters, delays, and fault of the actuators. This comparative method is capable of guaranteeing the overall boundary of all closed-loop system signals and the convergence of tracking errors to a small neighborhood around the origin. At the end, the simulation results show the effectiveness of the proposed control method

This paper presents, a novel evolutionary method for dynamic feeder reconfiguration at the presence of distributed generators (DGs), energy storage (ES) units and solar photovoltaic (PV) panels. Modern distribution networks, in addition to the importance of economic issues, must operate at an acceptable level of system reliability, Failure to pay attention to the reliability importance can lead to irreparable damages in the distribution network. Regarding the importance of the objective functions including voltage stability index (VSI), energy loss and energy not supplied (ENS) has led to the presentation of a stochastic multi-objective framework for dynamic feeder reconfiguration in the presence of distributed generators (DGs), energy storage (ES) units and solar photovoltaic (PV) panels considering uncertainty of PV units. A modified shuffled frog leaping algorithm is provided in this study to solve the proposed optimization problem. In order to show the efficiency and supremacy of the proposed method is applied on 33-node test system.

Breast cancer is one of the most common cancers among women. Many times, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a gold standard for over 40 years in diagnosing breast diseases. In recent years, artificial intelligence systems have been the focus of much attention in preventing the subjective analysis of mammograms and physicians by radiologists and enhancing the accuracy of breast cancer detection. In this study, combining the firefly algorithm and applying appropriate image processing to detect breast cancer in mammographic images has been investigated. In this paper, mammographic images in the DDSM dataset were used. Three performance metrics such as sensitivity, specificity and accuracy (93.4%, 91%, 95%) were used to analyze the detection performance. The proposed work shows better performance when compared to existing work in literature.

Nowadays, saving time and economy of each country requires proper planning, decision making, and rational forecasts in different areas. One of the most well-known areas that has received a lot of attention is electricity forecasting. The features of the electricity which makes it distinguished from other commodities are the impossibility of storing it and the existence of seasonality and nonlinear and ambiguity pattern in electricity data set. These features of the electricity makes it more difficult to forecast using traditional methods. Therefore, in this paper, a parallel optimal hybrid model using seasonal linear and nonlinear methods is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of individual models in the modeling of complex systems in a structure, simultaneously. Experimental results indicate that in this method due to the use of a direct weighting method, the computational cost of modeling it is significantly lower than other parallel hybrid methods.

The sympathetic Inrush current that occurs in series and parallel transformers can cause the creation of two major problems in the protection of power transformers. The main problem is that the peak of the inrush current for this case is more severe rather than common inrush current that the protection system may mal-operate. Besides, when a common protection system is designed for parallel transformers, the line input current, the vector summation of the input currents, will be detected as a single-phase fault that can cause the incorrect operation of the protection system. For this purpose, this paper presents a novel approach based on Lissajous of current signal for detection of inrush current phenomenon. The proposed method detects the faults very quickly; as well as, this method has appropriate operation under noisy conditions, like white Gaussian noise. This method is evaluated using a network consist of 230 to 63 kV parallel transformers with vector group ynd11. The results show that the proposed method performs better than conventional industrial schemes, such as second harmonic, zero-crossing, and waveform analysis method.

In this paper, a wearable dual-band filter is designed, simulated, and fabricated to operate at GPS and Bluetooth telecommunication frequency bands. In this filter, the frequency bands of 1.563-1.678 GHz and 2.4-2.48 GHz are achieved. These two frequency bands are widely used in the design of telecommunication-medical systems. The textures are generally used in place of conventional dielectric for the design of wearable devices. These include texture, denim, wool, and felt. In this filter, a felt substrate with dielectric constant 1.3 and 2 mm height is used. In this paper, CST Microwave Studio 2019 full-wave software is used to simulate all structures. The final dimensions of the resonators of this filter are 6 × 2 cm2 or 0.25 × 0.03 λg2 at 1.57 GHz. The filter was then fabricated to verify the simulations performed. The results of the measurement of the characteristics of this filter as well as the simulation results are in good agreement with each other.