فصلنامه روشهای هوشمند در صنعت برق
پیاپی 49 (بهار 1401)

Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. Planning in a large-scale electricity supply chain, in a space full of uncertainty, is begin with electricity demand forecasting.In this paper, a hybrid long-term demand forecasting method in the electricity supply chain of Isfahan's ironstone industries using a combined data mining method including wavelet transform,deep learning and intensive learning machine is proposed. The used data in this study is according to the recorded information from the electrical energy demand signal of Espidan ironstone industries in a period of 40 months in the form of 24-hours. The data in a part of the study period due to the lack of production of this industry in some hours are interrupted. So that only 40% of the data had a value and the remaining, 60% were zero. This subject led to information deficiencies and increases the forecasting error up to 40% in the first step of the proposed algorithm. By completing the first step of the proposed model with intense learning machine (ELM) the forecasting error is reduced and it was possible to create an improved forecasting model for supervised training. Finally, simulation results are compared with other available approaches such as support vector machine and decision tree. The results show the improvement and reduction of error and a significant increase in the accuracy of the proposed method in long-term demand forecasting in the electricity supply chain of Espidan ironstone industries.

Due to the possibility of fault in any industrial system's actuators, using a fault-tolerant control structure to compensate for the fault and maintain the system stability seems necessary. In this paper, the Continuously Stirred Tank Reactor model is evaluated, which has a nonlinear model with temperature outputs and heating inlets of interconnected tanks. An Unscented Kalman filter is used to estimate the model's output dynamics, which has a suitable convergence speed and higher accuracy than other estimators. The nonlinear predictive control approach is used to apply the appropriate heating rate to the system to achieve the desired temperatures for each tank when there is no fault in the system. In the proposed design, to compensate for the fault, a sliding mode observer has been used to identify the fault. When a fault is detected, a fuzzy proportional derivative controller is used to control the system's fault. MATLAB software has been to evaluate the proposed method in different working modes of the reactor model. The simulation results show the good performance of the proposed method to compensate for the fault

In the last decade, to reduce the costs of environmental monitoring, the data aggregation based on the joint dictionary learning and compressive sensing technique in wireless sensor networks has been considered. In this article, a deterministic and non-random sampling design for use in this data aggregation method is presented. This method is based on estimating the amount of mutual information of sensor data and is obtained by sampling all of them in a short part of the data collection round named the training phase. In the next and main stage of the data collection period, only the nodes that provide the most information about the non-sampled nodes are scheduled to sample. Simulation results for real signals show that when the number of sampling sensors comprises still about 25% of the total network nodes, average energy savings of more than 12% can be achieved over a reference sampling method.

Today, the use of distributed generation resources connected to the network has remarkable growth therefore network-connected to the converters must be able to provide services beyond the injection of power into network, including maintaining network stability. In this paper the control and management of energy for battery wind, photovoltaic and diesel connected to a three-phase network by activating the current limitation under unbalanced fault has been introduced. In this method injected currents are limited to a certain amount along the faults Also, the operation mode without tracking the maximum power point for the converter is included. This state is activated during severe faults, when the converter cannot control Maximum power of the system. Therefore, in a microgrid with, the operation of a fuzzy logic-based controller the transformation operation of DC to DC converter control is performed sparsely by two-way battery and distributed generation resources management. Also proportional resonance controller is used due to its proper performance. In fact, with the use of reference current generation, sinusoidal current with a total harmonic distortion (THD) value of less than 5% is injected. The results show that the control strategy along with energy management with multiple energy generation resources is able to limit and maintain DC link voltage within an acceptable range in addition to The existence of an asymmetric fault. Therefore, the fault crossing operation has been performed correctly without the operation of the protection relays and secondly in comparison with microgridsolely with the presence of one photovoltaic production source the process of injecting active and reactive power in the upstream network is more appropriate.

Advances in the digital world are leading us to the development of digital forensic tools. The use of machine learning methods for source printer identification is one of the sub-fields of this area that is being developed. In this paper, a new method for extracting secondary features based on identity vector or i-vector to identify the print source is presented. In the proposed method, the classification process is accelerated only by extracting a low-dimension i-vector vector per page, without the use of optical character recognition (OCR) method, and by eliminating majority voting. Furthermore, the proposed method in extracting features is independent of the type and size of the font and the language of the text. Secondary features are obtained by splitting the document image into smaller patches and modeling the primary LBP features of the dark, border, and light areas in separate spaces. Modeling the primary features of different regions in separate total variability printer space makes it possible to extract class discriminator information from the remaining print texture in the bright area to increase classification accuracy. In this paper, the effect of using the texture of different regions and changing the patch dimensions using the SVM (Support Vector Machine) classifier through simulation has been carefully investigated. The simulation results show that only by refining the basic features of LBP we achieved 99.05% accuracy, which is more than the latest research in this field.

Taking advantage of advances in Nanotechnology, the quantum-dot cellular automata (QCA) has overcome many limitations that complementary metal-oxide-semiconductor (CMOS) had been confronted. Undesirable characteristics such as too many leakage currents limit the CMOS designs in nano dimensions. The idea of designing multiple-valued logic (MVL) systems rather than standard binary has gotten attractive to many designers. The application of MVL in the design of digital circuits offers so many advantages over traditional methods. D flip-flop is a primary sequential circuit in any register. In this paper, a novel quaternary D flip-flop based on introducing quaternary QCA (QQCA) is presented. The structure of our quaternary model is clarified. Also, we have proposed a 4-qubits register by utilizing the presented quaternary D flip-flop. Both circuits got simulated and evaluated by QCASim (quaternary edition). QCASim can illustrate the simulation result in a truth table and a waveform format. Our work got compared with other published works. The simulation results show that our proposed circuit is efficient in terms of latency and energy consumption.

Metal-Semiconductor-Metal photodetectors consist of two metallic contacts and an aperture for photon absorption. An MSM photodetector has low dark current, because one of the contacts is always in the reverse bias region. In this paper, we studied the impact of aluminum (metal contact) characteristics on the dark current, photocurrent, and photo-response of a silicon-based MSM photodetector. Metal work function has a small variation in different deposition processes. Impact of metal work function and layer thickness on the performance of MSM photodetector are studied. To this aim, an MSM photodetector is simulated and the dark current, photocurrent, and the photo-response of the structure are investigated. The analyses show that aluminum work function and thickness have a direct effect on the dark current, photocurrent, and photo-response of the photodetector which can change the structure from a photovoltaic to a photoconductive device if the mentioned parameters are not selected well. Therefore, these parameters should be optimized in the design stage. In this study, for a symmetric MSM photodetector, the optimal work function was achieved to be 4.26 eV. Work functions less than this value result in an ohmic contact between the aluminum layer and the silicon layer, and work functions more than this value result in a Schottky junction with a very low breakdown voltage in the reverse bias region which both regions degrade performance of the photodetector. Moreover, the best aluminum layer thickness was obtained to be 1.1 μm. This thickness leads to the maximized photo-response in the photodetector.

With increasing environmental concerns and reducing fossil fuels, the significance of producing electricity via renewable energy resources and replacing it with conventional fuels is increasing day by day. At the same time, the dependency of renewable energies on environmental conditions makes it challenging. One of these challenges has been managing the energy resources of the hybrid power system. Hence, in this research, a fuzzy intelligent controller has been designed and implemented to manage the energy resources of a grid-tied hybrid power system including solar energy and battery storage in laboratory dimensions. In the present study, by using the Arduino board as an energy management unit in the hybrid power system, tried to provide a basis for the use of renewable energy resources in real dimensions and mass production. The mathematical modeling of the system's equipment is presented and the hybrid power system is simulated using MATLAB software. Moreover, the ability of real-time data monitoring has also been added to the system. Eventually, the capabilities of the proposed smart fuzzy logic controller have been assessed by applying a usual day in springtime. The outcomes indicate that the suggested hybrid power system and the controller can save energy about 60 percent.

In this paper, a dual-beam end-fire Bow-tie antenna for millimeter-wave applications over 57–64 GHz is designed. By introducing a new structure in the decoration of metamaterial slabs in the horizontal plane of the Bowtie antenna, a dual-beam generated at 60° and 120°. The proposed antenna is composed of a 2×3 array of metamaterial unit cells, which leads to a considerable gain enhancement and generates a dual-beam pattern. To create a dual-beam in the E-plane, the array of metamaterial slabs integrated vertically in front of the Bowtie antenna and tilted by 15o with regard to the antenna axis to tailor the radiation beam. These unit cells are capable of creating two resonances that can play an important role to improve the gain in a wide frequency band. The resulting dual-beam radiation in the E-plane has maxima at +60 and 120 degrees with a maximum peak gain of 9.5 dBi at 60 GHz. The final design simulated with a parametric study, and the best configuration provided despite the maintenance of other radiation characteristics of the antenna.Keywords: End-Fire Antenna, dual-beam Antenna, ENZ Metamaterial unit-cell, Millimeter-Wave, High-Gain Antenna.