نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال بیستم شماره 3 (پیاپی 72، پاییز 1401)

Renewable energies outfitted with low latency assets as wind turbines and photovoltaic exhibits give significant adverse consequences through power framework dynamic protections. For this issue, in view of fostering a high voltage direct current (HVDC) interface, a versatile Neuro-Fuzzy-based damping regulator is introduced in this paper for working on unique execution of low inertia resources associated with power frameworks. The created power framework comprises of various age sources including seaward and inland wind turbines (WTs), photovoltaic exhibits (PVs) and limited doubly fed induction generators (DFIGs) which are incorporated together through an interconnected framework. For this situation, thinking about various functional and innovative conditions, damping execution of proposed ANFIS plot is assessed. The proposed plot is a non-model-based regulator which utilizes the benefits of both neural and fluffy rationales together for giving a quick and secure design of damping regulator through continuous recreations. To research ANFIS plot through genuine cases, considering a commonplace microgrid comprises of various low-latency assets (for example WT, PV, DFIG), the framework damping exhibitions through hamper occasions are assessed. Recreation results demonstrate viability and effectiveness of the proposed plot for damping dynamic motions of low inertia resources with high damping proportions with respect to extreme issue occasions.

One of the new and growing equipment in modern power networks is solid state or power electronic transformer. These types of transformers are based on power semiconductor switches and high frequency transformers. Compared to traditional transformers, it has several capabilities such as the ability to operate with input voltage variations in amplitude and frequency, automatic regulation of output voltage and input power factor correction. The investigated transformer has the ability to transfer power in both directions and has three stages, including the rectifier, the middle stage and the inverter stage. This transformer has a large number of semiconductor switches and its modeling, analysis, design and simulation is difficult and complex. In particular, real-time simulation of these transformers with conventional models is not possible. In these cases, the use of averaging theory seems to be the appropriate solution. In this paper, the averaging theory is applied to a solid-state transformer and its modeling is done in a simple and powerful way with the ability to study real-time, transient and steady states performance. The proposed modeling includes differential equations and equivalent circuits and offers an integrated transformer model with the ability to study the interaction between stages as a part of power system. The presented models are used in simulation of smart grids, DC microgrids and connection of distributed generation sources to the grid, as well as analysis and design of solid-state transformer behavior in areas such as renewable energies and electrical transportation. In addition to the proposed modeling, the closed-loop control structure has been implemented for all three stages. Transformer simulation is performed by implementing differential equations in SIMULINK/MATLAB software and verified the proposed model.

In this paper by using the radiation slots, the half power beam width (HPBW) of the SIW horn antenna is reduced in E-plane and the radiation pattern is improved. In addition to keeping the dimensions of the structure constant, these slots can have a significant effect on the characteristics of the antenna. Also placing the reflector plate at a suitable distance from aperture and slots leads to improve side lobe levels (SLLs) and front to back ration (FTBR). Then, to improve the impedance matching and increase the bandwidth of the antenna, the dielectric of the structure is completely removed and non-radiation slots added to the upper and lower plate of the antenna. Removing the insulation, increasing the bandwidth of the antenna compared to a conventional SIW horn and adding radiation slots significantly improves the gain of the antenna. The simulation results shows that the proposed antenna in this paper covers the frequency range of 27.2 GHz to 28.3 GHz and its gain changes between 10.1 dBi to 15.3 dBi with 98% radiation efficiency in this range. Finally, in order to increase the gain of the antenna, a two-dimensional array of the proposed antenna with suitable feeding structure is designed in the H-plane.

Under the smart power systems, determining the amount of Demand Response Resources(DRRs) potential is considered as a crucial issue due to affecting in all energy policy decisions. In this paper, the potential of DRRs in presence of cooling and heating equipment are identified using k-means clustering algorithm as a data mining technique. In this regard, the energy consumption dataset are categorized in different clusters by k-means algorithm based upon variations of energy price and ambient temperature during peak hours of hot (Spring and Summer) and cold (Autumn and Winter) periods. Then, the clusters with the possibility of cooling and heating equipment’s commitment are selected. After that, the confidence interval diagram of energy consumption in elected clusters is provided based upon energy price variations. The nominal potential of DRRs, i.e. flexible load, will be obtained regarding the maximum and minimum differences between the average of energy consumption in upper and middle thresholds of the confidence interval diagram. The energy consumption, ambient temperature and energy price related to BOSTON electricity network over a six-year horizon time is utilized to evaluate the proposed model.

Nowadays, due to the advances made in power electronics and the desire to use renewable energy resources, micro-grids have been developed considerably. One of the challenging operation modes of Micro-grids is named islanded mode, where the control of power and frequency is a challenging problem. Many distributed energy resources are operated based on electronic power converters and these converters have no inertia unlike synchronous generators, as a result, the issue of power and frequency control in micro-grids is considered as a serious problem. This issue will cause severe frequency fluctuations following to power changes which can lead to system instability. In this paper, first a sample micro-grid is simulated and modeled in synchronous reference frame then using the idea of inertia in synchronous machines, a novel control method with the capability of damping enhancement during system transients is proposed. The control scheme utilizes the idea of virtual inertia injection during the power and frequency fluctuations based on the synchronverter model. Finally, the effectiveness of the proposed method is verified using a set of the time domain simulations carried out in Matlab/Simulink software in an inverter based multi-source micro-grid operating in the island mode, and the results are compared with the vector control method implemented in the rotating reference frame under different scenarios.

Continuous changes besides increasing complexities of modern power systems cause emergence of new challenges in modeling of power systems. Nowadays, with development of wide-area monitoring systems, data from the overall system can be used to identify and estimate model for power systems. This paper focuses on power system stabilizer tuning using the derived measurements-based model. The derived low-order model includes dynamic characteristics of inter-area dominant modes and can be used to design the damping controller and evaluate its effectiveness in power system studies. The controller can be reinterpreted as power system stabilizer and may be designed in two different methods of i) robust and ii) Ziegler-Nichols. The numerical results show the effectiveness of this approach in improving the small signal stability behavior of two-area 4-machine system using measured data, obtained from time domain simulation in MATLAB software.

The purpose of designing and manufacturing prosthetic organs is to create their maximum behavioral similarity to human organs. The aim of this paper is to improve the robotic arm control via Model Reference Adaptive System (MRAS) based on Lyapunov theory using EMG data classification. In this paper, human arm is modeled with a robot with two degrees of freedom. The proposed control method is MRAS. The outcome of this research is a robotic arm with MRAS, using the classification of electromyogram (EMG) data recorded from human arm movements, results in proper tracking of the reference signal, less overshoot and steady-state error compared to the conventional PI controller. For this purpose, using two electrodes, EMG data is collected from the anterior deltoid and middle deltoid muscles of the arm of five female athletes and by performing two movements of abduction and flexion of the arm. Then, after eliminating noise, integral of absolute value (IAV), zero crossing (ZC), variance (VAR) and median frequency (MF) are extracted. Then, classification is done by linear discriminant analysis (LDA) method to detect movements based on data characteristics. Finally, the proposed controller and model are designed according to the EMG characteristics to achieve the proper control response and the appropriate command signal is sent to the controller to perform the corresponding movement. The results and the values of the obtained errors show the conformity of the model and controller behavior with the predefined movement pattern.