International Journal of Smart Electrical Engineering
Volume:12 Issue: 4, Autumn 2023

Every year, a large part of the energy produced by power plants is lost on the way to the consumer in the entire power system, and a significant share of these losses is related to the distribution networks. One of the most important factors affecting losses in distribution networks is the existence of load imbalance due to the random consumption of subscribers and the lack of equal distribution of subscribers among different phases of the network. The main challenge in this field is the lack of an effective method to study the effect of load balancing on the losses of a large-scale network. In this regard, this paper studies the effect of load balancing on network loss reduction by applying the concept of clustering and representative feeders. the effectiveness of the proposed method has been proved by the simulations on the low voltage distribution network of Alborz province, and the results show the high efficiency of the proposed method.

The appropriateness of the agricultural economy is very effective in sustainable food security. The appearance and shape of agricultural products change in different periods. The correct classification of the product in terms of quality after harvest affects the economy of farmers. Today, deep learning classifiers have greatly contributed to the correct classification of product quality. But the database challenges and the same conditions of the database in the training and testing phase affect the classification accuracy. The purpose of this article is to classify the quality of tomatoes in the challenging conditions of the database, including crowded backgrounds, noise in the image, leaves of the same color as the fruit in the image, and the similarity of growth stages. For this purpose, 3 databases with different challenges have been used in the stage of classification training and testing. In this article, the aim is to classify the quality of tomatoes into 3 classes ripe, unripe ,and semi-ripe using Efficientnet deep learning classifier. According to the conditions of the database, the first three processes of noise removal, image contrast improvement ,and image segmentation have been applied to the images. The results of the evaluation of the proposed method show the proper performance of EfficientnetB5.

In modern power networks, once the restructuring of production units is done, traditional power plants will operate as virtual power plants (VPPs), which are actually a collection of distributed generation (DG) units and energy storage systems (ESSs) that form an integrated power plant. Commercial VPPs can replace the current traditional power plants in the near future, because they have many advantages such as organizing distributed energy resources (DER) and hydrogen and electricity storage systems. Considering that energy management and planning of DER resources in VPP have challenging issues, therefore, thoughts such as changes in instantaneous power generation, consumption, energy price and availability of system components should be taken into consideration, so that simulations and future research with problems will not accompanied. Since microgrids have the ability to monitor and control real-time power in power grids, determining the number of DER resources in VPPs is deliberated essential in order to reduce planning costs. For this purpose, in this paper, the optimal sizing of DERs is done using speed particle swarm optimization (SPSO) algorithm. In proposed optimization algorithm, the coefficients c1 and c2 are not constant and is changing according to the number of iterations, which makes the search in the problem solving space more efficient and its convergence is improved by 26% compared to the traditional PSO algorithm. Consequently, the number and sizing of solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), electrolyzer, hydrogen storage and battery resources in a 20-year time horizon will be achieved with the lowest cost.

To address computational complexity in heating, ventilating, and air conditioning systems, two general reduced multiple model control designs based on gap metric, stability margin, and model order reduction are proposed. The difference between two designs lies in the sequence of implementing model order reduction and multiple model techniques, resulting in distinct control approaches. As the number and location of reduced multiple models are not necessarily the same in two cases, the selected models will also be different. This could make one approach preferable to another in terms of closed-loop performance. Therefore, we introduce a model selection criterion to predict the most suitable approach for improving indoor thermal comfort and air quality in considered system. This criterion is based on maximum gap metric, maximum stability margin, and number of nominal models. Finally, two new approaches called OR-MM and MM-OR and a new criterion called MSC are proposed. To validate the effectiveness of our method, we conduct computer simulations that demonstrate their achievements.

Misdiagnosis of skin diseases is a common occurrence. Psoriasis is a skin disease that has many similarities with other diseases, and its incorrect diagnosis causes many problems in the treatment process. Misdiagnosis of this disease causes doctors to face problems during treatment. The lack of images of the disease and the database of skin diseases reduces the diagnosis and the coordination of diagnostic methods, therefore, diagnosis using different images is very useful.
Today, diagnosis methods using deep features in medical images have received much attention.
Artificial intelligence is one of the automatic methods of diagnosis. These methods can detect new data entering the system and keep it in memory. Therefore, in this article, two different groups of data have been identified using deep features based on artificial intelligence.
In this method, the data of the first group in the form of training and testing and the data of the second group are studied gradually. If they are correctly identified, the next 0.1 chunks of data enter the network without testing. If they are wrongly recognized, they enter the training section and this reduces the training process. In this work, by training 20% of the data, i.e. the first 10% and the fourth 10%, there was no need for training because the accuracy was not less than98%.
In this article, deep features of images were first extracted using convolutional neural network, and then psoriasis and eczema were diagnosed with average accuracy of98.3%and sensitivity of 97.9% in skin images using artificial intelligence.

In the present study, the aim is to model and optimize the photovoltaic/thermal system (PVT) to achieve the highest thermal and electrical efficiencies by considering the physical characteristics. To model the proposed system, the governing relations were derived by considering the desired variables. Then to use the modeling, the validation of the code was done with a valid source, for which the variables of cell surface temperature, a bottom surface, and output fluid were used. After validation, it was observed that the proposed model has good accuracy to achieve the desired goals. To achieve maximum output power by the cell, open circuit voltage and short circuit current for this system was calculated for one day that has the highest radiation intensity, for which the highest voltage and current are 9.3 V and 6.7 A, respectively. Then, after extraction of voltage and short circuit current for the studied cell, the amount of thermal and electrical efficiencies was determined to be 40% and 10%, respectively. In the next step of this research, optimization is performed to achieve the highest efficiency, which is 67% for this system.

Produced power of wind, solar, run of the river, ocean thermal, tidal and wave power plants is respectively, dependent on wind velocity, sun radiation, river flow, temperature of ocean upstream, period & height of waves, tidal level or tidal stream velocity. Due to wide change in these quantities, produced power of these renewable resources changes a lot over time. As the penetration level of renewable resources in electric network is increased, reliability and other aspects of electric network may be affected that should be studied. Analytical method is not suitable to study uncertainties of output power of renewable resources in reliability analysis of electric network with these renewable power plants. Thus, the current research suggests Monte Carlo simulation method to study effect of renewable power plants on reliability indices. Renewable power plants studied in the research are wind turbines, solar farms, wave energy converters, run of the river power plants, both types of tidal units, and ocean thermal energy conversion systems. Numerical studies are performed on test electric networks, to study these renewable resources impact on reliability indices of electric networks with renewable power plants. It is concluded from numerical outcomes that these renewable power plants improve reliability performance of electric network. However, due to the variation of renewable resources, the impact of renewable power plants on reliability performance of the electric network is less than the conventional units with the same capacity.

The main cause of oscillation during the movement of the vehicle is the unevenness of the road. Therefore, in order to maintain the stability of the car in swing states, the suspension system plays an essential role. Therefore, the active suspension system is used to replace the conventional passive suspension system, to improve comfort and smoothness. To reduce the displacement of the spring mass in the active vehicle suspension system, a high-order sliding mode controller is proposed in this paper. Uncertainty of system parameters, nonlinear characteristic of damping and spring, load changes and unknown path disturbance are estimated by disturbance observer. The controller only needs the information of the spring mass state variables and therefore does not need separate sensors to measure the suspension mass state variables. Particle swarm optimization algorithm has been used to determine the control parameters. The efficiency of the proposed method has been shown using simulation in MATLAB software and the results have been compared with the passive suspension system.

In this paper, a novel device, namely heterojunction electron-hole bilayer tunnel field effect transistor (HJ-EHBTFET), is proposed which outperforms conventional tunnel field effect transistor (TFET) in terms of electrical performance. The use of lattice matched InAs/Al0.6Ga0.4Sb material combination results in a broken band gap configuration, making it highly suitable for high speed ultra-low applications, as it requires smaller gate bias for the onset of tunneling. The impact of critical design parameters on the device performance is comprehensively investigated. The proposed device utilizes electrical doping instead of physical doping for the creation of tunneling junction, which effectively addresses the problem of low solubility of dopants in heavily doped III-V materials. The top gate and bottom gate workfunction are critical design parameters that effectively modulated the electrically induced charges at the tunneling junction and consequently, affect the tunneling rate. In order to obtain the lowest possible transition voltage for the onset of tunneling, a variation matrix of threshold voltage variation is computed as a function of gate electrode workfunction. Through this process, a step-like behavior from off-state to on-state has been achieved, with a subthreshold swing of 3 mV/dec and on/off current ratio of 5.8×1012, thereby paving the way for the design of low-power high-speed digital computing systems.

This paper proposes a sub-optimal Extended State-Dependent Differential Riccati Equation (ESDDRE) controller for nonlinear Reaction-Advection-Diffusion (R-A-D) Partial Differential Equation (PDE) systems with multiple delays. A State-Dependent Riccati Equation (SDRE) is a nonlinear version of Linear Quadratic Regulator (LQR) in optimal control and it is used to analyze nonlinear optimal control problems. Instead of the linearization or the Jacobin procedure, the ESDDRE technique applies a State-Dependent Coefficients (SDC) for parameterization to construct an Extended Pseudo-Linearization (EPL) representation. All of the multiple delays sections in this presentation can be located in the system matrices and input vectors. The control effort of ESDDRE method is derived based on the Hamiltonian equation and also cost function according to the PDE systems. In addition, the L_2 stability is guaranteed by Poincaré inequality and as well as Lyapunov function regarded on the ESDDRE control strategy for the closed-loop system. The simulation results for the nonlinear R-A-D partial differential equation with one and two constant delays indicate that the proposed ESDDRE controller technique is efficient.