International Journal of Smart Electrical Engineering
Volume:11 Issue: 3, Summer 2022

The purpose using capacitors in distribution networks is to reduce the total losses of the network. Capacitors help regulate the power factor and voltage in the electrical distribution system, and can be controlled remotely, in and out of the system. Capacitor placement depends on the objective function, which is usually single objective or multi objective. In this paper, the amount of capacitor at minimum load is determined using a genetic algorithm. The calculation is done at peak load to determine the sensitivity of power losses. By using this method, the increase of the voltage caused by the lead phase of the system is prevented in the minimum load. A multi-purpose objective function to simultaneously reduce losses and improve the voltage profile of the optimal capacitor size in each section is detected by a genetic algorithm. To show the efficiency of the method, the capacitor placement results are compared using DIGSIENT software.

Liver cancer is one of the most common cancers that causes many deaths every year. In recent years, the risk of men and women getting liver cancer has increased by 40% and 23%, respectively. In order to identify a tumor in the liver, segmentation is performed on CT images. The use of data fusion methods in data mining techniques is one of the most practical methods to improve accuracy, which also has many applications in the field of medical image processing. Correct and efficient diagnosis of liver abnormalities leads to a significant reduction in human error and a more accurate diagnosis by physicians. This requires the use of methods based on automatic and semi-automatic detection. Combining clustering methods and considering cluster uncertainty is an appropriate tool in solving clustering problems in medical image processing, especially cancer diagnosis. The proposed method, in addition to having high accuracy, has a high convergence speed.

The most common way of communication between humans is the use of speech signals, which also includes the person's emotional states. Bionic wavelet transform entropy has been considered in this study for speaker-independent and context-independent emotion detection from speech. Bionic wavelet Transform decomposition, using wavelet type Morlet, is used after preprocessing and Shannon entropy in its nodes is calculated for feature selection. In addition, prosodic features such as the first four formants, jitter or pitch deviation amplitude, and shimmer or energy variation amplitude besides MFCC features are applied to complete the feature vector. Support Vector Machine (SVM) is used to classify multi-class samples of emotions. 46 different utterances of a single sentence from the Berlin emotional speech dataset are selected to be analyzed. The emotions that have been considered are sadness, happiness, fear, boredom, anger, and normal emotional state. Experimental results show that proposed features can improve emotional state detection accuracy in the multi-class situation.

Routing in a wireless sensor network is a very challenging task that allows nodes to be routed to transmit information from source to destination. As a result, optimal energy consumption is an important goal for designing a routing algorithm. In addition, due to the wireless communications and adverse environments, it is very important to ensure the security of communication links. In this research, a secure routing method for clustered and heterogeneous wireless sensor networks is presented. The proposed secure routing method consists of three phases: startup phase, inter-node routing phase and communication security phase. In the startup phase, the base station loads the system parameters and encryption functions into the memory of the sensor nodes. In the routing phase between cluster head nodes, the cluster head nodes calculate their score for rerouting the path request packet (RREQ) based on the information in the packet. If their score exceeds a threshold, they will redistribute the RREQ package. It should be noted that the score of cluster head nodes is calculated based on four parameters: the distance of the current cluster head node to the destination, the residual energy of the nodes, the quality of the communication link and the number of steps. The proposed secure routing method is implemented using NS2 emulator. Then, the results are compared with SMEER and LEACH-C routing methods. Experimental results indicate that the proposed routing method improves end-to-end delays, efficiency, energy consumption, packet delivery rate (PDR) and packet loss rate (PLR).

This paper presents a two-stage stochastic model for the management of microgrids. In the proposed model, the uncertainties related to the generation output of wind turbines, the consumption load, and the electrical energy price have been taken into account. The presented two-stage problem is modeled as a mixed-integer linear programming (MILP) problem and solved by the CPLEX solver of the GAMS software. In the first stage, the operation areas of individual microgrids are determined. To this end, the considered model is implemented on the 118-bus IEEE distribution system and the security constraints of the distribution system are considered. In the second stage, the microgrid operation problem is solved considering the microgrids' operation area. The second stage is solved as single-objective and multi-objective problems separately. Objective functions in the multi-objective case include the operating costs and the amount of pollutant emissions. The results show that an increase in the operating costs due to the reduction in the amount of emissions in the bi-objective case. It is worth noting that the multi-objective model provided in this study is solved using the fuzzy and the ε-constraint methods separately. The comparison indicates a more reduction in the operating costs in the ε -constraint method than the fuzzy method. However, the reduction of emissions in the fuzzy method is higher than that of the ε-constraint method. Further, more investigations prove the effectiveness of the DRP on the correction of the demand curve and the reduction in the operating costs.

Today, energy carriers are addressed as one of the main pillars of energy generation due to the increased growth of industries in developed and developing countries. According to the global view, increased consumption of energy carriers has increased the production of greenhouse gases, the global average temperature, resulting in the faster melting of ice in Antarctica. Moreover, increased pollution due to consumption of fossil fuels for electricity generation is causing numerous problems for industrial cities, photochemical smog, and numerous lung diseases for humans and often living organisms. Due to the above reasons, using methods to minimize energy carriers consumption and applying renewable energy in order to reduce the extent of generated pollution is highly important. Using energy HUB systems might help manage energy resources properly and minimize the extent of pollution. This article has proposed a smart management system of an energy hub that includes an input (consisting of fossil fuels, renewable energy, and electricity grid) and output (consisting of consumers). The proposed system can manage energy carries and maximize the use of renewable energies, and it can absorb high levels of generated CO2 and transform it to gas energy carries in addition to save energy. Such an approach has reduced CO2 level inside the energy hub, and reduced the total cost of the system and this is because a part of gas fuel needed by the system is supplied. This article has shown that using such a system reduces the cost imposed by system pollution by 9.08%.