International Journal of Smart Electrical Engineering
Volume:10 Issue: 1, Winter 2021

Energy supply has currently become one of the most strategic and critical issues of human societies; as many political, economic and social challenges have been directly or indirectly related to the energy crisis over the past few decades. Given that the energy sector has the greatest impact on increasing carbon dioxide emissions, the need to develop renewable energy to reduce the risks of pollution and climate change is highly significant. In Iran, with more than 85000 MW of installed capacity of the electricity network, only about 859MW of electricity (about 1% of total capacity) is extracted from renewable resources. There are various obstacles to the development of renewable energies, which is generally dependent on the economic and technical conditions, and one of the most important challenges is the lack of social acceptance and lack of awareness of citizens and energy stakeholders. The most important benefits of renewable energies includes increasing energy security, reduced global warming, economic growth, job creation, increased per capita income, increased social justice and environmental protection. In this article, the opportunities and obstacles to the use of renewable electricity sources in Iran are described and with the help of Vensim software, the status of renewable energies until 1430 is simulated and predicted based on existing conditions and different scenarios. Suggestions for expanding the use of renewable energy have been mentioned eventually.

Due to increased energy consumption in cities and industrial areas, many technical and economic issues arise for designers and beneficiaries of the system. Currently, unfortunately, in choosing the right place for installing arrester, the traditional methods are used and without considering the economic issues and the reliability of the arresters, they are used and this equipment is installed and putted into operation on this basis. In this paper, an intelligent optimization method called the Imperialist Competitive Algorithm (ICA) is proposed to select the appropriate protection plan. Considering the combined economic and technical factors, the location of the lightening arrester is determined in a distribution network with the aim of reducing risk Insulation. The proposed method has been implemented on one of the long feeders of Mazandaran Electricity Distribution Network, which results in identifying the efficiency of the proposed algorithm in comparison with traditional methods in the optimal location of the arrester.

This work aims to diagnose depression and isolate healthy people from depressed patients based on EEG brain signals via the k-nearest neighbor algorithm (KNN) and using 10-fold cross-validation. Five regular frequency bands (Gamma, Beta, Alpha, Theta, and Delta) were utilized from the signals. Band power and median band frequency were extracted by Welch’s periodogram method as features. After classification, the highest accuracy gained by using frequency features in the left hemisphere was from the Alpha and Beta waves which resulted in 100% output (p <0.05), and as for the right hemisphere highest accuracy was for the Gamma, alpha, and Beta oscillators, which also resulted in 100% (p <0.05). the lowest accuracy was assigned to the Delta band. In general, combining the two hemispheres boosted the accuracy.

This study investigated the effect of distributed generation resources and demand-response program on the placement of charging/discharging stations and optimal exploitation programming of electric vehicles in a distribution network. Effective factors in the sitting of stations and optimal charge/discharge power in stations are a combination of technical and economic parameters. Minimization of network losses, minimization of voltage loss in feeders, smoothing network load curve, and THD reduction were assumed as technical parameters. As to the economic scope, the placement of stations and charge/discharge power were considered the most effective parameters. In other words, the costs of charging/discharging operations needed to be minimized in the stations to reach the lowest costs spent on purchasing power. A price-based demand-response program was incorporated into the simulations to manage loads on the customer side and smooth the load curve. We implemented genetic, particle swarm optimization, and imperialist competitive hybrid meta-heuristic algorithms to find the optimum operating point. We performed simulations in an IEEE standard 69-bus network. The problem was solved using the former hybrid algorithm, and optimal sites of stations and exploitation program of charge/discharge were specified. This study evaluated the effects of renewable energy resources and price-based demand-response program on the optimal placement of stations and optimal exploitation program of stations. Furthermore, it addressed the effects of an increase in the number of stations and a rise in charge/discharge capacity.

Quantum-dot cellular automata (QCA) is a new and very attractive technology for implementing logic gates and digital circuits at the nanoscale. This technology has very attractive and amazing features such as: low area, high processing speed and low power consumption. Over time, with the advancement of science and technology, there is hope that QCA technology will replace today's VLSI technology. Minority gates are one of the most important elements in digital circuit design. In this paper, a smart four-input minority gate is presented and it is the first time that a priority four-input minority gate is proposed. The proposed minority gate architecture is evaluated and simulated using the QCADesigner tool version 2.0.3. The results show 100% accuracy. By using this type of gates, the hardware required to design QCA circuits can be significantly reduced.

This paper, firstly presents a model of solar cell is built using MATLAB SIMULINK and P-V, I-V & P-I characteristics are studied for various values of irradiance & a constant temperature, And then used of Genetic Algorithm (GA) for maximum power point tracking (MPPT) of Photovoltaic (PV) system using the direct control method. The main objective of this paper is to find out that optimal angle, which is used for positional control of solar module for optimal power tracking and also the main contribution of the proposed scheme is the elimination of PI control loop which normally exists to manipulate the duty cycle. Simulation results indicate that proposed controller outperforms the others method for all type of environmental conditions. For efficient utilization of solar energy, the solar PV system must be able to track MPP and extract available maximum power in real-time. Thus, it becomes necessary to properly interface the PV module with a fixed load.