Electronic-based loads such as computers, smart phones, etc. are fast expanding all around the world. On the other hand, the aging of distribution systems to deliver power to these users results in a low quality of power. To this aim, in this study, renewable energy sources (RESs) connected to networks by smart inverters, are allocated to support networks' weakness related to harmonic distortion, resulting in delivering high-quality power to customers. In other words, solar and wind energy resources are optimally allocated in reconfigurable distribution networks with the aim of minimizing total harmonic distortion (THD). Due to the nonlinearity of the problem, the proposed methodology is then optimized by using the differential evolution algorithm (DEA). The simulation results from a typical 33-bus IEEE test network demonstrate that network reconfiguration in the presence of renewable energies with the interface of smart inverters has an active role in THD compensation, loss minim
Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model
This new method makes it possible to decompose the Complex Cross Ambiguity Function of GNSS signals during malicious spoofing attacks. SAHIL AHMED, SAMER
A new paper in the journal Energies has investigated different methods for estimating state of charge in lithium-ion batteries, a key technology in applications such as electric vehicles. The research has been conducted by scientists from Brazil.
Owing to the acute shortage of electric power in the majority of countries, short-term measures such as installation of Distributed Generators (DGs) have attracted much attention in recent decades. Employment of DGs can provide numerous advantages for the power systems through reduction of losses, escalation of the voltage profile, as well as mitigation of pollutant emissions. However, in case they are not optimally allotted, they may even lead to aggravation of the network operation from different aspects. The aim of this paper is to explore the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced. The salient feature of the proposed strategy compared to the previous works is that it contemplates optimal allotment of DGs under various objectives, i.e. minimization of total network active and reactive power losses, and Cumulative Voltage Deviation (CVD), with different weight values. Furthermore, the impact of enhancem