جستجوی مقالات مرتبط با کلیدواژه "genetic algorithm" در نشریات گروه "برق"

The demand for non-renewable energy sources in power generation is crucial for residential and commercial uses, significantly impacting national development. However, with the depletion of fossil fuels, there is a shift towards renewable energy sources such as solar, water, and wind, which have seen a surge in use over recent decades. In Iran, despite abundant fossil fuel resources, solar energy is vital due to the country's favorable geographic conditions for solar exploitation. This study applies the analytic network process (ANP) and Genetic algorithm (GA) to identify optimal locations for Solar Power Plant Stations in Markazi province, Iran. Key morphological factors considered include slope, elevation, and solar radiation. The research identified the northwest and northern parts of Markazi province as the most suitable for solar photovoltaic systems, primarily due to their simpler topography. Using a genetic algorithm, which outperformed the ANP, it was found that about 24,000 km² in these areas are apt for solar power facilities, categorized into highly suitable (2,429.312 km²), moderately suitable (16,818.49 km²), and suitable (5,029.007 km²). Saveh showed the highest potential, while Ashtian, Khondab, and Shazand had the least. These findings provide crucial insights for stakeholders looking to develop solar energy projects in Markazi province.

In recent decades, because of some main and principle world problems such as increasing the population, global warming, climate changes, and fossil fuel sources reduction, the using of renewable energies has impressively increased that can solve and reduce the caused problems by traditional power plants, and also can control power system the important indexes such as losses, voltage drop, transferring capacity. Reactive power has an important role in controlling and minimizing of losses, the optimal distribution of reactive power in presence of Distributed generation (DG) units in distribution networks is an important and key problem. In this paper, for uncertainties modelling of DG units and optimizing the reactive power, the statistical-quality based Taguchi method and Genetic algorithm are used, respectively.  The simulation of this paper is checked and done in MATLAB and MINITAB using IEEE 57-bus standard network, and simulation results show 5.5 MW reduction of the distribution network losses.

In electrical distribution networks, inefficiencies and instabilities often arise from inductive loads like motors and transformers, which exhibit a lagging power factor. This reduces system capacity, increases losses, and can lead to lower voltage levels. To address these issues, integrating parallel capacitors proves effective, enhancing the power factor, improving voltage profiles, and reducing overall system losses and costs. This research explores the optimal placement of parallel capacitors within a distribution network to manage reactive power effectively, thereby minimizing losses and improving voltage stability and system efficiency. Utilizing DigSILENT Power Factory and MATLAB, a genetic algorithm optimizes the location and sizing of capacitors in a 33-bus distribution network, considering scenarios with and without distributed generation (DG) and the impact of harmonic currents. The study finds that incorrect sizing or placement of capacitors can worsen voltage deviations when higher harmonic levels are present. However, the optimization method accurately determines the best parameters for capacitor installation, ensuring compliance with voltage and harmonic constraints. Deploying more than three to four capacitors does not significantly affect outcomes, while a single busbar capacitor often fails to meet operational standards. In conclusion, strategic capacitor placement and sizing can significantly reduce losses, enhance voltage stability, and mitigate inefficiencies caused by inductive loads. Attention to harmonics is crucial to avoid negative impacts on the network. This approach offers a replicable framework for similar optimizations in other distribution systems, advancing smart grid technology implementation.

This paper proposes a new linearized mathematical model to solve integrated cell formation and job scheduling problem. The model aims to minimize the exceptional elements, voids and the make-span of the jobs. The results of test problems show that the proposed model is very effective to obtain best solutions for small sized problems in reasonable computation times. However, due to the NP-hard nature of the considered problem, the best solutions couldn’t be obtained in acceptable times for large sized test problems whereas the real-life applications of the problem addressed here are often much larger in size. To meet the requirement of solving larger sized problems, Genetic Algorithm, which is, today, considered as one of the artificial intelligence and machine learning technique and Marine Predators Algorithm as a new and a nature-inspired metaheuristic, are proposed. The success of the algorithms was investigated and compared. The test results reveal the fact that the Marine Predators algorithm with optimized parameters has a high potential to solve real life problems. At last, an attempt is made to re-design an existing real-life production system by the proposed algorithms. Eventually, a considerable improvement is obtained on performance compared to the current situation of the system.

This study presents a four-element compact Multi-Input Multi-Output antenna with enhanced isolation for applications with three bands. The four-port antenna elements have hook-shape multiband monopole elements with 50 ⦠microstrip feed lines and are placed such that they are perpendicular to one another to enhance the act of the MIMO system. Simulation and measurement consequences demonstrate that the antenna operates at a consistent gain and radiation patterns at the major frequency bands of 2.11-2.47 GHz, 3.14-3.54 GHz, and 5.15-5.85 GHz with S11<-10 dB. To verify the claimed MIMO antenna performance, data in the format of radiation patterns, peak gain (4.9,5.2,5.8 dB), diversity gain (DG) (9.95,9.92,9.92 dB), envelope correlation coefficient (ECC) (0.005,0.003,0.002), TRAC, channel capacity and MEG ratio are retrieved. These specifications of the suggested antenna make it a suitable candidate for WiMAX, Bluetooth, and WLAN uses. Additionally, the suggested antenna outperformed current work by providing a superior balance of size, bandwidth, and several performance characteristics.

Distribution systems pose a significant challenge within the power grid, primarily due to their high current, low voltage, and comparatively high ohmic resistance compared to transmission and sub-transmission systems.This results in substantial power losses, necessitating the need for effective mitigation strategies.To address this issue, a wide range of methods and algorithms have been proposed and continuously developed. Over the past half-century, reconfiguring the distribution network has emerged as a cost-effective and straightforward approach to reducing distribution losses. Distribution system reconfiguration has been extensively studied, with each study aiming to achieve distinct objectives. Additionally, numerous studies have explored the dynamics of distribution system reconfiguration, evaluating and comparing various approaches. This study comprehensively assesses both static and dynamic methods of reconfiguring distribution systems and introduces a novel dynamic reconfiguration technique. Unlike traditional methods that rely on real-time or hourly load models, this approach utilizes a load model to address the dynamic reconfiguration problem. Simulations were conducted on the well-established IEEE 33-bus test system, employing MATLAB software in conjunction with a genetic algorithm to minimize losses and optimize voltage profiles. Based on the simulation results, this novel dynamic reconfiguration method demonstrated superior performance compared to previously employed methods. It effectively reduced power losses and enhanced the voltage profile, demonstrating its potential for improving the overall efficiency of distribution systems.

‎ The development of digital technologies has improved the transfer of data over the Internet in recent years. Image encryption is a technique to ensure security in information transfers. The current paper presents an evolutionary model on the basis of a hybridization of DNA biomolecule operators and the LS2 Map ‎chaos function for encryption of image. The model proposed here includes three stages. In the initial stage, the MSC (Magic Square Chaotic) algorithm and a secret key are utilized with the SHA-256 algorithm t‎o determine the initiating the LS2 Map function value, which is then employed to manipulate the pixels of the image. Then, DNA biomolecule operators and the chaos function are used for propagation. Additionally, the previous stages process is iterated with the starting population of the genetic algorithm in the third stage. Afterward, the optimization is carried out through genetic algorithm operators. The results indicate ‎that the introduced model is superior to other rivals. Furthermore, as for the high level of entropy obtained, the model exhibits strong resistance to common attacks.‎

Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using segmentation and ensemble clustering to solve this issue is a key aspect that has received less attention in previous research. In this study, an algorithm based on the selection and combination of the best segments created from a time-series dataset was developed. In the first step, the dataset was divided into segments of equal lengths. In the second step, each segment is clustered using a hierarchical clustering algorithm. In the third step, a genetic algorithm selects different segments and combines them using combinatorial clustering. The resulting clustering of the selected segments was selected as the final dataset clustering. At this stage, an internal clustering criterion evaluates and sorts the produced solutions. The proposed algorithm was executed on 82 different datasets in 10 repetitions. The results of the algorithm indicated an increase in the clustering efficiency of 3.07%, reaching a value of 67.40. The obtained results were evaluated based on the length of the time series and the type of dataset. In addition, the results were assessed using statistical tests with the six algorithms existing in the literature.

In this paper, an adaptive controller is presented to control a quadrotor, whose parameters are extracted from the genetic algorithm optimization method. The advantage of this method is that based on the system states, the control coefficients are calculated online. For this purpose, a function between system states-space and control coefficients is obtained. From the database collected from the genetic algorithm optimization method, the parameters of the control coefficient function are obtained using the least squares method. The stability of the proposed controller is proved by the Lyapunov method. Finally, the performance of the proposed controller is compared with the PID controller, which is widely used in the literature. The results show that the proposed approach is promising.

The classification of emotions using electroencephalography (EEG) signals is inherently challenging due to the intricate nature of brain activity. Overcoming inconsistencies in EEG signals and establishing a universally applicable sentiment analysis model are essential objectives. This study introduces an innovative approach to cross-subject emotion recognition, employing a genetic algorithm (GA) to eliminate non-informative frames. Then, the optimal frames identified by the GA undergo spatial feature extraction using common spatial patterns (CSP) and the logarithm of variance. Subsequently, these features are input into a Transformer network to capture spatial-temporal features, and the emotion classification is executed using a fully connected (FC) layer with a Softmax activation function. Therefore, the innovations of this paper include using a limited number of channels for emotion classification without sacrificing accuracy, selecting optimal signal segments using the GA, and employing the Transformer network for high-accuracy and high-speed classification. The proposed method undergoes evaluation on two publicly accessible datasets, SEED and SEED-V, across two distinct scenarios. Notably, it attains mean accuracy rates of 99.96% and 99.51% in the cross-subject scenario, and 99.93% and 99.43% in the multi-subject scenario for the SEED and SEED-V datasets, respectively. Noteworthy is the outperformance of the proposed method over the state-of-the-art (SOTA) in both scenarios for both datasets, thus underscoring its superior efficacy. Additionally, comparing the accuracy of individual subjects with previous works in cross subject scenario further confirms the superiority of the proposed method for both datasets.

Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results. The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier. The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features. Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index.

Solar cell efficiency considers an important part of the PV system, the parameters (Io, IL, n, Rs, and Rsh) of solar cell is the main part that effected on efficiency. The Matlab simulation program was used to estimate the three parameters' optimization values and evaluated by the Fminsearch method, they calculated for solar cells measured from 0oC to 100oC for seven temperatures, then make comparing for the results between the Genetic Algorithm method with Neural Network Algorithm. This paper establishes the results are frequently in GA was better than NNA, with the Io being 3.0992 e-7 and IL being 3.8059 found by GA. GA is good if they have the same population size and number of iterations. The value of the objective function (fval) in GA is 0.002856 but in NNA is 0.005518. And also second objective function (fvaltemp) in GA is 0.1035 with a 0.1069 value in NNA. From the side, the execution time considers in the Fminsearch method is less than NNA and GA that being 64.9 s, 781 s, and 289 s respectively.