Power Quality Improvement of a Hybrid Renewable Energy Sources Based Standalone System Using Neuro-Fuzzy Controllers

Chen, Hanxin; Miao, Yuzhuo; Chen, Yongting; Fang, Lü; Zeng, Li; Shi, Jianhui

doi:10.13052/jwe1540-9589.2022

Cited by 18 publications

(9 citation statements)

References 16 publications

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“…To determine the optimal location and capacity, one SVC and TCSC are installed, which are considered as a sequence with three cells for the optimization problem, where the first two cells represent the location (SVC bus number and line number for the TCSC) and the third cell represents capacity. According to the generator bus model for the SVC, its optimal reactive capacity is calculated using the power flow [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The capacitance range of the SCV is between −50 to 50 MVAr.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

et al. 2021

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Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

et al. 2021

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“…In this procedure, the Relief-F algorithm can extend a random sample R as a targeted image from the set of training samples. After choosing a random sample, this algorithm selects a set of neighbors as R number of samples to update the weight of each pixel value as a targeted feature according to Equation (1) [33,34].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…According to Equation (1), function FðAÞ is defined as updated feature selection and previous features where Δð L, R, HjÞ represents the difference between sample R i as new feature selection and sample R j as previous features on the characteristic L, and as shown in Equation ( 2), H j represents the j nearest neighbor sample in class K [35]. Final value of the H is categorized into three types including a continuous value, 0, and 1 according to Equation (1) [34].…”

Section: Proposed Methodsmentioning

confidence: 99%

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

Yuqing

2022

Wireless Communications and Mobile Computing

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Today, energy management based on the digitalization of smart grids by the Internet of Things (IoT) is an emerging paradigm for power line systems. There are several environmental hazards to break down high-voltage power cables such as lightning, severe voltage fluctuations, and incorrect design of electric field distribution. So, identifying faulty high-voltage power lines is one of the most emerging challenges in smart grids to avoid disruption of the power distribution networks. This paper presents a new hybrid Convolutional Neural Network and Relief-F (CNN-RF) algorithm for an energy-aware collaborative learning approach to detect power line systems in smart grids. This hybrid approach ensures the stability and reliability of the defective power line system and improves the energy efficiency of the smart grids. This approach can detect the defective power line recognition using damaged power line images concerning automatic monitoring using Unmanned Aerial Vehicle (UAV) control system and IoT communications. By applying UAV control system and IoT communications on gathering damaged power line images, human faults and environmental hazards for extra data transmission are avoided. Experimental results show that the proposed CNN-RF model represents a high accuracy rate of 92.2% for recognizing damaged power lines. Also, the precision of damaged line detection ratio is higher than other prediction methods by the rate of 92.5%. Finally, the performance of the damaged line prediction approach in the CNN-RF method has a daily minimum cost in the IoT-based smart grids.

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“…AI techniques including machine learning and neural networks have been proposed and performed on a variety of engineering problems. Neural networks (NNs) are a common group of techniques that are employed to predict and analyse test results [58][59][60][61]. The development of different types of NNs has led to several algorithms including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) [62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques

Ding¹,

Alharbi

Almadhor

et al. 2022

Materials

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It is very important to keep structures and constructional elements in service during and after exposure to elevated temperatures. Investigation of the structural behaviour of different components and structures at elevated temperatures is an approach to manipulate the serviceability of the structures during heat exposure. Channel connectors are widely used shear connectors not only for their appealing mechanical properties but also for their workability and cost-effective nature. In this study, a finite element (FE) evaluation was performed on an authentic composite model, and the behaviour of the channel shear connector at elevated temperature was examined. Furthermore, a novel hybrid intelligence algorithm based on a feature-selection trait with the incorporation of particle swarm optimization (PSO) and multi-layer perceptron (MLP) algorithms has been developed to predict the slip response of the channel. The hybrid intelligence algorithm that uses artificial neural networks is performed on derived data from the FE study. Finally, the obtained numerical results are compared with extreme learning machine (ELM) and radial basis function (RBF) results. The MLP-PSO represented dramatically accurate results for slip value prediction at elevated temperatures. The results proved the active presence of the channels, especially to improve the stiffness and loading capacity of the composite beam. Although the height enhances the ductility, stiffness is significantly reduced at elevated temperatures. According to the results, temperature, failure load, the height of connector and concrete block strength are the key governing parameters for composite floor design against high temperatures.

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Power Quality Improvement of a Hybrid Renewable Energy Sources Based Standalone System Using Neuro-Fuzzy Controllers

Cited by 18 publications

References 16 publications

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques

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