Investigation of transient magnetoelectric response of magnetostrictive/piezoelectric composite applicable for lightning current sensing

Zhou, H. D.; Xu, Changbao; Lu, Caijiang; Jiang, Xueling; Zhang, Ziyan; Wang, Jingqi; Xiao, Xiaobing; Xin, Mingyong; Wang, Lei

doi:10.1016/j.sna.2021.112789

Cited by 55 publications

(15 citation statements)

References 19 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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“…Authors in [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] developed mathematical techniques to solve energy management problem in SG. In [7], Mixed Integer Linear Programming (MILP) is developed to solve energy management problem for PAR and cost minimization.…”

Section: Mathematical Techniquesmentioning

confidence: 99%

“…PV contributes 20%, WT contributes 30%, and utility contributes 50%. Equation (20) ensures that appliances are optimally scheduled, and Equation ( 21) represents the total operation time slots of appliances. Constraint (22) represents ON/OFF appliances status.…”

Section: Objective Functionmentioning

confidence: 99%

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

Albogamy

Ashfaq

Hafeez³

et al. 2022

Processes

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This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The framework includes microgrid with renewable energy sources (solar and wind), energy storage systems, electric vehicles (EVs), and building appliances like time flexible, power flexible, and base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules smart appliances, and EVs batteries charging/discharging with microgrid and without (W/O) microgrid under FPS to minimize energy cost, carbon emission, and peak to average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm and artificial neural network (ANN) is devised to predict solar irradiance and wind speed for accurate microgrid energy estimation. To endorse the applicability of the proposed framework, simulations are conducted. Moreover, the proposed framework based on the ACO algorithm is compared to mixed-integer linear programming (MILP) and W/O scheduling energy management frameworks in terms of energy cost, carbon emission, and PAR. The developed ACO algorithm reduces energy cost, PAR, and carbon emission by 23.69%, 26.20%, and 15.35% in scenario I, and 25.09%, 31.45%, and 18.50% in scenario II, respectively, as compared to W/O scheduling case. The results affirm the applicability of the proposed framework in aspects of the desired objectives.

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“…Today, by increasing information technology and computer science paradigms on Internet of Things (IoT) [1,2], wireless communications, and 5G technologies, smart environments can apply these paradigms to improve and facilitate resource management, optimized services, and energy efficiency factors [3,4]. Smart grid network is one of important smart environments that energy efficiency and fault tolerant methods are considering to manage optimized services using the IoT and intelligent applications [5,6].…”

Section: Introductionmentioning

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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Investigation of transient magnetoelectric response of magnetostrictive/piezoelectric composite applicable for lightning current sensing

Cited by 55 publications

References 19 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

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

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

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