Performance Enhancement of Solar Panels Using Adaptive Velocity-Particle Swarm Optimization (AVPSO) Algorithm for Charging Station as an Effort for Energy Security

The growth of public awareness of the environment is directly proportional to the development of the use of electric cars. Electric cars operate by consuming electrical energy from battery storage, which must be recharged periodically at the charging station. Solar panels are one source of energy that is environmentally friendly and has the potential to be applied to charging stations. The use of solar panels causes the charging station to no longer depend on conventional electricity networks, which the majority of it still use fossil fuel power plants. Solar panels have a problem that is not optimal electrical power output so that it has the potential to affect the charging parameters of the battery charging station. Adaptive Velocity-Particle Swarm Optimization (AV-PSO) is an artificial intelligence type MPPT optimization algorithm that can solve the problem of solar panel power optimization. This study also uses the Coulomb Counting method as a battery capacity estimator. The results showed that the average sensor accuracy is more than 91% with a DC-DC SEPIC converter which has an efficiency of 69.54%. In general, the proposed charging station system has been proven capable to enhance the energy security by optimizing the output power of solar panels up to 22.30% more than using conventional systems.

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Multiobjective Control of Power Plants Using Particle Swarm Optimization Techniques

IEEE Transactions on Energy Conversion ◽

10.1109/tec.2005.858078 ◽

2006 ◽

Vol 21 (2) ◽

pp. 552-561 ◽

Cited By ~ 133

Author(s):

J.S. Heo ◽

K.Y. Lee ◽

R. Garduno-Ramirez

Keyword(s):

Particle Swarm Optimization ◽

Power Plants ◽

Particle Swarm ◽

Optimization Techniques ◽

Swarm Optimization ◽

Multiobjective Control

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Developing a Decision Model Based on Decision Tree and Particle Swarm Optimization Algorithms to Identify Optimal Locations for Solar Power Plants Construction in Iran

SSRN Electronic Journal ◽

10.2139/ssrn.3900348 ◽

2021 ◽

Author(s):

Saman Nadizadeh Shorabeh ◽

Najmeh Neysani Samani ◽

Foad Minaei ◽

Hamzeh Karimi Firozjaei ◽

Mehdi Homaee ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Decision Tree ◽

Decision Model ◽

Power Plants ◽

Solar Power ◽

Optimization Algorithms ◽

Particle Swarm ◽

Swarm Optimization ◽

Model Based ◽

Solar Power Plants

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Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v10.i3.pp1287-1295 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1287

Author(s):

Aparna Pradeep Laturkar ◽

Sridharan Bhavani ◽

DeepaliParag Adhyapak

Keyword(s):

Particle Swarm Optimization ◽

Wireless Sensor Network ◽

Sensor Network ◽

Particle Swarm ◽

Wireless Sensor ◽

Sensor Deployment ◽

Packet Size ◽

Swarm Optimization ◽

Wide Range ◽

Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid & Force Based sensor deployment algorithms based on interval and packet size. From the results of Combination of Grid & Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.

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Random, PSO and MDBPSO based Sensor Deployment in Wireless Sensor Network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v10.i3.pp1278-1286 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1278 ◽

Cited By ~ 1

Author(s):

Aparna Pradeep Laturkar ◽

Sridharan Bhavani ◽

DeepaliParag Adhyapak

Keyword(s):

Particle Swarm Optimization ◽

Wireless Sensor Network ◽

Sensor Network ◽

Particle Swarm ◽

Wireless Sensor ◽

Sensor Deployment ◽

Binary Particle Swarm Optimization ◽

Swarm Optimization ◽

Wide Range ◽

Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deployment using Random; Particle Swarm Optimization (PSO) and grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization) methods. This paper analyzes the performance of Random, PSO based and MDBPSO based sensor deployment methods by varying different grid sizes and the region of interest (ROI). PSO and MDBPSO based sensor deployment methods are analyzed based on number of iterations. From the simulation results; it can be concluded that MDBPSO performs better than other two methods.

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