Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load

Srivastava, Spandan; Lata, Charu; Lohan, Prateek; Mosobi, Rinchin W.

doi:10.37391/ijeer.100309

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Another study by 21 aims to enhance the performance of microgrid systems by creating a self-adapting energy management model that integrates optimal ANN. The proposed model is composed of a series of artificial neural networks that have been optimized individually through the application of PSO.…”

Section: Related Work On Mppt Techniquesmentioning

confidence: 99%

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

Sarang,

Raza,

Panhwar

et al. 2024

Sci Rep

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A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

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Section: Related Work On Mppt Techniquesmentioning

confidence: 99%

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

Sarang,

Raza,

Panhwar

et al. 2024

Sci Rep

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“…PV modules are constructed from an arrangement of PV cells. PV arrays are created by connecting these modules in a parallelseries arrangement [12].…”

Section: Mathematical Modeling Of Pv Modulementioning

confidence: 99%

Enhancement of Agriculture Feeder Performance by Optimal Sizing and Placing of Solar PV Tree through AEO-Based Optimization Technique

Janamala

2023

IJEER

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Electrical demand, which makes up a large share of the overall power market, agriculture at the top of the list of priorities. To provide end users with a dependable and high-quality supply via various feeders and renewable energy sources, distribution generations are now being developed. In recent years, solar PV systems have been used to meet the demands of numerous applications, including boosting the efficiency of distribution networks. This paper presents the system with effective optimization method like Artificial Eco-System based Optimization Technique for identification of the best location to install distribution generation and the optimum size to minimize feeder losses. To meet service expectations, the integration of a solar PV system is swapped out for a solar tree in this suggested work. A 28-bus Indian agriculture feeder is considered for better understanding the proposed algorithm. MATLAB software is used for implementing the proposed optimization technique and CREO-2.0 is used for designing the 3-dimensional solar PV tree.

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“…PSO is a population-based algorithm, presented by Eberhart & Kennedy [10]. It is widely used for fine-tuning the parameters of neural networks [16] [17] [18]. (2) where 𝜔-inertia weight, c1and c2-acceleration coefficients, r1, r2 -random numbers [0,1].…”

Section: Particles Swarm Optimizationmentioning

confidence: 99%

A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics

Narayanan,

Pillai

2023

IJEER

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The field of Printed Electronics (PE) is experiencing significant growth in the industrial sector and generating considerable interest across various industries due to its ability to produce intricate components. The functionality of printed electronic products heavily relies on the utilization of conductive ink during the printing process, which plays a vital role in developing flexible electronic circuits and improving the communicative functionalities of objects. Selecting the right ink for printing is crucial to meet consumer requirements. However, the conventional approach to this process has been manual, labor-intensive, and time-consuming, relying on the expertise of designers. This paper presents an automated ink selection model for printed circuits. This novel method has been incorporated with Multilayer Perceptron Neural Network (MLPNN) and Particle Swarm Optimization (PSO), named PSO-MLPNN. A dataset containing material features is generated by gathering information from both literature and experimental observations. To ensure uniformity, the data undergoes preprocessing using the min-max method, which scales all features to a standardized range between 0 and 1. A four-layer MLPNN is constructed to choose the most suitable ink. The network is trained with the PSO algorithm. The bias and weight values of MLPNN are tuned using the PSO algorithm to attain high accuracy. The computed findings confirm that the ink selection is highly effective and more accurate when compared to both the standard MLPNN.

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Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load

Cited by 8 publications

References 16 publications

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

Enhancement of Agriculture Feeder Performance by Optimal Sizing and Placing of Solar PV Tree through AEO-Based Optimization Technique

A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics

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