Parameter estimation of solar
            <scp>PV</scp>
            models with a new proposed war strategy optimization algorithm

Ayyarao, Tummala.S. L. V.; Kumar, Polamarasetty P.

doi:10.1002/er.7629

Cited by 73 publications

(34 citation statements)

References 78 publications

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“…Where I ph is the photocurrent, I L is the output current of the PV cell, I s d is the diode current and I R sh is the shunt resistance current [1]. The current expressions are as follows:…”

Section: A Single Diode Modelmentioning

confidence: 99%

Optimizing Photovoltaic Solar Model Parameters

Jeridi,

messaoud,

zaafouri

2024

Preprint

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In the last decade, accurate parameter estimation in photovoltaic (PV) system modeling has gained significant attention due to its crucial role in overall system performance. Despite the complexity of this problem, meta-heuristic algorithms (MHA) show promise, although conventional MHAs often suffer from slow convergence. The challenge lies in developing algorithms that balance exploration and exploitation while accurately determining PV model parameters. A recent approach combines ”War Strategy Optimization” (WSO) and ”Mountaineering Team-Based Optimization” (MTBO), along with the Newton- Raphson technique, to precisely estimate parameters in solar cell models. Evaluations of this new model, WSO-MTBO, confirm its effectiveness, particularly demonstrated through robust testing on three distinct photovoltaic systems, including the RTC France solar cell, over a 30-iteration period. Results highlight the superiority of WSO-MTBO over conventional algorithms, suggesting promising prospects for parameter optimization in photovoltaic systems, thereby enhancing accuracy and design efficiency.

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Section: A Single Diode Modelmentioning

confidence: 99%

Optimizing Photovoltaic Solar Model Parameters

Jeridi,

messaoud,

zaafouri

2024

Preprint

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“…The machine learning approach SVM provides efficient intrusion detection, and swift implementation, and is simple in nature. However, it possessed some shortcomings like over fitting issues and higher complexities [28]. These were tackled by the WSO algorithm which increased the convergence speed and searchability and increased the detection speed and accuracy.…”

Section: ) Optimized Svm-based Collaborative Intrusion Detectionmentioning

confidence: 99%

Collaborative based Vehicular Ad-hoc Network Intrusion Detection System using Optimized Support Vector Machine

Azath¹,

Singh²

2023

IJACSA

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The Vehicular Ad hoc Network (VANET) can be used to provide secured information to the user vehicles. However, these days the immunity of safeguarding the information from vulnerabilities and threats are of great challenge. Therefore, it is necessary to provide a secured solution for the improvement of security with the deployment of advanced technology. In context with this, a blockchain based VANET structure for secured communication incorporated with the enhancement of confidentiality, scalability, and privacy is planned. The k-means clustering model forms cluster formation. The cluster head selection is carried out with Tabu Search-based Particle Swarm Optimization (TS-PSO) algorithm. The proposed approach aims to mitigate the delay with the enhancement of throughput and energy efficiency. Meanwhile, the deployed blockchain will enhance reliability and security. Moreover, the novel War Strategy Optimization (WSO) based Support Vector Machine (SVM) model (Optimized SVM) can be used for the trust-based collaborative intrusion detection in the VANET. Our work targets to detect the intrusion and non-intrusion classes. Meanwhile, our proposed work can be used for the prevention of repetitive detection processes and therein it enhances the security by rewarding the vehicles. An experimental analysis is carried out to ensure its usage in detecting the malicious node from the resource constraint vehicles and also used to achieve better security, energy utilization and end-to-end delay.

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“…Shark optimization [12] , War strategy optimization [1], fireworks algorithm [13] etc. To accelerate convergence and improve the exploration potential of metaheuristic algorithms, learning mechanisms, chaotic drifts, and adaptive weight mechanisms are added to the core algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

“…Where 𝐼 𝑝ℎ is the photocurrent, 𝐼 𝐿 is the output current of the PV cell, 𝐼 𝑑 is the diode current and 𝐼 𝑠ℎ is the shunt resistance current [1].…”

Section: A Single Diode Modelmentioning

confidence: 99%

“…Solar energy has gained popularity as a reliable and consistent renewable energy source in recent years, owing to its widespread availability, environmental friendliness, and low maintenance cost. Solar energy is utilized not only for generation of electric power but also in many applications such as farming, water heating, kitchen applications, lightning, charging of batteries in vehicles, agriculture and rooftops [1]. However, the unpredictable nature of solar energy has posed a significant challenge to large-scale power generation operations.…”

Section: Introduction Motivationmentioning

confidence: 99%

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Parameter Estimation of Solar PV Models with an Artificial Humming Bird Optimization Algorithm using Various Objective Functions

Ayyarao¹,

kishore²

2022

Preprint

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Accurate photovoltaic (PV) models are essential to optimize grid operations and dynamic energy management. This article proposes parameter estimation of solar PV models using Artificial Humming bird Optimization (AHO) algorithm. The AHO algorithm is inspired by hummingbird flight dynamics and mimics hummingbird foraging behaviour. Three objective functions are developed to minimize the root mean square difference between the experimental and estimated currents. The first objective function is based on the conventional RMSE, while the second is developed using the Lambert W function, and the third is developed using the iterative Newton Raphson approach. The proposed AHO algorithm is applied to estimate the parameters for a single diode model (SDM), a double diode model (DDM), and a PV module. The AHO algorithm achieves an RMSE of 7.2985E-04 for SDM and 7.4080E-04 for DDM. The proposed AHO algorithm's performance is compared to the findings of other algorithms reported in the literature.

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Parameter estimation of solar PV models with a new proposed war strategy optimization algorithm

Cited by 73 publications

References 78 publications

Optimizing Photovoltaic Solar Model Parameters

Optimizing Photovoltaic Solar Model Parameters

Collaborative based Vehicular Ad-hoc Network Intrusion Detection System using Optimized Support Vector Machine

Parameter Estimation of Solar PV Models with an Artificial Humming Bird Optimization Algorithm using Various Objective Functions

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