A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation

Cui, Yao; Yi, Zhehan; Duan, Jiajun; Shi, Di; Wang, Zhiwei

doi:10.1109/isgt.2019.8791596

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…Many authors used RP in neural network. For example, Cui et al [7] proposed a novel maximum power point tracking (MPPT) method which is based on Rprop neural network. Leholo et al [22] showed that the train_LM gives better performance than train_SCG (Scaled Conjugate Gradient), train_BFGS (Quasi-Newton) and train_RP.…”

Section: Resilient Backpropagation (Rp)mentioning

confidence: 99%

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Kumar¹,

Sodhi²

2022

Comm. Math. Appl.

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We use different types of training algorithms in the neural network. But, we cannot say which kind of training algorithm is fast for a given problem. So, in this survey paper, we are trying to find which types of training algorithm are better for categorization problems. For this purpose, we used ten types of training algorithms in the pattern network in MATLAB. We used Levenberg-Marquardt (LM), Bayesian regularization backpropagation (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate gradient backpropagation (SCG), Conjugate Gradient with Powell/Beale Restarts (CGB), Fletcher-Powell Conjugate Gradient (CGF), Polak-Ribiere Conjugate Gradient (GDM), One Step Secant (OSS), and Variable Learning Rate Backpropagation (GD) algorithm. In this survey paper, we also check, affects of these algorithms on neural network when we applied different types of hidden neuron size. During this survey we found some new facts. We found that RP, SCG, CGB, CGF and OSS are fastest algorithms. BFG takes more time with respect to hidden neuron size. GDM and GD take more epochs. BR algorithm is not acceptable for image categorization.

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Section: Resilient Backpropagation (Rp)mentioning

confidence: 99%

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Kumar¹,

Sodhi²

2022

Comm. Math. Appl.

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“…Research from [ 60 ] presents an MPPT algorithm that makes use of resilient networks with backpropagation (Rprop-NN) and supervision to limit the short-circuit I (Isc). This method predicts the MPP instantaneously by measuring T and G, which allows it to move to its optimal operating point without any oscillations.…”

Section: Artificial Neural Network For Mppt Control In Pv Systemsmentioning

confidence: 99%

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

et al. 2021

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The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

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“…Bolukbasi et al [36] introduced an adaptive trade-off between DNN model accuracy and model inference latency, which reduces inference latency and computational cost by exiting early in the early layers of the model. Leroux et al [37] presented a new architecture for cascaded networks that reduces the computational cost using an early exit mechanism in the recycling phase of the network. For latency optimization, Thaha et al [38] proposed a matching theory-based distributed DNN model partitioning method for solving the joint partitioning and offloading of DNN inference tasks in fog networks, which reduces the total DNN inference latency.…”

Section: Dutta Et Al [25]mentioning

confidence: 99%

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

Zhang

et al. 2021

Complex Intell. Syst.

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With the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

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A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation

Cited by 13 publications

References 19 publications

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

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