A Method to Estimate and Analyze the Performance of a Grid-Connected Photovoltaic Power Plant

Truong, Le Phuong; Quoc, Hoang An; Tsai, Huan‐Liang; Dung, Do Van

doi:10.3390/en13102583

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Shin et al proposed a weather-corrected index, linear regression method, temperature-correction equation, estimation error matrix, clearness index and proposed variable index, a one-class support vector machine (SVM) method, and a kernel technique to classify the fault state and anomaly output power of PV plants [39]. Phuong Truong et al presented a method to estimate the yield and analyze the performance of a grid-connected PV system in a MATLAB/Simulink environment for a rooftop PV system and a solar farm [40]. Dhimish et al presented degradation rates over a 10-year span for seven different PV systems located in England, Scotland, and Ireland using a power-irradiance technique that compares output measured power with a corresponding irradiance level [41].…”

Section: Photovoltaic Performance Analysis Approachesmentioning

confidence: 99%

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Ρουμπακιάς

Stamatelos

2023

Sustainability

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The increasing installed volume of grid-connected PV systems in modern electricity networks induces variability and uncertainty factors which must be addressed from several different viewpoints, including systems’ protection and management. This study aims to estimate the actual performance and degradation of photovoltaic (PV) parks in Central Greece after several years of operation. Monitoring data over several years are analyzed and filtered, the performance ratio and normalized efficiency are computed, and five different ANNs are employed: (i) a feed-forward network (one hidden layer); (ii) a deep feed-forward network (two hidden layers); (iii) a recurrent neural network; (iv) a cascade-forward network; and (v) a nonlinear autoregressive network. The following inputs are employed: in-plane irradiance; backsheet panel temperature; airmass; clearness index; and DC voltage of the inverter. Monitoring data from an 8-year operation of a grid-connected PV system are employed for training, testing, and validation of these networks. They act as a baseline, built from the first year, and the computed metrics act as indicators of faults or degradation. Best accuracy is reached with the DFFNN. The ANNs are trained with data from the first year of operation, and output prediction is carried out for the remaining years. Annual electricity generation exceeds 1600 kWh /kWp, and MAPE values show an increasing trend over the years. This fact indicates a possible change in PV performance.

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Section: Photovoltaic Performance Analysis Approachesmentioning

confidence: 99%

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Ρουμπακιάς

Stamatelos

2023

Sustainability

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“…Figure 14 depicts the variation in PR and CF over a predetermined time frame. This variance falls between the PR and CUF range for solar PV systems in nations in Southeast Asia [29][30] [31]. The variation in solar irradiation had no effect on the performance ratio.…”

Section: Energy Lossesmentioning

confidence: 99%

Performance Assessment of Large Scale Rooftop Solar PV System - A Case Study in a Malaysian Public University

Govindarajan¹,

Batcha²,

Abdullah³

2023

Preprint

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“…There is currently great worldwide interest in assessing: (i) the performance of a PV system, (ii) the price per PV module, (iii) the solar resource of the place of energy generation, etc. [3].…”

Section: Introductionmentioning

confidence: 99%

Towards Optimal Management in Microgrids: An Overview

et al. 2021

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A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches.

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A Method to Estimate and Analyze the Performance of a Grid-Connected Photovoltaic Power Plant

Cited by 7 publications

References 18 publications

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

Performance Assessment of Large Scale Rooftop Solar PV System - A Case Study in a Malaysian Public University

Towards Optimal Management in Microgrids: An Overview

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