Large grid-connected photovoltaic power plants

Woyte, Achim; Goy, Solène

doi:10.1016/b978-1-78242-336-2.00011-2

Cited by 19 publications

(12 citation statements)

References 3 publications

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“…Despite of the lack of information, it is important to mention that when a variable is measured, its availability is high, close to 100% [26]. Table 2 shows the available variables for the different identified cases.…”

Section: Description Of the Plants And Its Monitoring Control Systemmentioning

confidence: 99%

Operation and Maintenance in Solar Plants: Eight study cases

Talayero¹,

Melero²,

Llombart³

et al. 2024

RE&PQJ

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The use of solar photovoltaic generation is growing up nowadays. New developments have been achieved lowering the prices of the modules and their installation. Nevertheless, the operation and maintenance of these plants still needs improvements in order to enhance their efficiency and economic balance. This paper presents a detailed analysis of eight photovoltaic (PV) plants revealing improvements on the monitoring system and the maintenance procedures. The common failures in PV plants have been established and their losses have been estimated. A correct diagnostic method using good information and monitoring systems could have reduced the losses.

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Section: Description Of the Plants And Its Monitoring Control Systemmentioning

confidence: 99%

Operation and Maintenance in Solar Plants: Eight study cases

Talayero¹,

Melero²,

Llombart³

et al. 2024

RE&PQJ

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“…The innovation of the proposed method is its independence from the use of irradiance data. The performance evaluation is not based on the estimation of the energy generation/production of the studied PV systems and simulation of the losses due to shading (with the use of irradiance data and models) and its comparison with the real production[3]- [5] or the comparison of the production with the tilted irradiance, through the calculation of the performance ratio [5], [6]. It is based on the performance comparison and the detection of repeated or non-repeated errors/malfunctions between neighboring PV systems with similar characteristics (tilt and orientation).…”

Section: Scientific Innovationmentioning

confidence: 99%

Data Analysis for Effective Monitoring of Partially Shaded Photovoltaic Systems

Tsafarakis

Sinapis²,

Sark

2017

2017 IEEE 44th Photovoltaic Specialist Conference (PVSC)

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The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinct it from any additional energy loss due to other malfunctions.

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“…Photovoltaic solar energy is growing by over 20% worldwide yearly [1]. The decrease in the price of the modules and the improvements in the technology, increasing the performance by up to 85% on average, make it competitive in the electricity generation sector [2]. These facts together with the policy incentives, at European [3] and national [4] levels, pushed the installation of new plants.…”

Section: Introductionmentioning

confidence: 99%

“…The increase in the size of the plants requires the optimization of the operation and maintenance, which implies new challenges and trends for this task [2], such as minimising the timework in the maintenance tasks. It is mandatory to quickly identify the location of the faults in the plant in order to optimise the reparation time and its impact on production.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of failures in Solar Plants based on Performance monitoring

Talayero¹,

Llombart²,

Melero³

2024

RE&PQJ

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Photovoltaic (PV) solar energy has become a reference in electrical generation. The plants currently installed, and those planned have a huge capacity and occupy large areas. The increase in size of the plants presents new challenges in operation and maintenance areas, such as the optimization of the number of sensors installed, large data management and the reduction of the timework in maintenance. The aim of this paper is to show a methodology, to diagnose failures, based on the measured data in the plant. The methodology used is supervised regression machine learning and comparison algorithms. This methodology allows the study of the sensors, the inverters, the joint boxes and the power reduction caused by soiling. The result would allow the detection of around 1-5% of production loss in the plant. The algorithms have been tested with real data of PV plants, and have detected common failures such as production drops in strings and losses due to soiling.

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Large grid-connected photovoltaic power plants

Cited by 19 publications

References 3 publications

Operation and Maintenance in Solar Plants: Eight study cases

Operation and Maintenance in Solar Plants: Eight study cases

Data Analysis for Effective Monitoring of Partially Shaded Photovoltaic Systems

Diagnosis of failures in Solar Plants based on Performance monitoring

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