Risk mitigation of performance ratio guarantees in  commercial photovoltaic systems

The performance ratio (PR) based on IEC 61724 standard is calculated under the influence of seasonal variations and the capability of the system. On the other hand, The National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy proposed the weather-corrected performance ratio (PRcorr). This PRcorr index calculates the performance of the PV system which has taken the weather variations that influence the cell temperature into account. Both techniques were compared with the simulation program to analyze the PR index according to system conditions. The study site is the on-grid PV rooftop at Kasetsart University Chalermphakiat Sakon Nakhon (CSC). The PV panel is oriented to the southwest, 215 degrees azimuth. The angle of the inclination of the panel is 17 degrees. The result shows the trend of monthly PRcorr with little variations. At the same time, the PR value over the year has a lot of variations. The variation of PRcorr and PR is 2.39 and 5.07, respectively. PRcorr has low variability due to the correction of weather factors. The average cell temperature is an important variable. To calculate the average temperature of the panels, one year of data is needed to filter out distorted information. The system available condition is an important factor for the on-grid PV system at low voltage.

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“…The formula PRcorr calculates the cell temperature based on irradiation and ambient temperature. The equation is shown in (7).…”

Section: Weather Corrected Performance Ratiomentioning

confidence: 99%

“…The weather variation should calculate the only factors that affect the cell temperature. According to research by Basson, the objective of this research is to reduce the sensitivity to climate data [7]. The result shows that the temperature correction has a significant effect to reduce the seasonal changes.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Grid-Connected PV Rooftop, at Sakon Nakhon Province, Thailand

Sathiracheewin¹,

Sripadungtham²,

Kamuang³

2020

Adv. sci. technol. eng. syst. j.

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“…Then, the uncorrected PR is calculated by [64] PR = kWhAC actual /P DC kWh sun /1000 (11) This work also considers yield (PV systems of different sizes are not directly comparable), and PR corrected to STC (PV systems employing different PV models are not directly comparable), and capacity factor (ratio of the observed PV generation over a time period to its potential generation if it functioned at its full nameplate capacity continuously over the same time period). Given a time instance t in N total number of time instances and the observed generation (in kW) from the PV system at the time t denoted by kWAC actual (t), the yield, PR corrected to STC, and capacity factor are calculated as follows [65]:…”

Section: Metrics To Evaluate Pv Performancementioning

confidence: 99%

“…Given a time instance t in N total number of time instances and the observed generation (in kW) from the PV system at the time t denoted by

kWA C_{actual} false(t false)

, the yield, PR corrected to STC, and capacity factor are calculated as follows [65]:

Yiel d_{monthly} = \frac{\sum_{t = 1}^{N} kWhA {normalC}_{actual} (t)}{P_{DC}}

normalP R_{corrected} = \frac{()(, kWhA C_{actual} / P_{DC})}{()(, kW h_{sun} / 1000) X}

CapFa c_{monthly} = \frac{\sum_{t = 1}^{N} kWA {normalC}_{actual} (t)}{N \times P_{DC}}

Among them, yield is considered to be more of a measure of PV system value than performance, and capacity factor is regarded as a measure of the system's utilisation than efficiency or performance. Another set of metrics, the EPI and PPI, are also defined as follows, where

P_{estimate} false(t false)

and

E_{estimate}

are obtained from (6) [65]

PPI false(t false) = \frac{kWA C_{actual} false(t false)}{P_{estimate} false(t false)}

EPI = \frac{kWhA C_{actual}}{E_{estimate...}}

…”

Section: Metrics To Evaluate Pv Performancementioning

confidence: 99%

Hybrid data‐model method to improve generation estimation and performance assessment of grid‐tied PV: a case study

Sundararajan¹,

Sarwat²

2019

IET Renewable Power Generation

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Increased installed capacity of distributed photovoltaic (PV) systems has necessitated accurate measurement and tracking of PV performance under locality-specific conditions of irradiance, temperature, and derate factors. Existing PV generation estimation methods are strictly model based and not responsive to changes in weather and system losses. Metrics computed using these methods, therefore, do not capture the real PV behaviour well. This study proposes a hybrid data-model method (HDMM) that uses historical PV data in addition to model information to improve the accuracy of generation estimation. The generation estimated by HDMM is used to compute performance metrics-performance ratio, yield, capacity factor, energy performance index, and power performance index-for two real-world PV systems at Miami (ℳ, 1.4 MW) and Daytona (D, 1.28 MW) for 2017. The significance of these metrics is then evaluated, and a preliminary analysis of inverter efficiencies is provided. Results from this study show that when compared with the existing estimation method, HDMM performs better on an average by 75% for D and 10% for ℳ. Further, at a given point in time, system ℳ is likely to perform better than D. The study gives system installers and other stakeholders better PV system visibility, enabling aggregation and transactive energy. 2 Related work The performance of PV systems has been studied well in the literature, both at system level and module level [9, 10]. However, only system-level performance is of scope in this paper. In a prior work of the authors [11], an in-depth analysis of PV performance for a special case of the partial solar eclipse of 21 August 2017 was conducted to demonstrate how critical the problem of PV performance analysis is for operators under high penetration scenarios. A study with a similar scope was conducted in [12] for

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“…Therefore, most maintenance engineers want to develop economic, efficient and reliable methodologies and tools to monitor and quickly identify the fault state, produce more energy and minimize maintenance planning. The Performance Ratio (PR) index (IEC61724-1 standard) is a health index for photovoltaic power plants that analyzes the solar power plant fault state and decreased output power and evaluates the solar power plant's performance and aging [1][2][3][4][5][6]. If a fault occurs in a PV power plant, the operator recognizes the anomaly in output power by calculating the PR index, which will show a lower value than that in the normal state.…”

Section: Introductionmentioning

confidence: 99%

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

Shin¹,

Kim²

2020

Energies

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This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

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Risk mitigation of performance ratio guarantees in commercial photovoltaic systems

Cited by 10 publications

References 4 publications

Performance Analysis of Grid-Connected PV Rooftop, at Sakon Nakhon Province, Thailand

Performance Analysis of Grid-Connected PV Rooftop, at Sakon Nakhon Province, Thailand

Hybrid data‐model method to improve generation estimation and performance assessment of grid‐tied PV: a case study

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

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