Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

Sun, Xingshu; Chavali, Raghu Vamsi Krishna; Alam, Muhammad Ashraful

doi:10.1002/pip.3043

Cited by 44 publications

(26 citation statements)

References 55 publications

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“…Among the algorithms used, it should be noted that most are specific solutions to a given problem using probabilistic models [7][8][9]. As they are specific solutions, they cannot be adjusted to the particularity of each case, which means that they fall behind other types of solutions.…”

Section: Related Workmentioning

confidence: 99%

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

et al. 2021

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Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies.

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Section: Related Workmentioning

confidence: 99%

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

et al. 2021

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“…One common method for diagnosis of a PV system is to compare the reference power output and the measured power output [7][8][9][10][11][12][13][14]. The statistical forecasting models can be used to acquire the reference data [7,8].…”

Section: Related Workmentioning

confidence: 99%

“…The physical models can also be used to acquire the reference data [9][10][11][12]. In [9], the authors utilise the extended Kalman filter to model normal condition based on I-V and P-V curves.…”

Section: Related Workmentioning

confidence: 99%

“…A common method for assessment of a PV system output performance is to compare the reference power output and measured power output. The reference power output can be acquired by statistical forecasting models [7,8], physical models [9][10][11][12], and neighbouring PV systems [13,14]. If a difference between the reference power output and the measured power output exceeds a pre-defined threshold, a PV system is considered underperforming.…”

Section: Introductionmentioning

confidence: 99%

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Forecast uncertainty‐based performance degradation diagnosis of solar PV systems

Lee

2020

IET Renewable Power Generation

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In this study, the authors are interested in estimating how much a PV system underperforms than expected by exploiting forecast uncertainty. For this, they first study a forecast accuracy-related forecast uncertainty metric using the ensemble method based on the dropout technique, which is widely used in deep learning forecasting models. Given the forecast accuracy-related uncertainty metric, the rationale of the authors' approach is that forecast accuracy is likely to decrease compared to the normal case of similar uncertainty metric values if any performance degradation happens. It is because similar uncertainty metric values are likely to show similar forecast accuracy. Therefore, they generate a standard table by simulating possible performance degradation cases and conduct the performance degradation diagnosis by looking up the standard table based on the uncertainty metric. From the experiments, in the case of persistent degradation, they show that their approach estimates the performance degradation with the estimation error of around 1% while an uncertainty-unaware approach shows the estimation error of up to 5%. In the case of temporal degradation, their approach shows the estimation error of around 3%, while the uncertainty-unaware approach does not show meaningful result.

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“…The model parameters need to be re-extracted whenever the modules' BOMs change. The uncertainties associated with technology and geography-specific degradation rates make it difficult to calculate the levelized cost of energy and thus the economic viability of solar energy (Sun et al 7 ).…”

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confidence: 99%

Photovoltaic lifetime forecast model based on degradation patterns

Kaaya

Lindig

Weiß

et al. 2020

Progress in Photovoltaics

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The ever‐growing secondary market of photovoltaic (PV) systems (i.e., the transaction of solar plants ownership) calls for reliable and high‐quality long‐term PV degradation forecasts to mitigate the financial risks. However, when long‐term PV performance degradation forecasts are required after a short time with limited degradation history, the existing physical and data‐driven methods often provide unrealistic degradation scenarios. Therefore, we present a new data‐driven method to forecast PV lifetime after a small performance degradation of only 3%. To achieve an accurate and reliable forecast, the developed method addresses the fundamental challenges that usually affect long‐term degradation evaluation such as data treatment, choosing a good degradation model, and understanding the different degradation patterns. In the paper, we propose and describe an algorithm for degradation trend evaluation, a new concept of multiple “time‐ and degradation pattern‐dependent” degradation factors. The proposed method has been calibrated and validated using different PV modules and systems data of 5 to 35 years of field exposure. The model has been benchmarked against existing statistical models evaluating 11 experimental PV systems with different technologies. The key advantage of our model over statistical ones is the ability to perform more reliable forecasts with limited degradation history. With an average relative uncertainty of 7.0%, our model is outstanding in consistency for different forecasting time horizons. Moreover, the model is applicable to all PV technologies. The proposed method will aid in making reliable financial decisions and also in adequately planning operation and maintenance activities.

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Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

Cited by 44 publications

References 55 publications

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

Forecast uncertainty‐based performance degradation diagnosis of solar PV systems

Photovoltaic lifetime forecast model based on degradation patterns

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