This paper proposes an improved statistical failure detection technique for enhanced monitoring c... more This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.
Ensuring optimal performance of solar photovoltaic (PV) systems requires the extensive assessment... more Ensuring optimal performance of solar photovoltaic (PV) systems requires the extensive assessment and understanding of losses of different origin that affect these installations. Soiling is a key loss factor influencing the performance of PV systems, particularly in arid and dry climatic regions, and its thorough knowledge and modelling aspects including the seasonal evolution is challenging for the early stages of energy prospecting for PV power plants. The purpose of this study is to address this fundamental challenge by evaluating the loss of soiling and the performance of six soiling models based on both physical and machine learning (ML) approaches. Specifically, the case study is a soiling test-bench experimental apparatus installed at the outdoor test facility of the University of Cyprus in Nicosia, Cyprus. The climatic conditions of the site represent a dry climate with high PV potential due to high irradiation levels. The obtained results reported soiling rates ranging from 0.039%/day to 0.535%/day depending on the season and the presence of dust episodes. The average yield daily and monthly soiling losses were 1.9% and 2.4% over a 2-year period, respectively. Furthermore, the comparative analysis of the different soiling models illustrated that the physical models achieved slightly better performance than the ML models with root mean square error (RMSE) of 1.16% and 0.83% for daily and monthly losses, respectively. Finally, the findings provide evidence and useful information on the performance and limitations of the different soiling models for fielded PV systems located in arid and dry climatic zones.
A main challenge in the scope of integrating higher
shares of photovoltaic (PV) systems is to en... more A main challenge in the scope of integrating higher
shares of photovoltaic (PV) systems is to ensure optimal
operations. This can be achieved through next-generation
monitoring with automatic data-driven functionalities. This work
aims to address this fundamental challenge by presenting the stage
of implementation of an advanced cloud-based monitoring
platform and a control digital twin for PV power plants (MW
scale). The platform is fully equipped with a multitude of artificial
intelligent (AI) algorithms for health-state diagnostics and
analytics. The performance of the digital twin to act as a health state
monitor was validated against field and synthetic data from
PV systems at different locations and demonstrated high
accuracies for PV performance modelling and fault diagnosis.
A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and i... more A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and improving lifetime performance is proposed in this paper. The platform incorporates a decision support system (DSS) engine and data-driven functionalities for data cleansing, PV system modeling, early fault diagnosis and provision of O&M recommendations. It can ensure optimum performance by monitoring in real time the operating state of PV assets, detecting faults at early stages and suggesting field mitigation actions based on energy loss analysis and incidents criticality evaluation. The developed platform was benchmarked using historical data from a test PV power plant installed in the Mediterranean region. The obtained results showed the effectiveness of the incorporated functionalities for data cleansing and system modeling as well as the platform’s capability for automated PV asset diagnosis and maintenance by providing recommendations for resolving the detected underperformance iss...
Progress in Photovoltaics: Research and Applications
Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a... more Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum ... more Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An O&M decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a large-scale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 e/kW/year when performing O&M actions. For an electricity price of 59.98 e/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized O&M cost value of 7.45 e/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV O&M Cost Model.
It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifeti... more It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate (RD) may exhibit nonlinear behavior. Neglecting nonlinearities may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear RD. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the "rear RD value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.
The timely detection of photovoltaic (PV) system failures is important for maintaining optimal pe... more The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive m...
A key factor important for the future photovoltaic (PV) uptake and the PV value chain is to reduc... more A key factor important for the future photovoltaic (PV) uptake and the PV value chain is to reduce the Levelized Cost of Electricity (LCoE). This can be achieved by increasing lifetime performance and reducing operating costs through robust condition monitoring offering quality control, safeguarding guarantees and cost-effective operation and maintenance (O&M). Specifically, since monitoring systems can assist to reduce the LCoE of PV, specific novel features based on advanced data analytics need to be included, such as data quality routines (DQRs) providing data sanity and integrity, system health state monitors offering real time operating state information, failure diagnosis through data analysis and other added value services such as performance loss quantification and degradation rate (DR) estimation.
Photovoltaic (PV) power prediction is important for monitoring the performance of PV plants. The ... more Photovoltaic (PV) power prediction is important for monitoring the performance of PV plants. The scope of this work is to develop a methodology for deriving an optimized location and technology independent machine learning (ML) model for power prediction. The prediction accuracy results demonstrated that the performance of the ML model was primarily affected by the dataset split method. In particular, for a 70:30 % train and test set approach, the ML model achieved a normalized root mean square error (nRMSE) of 0.88 % when using randomly selected samples compared to 0.94 % when using continuous samples. The accuracy of the developed model was also affected by the duration of the train set. For a random 70:30 % train and test set approach, the constructed ML topology achieved a nRMSE of 0.88 %, while when the dataset was split into a 30:30 % portion, the nRMSE was 0.95 %. Moreover, when low irradiance conditions were filtered out and 70 % of the entire dataset was randomly chosen for model training, a nRMSE of 1.41 % was obtained demonstrating that the model’s accuracy was not improved. Finally, for a random 10:30 % train and test set approach, the FNNN achieved the lowest nRMSE of 1.10 % when the model was trained using the prevailing irradiance classes.
The scope of this paper is to present the development of failure detection routines (FDRs) that w... more The scope of this paper is to present the development of failure detection routines (FDRs) that will operate on acquired data sets of grid-connected PV systems and determine and classify accurately the exhibited failures. The developed FDRs comprise of a failure detection and a classification stage. Specifically, the implemented failure detection stage was based on a comparative algorithm that detected discrepancies between the measured and simulated electrical measurements (dc current, voltage and power of the array) of a test PV system. Furthermore, statistical algorithms for identifying outliers, anomalies and normal system operation limits were also used for failure detection. Accordingly, for each identified failure there was a subsequent decision stage which performed a classification process based on developed logic and decision trees. The decision trees were constructed with a supervised learning process, trained with continuous samples split in a 70:30 % train and test set ...
Real-time identification of failures in photovoltaic (PV) systems is crucial for achieving reacti... more Real-time identification of failures in photovoltaic (PV) systems is crucial for achieving reactive maintenance schemes that, in turn, will increase the system reliability and guarantee the lifetime output. Following this line, failure detection routines (FDRs) that operate on acquired data-sets of grid-connected PV systems were developed to diagnose the occurrence of failures. The developed FDRs comprise of a failure detection and a classification stage. The detection stage was based on the comparison between the measured and predicted DC power production against set threshold levels (TL). The classification stage was based on data-driven algorithms, which were used to post-process the detected failure patterns through the application of the developed decision trees (DT), k-nearest neighbours (k-NN), support vector machine (SVM) and fuzzy inference systems (FIS). The experimental results showed that the FDRs were capable of detecting all the different types of failures (open- and s...
The presence of defects in a solar cell device affects the electrical parameters causing possible... more The presence of defects in a solar cell device affects the electrical parameters causing possible performance deterioration. Point-like defects are expected to have significant impact on their local surroundings. Their effect weakens away from them. For this reason a physical model of a GaAs device taking into consideration the impact of local ohmic shunts was developed in order to examine the effect of the shunts on the electrical and physical parameters. Defect states have been treated as degenerate semiconductors and the impact of voltage bias and degeneracy on the main electrical parameters of the cell have been investigated.
2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018
Accurate identification of failures in photovoltaic (PV) systems, which can result in energy loss... more Accurate identification of failures in photovoltaic (PV) systems, which can result in energy loss or even serious safety issues, is crucial for ensuring reliability of the installations and the guaranteed lifetime output. The scope of this paper is to present the development of failure detection routines (FDRs) that operate on acquired data-sets of grid-connected PV systems in order to diagnose the occurrence of failures. The developed FDRs comprise of a failure detection and classification stage. Specifically, the failure detection stage was based on a comparative statistical approach between the measured and simulated electrical measurements. In parallel, fuzzy logic inference was performed in order to analyse the failure pattern and classify accurately the occurred fault. The fuzzy rule based classification system (FRBCS) models were constructed for each failure through a supervisory learning process, trained with continuous samples split in a 70:30 % train and test set approach of acquired data-sets which included the feature patterns exhibited during normal and faulty operation. The results obtained by emulating three failure patterns (partial shading, inverter shutdown and bypass diode failure), showed that the developed FDRs were capable of detecting accurately the faults upon their occurrence by signifying detectable discrepancies through the daily statistical comparisons of the measured and simulated electrical parameters. Finally, the developed classification models showed high accuracy of classifying each failure occurrence within the test set used for benchmarking. Specifically, the success rate obtained with the FRBCS models was 100 %, 96.9 % and 96.53 %, when classifying the inverter shutdown failure, bypass diode fault and partial shading, respectively.
This paper proposes an improved statistical failure detection technique for enhanced monitoring c... more This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.
Ensuring optimal performance of solar photovoltaic (PV) systems requires the extensive assessment... more Ensuring optimal performance of solar photovoltaic (PV) systems requires the extensive assessment and understanding of losses of different origin that affect these installations. Soiling is a key loss factor influencing the performance of PV systems, particularly in arid and dry climatic regions, and its thorough knowledge and modelling aspects including the seasonal evolution is challenging for the early stages of energy prospecting for PV power plants. The purpose of this study is to address this fundamental challenge by evaluating the loss of soiling and the performance of six soiling models based on both physical and machine learning (ML) approaches. Specifically, the case study is a soiling test-bench experimental apparatus installed at the outdoor test facility of the University of Cyprus in Nicosia, Cyprus. The climatic conditions of the site represent a dry climate with high PV potential due to high irradiation levels. The obtained results reported soiling rates ranging from 0.039%/day to 0.535%/day depending on the season and the presence of dust episodes. The average yield daily and monthly soiling losses were 1.9% and 2.4% over a 2-year period, respectively. Furthermore, the comparative analysis of the different soiling models illustrated that the physical models achieved slightly better performance than the ML models with root mean square error (RMSE) of 1.16% and 0.83% for daily and monthly losses, respectively. Finally, the findings provide evidence and useful information on the performance and limitations of the different soiling models for fielded PV systems located in arid and dry climatic zones.
A main challenge in the scope of integrating higher
shares of photovoltaic (PV) systems is to en... more A main challenge in the scope of integrating higher
shares of photovoltaic (PV) systems is to ensure optimal
operations. This can be achieved through next-generation
monitoring with automatic data-driven functionalities. This work
aims to address this fundamental challenge by presenting the stage
of implementation of an advanced cloud-based monitoring
platform and a control digital twin for PV power plants (MW
scale). The platform is fully equipped with a multitude of artificial
intelligent (AI) algorithms for health-state diagnostics and
analytics. The performance of the digital twin to act as a health state
monitor was validated against field and synthetic data from
PV systems at different locations and demonstrated high
accuracies for PV performance modelling and fault diagnosis.
A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and i... more A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and improving lifetime performance is proposed in this paper. The platform incorporates a decision support system (DSS) engine and data-driven functionalities for data cleansing, PV system modeling, early fault diagnosis and provision of O&M recommendations. It can ensure optimum performance by monitoring in real time the operating state of PV assets, detecting faults at early stages and suggesting field mitigation actions based on energy loss analysis and incidents criticality evaluation. The developed platform was benchmarked using historical data from a test PV power plant installed in the Mediterranean region. The obtained results showed the effectiveness of the incorporated functionalities for data cleansing and system modeling as well as the platform’s capability for automated PV asset diagnosis and maintenance by providing recommendations for resolving the detected underperformance iss...
Progress in Photovoltaics: Research and Applications
Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a... more Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum ... more Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An O&M decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a large-scale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 e/kW/year when performing O&M actions. For an electricity price of 59.98 e/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized O&M cost value of 7.45 e/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV O&M Cost Model.
It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifeti... more It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate (RD) may exhibit nonlinear behavior. Neglecting nonlinearities may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear RD. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the "rear RD value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.
The timely detection of photovoltaic (PV) system failures is important for maintaining optimal pe... more The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive m...
A key factor important for the future photovoltaic (PV) uptake and the PV value chain is to reduc... more A key factor important for the future photovoltaic (PV) uptake and the PV value chain is to reduce the Levelized Cost of Electricity (LCoE). This can be achieved by increasing lifetime performance and reducing operating costs through robust condition monitoring offering quality control, safeguarding guarantees and cost-effective operation and maintenance (O&M). Specifically, since monitoring systems can assist to reduce the LCoE of PV, specific novel features based on advanced data analytics need to be included, such as data quality routines (DQRs) providing data sanity and integrity, system health state monitors offering real time operating state information, failure diagnosis through data analysis and other added value services such as performance loss quantification and degradation rate (DR) estimation.
Photovoltaic (PV) power prediction is important for monitoring the performance of PV plants. The ... more Photovoltaic (PV) power prediction is important for monitoring the performance of PV plants. The scope of this work is to develop a methodology for deriving an optimized location and technology independent machine learning (ML) model for power prediction. The prediction accuracy results demonstrated that the performance of the ML model was primarily affected by the dataset split method. In particular, for a 70:30 % train and test set approach, the ML model achieved a normalized root mean square error (nRMSE) of 0.88 % when using randomly selected samples compared to 0.94 % when using continuous samples. The accuracy of the developed model was also affected by the duration of the train set. For a random 70:30 % train and test set approach, the constructed ML topology achieved a nRMSE of 0.88 %, while when the dataset was split into a 30:30 % portion, the nRMSE was 0.95 %. Moreover, when low irradiance conditions were filtered out and 70 % of the entire dataset was randomly chosen for model training, a nRMSE of 1.41 % was obtained demonstrating that the model’s accuracy was not improved. Finally, for a random 10:30 % train and test set approach, the FNNN achieved the lowest nRMSE of 1.10 % when the model was trained using the prevailing irradiance classes.
The scope of this paper is to present the development of failure detection routines (FDRs) that w... more The scope of this paper is to present the development of failure detection routines (FDRs) that will operate on acquired data sets of grid-connected PV systems and determine and classify accurately the exhibited failures. The developed FDRs comprise of a failure detection and a classification stage. Specifically, the implemented failure detection stage was based on a comparative algorithm that detected discrepancies between the measured and simulated electrical measurements (dc current, voltage and power of the array) of a test PV system. Furthermore, statistical algorithms for identifying outliers, anomalies and normal system operation limits were also used for failure detection. Accordingly, for each identified failure there was a subsequent decision stage which performed a classification process based on developed logic and decision trees. The decision trees were constructed with a supervised learning process, trained with continuous samples split in a 70:30 % train and test set ...
Real-time identification of failures in photovoltaic (PV) systems is crucial for achieving reacti... more Real-time identification of failures in photovoltaic (PV) systems is crucial for achieving reactive maintenance schemes that, in turn, will increase the system reliability and guarantee the lifetime output. Following this line, failure detection routines (FDRs) that operate on acquired data-sets of grid-connected PV systems were developed to diagnose the occurrence of failures. The developed FDRs comprise of a failure detection and a classification stage. The detection stage was based on the comparison between the measured and predicted DC power production against set threshold levels (TL). The classification stage was based on data-driven algorithms, which were used to post-process the detected failure patterns through the application of the developed decision trees (DT), k-nearest neighbours (k-NN), support vector machine (SVM) and fuzzy inference systems (FIS). The experimental results showed that the FDRs were capable of detecting all the different types of failures (open- and s...
The presence of defects in a solar cell device affects the electrical parameters causing possible... more The presence of defects in a solar cell device affects the electrical parameters causing possible performance deterioration. Point-like defects are expected to have significant impact on their local surroundings. Their effect weakens away from them. For this reason a physical model of a GaAs device taking into consideration the impact of local ohmic shunts was developed in order to examine the effect of the shunts on the electrical and physical parameters. Defect states have been treated as degenerate semiconductors and the impact of voltage bias and degeneracy on the main electrical parameters of the cell have been investigated.
2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018
Accurate identification of failures in photovoltaic (PV) systems, which can result in energy loss... more Accurate identification of failures in photovoltaic (PV) systems, which can result in energy loss or even serious safety issues, is crucial for ensuring reliability of the installations and the guaranteed lifetime output. The scope of this paper is to present the development of failure detection routines (FDRs) that operate on acquired data-sets of grid-connected PV systems in order to diagnose the occurrence of failures. The developed FDRs comprise of a failure detection and classification stage. Specifically, the failure detection stage was based on a comparative statistical approach between the measured and simulated electrical measurements. In parallel, fuzzy logic inference was performed in order to analyse the failure pattern and classify accurately the occurred fault. The fuzzy rule based classification system (FRBCS) models were constructed for each failure through a supervisory learning process, trained with continuous samples split in a 70:30 % train and test set approach of acquired data-sets which included the feature patterns exhibited during normal and faulty operation. The results obtained by emulating three failure patterns (partial shading, inverter shutdown and bypass diode failure), showed that the developed FDRs were capable of detecting accurately the faults upon their occurrence by signifying detectable discrepancies through the daily statistical comparisons of the measured and simulated electrical parameters. Finally, the developed classification models showed high accuracy of classifying each failure occurrence within the test set used for benchmarking. Specifically, the success rate obtained with the FRBCS models was 100 %, 96.9 % and 96.53 %, when classifying the inverter shutdown failure, bypass diode fault and partial shading, respectively.
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Papers by Andreas Livera
shares of photovoltaic (PV) systems is to ensure optimal
operations. This can be achieved through next-generation
monitoring with automatic data-driven functionalities. This work
aims to address this fundamental challenge by presenting the stage
of implementation of an advanced cloud-based monitoring
platform and a control digital twin for PV power plants (MW
scale). The platform is fully equipped with a multitude of artificial
intelligent (AI) algorithms for health-state diagnostics and
analytics. The performance of the digital twin to act as a health state
monitor was validated against field and synthetic data from
PV systems at different locations and demonstrated high
accuracies for PV performance modelling and fault diagnosis.
shares of photovoltaic (PV) systems is to ensure optimal
operations. This can be achieved through next-generation
monitoring with automatic data-driven functionalities. This work
aims to address this fundamental challenge by presenting the stage
of implementation of an advanced cloud-based monitoring
platform and a control digital twin for PV power plants (MW
scale). The platform is fully equipped with a multitude of artificial
intelligent (AI) algorithms for health-state diagnostics and
analytics. The performance of the digital twin to act as a health state
monitor was validated against field and synthetic data from
PV systems at different locations and demonstrated high
accuracies for PV performance modelling and fault diagnosis.