AI-Based Power Degradation Assessment in PV Cells

Monitoring and remotely controlling off-grid solar power systems using machine learning to predict and compare operating parameters versus actual measurements to detect abnormal operating conditions. A first ML model predicts solar panel power, intensity, load profile, and battery voltage based on sensor readings. A second ML model compares predicted vs actual values to detect anomalies. If abnormalities are found, the second ML model sends alerts and provides maintenance recommendations like UAV inspection, cleaning, or load reduction.

Source

A method for monitoring solar panel performance that separates short-term and long-term variations. The method uses a trained model to simulate average panel performance based on environmental parameters, and compares this simulated performance to actual panel output. By analyzing the difference between simulated and actual performance over time, the method can detect long-term degradation such as soiling or UV damage, and identify maintenance or weather-related events. The method can be applied to photovoltaic panels, thermal solar panels, and hybrid solar panels.

Source

System and method for determining degradation loss in solar panels of photovoltaic power plants using machine learning models. The system obtains location, weather, and configuration data for the panels, as well as real-time operating parameters from sensors. This data is input into a pre-trained machine learning model to determine degradation loss for each panel, with alerts triggered when loss exceeds a threshold.

Source

A process for maximum power point tracking (MPPT) of photovoltaic modules that monitors degradation of the modules. The process involves periodic I/V operating point measurements of the module, positioning of the operating point at the maximum power point (MPP), and monitoring of the degradation of the module. The monitoring includes measuring electrical parameters associated with the current-voltage characteristics of the module, comparing the measured parameters with modeled data, and deducing degradation paths and causes from the comparison.

Source

Real-time monitoring of photovoltaic (PV) systems through simultaneous measurement of individual panels and strings under varying environmental conditions, enabling continuous performance assessment without inverter shutdown.

Source

A monitoring system for photovoltaic systems that identifies the faulty component causing reduced performance without requiring field intervention. The system comprises electronic control means that analyze data from the photovoltaic panel, inverter, and optimizer to pinpoint the source of the degradation. This enables rapid diagnosis and repair, reducing downtime and maintenance costs.

Source

Method for determining degradation rate of photovoltaic installations without weather data, using production data from clear-sky days to estimate degradation rate through monthly aggregation and comparison of production decreases across years.

Source

Method for diagnosing internal loss mechanisms in solar cells by analyzing the type of simulated current density-voltage (JV) curve, enabling identification of specific loss mechanisms such as recombination, series resistance, and shunt resistance.

Source

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The result... Read More

Source

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-b... Read More

Source

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability.

Source

System and method for detecting and reversing potential-induced degradation (PID) in solar modules based on their positioning in a string. The system monitors negative-to-ground voltage and fuse conditions in real-time to identify PID-prone modules, which are then switched from the negative side to the positive side to reverse the degradation. The method enables early detection and prevention of PID, which can cause up to 80% power loss in solar modules.

Source

A method for predicting performance of solar cell structures using machine learning algorithms. The method involves training a machine learning model on a dataset of known solar cell structures and their corresponding performance characteristics. The trained model is then used to predict the performance of new solar cell structures based on their structural parameters. The method enables rapid and accurate prediction of solar cell performance, enabling optimization of solar cell design and fabrication processes.

Source

Photovoltaic (PV) systems are integral to renewable energy, demanding accurate performance modeling for optimal functionality. This paper presents a pragmatic, data-driven approach employing Polynomial Regression (PR) for solar panel modeling to boost accuracy and adaptability to environmental variables. By emphasizing the advantages of data-driven models in attaining predictive precision, this study aims to reconcile theoretical concepts with practical solar panel performance. PR is employed as a black-box machine learning (ML) algorithm to transcend the limitations of traditional modeling methods, revealing ML's ability to grasp intricate relationships and ensure precise predictions. Utilizing real and simulated datasets encompassing solar irradiance, ambient temperature, and applied load through hardware in the loop (HIL), the model is trained to forecast the electrical outputs of solar panels in two approaches to estimate output voltage and current as well as key points on the panels' I-V curve. Assessment of model accuracy using Root Mean Square Error (RMSE) showcases the PR mod... Read More

Source

A method for predicting the performance and reliability of photovoltaic systems over time, taking into account factors such as installation quality, component specifications, environmental conditions, and maintenance practices. The method determines system parameters representing expected output, availability, and failure probability by analyzing parameters associated with the system, inverter, and components, and modeling their degradation over time. The system parameters are calculated based on the expected efficiency of the photovoltaic system, which is determined by analyzing the expected time course of output power and the degree of aging.

Source

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

Source

A system for monitoring solar power plant performance that quantifies losses in energy generation using data from multiple inverters. The system collects inverter and weather station data, analyzes it using machine learning models to segregate and quantify losses, and generates maintenance tickets for underperforming inverters. The system compares inverter attributes to determine actual power losses, enabling real-time analysis and diagnosis of performance issues.

Source

This paper presents a case study for analysis and prediction of photovoltaic generation in the metropolitan region of Fortaleza-CE, applying machine learning techniques in the paradigms of time series prediction and system identification. Computational simulations were performed using linear (least squares) and non-linear (artificial neural networks and kernel methods) estimators that are part of the state-of-the-art in machine learning. In time series scenario, using only power measurements, the best results were obtained with the MLP network, with a prediction horizon of seven days ahead. In system identification, using power and solar radiation measurements, the least squares estimator achieved the best performance among all tested estimators, even in the free simulation scenario, i.e., infinite steps ahead.

Source

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.

Source

The integration of renewable energy sources and the advancement of smart grid technologies have revolutionized the power distribution landscape. As the smart grid evolves, the monitoring and control of power converters play a crucial role in ensuring the stability and efficiency of the overall system. This research paper introduced a converter monitoring system in photovoltaic systems, the main concern is to protect the electrical system from disastrous failures that occur when the system is in operating condition. The reliability of the converters is significantly influenced by the degradation of their passive components, which can be characterized in various ways. For instance, the aging of inductors and capacitors can be char-acterized by a decrease in their inductance and capacitance values. Identifying which component is undergoing degradation and assessing whether it is in a critical condition or not, is crucial for implementing cost-effective maintenance strategies. This paper explores a set of classification algorithms, leveraging machine learning, trained on data collected f... Read More

Source