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This poster discusses how PV inverter failures cause AC breaker damage and is intended to help system owners identify power quality issues and origins, help inverter and/or circuit breaker manufacturers develop safer failure modes, and develop addendums to standards for failure testing of breakers and inverters.
Case study examines a common PV inverter failure that can drive uncommon issues in the field - namely, damage to the AC protection equipment from DC fault currents for short periods during transformer-less inverter power electronic failures.
Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.
The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.
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While PV panel reliability continues to increase, PV inverters become the limiting factor for PV system reliability. Consequently, it is critical to have a generic tool from a third party for PV inverter reliability assessment to help 1) utilities/PV farm operators schedule maintenance in advance, and 2) inverter developers improve the next-generation design. However, these two things cannot be accomplished without first understanding the reasons behind inverter failure. Following this idea, as the first step, it is essential to identify and investigate the most failure-prone components within a PV inverter system. After all, any system is only as reliable as the components that are contained within it. This motivates the failure mode and effects analysis (FMEA) work presented for this workshop. The FMEA is conducted as follows: first, the overview of the methodology on the development of the FMEA is presented; then, based on a top-down approach starting from the PV inverter system, critical inverter components with high failure rates are identified and summarized; afterward, a thorough FMEA study at a component-level is performed and its results, including failure modes, failure mechanisms, and critical stressors, are tabulated; finally, according to three rankings (chance of occurrence, severity of occurrence, and ease of detection prior to failure) for each failure mechanism provided by the FMEA, risk priority numbers are calculated and the failure mechanisms along with the critical stressors are ranked in terms of their potentially detrimental effect on the PV inverter.
Physics-of-Failure (PoF) based reliability assessment for photovoltaic (PV) inverters requires long-duration electrical and electrothermal stress histories, yet generating such stress histories with high-fidelity switching models over year long mission profiles is computationally prohibitive. Conventional methods either sacrifice modeling fidelity for speed or require runtimes that are impractical for design iteration and uncertainty studies. To address this bottleneck, this paper presents a High-Performance Computing (HPC) based simulation frame work for faster-than-real-time reliability-oriented simulation. The proposed framework integrates the Average-to-Switching (A2S) method with parallel computing techniques to accelerate switching-level waveform reconstruction. We further introduce optimization strategies, including cluster merging and sensitivity based mission profile screening, to reduce the computational burden. Evaluated using real-world mission profile inputs and a MATLAB/Simulink switching-model reference, the framework reduces the simulation time for a one-year mission from an intractable multi-year duration to approximately 7.3 minutes while maintaining low waveform error. This acceleration provides a practical reliability-oriented simulation engine that can be coupled with component-specific aging models for subsequent PV inverter PoF assessment.
Photovoltaic (PV) systems represent a cornerstone in the global shift toward sustainable energy generation, with inverters serving as the crucial link between solar panels and the grid. Despite their pivotal role, inverters are susceptible to failures, contributing significantly to maintenance events and operational disruptions in large-scale PV plants. This white paper investigates the importance and methodologies of predictive maintenance strategies that monitor the component-level pre-failure signatures on PV inverters. Through a comprehensive survey of literature and industry professionals, insights on preventive maintenance and retrofitting practices have been gathered. The expert elicitation helps shed light on common challenges and opportunities for improving PV system reliability, particularly inverter reliability. By addressing these challenges, the white paper aims to enhance the long-term viability and effectiveness of solar PV plants in the renewable energy landscape, contributing to the global transition toward environmentally friendly energy generation.
Photovoltaic (PV) inverters are critical yet vulnerable components in modern energy systems, often acting as reliability bottlenecks that increase the levelized cost of energy (LCOE). To address this, this paper presents a comprehensive Failure Mode and Effects Analysis (FMEA) tailored for PV inverters. Leveraging field data and literature, we identify failure-prone components, such as capacitors,, and relays, and prioritize their risks based on quantitative Risk Priority Numbers (RPNs). The analysis reveals that surge-induced MOV short circuits, capacitor degradation, and environmental cooling fan failures dominate the risk profile. These findings provide a targeted framework for reliability improvement, guiding future efforts in predictive diagnostics, design optimization, and accelerated life testing strategies.
Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.
Solar tracking system failures can dramatically reduce photovoltaic (PV) system energy output through suboptimal irradiance capture and self-shading loss. Detecting tracker failures is not always straightforward; unlike other common PV system failures like inverter outages and blown fuses which take portions of a system completely offline, tracker failures only partially reduce output power and can go unnoticed as a result. Additionally, tracker failures can take one of several forms, each with their own loss characteristics. Here we present two methods of detecting tracker failure events from time-series production data and a method for estimating the associated production loss. Compared with existing detection methods, the proposed power-based detection method showed 0-17% reductions in Type I error rate depending on weather conditions. Here, the loss model estimated production loss with low error (mean bias error = -2.3 %, root-mean-squared error = 6%) in a rudimentary validation.
This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.
The reliability of photovoltaic (PV) inverters is critical for long-term solar system performance, with cooling fan failures frequently leading to costly downtime. While much research exists on general cooling fan reliability, little attention has been given to fans operating within PV inverters and their unique environmental challenges. Here, this article proposes a comprehensive methodology to address this gap. First, a failure mode and effects analysis is performed on fans to identify the key failure mechanisms in PV applications, their corresponding stressors, and the models necessary for lifetime prediction. Second, an accelerated life test is designed and conducted to collect valuable experimental data for PV inverter fans in a reasonable amount of time. Third, a mathematical conversion of dynamic mission profiles into effective constant stress levels is derived. Fourth, case studies are given, showcasing lifetime estimates that account for geographic variations in mission profile data. The results demonstrate that this integrated approach leads to an accurate reliability assessment for PV inverter cooling fans.
This presentation talks about different failure modes and corresponding detection schemes of PV panels, PV inverters, electric machines such as motors and power converter. Several novel techniques have been presented that are capable of measuring degradation as well as detecting faults in various location in a PV based power system.
The performance metrics of the state-of-the-art commercial solar inverters, such as system cost, operation and maintenance (O&M) cost, service life, reliability, maintainability, and power density are much lower than the target metrics needed to achieve SunShot’s 2030 levelized cost of energy (LCOE) goals. To overcome the shortcomings of the existing solar inverters, this project proposed a novel Hot-Swappable, Fault-Tolerant, Modular Power Converter (HSFT-MPC) concept for solar photovoltaic (PV) plants and proved the concept through the design, fabrication, and laboratory test validation of a single-phase HSFT-MPC prototype. The HSFT-MPC has the following distinct advantages over the state-of-the-art: 1) elimination of harmonic/ electromagnetic interference (EMI) filter in the inverter stage due to the novel topology, 2) lower system cost and higher power density due to the modular design, elimination of harmonic/EMI filter, and lower cooling requirement, 3) higher efficiency due to lower switching frequencies, 4) higher reliability and longer (50 years) service life due to simpler cooling and fault tolerance capability, 5) easier installation, lower O&M cost, and improved maintainability due to the modular design and hot-swappable power electronic building blocks (PEBBs), and 6) improved manufacturability due to the modular design. This project developed a single-phase HSFT-MPC prototype with 25kW nominal output power, 2.4kV, 60Hz nominal AC output, lower than 5% AC output voltage total harmonic distortion, over 5 kW/L inverter power density, and 99.4% inverter peak efficiency, being tolerant to failure of single and multiple PEBBs, and capable of hot swapping of the failed PEBB(s). The HSFT-MPC enables uninterruptable operation of the solar PV plant when failure of single or multiple PEBBs or PV modules occurs. Compared with the existing solar inverters in the market, the HSFT-MPC is expected to reduce the inverter failure-caused downtime and energy losses of solar PV plants by more than 60% and 50%, respectively. Project findings have been presented at major conferences in the field and published in peer-reviewed papers, which added new knowledge to the field of power electronics for solar PV systems. A minicourse on Solar PV Systems was developed for outreach activities. The minicourse will help attract young individuals to the renewable energy profession which has a significant talent shortage. The HSFT-MPC is expected to overcome all of the shortcomings of the state-of-the-art solar inverters in terms of cost, efficiency, service life, reliability, maintainability, and manufacturability targets needed to achieve SunShot’s 2030 LCOE goals. Therefore, the proposed HSFT-MPC concept has the great potential to disrupt the current solar inverter market. This project created a pathway towards industry adoption of the HSFT-MPC to help achieve 50-year service life solar PV systems. Since the solar PV plants using the HSFT-MPC will feature with higher reliability, longer service life, and easier maintenance, they are particularly useful for the rural areas with underserved populations that demand reliable and affordable clean electricity. The outcomes of the project have the strong potential to address national needs in the field of renewable energy to reduce CO 2 emissions from the electricity sector, reduce imports of energy from foreign sources, and improve energy security, efficiency, and sustainability. Since electricity is used in almost all of society’s sectors, the outcomes of the project will benefit various sectors of society and economy.
Despite the importance of reliability to the cost competitiveness of PV, large data sets enabling high-level investigation of the technology’s performance in the field are relatively scarce. Dirk C. Jordan, Chris Deline, Bill Marion and Teresa Barnes of the National Renewable Energy Laboratory, and Mark Bolinger of the Lawrence Berkeley National Laboratory study a unique data set of 100,000 PV systems in the US, drawing out tips for better reliability that have relevance to other parts of the world.
Inverters are often reported to be the highest-impact failure point in PV systems. This importance is belied by the simplistic assumptions about inverter downtime losses used in industrial energy modeling. Energy models often assume the extent of inverter-related losses is limited to 1% of annual production from scheduled inverter preventative maintenance. In reality, inverter-related production losses are much more variable, although data from large-scale surveys of fielded systems are rare. Here we present a method of detecting inverter downtime events and estimating the associated lost production using inverter- and meter-level power data. Because communications outages are of similar frequency to true production outages, the method pays particular attention to distinguishing communications outages from true production outages. The results of applying the method at fleet-scale are presented and discussed.
Abstract 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.