A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems
Abstract
:1. Introduction
2. Materials and Methods
- In the first phase, anomalies and faults of the components were categorized through a search activity on digital collections such as Scopus, ScienceDirect, Web of Science, and IEEE Xplore, using concatenated keywords related to the component typology (such as “PV” or “Photovoltaic” or “Wind” or “Wind turbine” or “Electrolyzer” or “Electrolysis” or “Fuel Cell” or “FC” or “Battery” or “Battery system” or “BESS” or “Conversion” or “Power conversion” or “Converter” or “AC/DC” or “DC/DC” or “Monitoring systems” or “Communication” or “Communication systems”) and the investigated issue (such as “Failure” or “Anomalies” or “Rupture” or “Degradation” or “Performance decay” or “Reliability” or “Stress test”). The analysis of the identified papers and their related bibliographies was used to extend the investigation to other relevant papers.
- In the second phase, empirical datasets or mathematical models for the identified issues of each technology were searched. Through a detailed analysis of the papers highlighted in the previous phase, useful mathematical models or the existence of dedicated datasets were identified. Datasets were also found using platforms such as Google Dataset Search, IEEE DataPort, Kaggle, and Mendeley Data by using as combined keywords the technology and the investigated issue.
2.1. Caveats
2.2. Anomalies and Faults in PV Systems
Research Highlights
2.3. Anomalies and Faults in Wind Turbines
Research Highlights
2.4. Anomalies and Faults in Electrolyzers
Research Highlights
2.5. Anomalies and Faults in Fuel Cells
Research Highlights
2.6. Anomalies and Faults in Battery Systems
Research Highlights
Target Component | Description | Cause | References |
---|---|---|---|
Cell | Loss of active material | Battery degradation | [127] |
Electrolyte consumption | [128] | ||
Increase in internal resistance | [129] | ||
Lithium deposition | [130] | ||
Gas generation | [123] | ||
SEI thickening | [124] | ||
Current collector corrosion | [131] | ||
Internal short circuits | [132] | ||
Thermal runaway | [133] | ||
Capacity diving | [123] | ||
Liquid leakage | [123] | ||
System | Overcharge | Battery management system anomaly/fault | [134] |
Overdischarge | [135] | ||
Reduced battery life | [136] | ||
Thermal runaway | [133] | ||
Reduced battery performance | Sensory system | [136] | |
Equalization errors | [123] | ||
Reduced battery life | [136] | ||
Thermal runaway accidents | [133] | ||
Increase internal resistance | Cables and connections | [129] | |
Thermal runaway safety accidents | [133] |
Dataset Name | Source | Description | References | Related Papers |
---|---|---|---|---|
NASA Data Repository | Lab testing | Data sets suitable to develop algorithms useful as prognostic tools | [138] | |
IEEE Data Port | Simulations | Data set obtained by simulating a lithium polymer cell model ePLB C020, with an effective capacity of 15 Ah, related an electric car | [139] | |
Stanford Fast Charging Datasets | Lab testing | Dataset obtained through tests performed on commercial lithium-ion batteries under fast charging conditions. In particular, the cells, of the lithium-iron-phosphate (LFP)/graphite type, produced by A123 Systems (APR18650M1A), were tested on a 48-channel Arbin LBT device. The cells considered are characterized by a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V | [140] | [141] |
Lifecycle Prediction Dataset | Lab testing | Data set obtained by testing commercial lithium-ion batteries under fast charging conditions. The lithium-ion phosphate (LFP)/graphite cells, manufactured by A123 Systems (APR18650M1A), were tested using the 48-channel Arbin LBT device in a forced convection temperature chamber set to 30 °C. The cells have a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V | [142] | [143] |
University of Wisconsin Madison | Lab testing | Operational dataset for the Panasonic 18650PF lithium-ion battery | [144] | [145] |
BEEPt | Lab testing | Set of tools designed to support Battery Evaluation and Early Prediction of life cycle corresponding to the research of the d3batt program and the Toyota Research Institute | [146] | [147] |
Universal Battery Database | Lab testing | Open source Li-ion data management and modelling software | [148] | |
Alawa-toolbox | Lab testing and simulations | Dataset from University of Hawaii, which provides a large number of curves with different degradation modes, LLI and LAM | [149] | [150] |
2.7. Anomalies and Faults in DC/x Conversion Systems
Research Highlights
2.8. Anomalies and Faults in Monitoring Systems
Research Highlights
2.9. Anomalies and Faults in Communication Systems
Research Highlights
3. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
BS | Battery System |
EIS | Electrochemical Impedance Spectrometry |
FC | Fuel Cell |
GDL | Gas Distribution Layer |
HAWT | Horizontal-Axis Wind Turbine |
IGBT | Insulated-Gate Bipolar Transistor |
LAM | Loss of Active Material |
Li-ion | Lithium-ion |
LLI | Loss of Lithyum Inventory |
ML | Machine Learning |
MOSFET | Metal–Oxide–Semiconductor Field-Effect Transistor |
MPPT | Maximum Power Point Tracking |
PEM | Polymeric Electrolyte Membrane |
PV | Photo Voltaic |
PWM | Pulse-Width Modulation/Modulated |
SEI | Solid Electrolyte Interphase |
STC | Standard Test Condition |
VAWT | Vertical-Axis Wind Turbine |
WT | Wind Turbine |
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Target Component | Description | Cause | References |
---|---|---|---|
PV Module | Partial shading | Clouds, trees, building, etc. | [22,23] |
Dust Accumulation | Environmental pollution | [24,25] | |
Leaves fall, bird droppings | Environmental pollution | [25] | |
Hot Spot | Mechanical and optical degradation of encapsulation | [33] | |
Glass breakage | Bad installation | [27] | |
Welding | Leaching of silver or copper, solder joint fatigue, bad welding | [28] | |
Frame issues | Snowing | [27] | |
Microcracks | Multiple (transportation, incorrect installation, vibrations, excessive loads, environmental stress, improper cleaning, etc.) | [34] | |
Busbar failure | Incorrect packaging, installation, hail, and/or stone throwing | [35] | |
Module degradation | Multiple | [36] | |
Discoloration | Multiple | [37] | |
Delamination | Multiple | [38] | |
Cell breakage | Multiple (production, transport, installation, vibrations, environmental stress, improper cleaning, and maintenance, etc.) | [27,39] | |
Connection System | Short circuit | Bad wiring, bad production process | [17,19] |
Open circuit | Multiple (bad/obsolete wiring, hot spots, cell breakage, bad connections in the junction box, etc.) | [17,18,19] | |
Bypass diode failure | Short-/open-circuit | [17,18] | |
Bridging faults | Improper connection between PV modules | [19] | |
Ground fault | Insulation deterioration, corrosion, wire cutting, or poor/incorrect connection | [29,30] | |
Line-to-line fault | Short circuits by unintentional connections (wearing, bad connection, etc.) between current-carrying conductors with ground/neutral conductors and/or other PV system’s parts (e.g., the PV module’s frame) | [29,30] | |
Arc fault | Gap between conductors by corrosion of connectors, cell damage, solder disconnection, insulation breakage | [29] | |
Junction Box | Junction box fault | Human errors (insufficient fastening of the junction to the back panel, poor wiring, inadequate assembly, moisture penetration into connectors) | [40] |
MPPT | MPPT control system failure | MPPT charge controller or sensors failure | [31] |
Inverter | Inverter failure | IGBT, capacitors, inductors, etc. failure | [32] |
PV System | Lightning strike fault | Lightning strikes | [26] |
PV array | PV array fault | Bad connections | [29,30] |
Network grid connection | Line fault | Line interruptions, equipment failures, maintenance services, network configuration, accidents, human error, etc. | [41] |
Dataset Name | Source | Description | References | Related Papers |
---|---|---|---|---|
Fault Detection Dataset in Photovoltaic Farms | Simulations | Simulated 25 PV system used for generating data during normal operations, string fault, string-to-ground fault and string-to-string fault | [42] | [43] |
PVEL-AD dataset | Real plant | 36,543 electroluminescence images of PV panels with no/various defects and backgrounds | [44] | [45] |
GPVS-Faults | Lab-scale real plant | Array, inverter, feedback sensor, MPPT controller and grid anomalies/faults | [46] | [47] |
PV System Thermography Dataset | Real plant | 120 thermal images obtained from a drone | [48] | [49,50] |
Mismatching and partial shading dataset | Simulations and real plant | 10,000 simulated IV curves (5000 in normal operations and 5000 under mismatch faults), and 2000 real IV curves (1000 in normal operations and 1000 during faults) | [51] | [52] |
Partial Shading and Fault Simulation Dataset | Simulations | Simulations of 10 PV panels under variations in temperature and partial shading conditions | [53] | |
PV Fault Dataset | Real plant | System with 2 strings of 8 C6SU-330P PV modules under degradation, short circuit, open circuit and shading anomalies/faults | [54] | [55] |
Elpv dataset | Real plant | 2624 electroluminescence images (300 × 300 pixels, 8 bit-grayscale), of intact and damaged PV cells with different degradations | [56] | [57,58,59] |
PVWatts calculator | Web tool | Can generate hourly data based on the input PV system’s size and location. Can account for losses due to, e.g., soiling, shading, mismatch, etc. | [60] |
Dataset Name | Source | Description | References | Related Papers |
---|---|---|---|---|
Wind turbine gearbox monitoring vibration analysis benchmark dataset | Real | Data collected from a functioning gear and a damaged one. The healthy gear was tested only with a dynamometer, while the damaged one was first tested with a dynamometer and then sent to a wind farm for a field test | [65] | |
Wind Turbine Blades Fault Diagnosis based on Vibration Dataset Analysis | Real | Uniaxial vibration measurements of a wind turbine operating at various wind speeds. There are three types of issues (blade damage, blade surface degradation, and unbalanced blade) in addition to measurements taken under normal operating conditions | [66] | |
Vibration Signals Feature for Fault Diagnosis of wind turbine blade | Real | The Vibration measurements under both normal and fault conditions (blade damage, blade surface degradation, and unbalanced blade) | [67] | |
YOLO Annotated Wind Turbine Surface Damage | Real | Surface images of wind turbines with annotated damages | [68] | [69] |
Wind turbine fault diagnosis dataset | Real | Measurements from several wind turbines | [70] | [71] |
Wind turbine PMSG- Short-Circuit Fault | Simulations | Simulation of a mathematical model at 1 kHz of sampling frequence | [72] | [73] |
Vibration and Motor Current Dataset of Rolling Element Bearing Under Varying Speed Conditions for Fault Diagnosis | Real | Dataset containing vibration, current, temperature, and acoustic measurements of a rotating machine. Both normal conditions and malfunctions (e.g., bearing failures at different rotation speeds, shaft misalignment, and rotor imbalance) are considered. It is not directly related to wind turbines but to a rotating machine. | [74,75,76] | [77] |
Gearbox Fault Diagnosis Data | Real | Vibration dataset recorded varying load from 0 to in healthy condition to broken tooth condition | [78] | |
EDP Open Data | Real | Historical data of faults occurred in a Wind Farm | [79] |
Target Component | Description | Cause | References |
---|---|---|---|
Membrane | Mechanical degradation | Current collector hole; Widening and narrowing; Non-uniform hydration; Lack of water | [83,85] |
Thermal degradation | Thermal stresses; Thermal cycles | ||
Chemical and electrochemical degradation | Contamination; Radical attacks | ||
Catalysts | Dissolution | Too high potential; Formation of soluble iridium complexes during the oxygen evolution reactions; Current inversion in the shut-off procedure | [84,86,92] |
Support passivation | Too high potential; Highly oxidant environment | ||
Agglomeration | Sinterning of active sites; Start-up and shut-down load cycles | ||
Ionomer dissolution | High current density, radical chemical attack | ||
Cations contamination | Locking of active sites for potential deposition; Replacement of protons in ionomer by cations | ||
Mechanical damages | Non-uniform tightening pressure; Non-uniform membrane dilatation | ||
Bipolar plates | Embrittlement for hydrogen | Hydrogen adsorption by cathodic metallic plates | [86,93] |
Passivation | Oxide layer formation | ||
Corrosion | Titanium oxidation; Iron corrosion by acids | ||
Current collectors | Chemical embrittlement | Metallic plates passivation and corrosion | [94] |
Mechanical embrittlement | Non regular compression; Hydrogen embrittlement |
Target Phoenomenon | Typology | Description | References |
---|---|---|---|
Membrane degradation | Predictive mathematical model of membrane degradation | The model accounts for the load cycle degradation mechanism | [95] |
Predictive mathematical model of cell performances based on temperature and load | The model accounts for the degradation mechanism based on radical attack to the membrane. The degradation curve depends by cell temperature and load | [87] | |
Predictive mathematical model of membrane thinning | The model accounts for the degradation curve depending on cell temperature and load | [87] |
Target Component | Description | Cause | References |
---|---|---|---|
Membrane | Mechanical degradation | Mechanical stresses due to non-uniform pressure in assembling procedure; Non-uniform humidification; Catalyst penetration in the membrane; Sealing material traces | [97,98,101,105,106,107] |
Thermal degardation | Thermal stresses and cycles | ||
Chemical and electrochemical degradation | Contamination; Radical attacks | ||
Electrodes | Activation loss | Catalyst sintering and unsoldering | [102,105,107,108,109] |
Conductivity loss | Catalytic support corrosion | ||
Reactants mass transport efficiency loss Reduction in tolerance to reactants | Mechanical stresses Contamination Materials hydrophobicity variation due to Nafion or PTFE dissolution | ||
GDL | Structure reduction | Support material embitterment; Carbon layer corrosion | [103] |
Water management ability reduction | Mechanical stresses; Materials hydrophobicity variation | ||
Conductivity loss | Corrosion | ||
Bipolar plate | Conductivity loss | Corrosion; Formation of a resistant surface layer | [101] |
Fracture/deformation | Mechanical stresses; Thermal cycles | ||
Seals (gaskets) | Mechanical fractures | Corrosion; Thermal stresses | [101] |
Target Phoenomenon | Typology | Description | References |
---|---|---|---|
Membrane degradation | Predictive mathematical model of membrane degradation | The proposed method is validated against polarization mechanisms due to over-current and over-voltage phenomena. The approach is based on finite elements method | [107] |
Predictive mathematical model of membrane degradation | The semi-empirical model accounts for the current losses, catalyst polarization and ohmic resistance | [116] | |
Predictive mathematical model of membrane degradation | The model accounts for polarization resistance as the sum of all polarization losses | [117] | |
Catalyst degradation | Predictive mathematical model of catalyst dissolution | The model is based on catalyst transformation theory | [118] |
Predictive mathematical model of catalyst dissolution | The model accounts for several phenomena determining the catalyst deactivation | [109] | |
Predictive mathematical model of catalyst dispersion and sintering | The model analyzes, at cathode-side, the platinum-based catalyst dispersion and agglomeration phenomena, leading to catalytic activity reduction | [119] | |
Stack potential degradation | Mathematical model of stack potential decay | The model determines the stack potential decay equation and the multiplicative factors based on start/stop, IDLE and over-potential phenomena | [120] |
Target Component | Description | Cause | References |
---|---|---|---|
Magnetic/ | Switches damage | Thermal stress | [156] |
capacitive/ | Capacitor damage | Electrical stress | [157] |
switchingdevices | Inductor damage | Thermal and electrical stress | [158] |
Printed circuit board | Delamination | Aging | [159] |
Cracks | |||
Weld deterioration | |||
Converter terminals | Power stage devices overcurrent and overtemperature | Terminals short-circuit | [160] |
Converter power stage | Ground fault | Worn, frayed, or damaged insulation due to mechanical, environmental, electrical stressing | [160] |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mariani, V.; Adinolfi, G.; Buonanno, A.; Ciavarella, R.; Ricca, A.; Sorrentino, V.; Graditi, G.; Valenti, M. A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems. Sustainability 2024, 16, 6042. https://doi.org/10.3390/su16146042
Mariani V, Adinolfi G, Buonanno A, Ciavarella R, Ricca A, Sorrentino V, Graditi G, Valenti M. A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems. Sustainability. 2024; 16(14):6042. https://doi.org/10.3390/su16146042
Chicago/Turabian StyleMariani, Valerio, Giovanna Adinolfi, Amedeo Buonanno, Roberto Ciavarella, Antonio Ricca, Vincenzo Sorrentino, Giorgio Graditi, and Maria Valenti. 2024. "A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems" Sustainability 16, no. 14: 6042. https://doi.org/10.3390/su16146042