Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-30, pub-electronic 2021-10-03Publication status: PublishedTo ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future

New records and noteworthy data of plants, algae and fungi in SE Europe and adjacent regions, 14

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: diatom algae Cyclostephanos invisitatus, Cyclotella meduanae, and Stephanodiscus lacustris, mycorrhizal fungi Alessioporus ichnusanus and Amanita mairei, saprotrophic fungi Diaporthe oncostoma, Stropharia albonitens and Pseudomassaria chondrospora, lichenised fungus Acrocordia subglobosa, stonewort Chara connivens, mosses Buxbaumia viridis, Tortella fasciculata and Tortula protobryoides, monocots Epipactis pontica Gymnadenia frivaldii, and Orchis italica and dicots Callitriche brutia, Callitriche platycarpa and Epilobium nutans are given within SE Europe and adjacent regions

New records and noteworthy data of plants, algae and fungi in SE Europe and adjacent regions, 15

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: saprotrophic fungus Geastrum morganii, Guignardia istriaca and Hypoxylon howeanum, mycorrhizal fungus Amanita friabilis and Suillus americanus, xanthophyte Vaucheria frigida, stonewort Chara hispida, liverwort Calypogeia integristipula and Ricciocarpus natans, moss Campylopus introflexus, Dicranum transsylvanicum, Tortella pseudofragilis and Trematodon ambiguus, fern Ophioglossum vulgatum subsp. vulgatum, monocots Epipactis exilis, Epipactis purpurata and Epipogium aphyllum and dicots Callitriche cophocarpa, Cornus sanguinea subsp. hungarica and Viscum album subsp. austriacum are given within SE Europe and adjacent regions

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies

Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study

With a continued strong increase in wind generator applications, the condition monitoring of wind turbine systems has become ever more important in ensuring the availability and reduced cost of produced power. One of the key turbine conditions requiring constant monitoring is the generator shaft alignment, which if compromised and untreated can lead to catastrophic system failures. This study explores the possibility of employing supervised machine learning methods on the readily available generator controller loop signals to achieve detection of shaft misalignment condition. This could provide a highly noninvasive and low-cost solution for misalignment monitoring in comparison with the current misalignment monitoring field practice that relies on invasive and costly drivetrain vibration analysis. The study utilises signal datasets measured on a dedicated doubly fed induction generator test rig to demonstrate that high consistency and accuracy recognition of shaft angular misalignment can be achieved through the application of supervised machine learning on controller loop signals. The average recognition accuracy rate of up to 98.8% is shown to be attainable through analysis of a key feature subset of the stator flux-oriented controller signals in a range of operating speeds and loads

An FEA model study of spectral signature patterns of PM demagnetisation faults in synchronous PM machines

This work reports a finite element analysis (FEA) model study of the stator current spectra of a commercial permanent magnet (PM) AC machine operating with rotor PM demagnetisation faults. A range of PM uniform and local demagnetisation fault scenarios at different severity conditions are examined. The aim of this study is to investigate the extent to which different modes of PM demagnetisation could be distinguished by inspecting the spectral signature patterns these generate in the stator current signal. To this end, a generalised mathematical characterisation of PM fault spectral signature in the stator current is examined and a 2D FEA software used to establish a model of the studied PM machine design. The FEA model was employed to predict the current signal spectral signature patterns of a range of different PM demagnetisation faults. These are then correlated with those arising from the derived expressions to examine whether the predicted and expected spectral trends are in agreement and whether their observation as the potential to provide diagnostic information

Implementation and performance evaluation of controller signal embedded sensorless speed estimation for wind turbine doubly fed induction generators

This paper reports a novel sensorless speed estimation method for wind turbine doubly fed induction generators that is highly non-invasive and utilises the readily available generator drive controller signals. A generalised analytic analysis is first presented that allows the assessment, identification and study of the controller signal spectral content needed to allow the establishment of real time speed estimation. An overlapping window parabolic interpolation algorithm is then proposed for tracking the controller signal speed dependent spectral content, with a view to provision of an increased real-time estimation rate. The reported method is implemented on a vector controlled doubly fed induction generator laboratory system and tested in transient operating regimes representative of wind generator application. The test results show that high accuracy high estimation rate is achievable in field application characteristic transient dynamics

Early life failure modes and downtime analysis of onshore type-III wind turbines in Turkey

Operations and maintenance costs, and unplanned downtime accounts for a significant proportion of the total expenditure of windfarms. Therefore, reduction of these costs is essential, which requires a better understanding of the wind turbines' reliability in terms of failure rates and downtime with operational lifetime. Failure rates and downtime are generally logged using condition monitoring systems, which mainly focus on Supervisory Control and Data Acquisition (SCADA) alarm signals. The aim of this paper is to use SCADA alarm statistics to provide a new failure rate and downtime survey and thus to evaluate reliability performance of the major wind turbine components and subsystems. The paper focuses on a modern onshore windfarm located in Turkey with Type-III wind turbines over the course of the first two years of operations, which is the first time reliability data from Turkey has been published in literature. The presented data can help to provide a better understanding of early life operations, since all maintenance activities, as well as stoppages that caused the wind turbines not to generate electricity were considered in this paper. Furthermore, the evaluation and categorisation of the recorded SCADA alarms, their origins and whether they were associated with planned or unplanned downtime is presented. This analysis shows that early life modern wind turbines have the highest alarm rates and downtime associated with ‘safety’ factors, followed by the ‘electrical systems’, which was found to be the most critical (or unreliable) subsystem. The presented results therefore suggest that early life focus should be on the electrical systems of wind turbines for maximising their operating time and availability. Monthly distributions of both SCADA alarms and downtime rates are also presented to highlight the effects of environmental conditions