Cluster analysis of wind turbine alarms for characterising and classifying stoppages

Turbine alarm systems can give useful information to remote technicians on the cause of a fault or stoppage. However, alarms are generally generated at much too high a rate to gain meaningful insight from on their own, so generally require extensive domain knowledge to interpret. By grouping together commonly occurring alarm sequences, the burden of analysis can be reduced. Instead of analysing many individual alarms that occur during a stoppage, the stoppage can be linked to a commonly occurring sequence of alarms. Hence, maintenance technicians can be given information about the shared characteristics or root causes of stoppages where that particular alarm sequence appeared in the past. This research presents a methodology to identify relevant alarms from specific turbine assemblies and group together similar alarm sequences as they appear during stoppages. Batches of sequences associated with 456 different stoppages are created, and features are extracted from these batches representing the order the alarms appeared in. The batches are grouped together using clustering techniques, and evaluated using silhouette analysis and manual inspection. Results show that almost half of all stoppages can be attributed to one of 15 different clusters of alarm sequences

Automatically identifying and predicting unplanned wind turbine stoppages using SCADA and alarms system data: case study and results

Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbine's sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine fault

Issues with data quality for wind turbine condition monitoring and reliability analyses

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting

From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

The European Union's Energy Efficiency Directive is placing an increased focus on the measurement and verification (M&V) of demand side energy savings. The objective of M&V is to quantify energy savings with minimum uncertainty. M&V is currently undergoing a transition to practices, known as M&V 2.0, that employ automated advanced analytics to verify performance. This offers the opportunity to effectively manage the transition from short-term M&V to long-term monitoring and targeting (M&T) in industrial facilities. The original contribution of this paper consists of a novel, robust and technology agnostic framework that not only satisfies the requirements of M&V 2.0, but also bridges the gap between M&V and M&T by ensuring persistence of savings. The approach features a unique machine learning-based energy modelling methodology, model deployment and an exception reporting system that ensures early identification of performance degradation. A case study demonstrates the effectiveness of the approach. Savings from a real-world project are found to be 177,962 +/- 12,334 kWh with a 90% confidence interval. The uncertainty associated with the savings is 8.6% of the allowable uncertainty, thus highlighting the viability of the framework as a reliable and effective tool

A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study

Using 10-minute wind turbine supervisory control and data acquisition (SCADA) system data to predict faults can be an attractive way of working toward a predictive maintenance strategy without needing to invest in extra hardware. Classification methods have been shown to be effective in this regard, but there have been some common issues in their application within the literature. To use these data-driven methods effectively, historical SCADA data must be accurately labelled with the periods when turbines were down due to faults, as well as with the reason for the fault. This can be manually achieved using maintenance logs, but can be highly tedious and time-consuming due to the often unstructured format in which this information is stored. Alarm systems can also help, but the sheer volume of alarms and false positives generated complicate efforts. Furthermore, a way to implement and evaluate the field deployed system beyond simple classification metrics is needed. In this work, we present a prescribed and reproducible framework for: (i) automatically identifying periods of faulty operation using rules applied to the turbine alarm system; (ii) using this information to perform classification which avoids some of the common pitfalls observed in literature; and (iii) generating alerts based on a sliding window metric to evaluate the performance of the system in a real-world scenario. The framework was applied to a dataset from an operating wind farm and the results show that the system can automatically and accurately label historical stoppages from the alarms data. For fault prediction, classification scores are quite low, with precision of 0.16 and recall of 0.49, but it is envisaged that this can be greatly improved with more training data. Nonetheless, the sliding window metric compensates for the low raw classification scores and shows that 71% of faults can be predicted with an average of 30 h notice, with false alarms being active for 122 h of the year. By adjusting some of the parameters of the fault prediction alerts, the duration of false alarms can be drastically reduced to 2 h, but this also reduces the number of predicted faults to 8%

Planet Hunters: Assessing the Kepler Inventory of Short Period Planets

We present the results from a search of data from the first 33.5 days of the Kepler science mission (Quarter 1) for exoplanet transits by the Planet Hunters citizen science project. Planet Hunters enlists members of the general public to visually identify transits in the publicly released Kepler light curves via the World Wide Web. Over 24,000 volunteers reviewed the Kepler Quarter 1 data set. We examine the abundance of \geq 2 R\oplus planets on short period (< 15 days) orbits based on Planet Hunters detections. We present these results along with an analysis of the detection efficiency of human classifiers to identify planetary transits including a comparison to the Kepler inventory of planet candidates. Although performance drops rapidly for smaller radii, \geq 4 R\oplus Planet Hunters \geq 85% efficient at identifying transit signals for planets with periods less than 15 days for the Kepler sample of target stars. Our high efficiency rate for simulated transits along with recovery of the majority of Kepler \geq 4 R\oplus planets suggest suggests the Kepler inventory of \geq 4 R\oplus short period planets is nearly complete.Comment: 41 pages,13 figures, 8 tables, accepted to Ap

Thermal Emission of WASP-14b Revealed with Three Spitzer Eclipses

Exoplanet WASP-14b is a highly irradiated, transiting hot Jupiter. Joshi et al. calculate an equilibrium temperature Teq of 1866 K for zero albedo and reemission from the entire planet, a mass of 7.3 +/- 0.5 Jupiter masses and a radius of 1.28 +/- 0.08 Jupiter radii. Its mean density of 4.6 g/cm3 is one of the highest known for planets with periods less than 3 days. We obtained three secondary eclipse light curves with the Spitzer Space Telescope. The eclipse depths from the best jointly fit model are $0.224\%$ +/- $0.018\%$ at 4.5 {\mu}m and $0.181\%$ +/- $0.022\%$ at 8.0 {\mu}m. The corresponding brightness temperatures are 2212 +/- 94 K and 1590 +/- 116 K. A slight ambiguity between systematic models suggests a conservative 3.6 {\mu}m eclipse depth of $0.19\%$ +/- $0.01\%$ and brightness temperature of 2242 +/- 55 K. Although extremely irradiated, WASP-14b does not show any distinct evidence of a thermal inversion. In addition, the present data nominally favor models with day night energy redistribution less than $~30\%$. The current data are generally consistent with oxygen-rich as well as carbon-rich compositions, although an oxygen-rich composition provides a marginally better fit. We confirm a significant eccentricity of e = 0.087 +/- 0.002 and refine other orbital parameters.Comment: 16 pages, 16 figure

Pan-viral serology implicates enteroviruses in acute flaccid myelitis.

Since 2012, the United States of America has experienced a biennial spike in pediatric acute flaccid myelitis (AFM)1-6. Epidemiologic evidence suggests non-polio enteroviruses (EVs) are a potential etiology, yet EV RNA is rarely detected in cerebrospinal fluid (CSF)2. CSF from children with AFM (n = 42) and other pediatric neurologic disease controls (n = 58) were investigated for intrathecal antiviral antibodies, using a phage display library expressing 481,966 overlapping peptides derived from all known vertebrate and arboviruses (VirScan). Metagenomic next-generation sequencing (mNGS) of AFM CSF RNA (n = 20 cases) was also performed, both unbiased sequencing and with targeted enrichment for EVs. Using VirScan, the viral family significantly enriched by the CSF of AFM cases relative to controls was Picornaviridae, with the most enriched Picornaviridae peptides belonging to the genus Enterovirus (n = 29/42 cases versus 4/58 controls). EV VP1 ELISA confirmed this finding (n = 22/26 cases versus 7/50 controls). mNGS did not detect additional EV RNA. Despite rare detection of EV RNA, pan-viral serology frequently identified high levels of CSF EV-specific antibodies in AFM compared with controls, providing further evidence for a causal role of non-polio EVs in AFM

The Onconeural Antigen cdr2 Is a Novel APC/C Target that Acts in Mitosis to Regulate C-Myc Target Genes in Mammalian Tumor Cells

Cdr2 is a tumor antigen expressed in a high percentage of breast and ovarian tumors and is the target of a naturally occurring tumor immune response in patients with paraneoplastic cerebellar degeneration, but little is known of its regulation or function in cancer cells. Here we find that cdr2 is cell cycle regulated in tumor cells with protein levels peaking in mitosis. As cells exit mitosis, cdr2 is ubiquitinated by the anaphase promoting complex/cyclosome (APC/C) and rapidly degraded by the proteasome. Previously we showed that cdr2 binds to the oncogene c-myc, and here we extend this observation to show that cdr2 and c-myc interact to synergistically regulate c-myc-dependent transcription during passage through mitosis. Loss of cdr2 leads to functional consequences for dividing cells, as they show aberrant mitotic spindle formation and impaired proliferation. Conversely, cdr2 overexpression is able to drive cell proliferation in tumors. Together, these data indicate that the onconeural antigen cdr2 acts during mitosis in cycling cells, at least in part through interactions with c-myc, to regulate a cascade of actions that may present new targeting opportunities in gynecologic cancer