Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Selecting appropriate machine learning classifiers for DGA diagnosis

Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machine learning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data

Determining appropriate data analytics for transformer health monitoring

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring

Ectoplasm & Superspace Integration Measure for 2D Supergravity with Four Spinorial Supercurrents

Building on a previous derivation of the local chiral projector for a two dimensional superspace with eight real supercharges, we provide the complete density projection formula required for locally supersymmetrical theories in this context. The derivation of this result is shown to be very efficient using techniques based on the Ectoplasmic construction of local measures in superspace.Comment: 18 pages, LaTeX; V2: minor changes, typos corrected, references added; V3: version to appear in J. Phys. A: Math. Theor., some comments and references added to address a referee reques

Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%

Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the non-linearities of the thermal model and improves the prediction accuracy amongst a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a Particle Filtering framework to improve thermal modelling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant

Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Transformers are critical assets for the reliable operation of the power grid. Transformers may fail in service if monitoring models do not identify degraded conditions in time. Dissolved gas analysis (DGA) focuses on the examination of dissolved gasses in transformer oil to diagnose the state of a transformer. Fusion of black-box (BB) classifiers, also known as an ensemble of diagnostics models, have been used to improve the accuracy of diagnostics models across many fields. When independent classifiers diagnose the same fault, this method can increase the veracity of the diagnostics. However, if these methods give conflicting results, it is not always clear which model is most accurate due to their BB nature. In this context, the use of white-box (WB) models can help resolve conflicted samples effectively by incorporating uncertainty information and improve the classification accuracy. This paper presents an uncertainty-aware fusion method to combine BB and WB diagnostics methods. The effectiveness of the proposed approach is validated using two publicly available DGA datasets

Cryptosporidium Priming Is More Effective than Vaccine for Protection against Cryptosporidiosis in a Murine Protein Malnutrition Model

Cryptosporidium is a major cause of severe diarrhea, especially in malnourished children. Using a murine model of C. parvum oocyst challenge that recapitulates clinical features of severe cryptosporidiosis during malnutrition, we interrogated the effect of protein malnutrition (PM) on primary and secondary responses to C. parvum challenge, and tested the differential ability of mucosal priming strategies to overcome the PM-induced susceptibility. We determined that while PM fundamentally alters systemic and mucosal primary immune responses to Cryptosporidium, priming with C. parvum (106 oocysts) provides robust protective immunity against re-challenge despite ongoing PM. C. parvum priming restores mucosal Th1-type effectors (CD3+CD8+CD103+ T-cells) and cytokines (IFNγ, and IL12p40) that otherwise decrease with ongoing PM. Vaccination strategies with Cryptosporidium antigens expressed in the S. Typhi vector 908htr, however, do not enhance Th1-type responses to C. parvum challenge during PM, even though vaccination strongly boosts immunity in challenged fully nourished hosts. Remote non-specific exposures to the attenuated S. Typhi vector alone or the TLR9 agonist CpG ODN-1668 can partially attenuate C. parvum severity during PM, but neither as effectively as viable C. parvum priming. We conclude that although PM interferes with basal and vaccine-boosted immune responses to C. parvum, sustained reductions in disease severity are possible through mucosal activators of host defenses, and specifically C. parvum priming can elicit impressively robust Th1-type protective immunity despite ongoing protein malnutrition. These findings add insight into potential correlates of Cryptosporidium immunity and future vaccine strategies in malnourished children

Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma

PURPOSE In the phase III CheckMate 067 trial, durable clinical benefit was demonstrated previously with nivolumab plus ipilimumab and nivolumab alone versus ipilimumab. Here, we report 6.5-year efficacy and safety outcomes. PATIENTS AND METHODS Patients with previously untreated unresectable stage III or stage IV melanoma were randomly assigned 1:1:1 to receive nivolumab 1 mg/kg plus ipilimumab 3 mg/kg once every 3 weeks (four doses) followed by nivolumab 3 mg/kg once every 2 weeks (n = 314), nivolumab 3 mg/kg once every 2 weeks (n = 316), or ipilimumab 3 mg/kg once every 3 weeks (four doses; n = 315). Coprimary end points were progression-free survival and overall survival (OS) with nivolumab plus ipilimumab or nivolumab versus ipilimumab. Secondary end points included objective response rate, descriptive efficacy assessments of nivolumab plus ipilimumab versus nivolumab alone, and safety. Melanoma-specific survival (MSS; descriptive analysis), which excludes deaths unrelated to melanoma, was also evaluated. RESULTS Median OS (minimum follow-up, 6.5 years) was 72.1, 36.9, and 19.9 months in the combination, nivolumab, and ipilimumab groups, respectively. Median MSS was not reached, 58.7, and 21.9 months, respectively; 6.5-year OS rates were 57%, 43%, and 25% in patients with BRAF-mutant tumors and 46%, 42%, and 22% in those with BRAF–wild-type tumors, respectively. In patients who discontinued treatment, the median treatment-free interval was 27.6, 2.3, and 1.9 months, respectively. Since the 5-year analysis, no new safety signals were observed. CONCLUSION These 6.5-year CheckMate 067 results, which include the longest median OS in a phase III melanoma trial reported to date and the first report of MSS, showed durable, improved clinical outcomes with nivolumab plus ipilimumab or nivolumab versus ipilimumab in patients with advanced melanoma and, in descriptive analyses, with the combination over nivolumab monotherapy

A novel, highly potent and selective phosphodiesterase-9 inhibitor for the treatment of sickle cell disease

The most common treatment for patients with sickle cell disease (SCD) is the chemotherapeutic hydroxyurea, a therapy with pleiotropic effects, including increasing fetal hemoglobin (HbF) in red blood cells and reducing adhesion of white blood cells to the vascular endothelium. Hydroxyurea has been proposed to mediate these effects through a mechanism of increasing cellular cGMP levels. An alternative path to increasing cGMP levels in these cells is through the use of phosphodiesterase-9 inhibitors that selectively inhibit cGMP hydrolysis and increase cellular cGMP levels. We have developed a novel, potent and selective phosphodiesterase-9 inhibitor (IMR-687) specifically for the treatment of SCD. IMR-687 increased cGMP and HbF in erythroid K562 and UT-7 cells and increased the percentage of HbF positive erythroid cells generated in vitro using a two-phase liquid culture of CD34+ progenitors from sickle cell blood or bone marrow. Oral daily dosing of IMR-687 in the Townes transgenic mouse SCD model, increased HbF and reduced red blood cell sickling, immune cell activation and microvascular stasis. The IMR-687 reduction in red blood cell sickling and immune cell activation was greater than that seen with physiological doses of hydroxyurea. In contrast to other described phosphodiesterase-9 inhibitors, IMR-687 did not accumulate in the central nervous system, where it would inhibit phosphodiesterase-9 in neurons, or alter rodent behavior. IMR-687 was not genotoxic or myelotoxic and did not impact fertility or fetal development in rodents. These data suggest that IMR-687 may offer a safe and effective oral alternative for hydroxyurea in the treatment of SCD