Browsing by Subject "anomaly detection"

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    Open Access
    A modelling methodology to quantify the impact of plant anomalies on ID fan capacity in coal fired power plants
    (2020) Khobo, Rendani Yaw-Boateng Sean; Rousseau, Pieter; Gosai, Priyesh
    In South Africa, nearly 80 % of electricity is generated from coal fired power plants. Due to the complexity of the interconnected systems that make up a typical power plant, analysis of the root causes of load losses is not a straightforward process. This often leads to losses incorrectly being ascribed to the Induced Draught (ID) fan, where detection occurs, while the problem actually originates elsewhere in the plant. The focus of this study was to develop and demonstrate a modelling methodology to quantify the effects of major plant anomalies on the capacity of ID fans in coal fired power plants. The ensuing model calculates the operating point of the ID fan that is a result of anomalies experienced elsewhere in the plant. This model can be applied in conjunction with performance test data as part of a root cause analysis procedure. The model has three main sections that are integrated to determine the ID fan operating point. The first section is a water/steam cycle model that was pre-configured in VirtualPlantTM. The steam plant model was verified via energy balance calculations and validated against original heat balance diagrams. The second is a draught group model developed using FlownexSETM. This onedimensional network is a simplification of the flue gas side of the five main draught group components, from the furnace inlet to the chimney exit, characterising only the aggregate heat transfer and pressure loss in the system. The designated ID fan model is based on the original fan performance curves. The third section is a Boiler Mass and Energy Balance (BMEB) specifically created for this purpose to: (1) translate the VirtualPlant results for the steam cycle into applicable boundary conditions for the Flownex draught group model; and (2) to calculate the fluid properties applicable to the draught group based on the coal characteristics and combustion process. The integrated modelling methodology was applied to a 600 MW class coal fired power plant to investigate the impact of six major anomalies that are typically encountered. These are: changes in coal quality; increased boiler flue gas exit temperatures; air ingress into the boiler; air heater inleakage to the flue gas stream; feed water heaters out-of-service; and condenser backpressure degradation. It was inter alia found that a low calorific value (CV) coal of 14 MJ/kg compared to a typical 17 MJ/kg reduced the fan's capacity by 2.1 %. Also, having both HP FWH out of service decreased the fan's capacity by 16.2 %.
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    Open Access
    Deep adaptive anomaly detection using an active learning framework
    (2022) Sekyi, Emmanuel; Bassett, Bruce
    Anomaly detection is the process of finding unusual events in a given dataset. Anomaly detection is often performed on datasets with a fixed set of predefined features. As a result of this, if the normal features bear a close resemblance to the anomalous features, most anomaly detection algorithms exhibit poor performance. This work seeks to answer the question, can we deform these features so as to make the anomalies standout and hence improve the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each round to incorporate the feedback from the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be suboptimal for detecting anomalies in the data, learning more appropriate features. We also explore the role of the loss function and Active Learning query strategy, showing these are important, especially when there is a significant variation in the anomalies.