Failure forecast engine for power plant expert system shell

Mayadevi, N.; Vinodchandra, S. S.; Ushakumari, S.

doi:10.1109/icaccct.2012.6320807

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Short-term prediction approaches available in the literature can be divided into two main categories: statistical methods and artificial intelligence-based methods. Artificial intelligence includes artificial neural networks [34,38], fuzzy inference systems [32,33,37] and expert systems [39,40]. In the statistical methods category are ARIMAX [35,37] support vector regression (SVR) [41][42][43], and stochastic time series [26,34].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems

2022

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Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to model and predict the reactive power produced by an AC locomotive. This paper presents a study on the modelling and prediction of reactive power produced by AC-powered electric locomotives. Reactive power flow has a significant impact on network voltage levels and power efficiency. The research was conducted by using intelligent techniques—more precisely, by using the adaptive neuro fuzzy inference system (ANFIS). Several approaches to the ANFIS structure were used in the research. Of these, we mention the ANFIS-grid partition, ANFIS subtractive clustering and ANFIS fuzzy c-means (FCM) clustering. Thus; for modelling and predicting reactive power, ANFIS was trained, then tested. For the training of ANFIS, experimental data obtained from measurements performed in a train supply sub-station were used. The measurements were taken over a period of time when the locomotives were far away from the station, close to the station, and at the station, respectively. The currents and voltages from the supply substation, respectively the active, reactive, and distorted powers, were measured on the data acquisition board. With the measured data of the reactive power, the modelling with ANFIS was performed, and a prediction of the variation in the reactive power was made. The paper analysed the results of the modelling by comparing between several types of ANFIS architectures. The values of RMSE, RMS and the training time of ANFIS were compared for several structures of ANFIS.

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Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems

2022

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“…The in-depth knowledge of the plant environment, with regard to the condition of its main assets, is an important factor to gain information on the stress levels and wear and tear of the assets, as well as to perform maintenance control more effectively, which can reduce costs and postpone investments. Several approaches to the implementation of supervisory systems based on the resources of the environment in which they are inserted have been proposed in the literature, such as the methods presented in [12][13][14][15]. In the work of Audas [12], a database scheme was proposed in order to solve issues related with manual workload (spreadsheet-based) of TPP signal data.…”

Section: Introductionmentioning

confidence: 99%

“…In Samtani et al [14], an approach was presented to rate and identify the vulnerability of an Internet-enabled data acquisition and supervision system (SCADA). In Mayadevi et al [15], a new technique was proposed that is capable of predicting failures in plants that use SCADA as a supervisory system. In the present context, special emphasis is given to the Wärtsilä Operator's Interface System (WOIS), which is discussed in depth in [16,17].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Internal Combustion Engines in Thermal Power Plants Based on Control Charts and Adapted Nelson Rules

et al. 2021

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In thermal power plants, the internal combustion engines are constantly subjected to stresses, requiring a continuous monitoring system in order to check their operating conditions. However, most of the time, these monitoring systems only indicate if the monitored parameters are in nonconformity close to the occurrence of a catastrophic failure—they do not allow a predictive analysis of the operating conditions of the machine. In this paper, a statistical model, based on the statistical control process and Nelson Rules, is proposed to analyze the operational conditions of the machine based on the supervisory system data. The statistical model is validated through comparisons with entries of the plant logbook. It is demonstrated that the results obtained with the proposed statistical model match perfectly with the entries of the logbook, showing our model to be a promising tool for making decisions concerning maintenance in the plant.

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