Machine Learning Based Data Driven Modelling of Time Series of Power Plant Data
Accurate modeling and simulation of data collected from a power plant system are important factors in the strategic planning and maintenance of the unit. Several non-linearities and multivariable couplings are associated with real-world plants. Therefore, it becomes almost impossible to model the system using conventional mathematical equations. Statistical models such as ARIMA, ARMA are potential solutions but their linear nature cannot very well t a system with non-linear, multivariate time series data. Recently, deep learning methods such as Arti cial Neural Networks (ANNs) have been extensively applied for time series forecasting. ANNs in contrast to stochastic models such as ARIMA can uncover the non-linearities present underneath the data. In this thesis, we analyze the real-time temperature data obtained from a nuclear power plant, and discover the patterns and characteristics of the sensory data. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the time series data; k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of nuclear power plants using system states and state transitions. Dependent and independent parameters of the system are de ned based on co-relation among themselves. Various forecasting models are then applied over multivariate time-stamped data. We discuss thoroughly the implementation of a key architecture of neural networks, Long Short-Term Neural Networks (LSTMs). LSTM can capture nonlinear relationships in a dynamic system using its memory connections. This further aids them to counter the problem of back-propagated error decay through memory blocks. Poly-regression is applied to represent the working of the plant by de ning an association between independent and dependent parameters. This representation is then used to forecast dependent variates based on the observed values of independent variates. Principle of sensitivity analysis is used for optimisation of number of parameters used for predicting. It helps in making a compromise between number of parameters used and level of accuracy achieved in forecasting. The objective of this thesis is to examine the feasibility of the above-mentioned forecasting techniques in the modeling of a complex time series of data, and predicting system parameters such as Reactor Temperature and Linear Power based on past information. It also carries out a comparative analysis of forecasts obtained in each approach.
Cite this version of the work
Kunal Taneja (2020). Machine Learning Based Data Driven Modelling of Time Series of Power Plant Data. UWSpace. http://hdl.handle.net/10012/15792