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1 Chapter 1Figure 1.1 Equivalent circuit representation of a six-phase synchronous machine.Figure 1.2 Dynamic response of motor following the change in load torque showing...Figure 1.3 Dynamic response of motor following the change in load torque showing...Figure 1.4 d-q component of stator winding currents (a) Iq₁, (b) Id₁, (c) Iq₂, a...Figure 1.5 Variation in eigenvalue of three-phase synchronous machine with stato...Figure 1.6 Variation in eigenvalue of six-phase synchronous machine with stator ...Figure 1.7 Variation in eigenvalue of three-phase synchronous machine with stato...Figure 1.8 Variation in eigenvalue of six-phase synchronous machine with stator ...Figure 1.9 Variation in eigenvalue of three-phase synchronous machine with field...Figure 1.10 Variation in eigenvalue of six-phase synchronous machine with field ...Figure 1.11 Variation in eigenvalue of three-phase synchronous machine with fiel...Figure 1.12 Variation in eigenvalue of six-phase synchronous machine with field ...Figure 1.13 Variation in eigenvalue of three-phase synchronous machine with damp...Figure 1.14 Variation in eigenvalue of six-phase synchronous machine with damper...Figure 1.15 Variation in eigenvalue of three-phase synchronous machine with damp...Figure 1.16 Variation in eigenvalue of six-phase synchronous machine with damper...Figure 1.17 Variation in eigenvalue of three-phase synchronous machine with damp...Figure 1.18 Variation in eigenvalue of six-phase synchronous machine with damper...Figure 1.19 Variation in eigenvalue II with damper leakage reactance change alon...Figure 1.20 Variation in eigenvalue of three-phase synchronous machine with magn...Figure 1.21 Variation in eigenvalue of six-phase synchronous machine with magnet...Figure 1.22 Change in real/real component of generator eigenvalue due to load va...

2 Chapter 2Figure 2.1 Radial basis neural network architecture. Reprint with copyright perm...Figure 2.2 Back-propagation architecture. Reprint with copyright permission from...Figure 2.3 Feed-forward neural network architecture. Reprint with copyright perm...Figure 2.4 Cascaded systems of neural networks (ISSO). Reprint with copyright pe...Figure 2.5 ANN-based condition monitoring method using SCADA data. Reprint with ...Figure 2.6 Procedure for Artificial Bee Colony (ABC) algorithm. Reprint with per...

3 Chapter 3Figure 3.1 Clustered WSNs.

4 Chapter 4Figure 4.1 Principal of biogas generation.Figure 4.2 Biogas generation plant at the LNMIIT, Jaipur.Figure 4.3 Basic artificial neural network architecture.Figure 4.4 Feedback propagation artificial neural network.Figure 4.5 Genetic algorithm evaluation flow.Figure 4.6 Path of ant when there is an obstruction.Figure 4.7 Ant colony optimization.Figure 4.8 Swarm of birds [30].Figure 4.9 Flowchart of particle swarm optimization.

5 Chapter 5Figure 5.1 First-order battery model.Figure 5.2 Block diagram for the simulation setup.Figure 5.3 I-V and P-V curve for solar PV array.Figure 5.4 Irradiance, current, voltage, and SoC for the simulation setup.Figure 5.5 Curve fitting for OCV-SoC based on second-, third-, 4, and fifth-orde...

6 Chapter 6Figure 6.1 Classification of WPF models.Figure 6.2 Wind forecasting methodologies.Figure 6.3 Deep learning paradigm.Figure 6.4 Deep learning approaches for WPF.

7 Chapter 7Figure 7.1 (a) Total installed wind capacity around the world. (b) Total install...Figure 7.2 Components of wind energy conversion system.Figure 7.3 Wind power characteristics curve.Figure 7.4 Classification of wind energy technologies.Figure 7.5 Wind forecasting system [4].Figure 7.6 General forecasting framework.Figure 7.7 Classification of statistical methods.Figure 7.8 Classification of time series method.Figure 7.9 Multilayer feed-forward network.Figure 7.10 One-level decomposition.Figure 7.11 Wavelet tree decomposition with three detailed levels.Figure 7.12 Architecture of ANFIS network.Figure 7.13 Hyperplane SVM architecture.Figure 7.14 Architecture of Kernel-type SVM.Figure 7.15 Flowchart depicting data pre-processing.Figure 7.16 Different network architectures used in deep learning.Figure 7.17 Unsupervised pre-trained network architecture.Figure 7.18 Deep CNN architecture.Figure 7.19 RNN architecture.Figure 7.20 Support vector machine network structure.Figure 7.21 Prediction comparisons between various models.Figure 7.22 Daily variation of averaged wind speed.Figure 7.23 Structure of the proposed ANN model.Figure 7.24 Comparison between the measured and predicted wind speed.

8 Chapter 8Figure 8.1 LSTM with four interacting layers.Figure 8.2 No. of epochs vs. loss.Figure 8.3 Actual and predicted test value.Figure 8.4 Visualization over full data.Figure 8.5 Plot over small part of the data.Figure 8.6 Plot over very small part of the data.Figure 8.7 GRU.Figure 8.8 Number of epochs vs. loss.Figure 8.9 Plot of actual and predicted test value.Figure 8.10 Visualization over full data.Figure 8.11 Plot over the small part of the data.Figure 8.12 Plot over very small part of data.Figure 8.13 Bidirectional LSTM.Figure 8.14 No. of epoch vs. loss.Figure 8.15 Actual and predicted test value.Figure 8.16 Visualization over full data.Figure 8.17 Plot over small part of the data.Figure 8.18 Plot over very small part of the data.

9 Chapter 9Figure 9.1 AMI architecture.

10 Chapter 10Figure 10.1 Traditional RNN unrolled over t timesteps [12].Figure 10.2 LSTM cell [16].Figure 10.3 GRU cell [12].Figure 10.4 ConvLSTM cell.Figure 10.5 Bidirectional RNN.Figure 10.6 Demonstration of the sliding window approach.Figure 10.7 Predictions done by models trained with window size = 6.

11 Chapter 11Figure 11.1 Classification of biofuels.Figure 11.2 AI-based biodiesel model.