Fault Diagnosis for Electric Power Systems and Electric Vehicles

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Author
Chapter 1: Fault diagnosis with model-based and model-free techniques
	1.1	Model-based fault diagnosis
		1.1.1. Outline
		1.1.2. Dynamic model of the three-phase inverter
		1.1.3. Approximate linearization of the three-phase inverter
		1.1.4. Statistical fault diagnosis with the H-infinity Kalman Filter
		1.1.5. Simulation tests
	1.2. Model-free fault diagnosis
		1.2.1. Outline
		1.2.2. Dynamic model of the PMLSM
		1.2.3. Neural-network model of the PMLSM
		1.2.4. Statistical fault diagnosis using the neural network
		1.2.5. Simulation tests
Chapter 2: Fault diagnosis for SG-based renewable energy systems
	2.1. Control of marine-turbine and SG
		2.1.1. Outline
		2.1.2. Dynamic model of the marine-turbine power unit
		2.1.3. Nonlinear optimal control for marine turbine with SG
		2.1.4. Nonlinear optimal control of marine turbine and SG
		2.1.5. Flatness-based control for marine-turbine and SG
		2.1.6. Multi-loop flatness-based control for marine-turbine and SG
		2.1.7. Sliding-mode control for marine-turbine and SG
		2.1.8. Multi-model fuzzy control for marine-turbine and SG
		2.1.9. Simulation tests
	2.2. Fault diagnosis of marine-turbine and SG
		2.2.1. Outline
		2.2.2. Differential flatness for marine-turbine and SG power units
		2.2.3. Design of a Kalman Filter-based disturbance observer
		2.2.4. Statistical tests for fault detection and isolation
		2.2.5. Simulation tests
Chapter 3: Fault diagnosis for electricity microgrids and gas processing units
	3.1. Fault diagnosis of DC microgrids
		3.1.1. Outline
		3.1.2. Dynamic model of the DC microgrids
		3.1.3. Linearization of the state-space model of DC microgrids
		3.1.4. Statistical fault diagnosis with the H-infinity Kalman Filter
		3.1.5. Simulation tests
	3.2. Fault diagnosis for 5-phase IM-driven gas compressors
		3.2.1. Outline
		3.2.2. Dynamic model of the 5-phase IM-driven gas compressor
		3.2.3. Linearization of the 5-phase IM-driven gas-compressor
		3.2.4. State estimation and fault diagnosis
		3.2.5. Simulation tests
Chapter 4: Fault diagnosis for gas and steam-turbine power generation units
	4.1. Fault diagnosis of gas-turbine and SG
		4.1.1. Outline
		4.1.2. Dynamic model of the gas-turbine power unit
		4.1.3. Differential flatness of the gas-turbine power unit
		4.1.4. Input-output linearization of the power unit
		4.1.5. State estimation with nonlinear Kalman Filtering
		4.1.6. Statistical fault diagnosis using the Kalman Filter
		4.1.7. Simulation tests
	4.2. Fault diagnosis of steam-turbine and SG
		4.2.1. Outline
		4.2.2. Dynamic model of the steam-turbine and SG
		4.2.3. Differential flatness of the steam-turbine and SG
		4.2.4. Differential flatness theory-based Kalman Filtering
		4.2.5. Statistical fault diagnosis with the Kalman Filter
		4.2.6. Simulation tests
Chapter 5: Fault diagnosis for wind power units and the distribution grid
	5.1. Fault diagnosis for wind-power generators
		5.1.1. Outline
		5.1.2. Dynamic model of the wind power unit
		5.1.3. Differential flatness of the wind-turbine and SG
		5.1.4. Input-output linearization of the wind power unit
		5.1.5. State estimation with Kalman Filtering
		5.1.6. Simulation tests
	5.2. Fault diagnosis for the electric grid
		5.2.1. Introduction
		5.2.2. Model of the power grid
		5.2.3. Sensors’ condition monitoring with Kalman Filtering
		5.2.4. Fault detection with the use of statistical criteria
		5.2.5. Use of the Kalman Filter as a disturbance observer
		5.2.6. Simulation tests
References
Index