Fault Diagnosis for Electric Power Systems and Electric Vehicles

The present monograph offers a detailed and in-depth analysis of the topic of fault diagnosis for electric power systems and electric vehicles. First, the monograph treats the problem of Fault diagnosis with model-based and model-free techniques (Model-based fault diagnosis techniques and Model-free fault diagnosis techniques). Next, the monograph provides a solution for the problem of Control and fault diagnosis for Synchronous Generator-based renewable energy systems (Control of the marine-turbine and synchronous-generator unit and Fault diagnosis of the marine turbine and synchronous-generator unit. Additionally, the monograph introduces novel solutions for the problem of Fault diagnosis for electricity microgrids and gas processing units (Fault diagnosis for electric power DC microgrids and Fault diagnosis for electrically actuated gas compressors). Furthermore, the monograph analyzes and solves the problem of Fault diagnosis for gas and steam-turbine power generation units (Fault diagnosis for the gas-turbine and Synchronous Generator electric power unit and for the steam-turbine and synchronous generator power unit). Finally, the monograph provides a solution for the problem of Fault diagnosis for wind power units and for the distribution grid (Fault diagnosis for wind power generators and Fault diagnosis for the electric power distribution grid).

• The new fault detection and isolation methods that the monograph develops are of generic use and are addressed to a wide class of nonlinear dynamical systems, with emphasis on electric power systems and electric vehicles.
• On the one side, model-based fault detection and isolation methods are analyzed. In this case, known models about the dynamics of the monitored system are used by nonlinear state observers and Kalman Filters, which emulate the system’s fault-free condition.
• On the other side, model-free fault detection and isolation methods are analyzed. In this case, raw data are processed by neural networks and nonlinear regressors to generate models that emulate the fault-free condition of the monitored system.
• Statistical tests based on the processing of the residuals, which are formed between the outputs of the monitored system and the outputs of the fault-free model provide objective and almost infallible criteria about the occurrence of failures.
• The new fault detection and isolation methods with statistical procedures for defining fault thresholds enable early fault diagnosis and reveal incipient changes in the parameters of the monitored systems.

Dr. Gerasimos Rigatos obtained his diploma (1995) and his Ph.D. (2000) both from

the Department of Electrical and Computer Engineering, of the National Technical

University of Athens (NTUA), Greece. In 2001 he was a post-doctoral researcher

at IRISA-INRIA, Rennes, France. He is currently a Research Director (Researcher

Grade A’) at the Industrial Systems Institute, Greece. He is a Senior Member of

IEEE, and a Member and CEng of IET. He has led several research cooperation

agreements and projects which have given accredited results in the areas of nonlinear

control, nonlinear filtering and control of distributed parameter systems. His

results appear in 8 research monographs and in several journal articles. According

to Elsevier Scopus his research comprising 135 journal articles where he is the first

or sole author, has received more than 3000 citations with an H-index of 26. Since

2007, he has been awarded visiting professor positions at several academic institutions

(University Paris XI, France, Harper-Adams University College, UK, University

of Northumbria, UK, University of Salerno, Italy, Ecole Centrale de Nantes,

France). He is an editor of the Journal of Advanced Robotic Systems and of the SAE

Journal of Electrified Vehicles.

Dr.Masoud Abbaszadeh obtained a B.Sc and aM.Sc in Electrical Engineering from

Amirkabir University of Technology and Sharif University of Technology, in Iran,

respectively. Next, he received a Ph.D. degree in Electrical Engineering (Controls)

in 2008 from the University of Alberta, Canada. From 2008 to 2011, he was with

Maplesoft, Waterloo, Ontario, Canada, as a Research Engineer. He was the principal

developer of MapleSim Control Design Toolbox and was a member of a research

team working on the Maplesoft-Toyota joint projects. From 2011 to 2013, he was

a Senior Research Engineer at United Technologies Research Center, East Hartford,

CT, USA, working on advanced control systems, and complex systems modeling and

simulation. Currently he is a Principal Research Engineer at GE Research Center,

Niskayuna, NY, USA. He has also held an Adjunct Professor position at Rensselaer

Polytechnic Institute, NY, USA. He has over 150 peer-reviewed papers, 9 book

chapters, and holds 39 issued US patents, with over 40 more patents pending.. His

research interests include estimation and detection theory, robust and nonlinear control,

and machine learning with applications in diagnostics, cyber-physical resilience

and autonomous systems. He serves as an Associate Editor of IEEE Transactions

on Control Systems Technology, and a member of IEEE CSS Conference Editorial

Board.

Dr.Mohamed-Assaad Hamida was born in El Oued, Algeria, in 1985. He received

the B.Sc . degree in electrical engineering from the University of Batna, Batna, Algeria,

in 2009, the M.Sc. degree in automatic control from Ecole Nationale Superieure

d’Ingenieurs de Poitiers (ENSIP), Poitiers, France, in 2010, and the Ph.D degree in

automatic control and electrical engineering from Ecole centrale de Nantes, Nantes,

France, in 2013. From 2013 to 2017, he was an Associate Professor of Electrical

Engineering with the University of Ouargla, Algeria. In 2017, he joined the Ecole

Centrale de Nantes and the Laboratory of Digital Sciences of Nantes (LS2N), as an

Associate Professor. Dr. Hamida is the local coordinator of the European project EPiCo

on Electric Vehicles Propulsion and Control at Ecole Centrale of Nantes and

the head of the real-time systems unit in the same university. His research interests

include robust nonlinear control (higher order sliding mode, backstepping, adaptive

control, optimal control), theoretical aspects of nonlinear observer design, control

and fault diagnosis of electrical systems and renewable energy applications. His current

research interests include robust nonlinear control, theoretical aspects of nonlinear

observer design, control, and fault diagnosis of electrical systems and renewable

energy applications.

Dr. Pierluigi Siano received the M.Sc. degree in electronic engineering and the

Ph.D. degree in information and electrical engineering from the University of

Salerno, Salerno, Italy, in 2001 and 2006, respectively. He is Full Professor of Electrical

Power Systems and Scientific Director of the Smart Grids and Smart Cities

Laboratory with the Department ofManagement and Innovation Systems, University

of Salerno. Since 2021 he has been a Distinguished Visiting Professor in the Department

of Electrical and Electronic Engineering Science, University of Johannesburg.

His research activities are centered on demand response, energy management, the

integration of distributed energy resources in smart grids, electricity markets, and

planning and management of power systems. In these research fields, he has coauthored

more than 700 articles including more than 410 international journals that

received in Scopus more than 19200 citations with an H-index equal to 66. Since

2019 he has been awarded as a Highly Cited Researcher in Engineering by Web of

Science Group. He has been the Chair of the IES TC on Smart Grids. He is Editor for

the Power & Energy Society Section of IEEE Access, IEEE Transactions on Power

Systems, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial

Electronics, and IEEE Systems.