In an uncertain and complex environment, to ensure secure and stable operations of large-scale power systems is one of the biggest challenges that power engineers have to address today. Traditionally, power system operations and decision-making in controls are based on power system computations of physical models describing the behavior of power systems. Largely, physical models are constructed according to some assumptions and simplifications, and such is the case with power system models. However, the complexity of power system stability problems, along with the system's inherent uncertainties and nonlinearities, can result in models that are impractical or inaccurate. This calls for adaptive or deep-learning algorithms to significantly improve current control schemes that solve decision and control problems.
Cyberphysical Infrastructures in Power Systems: Architectures and Vulnerabilities provides an extensive overview of CPS concepts and infrastructures in power systems with a focus on the current state-of-the-art research in this field. Detailed classifications are pursued highlighting existing solutions, problems, and developments in this area.
1. 3.1: Introduction - p. 105 2. 3.2: State feedback controller - p. 109 3. 3.3: Observer-based controller - p. 116 4. 3.4: Performance-degradation issues - p. 125 5. 3.5: Decentralized secure control - p. 145 6. 3.6: Notes - p. 157 7. References - p. 159 8. Chapter 4 Event-triggering control of cyberphysical power systems - p. 163 9. 4.1: Introduction - p. 163 10. 4.2: Problem formulation and the control scheme - p. 167 11. 4.3: Design results - p. 173 12. 4.4: Illustrative examples - p. 178 13. 4.5: Conclusions - p. 184 14. Appendix: proof of Theorem 14 - p. 185 15. References - p. 191 16. Chapter 5 Wide-area monitoring and estimation systems - p. 195 17. Chapter 6 Future grid architectures - p. 233 18. Chapter 7 Mature industrial functions - p. 271 19. 7.1: Secure remote state estimation - p. 271 20. 7.1.1: Introduction - p. 271 21. 7.1.2: Problem formulation - p. 273 22. 7.1.3: Secure modules for data transmission - p. 276 23. 7.1.4: Detection and performance analysis in various attack scenarios - p. 280 24. 7.1.5: Extension to detect other attacks - p. 288 25. 7.1.6: Proofs of the lemmas and theorems - p. 290 26. 7.1.7: Simulation results - p. 296 27. 7.2: Notes - p. 300 28. References - p. 301 29. Chapter 8 Secure filtering in power systems - p. 305 30. Chapter 9 Basic mathematical tools - p. 327 31. 9.1: Finite-dimensional spaces - p. 328 32. 9.2: Matrix theory - p. 336 33. 9.3: Some bounding inequalities - p. 352 34. 9.4: Gronwall-Bellman inequality - p. 354 35. 9.5: Schur complements - p. 355 36. 9.6: Some useful lemmas - p. 357 37. 9.7: Fundamental stability theorems - p. 362 38. 9.8: Elements of algebraic graphs - p. 369 39. 9.9: Linear matrix inequalities - p. 381 40. 9.10: Some formulas on matrix inverses - p. 384 41. 9.11: Notes - p. 386 42. References - p. 386 43. Index - p. 389
In an uncertain and complex environment, to ensure secure and stable operations of large-scale power systems is one of the biggest challenges that power engineers have to address today. Traditionally, power system operations and decision-making in controls are based on power system computations of physical models describing the behavior of power systems. Largely, physical models are constructed according to some assumptions and simplifications, and such is the case with power system models. However, the complexity of power system stability problems, along with the system's inherent uncertainties and nonlinearities, can result in models that are impractical or inaccurate. This calls for adaptive or deep-learning algorithms to significantly improve current control schemes that solve decision and control problems.
Cyberphysical Infrastructures in Power Systems: Architectures and Vulnerabilities provides an extensive overview of CPS concepts and infrastructures in power systems with a focus on the current state-of-the-art research in this field. Detailed classifications are pursued highlighting existing solutions, problems, and developments in this area.
1. 3.1: Introduction - p. 105 2. 3.2: State feedback controller - p. 109 3. 3.3: Observer-based controller - p. 116 4. 3.4: Performance-degradation issues - p. 125 5. 3.5: Decentralized secure control - p. 145 6. 3.6: Notes - p. 157 7. References - p. 159 8. Chapter 4 Event-triggering control of cyberphysical power systems - p. 163 9. 4.1: Introduction - p. 163 10. 4.2: Problem formulation and the control scheme - p. 167 11. 4.3: Design results - p. 173 12. 4.4: Illustrative examples - p. 178 13. 4.5: Conclusions - p. 184 14. Appendix: proof of Theorem 14 - p. 185 15. References - p. 191 16. Chapter 5 Wide-area monitoring and estimation systems - p. 195 17. Chapter 6 Future grid architectures - p. 233 18. Chapter 7 Mature industrial functions - p. 271 19. 7.1: Secure remote state estimation - p. 271 20. 7.1.1: Introduction - p. 271 21. 7.1.2: Problem formulation - p. 273 22. 7.1.3: Secure modules for data transmission - p. 276 23. 7.1.4: Detection and performance analysis in various attack scenarios - p. 280 24. 7.1.5: Extension to detect other attacks - p. 288 25. 7.1.6: Proofs of the lemmas and theorems - p. 290 26. 7.1.7: Simulation results - p. 296 27. 7.2: Notes - p. 300 28. References - p. 301 29. Chapter 8 Secure filtering in power systems - p. 305 30. Chapter 9 Basic mathematical tools - p. 327 31. 9.1: Finite-dimensional spaces - p. 328 32. 9.2: Matrix theory - p. 336 33. 9.3: Some bounding inequalities - p. 352 34. 9.4: Gronwall-Bellman inequality - p. 354 35. 9.5: Schur complements - p. 355 36. 9.6: Some useful lemmas - p. 357 37. 9.7: Fundamental stability theorems - p. 362 38. 9.8: Elements of algebraic graphs - p. 369 39. 9.9: Linear matrix inequalities - p. 381 40. 9.10: Some formulas on matrix inverses - p. 384 41. 9.11: Notes - p. 386 42. References - p. 386 43. Index - p. 389
