Intelligent wide‐area damping controller for static var compensator considering communication delays

• Published in: International journal of circuit theory and applications Vol. 51; no. 7; pp. 3376 - 3403
• Main Authors: Jamsheed, Faisal, Javed Iqbal, Sheikh
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.07.2023
• Subjects: adaptive control; Algorithms; Communication; Compensators; Controllers; Damping; low‐frequency oscillations; Machine learning; neural network; Observability (systems); static var compensator; System identification; wide‐area damping control
• ISSN: 0098-9886; 1097-007X
• DOI: 10.1002/cta.3560

Summary This paper utilizes a static var compensator in coordination with an intelligent online controller, to enhance the oscillation damping of the power system at various operating conditions and communication delays. For system identification, a linear identifier is used that ensures a low computational burden and facilitates its real‐time implementation. This identifier accurately estimates the system's local linear model using local measurements and continuously updates its parameters in real time to account for system's changing operational states. The learning rate of the identifier is also adaptively adjusted such that the stability of the learning algorithm and optimal speed of convergence are guaranteed. The location of the controller is selected based upon the controllability and bus voltage participation factors, and an effective feedback signal is obtained using the observability. An empirical expression has also been proposed to offset the effects of communication time delays caused due to use of wide‐area signals. Time‐domain simulations are performed on single‐ and multi‐machine power systems to check the efficacy of the proposed intelligent controller. The performance of the controller is compared with the residue‐based conventional controller and also with the recently proposed wide‐area damping controller. A static var compensator is applied in coordination with an online intelligent controller to enhance the oscillation damping of the power system. For system identification, a linear identifier is used, which approximates the system's local linear model using local measurements and also ensures optimal speed of convergence, a low computational burden, and facilitates its real–time implementation. The performance of the proposed neuro–adaptive controller is robust, and its implementation in the actual power system is quite realizable.