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Multi-objective dynamic VAR planning against fault-induced delayed voltage recovery using heuristic optimization

Bahramgiri, M ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1007/s00202-024-02326-7
  3. Publisher: 2024
  4. Abstract:
  5. The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024
  6. Keywords:
  7. Composite load model ; Fault-induced delayed voltage recovery ; Air conditioning ; Bayesian networks ; Electric current regulators ; Electric loads ; Electric power transmission networks ; Flexible AC transmission systems ; Genetic algorithms ; Induction motors ; Multiobjective optimization ; Neural networks ; Power quality ; Reactive power ; Recovery ; Static var compensators ; Value engineering ; Bayesian ; Bayesian regularized artificial neural network ; Heuristic optimization ; Multi objective ; Non dominated sorting genetic algorithm ii (NSGA II) ; Short-term voltage instability ; Trajectory sensitivity ; Voltage instability ; Static synchronous compensators
  8. Source: Electrical Engineering ; Volume 106, Issue 5 , 2024 , Pages 6281-6293 ; 09487921 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s00202-024-02326-7