Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Abstract

A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

“…In the paper authored by Cao et al [26], reactive power optimization was investigated for large-scale power systems. A novel transfer bees optimizer was used, where Q-learning was employed to construct the learning mode of bees in order to improve the intelligence of bees through task division and cooperation.…”

Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow

“…As all individuals are exploring and learning, they are faced with action selections. When the individual j prepares to determine the variable x i , its action selection is based on the following equation [41]:…”

“…According to the OCECF model described by Equation 5, the inequality constraint is brought in by the objective function, and then the objective function value obtained by the individual j becomes [41]…”

“…For purpose of testing the optimization performance of MCR-Q(λ) learning, the simulation results of Q(λ) learning, Q learning [41], quantum genetic algorithm (QGA) [42], GA [43], PSO [44], ant colony system (ACS) [45], group search optimizer (GSO) [46] and artificial bee colony (ABC) [47] were also introduced for comparison. Note that the weight coefficient in Equation (5) can be adjusted according to the preference on different components of the objective function.…”

Optimal Nonlinear Adaptive Control for Voltage Source Converters via Memetic Salp Swarm Algorithm: Design and Hardware Implementation

“…Disturbances are very common in many industrial processes which often have a malignant impact on the predesigned control performance of the studied system. As a result, disturbance rejection performance is a very crucial property for advanced controller design in industries [31][32][33]. This test aimed to evaluate the disturbance rejection performance of ONAC, while the perturbation estimation performance of HGPO under 20% voltage drop lasting for 15 ms (t = 0.1 s-0.115 s) of VSC under rectifier mode was recorded and illustrated in Figure 11.…”