Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth–flame optimizer

Abstract

This paper proposes a novel swarm moth–flame optimizer (SMFO) to obtain the optimal parameters of four interacting proportional–integral (PI) loops of a doubly fed induction generator (DFIG)-based wind turbine, so that maximum power point tracking (MPPT) may be achieved together with an improved fault ride-through (FRT) capability. The SMFO is inspired by a moth swarm encircling a flame at night, in which each flame is simultaneously encircled by multiple moths for a greater exploitation, whereas the flame with a higher brightness (i.e. a smaller fitness function) will attract more moths than those of its adjacent flames. In order to achieve a wider exploration, a ring network is then constructed among the flames such that the moths may be guided to search for a brighter flame more effectively. Three case studies are undertaken, which verify that an improved global convergence, more optimal power tracking and enhanced FRT capability may be achieved by SMFO compared with those of existing meta-heuristic techniques.

Model-based model predictive control for a direct-driven permanent magnet synchronous generator with internal and external disturbances

“…The simulation results of three case studies can verify that the proposed PI controller can achieve better convergence, robustness and tracking performance compared with other heuristic algorithms. A novel MPPT controller based on a swarm moth-flame optimization PI method is proposed to extract maximum power for a wind energy conversion system with doubly fed induction generator (DFIG) in Huang et al (2019). The simulation results verify that the proposed swarm moth–flame optimizer (SMFO) method can achieve a better optimal power tracking performance and enhance the fault ride-through capability compared with several traditional optimization methods.…”

Composite control for disturbed direct-driven surface-mounted permanent magnet synchronous generator with model prediction strategy

“…4 Simulation results show that this method can provide an optimal control scheme for wind energy control system based on permanent magnet synchronous generator. In Huang et al, 5 the nonlinear time-varying evolutionary particle swarm optimization algorithm as the training stage of radial basis function neural network (RBFNN) is adopted to optimize the timing prediction parameters of various electrical models.…”

Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems

“…grey wolf optimizer (GWO) [39]) have been proposed by Mirjalili, who proposed the DA [34]. In general, a metaheuristic optimization algorithm can be improved by at least five types of operations: (1) more grouped operation: grouped grey wolf optimizer has been proposed for the parameters optimization of the maximum power point tracking of doublyfed induction generator based wind turbine [40]; swarm moth-flame optimizer has been proposed for the racking of doubly-fed induction generator [41]; whale optimization algorithm has been grouped for standard benchmark functions [42]; (2) combined operation: the combined genetic algorithm (GA) and the PSO has been developed for hybrid wind-photovoltaic-battery system [43]; the GWO has been combined with a whale optimization algorithm for pressure vessel design [44] (3) adaptive parameters operation: an improved Jaya with self-adaptive weight has been applied for the parameters identification of photovoltaic models [45]; a teaching-learning-based optimization algorithm has been improved by adaptive inertia weights [46]; epsilon multiobjective genetic algorithm has been applied for PID parameters optimization [47]; (4) knowledge matrix based operation: knowledge matrix is employed to remember the optimization task [48]; transfer reinforcement learning with Q-value matrix has been proposed for reactive power optimizations [49]; a transfer matrix with Kriging model has been introduced into a multi-objective optimization algorithm [50]; (5) different coded operation: a real-coded GA has been applied into numerical optimizations [51]; binary coded GA has been employed to solve the path planning of mobile robots [52]; a binary operation has been added into the social minic optimization method [53]; hexadecimal coded optimization algorithm based on field-programmable gate-array has been applied for parallel computing [54]; complex-valued encoding operation has been employed to improve the optimal performance of the wind-driven optimization [55]. Furthermore, both more grouped operation and combined operation can obtain the global solution rather than a local solution; both adaptive parameters operation and knowledge matrix based operation can accelerate the convergence process of optimization algorithm; different coded operations can fit for different fitness functions or different types of optimization problems.…”