Design of multivariable PID controllers using real-coded population-based extremal optimization

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

“…Clearly, MPC is superior to FA-PI, GA-PI and PEO-PI in terms of all indices. In other words, the proposed MPC method in this paper also outperforms these state-of-the-art PI control methods [32,43] for the LFC issue of a multi-area interconnected power system with PV generations even under the dynamical loads fluctuations. …”

“…The comparative methods include firefly algorithm (FA)-based PI controller abbreviated as FA-PI [32], genetic algorithm (GA)-based PI controller abbreviated as GA-PI [32], and our recently reported population extremal optimization (PEO)-based PI controller abbreviated as PEO-PI [42,43]. For fair comparison, the lower and upper limits of the optimized PI controllers' parameters are set as −2 and 2 for FA-PI, GA-PI and PEO-PI, respectively [32].…”

“…The key operations of this proposed method include formulating the LFC issue as a discrete-time state space model, obtaining the dynamic predictive model by introducing an expanded state vector, and rolling optimization of control output signal by gradient descent method based on a cost function minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have shown that the proposed MPC method is superior to evolutionary algorithms-based PI control methods such as FA-PI [32], GA-PI [32], and PEO-PI [42,43] in terms of dynamic and steady-state performance in cases of normal condition, load disturbance and parameters uncertainty. To the best of the authors' knowledge, this work can be considered as the first contribution of MPC to the optimal LFC issue of a multi-area interconnected power system with PV generation.…”

Filter choice for an effective measurement noise attenuation in PI and PID controllers

“…There exist several possible performance measures for evaluation of the analyzed transient responses including traditional [11], as well as less frequently used measures as SDU, or a noise gain [3]. In this paper, to characterize the actual control effort, the total variance [12] will be used.…”

A novel real-coded population-based extremal optimization algorithm with polynomial mutation: A non-parametric statistical study on continuous optimization problems

“…In our recent research work [23], random mutation is adopted in an improved real-coded population-based EO algorithm called IRPEO-RM. Moreover, we have proposed a real-coded population-based EO algorithm with multi-non-uniform mutation to optimize multi-variable PID and PI controller [24]. Nevertheless, these reported experimental results are still for small size continuous optimization problems.…”