A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm.

The maximum power point tracking (MPPT) controller can monitor the voltage and current of photovoltaic (PV) array in real-time to acquire the maximum power output for battery charging in PV system. In order to improve the accuracy and rapidity of tracking process, a bionic two-stage MPPT control strategy composed of fast positioning stage and precise determination stage is proposed in this paper for optimizing the duty cycle d of DC-DC converter. Firstly, an improved artificial bee colony algorithm with simplified probability selection mechanism and novel employed bee phase is presented to balances exploration and exploitation for excellent rapidity in positioning rough search region around global peak. Then, the simultaneous heat transfer search (SHTS) algorithm is adopted for accurately acquiring the global maximum power point in obtained search region. On the one hand, the SHTS can reduce the subjectivity in artificial setting of the parameters to ensure the universality and accuracy of search process. On the other hand, the parallel search in SHTS can decrease the optimization time effectively. Finally, extensive simulation results illustrate that this two-stage MPPT strategy shows excellent performance in precisely identifying the GMPP of PV system with phenomenal rapidity among multiple peaks. Moreover, it outperforms all the counterparts in tracking speed and accuracy under partial shading conditions.

A novel linear active disturbance rejection controller for main steam temperature control based on the simultaneous heat transfer search.

The main steam temperature of boiler outlet has been deemed as a significant parameter of the safety and economic performances in the thermal power plant operation. The complex working status of the thermal generation endures highly uncertain factors and remarkable disturbance, which call for effective controlling approaches in the corresponding temperature management. The linear active disturbance rejection controller (LADRC) is a conducive and powerful controlling method, whereas strong correlation between LADRC parameters leads to difficulties in optimally determining the controller parameters. Aiming at eliminating the negative effects on main steam temperature control caused by uncertainties factors and disturbances, a high performance LADRC based on a novel parameters optimization strategy, the simultaneous heat transfer search (SHTS) algorithm, is designed to deliver a stability, rapidity, and precision of control process. In the presented SHTS algorithm, all the three phases of heat transfer are randomly and parallel operated, providing a significant improvement towards the optimization performance. The proposed algorithm is first verified on various benchmark functions contrasted to state-of-the-art counterparts in performance validating, and then adopted in the parameter selection of LADRC in the main steam temperature control system. The excellent control performance, strong robustness and disturbance rejection ability of the designed approach are illustrated through the simulation results on main steam temperature control system.

Novel fuzzy modeling and energy-saving predictive control of coordinated control system in 1000 MW ultra-supercritical unit.

In order to satisfy the growing demands of control performance and energy conservation in power generation process, a novel T-S fuzzy modeling method combined with the quantum artificial bee colony (QABC) algorithm is proposed and applied to the coordinated control system (CCS) of ultra-supercritical unit in 1000MW power plant. The T-S fuzzy modeling consists of the identifications of premise part and consequence part. In the premise part identification, the cluster number and initial cluster centers are obtained at first by using entropy-based clustering method. Secondly, the initial cluster centers are modified through QABC algorithm to guarantee the integral of data and avoid possible marginalization. Then, the consequence part is identified through exponentially-weighted least squares. Furthermore, on account of the obtained fuzzy model, an energy-saving predictive control (ESPC) algorithm based on the generalized predictive control is introduced. In the rolling optimization process of ESPC, the values of manipulated variables taken as energy consumption indicator are introduced into objective function to decrease the consumption of energy and improve the performance of control process. Meanwhile, the addition of manipulated variables constraints can obtain further improvements of energy-saving efficiency and control performance. The simulation results demonstrate the high precision of identified model and ideal performance along with energy-saving ability of ESPC.

Coordinated control system modelling of ultra-supercritical unit based on a new T-S fuzzy structure.

The thermal power plant, especially the ultra-supercritical unit is featured with severe nonlinearity, strong multivariable coupling. In order to deal with these difficulties, it is of great importance to build an accurate and simple model of the coordinated control system (CCS) in the ultra-supercritical unit. In this paper, an improved T-S fuzzy model identification approach is proposed. First of all, the k-means++ algorithm is employed to identify the premise parameters so as to guarantee the number of fuzzy rules. Then, the local linearized models are determined by using the incremental historical data around the cluster centers, which are obtained via the stochastic gradient descent algorithm with momentum and variable learning rate. Finally, with the proposed method, the CCS model of a 1000 MW USC unit in Tai Zhou power plant is developed. The effectiveness of the proposed approach is validated by the given extensive simulation results, and it can be further employed to design the overall advanced controllers for the CCS in an USC unit.

Improved single neuron controller for multivariable stochastic systems with non-Gaussianities and unmodeled dynamics.

In this paper, a new adaptive control approach is presented for multivariate nonlinear non-Gaussian systems with unknown models. A more general and systematic statistical measure, called (h,ϕ)-entropy, is adopted here to characterize the uncertainty of the considered systems. By using the "sliding window" technique, the non-parameter estimate of the (h,ϕ)-entropy is formulated. Then, the improved neuron based controllers are developed for multivariate nonlinear non-Gaussian systems by minimizing the entropies of the tracking errors in closed loops. The condition to guarantee the strictly decreasing entropy of tracking error is presented. Moreover, the convergence in the mean-square sense has been analyzed for all the weights in the neural controllers. Finally, the comparative simulation results are presented to show that the performance of the proposed algorithm is superior to that of PID control strategy.

Cascade control of superheated steam temperature with neuro-PID controller.

In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neuro-PID controller besides the sequences of tracking error in outer loop control system, hence, feedback control is combined with feedforward control in the proposed neuro-PID controller. The convergent condition of the neural networks is analyzed. The implementation procedures of the proposed cascade control approach are summarized. Compared with the neuro-PID controller using minimizing squared error criterion, the proposed neuro-PID controller using minimizing error entropy criterion may decrease fluctuations of the superheated steam temperature. A simulation example shows the advantages of the proposed method.