Fuzzy modeling and fast model predictive control of gas turbine system

A dynamic nonlinear model for a once-through boiler-turbine unit in low load

A dynamic mathematical model for once-through boiler-turbine units with superheated steam temperature

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.

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

The dynamic economic optimization of the ultrasupercritical (USC) boiler-turbine unit has become an important task in modern power plants. Economic model predictive control (EMPC) has recently developed to be a promising method for realizing the dynamic economy. This EMPC essentially requires a highly reliable model for USC dynamic prediction which could reflect the internal mechanism of USC with big data feature. This article constitutes a deep-neural-network-based EMPC for the USC unit. Deep belief network (DBN) is used to model the USC unit with mathematical structure. To overcome the nonlinearity and time delay existing in the pulverized channel, an augmented model with predictor embedded is also incorporated into the EMPC design. The auxiliary controller and stability region have been constituted to guarantee closed-loop stability. Simulation results on a 1000-MW USC unit fully demonstrate the effectiveness of the proposed DBN-based EMPC.

Deep-Neural-Network-Based Economic Model Predictive Control for Ultrasupercritical Power Plant

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.

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

Faulty feeder detection based on the integrated inner product under high impedance fault for small resistance to ground systems

In this paper, an extended state space predictive control (ESSPC) strategy has been applied to gas turbine in combined cycle power plant. This proposed predictive control needn’t solve Diophantine equation online. In addition, in order to overcome the shortcoming of the conventional state space predictive control (SSPC) which only takes output errors into consideration, the objective function of ESSPC includes both output errors and the variation of the system states. In the rolling optimization part, the quadratic program (QP) method is applied to deal with the limitations on the inputs of system. Simulation results show that the proposed algorithm has better tracking ability and stability compared with conventional state space predictive controller in the same condition.

Extended State Space Predictive Control of Gas Turbine System in Combined Cycle Power Plant

Combustion control system is a typical system with large time delays and complex couplings. The traditional PID control approach is a classical method often used. However, control performances of PID are unsatisfied such as overshoot and switch time. Internal model control (IMC) is a new control strategy with controller design based on process mathematical model. IMC is widely applied in combustion system because of its outstanding control performance and the simplicity of design. In this paper, an IMC structure is established firstly. Then, adaptive weight particle swarm optimization (AWPSO) algorithm is used in optimizing parameters of IMC and PID with the control evaluation index based on integral of time-weighted absolute value of the error (ITAE). Finally, these adjusted parameters are applied in process object. Result shows that the proposed approach can achieve better control performance than PID.

Design of internal model controller based on ITAE index and its application in boiler combustion control system

Abstract To address the challenges in fault location in distribution networks, the distribution of magnetic field under overhead line and its relationship with three-phase currents are explored in this paper. At the same time, considering the influence of sensor installation position, line sag and galloping on magnetic field, a grounding fault location method of an overhead line based on dual-axis magnetic field trajectory is proposed. The analytical expressions of the magnetic field on the x -axis and y -axis under the overhead line are obtained by least squares fitting. The Lissajous figure synthesized by dual-axis is then compared with the general equation of an ellipse, and the characteristic quantity expression characterizing the magnetic field trajectory structure is obtained. Finally, a fault location criterion is constructed using the difference of the characteristic quantities of the ellipses synthesized by x -axis and y -axis magnetic fields upstream and downstream of the fault point, i.e., the difference of the length of the major axis and the minor axis, and the sign for the ratio of the cosine value of the inclination angle. Compared with other location methods based on electrical quantity, the principle of this method is simpler and it can locate faults more quickly and accurately. A large number of simulation results show that the proposed method is suitable for different types of fault conditions. 

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Huan Du

Papers

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

Faulty feeder detection based on the integrated inner product under high impedance fault for small resistance to ground systems

Extended State Space Predictive Control of Gas Turbine System in Combined Cycle Power Plant

Design of internal model controller based on ITAE index and its application in boiler combustion control system

Grounding fault location method of overhead line based on dual-axis magnetic field trajectory