Adaptive intelligent hydro turbine speed identification with water and random load disturbances

In this paper, the hydro power plant model (with penstock-wall elasticity and compressible water column effect) is simulated at random load disturbance variation with output as turbine speed for random gate position as input. The multilayer perceptron neural network (i.e. NNARX) and fused neural network and fuzzy inference system (i.e. ANFIS) for identification of turbine speed as output variable are reported. Emphasis is put on obtaining a generalized model, using (i) NNARX model and (ii) ANFIS model with membership functions defined by subtractive clustering for plant model representation under different values of water time constant. The comparative performance study between the two approaches is also addressed. In the end of the paper, an application of adaptive noise cancellation based on ANFIS model to identify the turbine speed dynamics is also discussed.     

An Investigation on Pruned NNARX Identification Model of Hydropower Plant

The aim of this paper is to determine an accurate nonlinear system model for identification of dynamics. A small hydropower plant connected as single machine infinite bus (SMIB) system is considered in the study. It is modeled by a neural network configured as a feedforward multilayer perceptron neural network (MLPNN). An investigation is conducted on various NN structures to determine the optimally pruned neural network nonlinear autoregressive with exogenous signal (NNARX) identification model. The structure selection is based on validation tests performed on these network models. The proposed structure identifies the model characteristics, which represent the dynamics of a power plant accurately. The results show an improved performance in identification of power plant dynamics by optimal brain surgeon (OBS) pruned network as compared to un-pruned (i.e., fully connected) network.     

Comparative performance study of QN and LM algorithms in predictive control for NNARX-identified model of hydro-power plant

In this paper input-constrained predictive control strategy for NNARX (neural network non-linear auto-regression with exogenous signal) model of hydro-turbine is presented. The input (gate position) and output (turbine power) data are generated by means of dynamic plant model. The collected data are utilized to develop the NNARX model of the plant. Then NN-based predictive control (NNPC) scheme is applied to control the turbine power. The control cost function (CCF) includes the squared difference between the model predicted output and desired response and a weighted squared change in the control signal. The CCF is minimized with both Quasi-Newton and Levenberg–Marquardt iterative algorithms. To demonstrate the suitability of the strategy, the plant has been simulated on two different reference signals. An erratum to this article can be found at     

Nonlinear predictive control for a NNARX hydro plant model

A neural network (NN)-based nonlinear predictive control (NPC) is described for control of turbine power with variation in gate position. The studied plant includes the tunnel, surge tank and penstock effect dynamics. Multilayer perceptron neural network is chosen to represent a neural network nonlinear autoregressive with exogenous signal model of hydro power plant. With the said NN model configuration, quasi-Newton and Levenberg–Marquardt iterative optimization algorithms are applied in order to determine optimal predictive control parameters. The controlled response is simulated on different amplitude step function and trapezoidal shape reference signal. The study also discusses comparison with an approximate predictive control approach, being linearized around operating points. It is shown that NPC strategy gives impressive results in comparison to the approximated one.     

Small hydro power plant identification using NNARX structure

A feedforward multi-layer perceptron neural network structure is developed to model the nonlinear dynamic relationship between input and output of a hydro power plant connected as single machine infinite bus system. Two independent second-order neural network nonlinear auto-regressive with exogenous signal models are used in the study. The structure selection of each independent model is based on various validation tests. The optimal brain surgeon pruning strategy adopted for optimizing the neural network structure. The network performance is studied for fixed and change in operating point.     

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.     

Design of a fuzzy-PID controller for a nonlinear hydraulic turbine governing system by using a novel gravitational search algorithm based on Cauchy mutation and mass weighting

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system, which regulates frequency and power of hydropower generating unit. In previous study, control model of HTGS is always overly simplified and the elastic water hammer model is seldom considered. In this paper, a nonlinear HTGS model with elastic water hammer effect has been studied and a fuzzy-PID controller is designed to improve control quality of this system. In order to optimize the fuzzy-PID controller, a novel gravitational search algorithm based on Cauchy mutation and mass weighing (GSA-CW) has been proposed with two improvements: a weighting strategy is designed to accelerate the convergence by assigning weights to agents in mass calculation; a combined mutation strategy based on Cauchy and Gaussian distribution is proposed to balance the exploration and exploitation ability of the proposed algorithm. At first, the searching ability of the GSA-CW has been verified on a set of 13 complex benchmark functions by statistical analysis. And then, the GSA-CW is applied to optimize the fuzzy-PID controller, while different meta-heuristics and different PID controllers are employed for comparison. Experimental results indicate that the fuzzy-PID controller optimized by the GSA-CW is more effective to improve the control quality of the nonlinear HTGS.     

PEMFC发电系统的自适应模糊控制与动态分析

监测和控制燃料电池的过程中,需要获得各种实时数据.质子交换膜燃料电池(PEMFC)发电系统中的参数强耦合、高度非线性特性增加了对其控制的难度,传统的PI控制虽然对模型精确的系统有较好的控制效果,但对于参数波动的系统则无法获得较高的控制性能.针对以上情况,基于PEMFC发电系统的动态仿真模型,根据重整器在燃料电池发电系统中的作用,设计了自适应模糊控制器.利用模糊控制规则在线控制氢气摩尔流,从而控制PEMFC发电系统的输出功率.仿真结果表明,该动态模型能够预测输出电压.响应曲线显示出自适应模糊控制算法能够较好控制燃料电池有功和无功功率的输出.模型具有良好的负载跟踪特性.     

Load Frequency Control of Multi-interconnected Renewable Energy Plants Using Multi-Verse Optimizer

A reliable approach based on a multi-verse optimization algorithm (MVO) for designing load frequency control incorporated in multi-interconnected power system comprising wind power and photovoltaic (PV) plants is presented in this paper. It has been applied for optimizing the control parameters of the load frequency controller (LFC) of the multi-source power system (MSPS). The MSPS includes thermal, gas, and hydro power plants for energy generation. Moreover, the MSPS is integrated with renewable energy sources (RES). The MVO algorithm is applied to acquire the ideal parameters of the controller for controlling a single area and a multi-area MSPS integrated with RES. HVDC link is utilized in shunt with AC multi-areas interconnection tie line. The proposed scheme has achieved robust performance against the disturbance in loading conditions, variation of system parameters, and size of step load perturbation (SLP). Meanwhile, the simulation outcomes showed a good dynamic performance of the proposed controller.     

基于神经网络的单相有源滤波器

有源滤渡器的控制是一个典型的非线性控制过程．非常适合用神经网络来实现．本文提出了一种应用于有源滤波器系统的神经网络控制器,神经网络控制器的输入是负载电流和补偿电流。输出是开关控制信号甩于控制有源滤波器产生补偿电流来抵消非线性负载的畸变电流。基于MATLAB/SIMULINK平台．建立了单相有源滤波器仿真模型．仿真结果表明所提出的神经网络控制器的有效性。     

An indirect model reference robust fuzzy adaptive control for a class of SISO nonlinear systems

In this paper we are interested in robust adaptive fuzzy control of nonlinear SISO systems in the presence of parametric uncertainties. The plant model structure is represented by the Takagi-Sugeno (T-S) type fuzzy system. An indirect adaptive fuzzy controller based on model reference control scheme is proposed to provide asymptotic tracking of reference signal. The controller parameters are computed at each time. The plant state tracks asymptotically the state of the reference model for any bounded reference input signal. Inverted pendulum and mass spring damper are used to check the performance of the proposed controller.     

Simulation study for automatic generation control of a multi-area power system by ANFIS approach

This paper deals with the application of artificial neural network (ANN) based ANFIS approach to automatic generation control (AGC) of a three unequal area hydrothermal system. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. Appropriate generation rate constraints (GRC) have been considered for the thermal and hydro plants. The hydro area is considered with an electric governor and thermal area is considered with reheat turbine. The design objective is to improve the frequency and tie-line power deviations of the interconnected system. 1% step load perturbation has been considered occurring either in any individual area or occurring simultaneously in all the areas. It is a maiden application of ANFIS approach to a three unequal area hydrothermal system with GRC considering perturbation in a single area as well as in all areas. The performance of the ANFIS controller is compared with the results of integral squared error (ISE) criterion based integral controller published previously. Simulation results are presented to show the improved performance of ANFIS controller in comparison with the conventional integral controller. The results indicate that the controllers exhibit better performance. In fact, ANFIS approach satisfies the load frequency control requirements with a reasonable dynamic response.     

Neuro-fuzzy generalized predictive control of boiler steam temperature

Power plants are nonlinear and uncertain complex systems.Reliable control of superheated steam temper-ature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant.A nonlinear generalized predictive controller based on neuro-fuzzy network(NFGPC)is proposed in this paper.The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant.From the experiments on the plant and the simulation of the plant,much better performance than the traditional controller is obtained.     

Neuro-fuzzy generalized predictive control of boiler steam temperature

Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained.     

连通式油气悬架三轴重型车辆模型非线性动力学分析

建立了基于连通式油气悬架的三轴重型车辆模型,分别将路面不平度考虑为冲击激励、随机激励和正弦激励,分析了连通式油气悬架的非线性特性对三轴重型车辆振动特性的影响,并分析了连通式油气悬架的抗俯仰性能及抗侧倾性能;将路面不平度考虑为正弦激励,以路面不平度激励频率为参数,通过分叉图、波形图、相图以及庞加莱截面分析了正弦激励作用下三轴重型车辆的非线性动力学响应,仿真结果表明系统在不同激励条件下存在周期运动和混沌运动;连通式油气悬架对重型车辆具有较好的抗侧倾和俯仰特性.     

State-space approach to nonlinear predictive generalised minimum variance control

A nonlinear predictive generalised minimum variance control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process, but because of the assumed structure of the system, the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well-known GPC controller.     

基于吹灰专家监督系统的主汽温度广义预测控制

针对国电山东石横发电厂300MW汽包锅炉中主汽温度大滞后、强非线性等特点,设计了基于负荷前馈的多模型阶梯式广义预测控制器;并针对锅炉吹灰过程中主汽温度大幅度不确定波动的情况,基于经验知识和推理,构建了专家监督系统。两者结合,主汽温控制效果良好,实现了先进控制的长期良好投用。     

Optimization of boiler control to improve the load-following capability of power-plant units

The capability to perform fast load changes has been an important issue in the power market, and will become increasingly more so due to the increasing commercialization of the European power market. An optimizing control system for improving the loadfollowing capability of power-plant units has therefore been developed. The system is implemented as a complement, producing control signals to be added to those of the existing boiler control system, a concept which has various practical advantages in terms of implementation and commissioning. The optimizing control system takes account of the multivariable and load-dependent nonlinear characteristics of the boiler process, as a scheduled LQG controller with feedforward action is utilized. The LQG controller improves the control of critical process variables, making it possible to increase the load-following capability of a specific plant. Field tests on a 265 MW coal-fired power-plant unit reveals that the maximum allowable load gradient that can be imposed on the plant, can be increased from 4 to 8 MW/min.     

An H∞ suboptimal robust control approach for systems with uncertainties and data dropouts

This paper studies the design of control systems subject to plant uncertainties and data losses in the channel connecting the plant sensor with the controller. The controller design has two main objectives. The first one is to robustify the control law against plant uncertainties. The other one is to achieve good performance by minimising the variance of the error signal. Data losses are modelled as an independent and identically distributed sequence of Bernoulli random variables. For analysis and design, this random variable is replaced by an additive noise plus gain channel model. To cope with structural uncertainties in the model of the plant, an H_∞ control technique is employed. The controller is synthesised in order to make the closed-loop system robust against structural uncertainties of the nominal model, while achieving optimal performance of the system in the presence of dropouts.     

Nonlinear controller tuning based on a sequence of identifications of linearized time-varying models

A novel algorithm for tuning controllers for nonlinear plants is presented. The algorithm iteratively minimizes a criterion of the control performance. In each iteration one experiment is performed with a reference signal slightly different from the previous reference signal. The input–output signals of the plant are used to identify a linear time-varying model of the plant which is then used to calculate an update of the controller parameters. The algorithm requires an initial feedback controller that stabilizes the closed loop for the desired reference signal and in its vicinity, and that the closed-loop outputs are similar for the previous and current reference signals. The tuning algorithm is successfully tested on a laboratory set-up of the Furuta pendulum.