Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse

This paper describes one practical approach that suggests a model based technique to control in real time the relative humidity under greenhouse. The humidity level is one of the most difficult environmental factors to be regulated in greenhouse. Moreover, maintaining and correcting for more or less humidity can be a challenge for even the most sophisticated monitoring and control equipment. For these raisons, a Linear Quadratic Gaussian (LQG) controller for relative humidity regulation under greenhouse turns out to be useful. Indeed a LQG controller is proposed for a relative humidity under a greenhouse control task. So, the state space model, which is best fitting the acquired data, was identified using the Numerical Subspace State Space System IDentification (N4SID) algorithm. The mathematical model that is obtained will be used for evaluating the parameters of LQG strategy. The proposed controller is implemented in two steps, in one hand, Kalman filter (KF) is used to develop an observer that estimates the state of relative humidity under greenhouse. In the other hand, the state feedback controller gain is estimated using a linear quadratic criterion function. The suggested optimal implemented controller using Matlab/Simulink environment is applied to an experimental greenhouse. We found, according to the results, that the controller is able to lead the inside relative humidity to the desired value with high accuracy, regardless of the external disturbances

Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

Data-driven adaptive model-based predictive control with application in wastewater systems

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms

Closed-loop Identification of an Industrial Extrusion Process

This paper deals with the challenging problem of closed-loop identification for multivariable chemical processes and particularly the estimation of an open-loop plant model for a lab-scale industrial twin-screw extruder used in a powder coatings manufacturing line. The aim is to produce a low order efficient model in order to assist the scaling-up and the model-based control design of the manufacturing process. To achieve this goal, a two-stage indirect approach has been deployed which relies on the a-priori knowledge of the controller parameters in order to extract good estimates of the open-loop dynamics of the underlying process. As input excitation signals we have used multiple single variable step tests at various operating conditions (current industrial practice) carried out manually in order to generate the data-set which captures the dynamics of the extrusion process. In order to increase the efforts for obtaining a suitable plant model, we have employed various identification techniques, such as Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) in order to generate candidate closed-loop models that fit to the original input-output process data. Then, a comparison of the estimated models was performed by means of the mean square error and data fitting criteria in order to select the model that best describes the dynamic behaviour of the extrusion process. Model validation based on closed-loop step responses also used as verification of the results

An optimization-based control strategy for energy efficiency of discrete manufacturing systems

In order to reduce the global energy consumption and avoid highest power peaks during operation of manufacturing systems, an optimization-based controller for selective switching on/off of peripheral devices in a test bench that emulates the energy consumption of a periodic system is proposed. First, energy consumption models for the test-bench devices are obtained based on data and subspace identification methods. Next, a control strategy is designed based on both optimization and receding horizon approach, considering the energy consumption models, operating constraints, and the real processes performed by peripheral devices. Thus, a control policy based on dynamical models of peripheral devices is proposed to reduce the energy consumption of the manufacturing systems without sacrificing the productivity. Afterward, the proposed strategy is validated in the test bench and comparing to a typical rule-based control scheme commonly used for these manufacturing systems. Based on the obtained results, reductions near 7% could be achieved allowing improvements in energy efficiency via minimization of the energy costs related to nominal power purchased.Peer ReviewedPostprint (author's final draft

Wide-Area Measurement-Driven Approaches for Power System Modeling and Analytics

This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising. Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses. Furthermore, since auto-regressive models are in a big family, the ARX model can be modified as other models for various purposes. A multi-input multi-output (MIMO) auto-regressive moving average with exogenous inputs (ARMAX) model is introduced to identify a low-order transfer function model of power systems for adaptive and coordinated damping control. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify an online measurement-based transfer function model that can be used to tune the oscillation damping controller. A demonstration on hardware testbed may illustrate the effectiveness of the proposed adaptive and coordinated damping controller. In fact, measurement-driven approaches for power system modeling and analytics are also attractive to the power industry since a huge number of monitoring devices are deployed in substations and power plants. However, most current systems for collecting and monitoring data are isolated, thereby obstructing the integration of the various data into a holistic model. To improve the capability of utilizing big data and leverage wide-area measurement-driven approaches in the power industry, this dissertation also describes a comprehensive solution through building out an enterprise-level data platform based on the PI system to support data-driven applications and analytics. One of the applications is to identify transmission-line parameters using PMU data. The identification can obtain more accurate parameters than the current parameters in PSS®E and EMS after verifying the calculation results in EMS state estimation. In addition, based on temperature information from online asset monitoring, the impact of temperature change can be observed by the variance of transmission-line resistance

SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented

Development of a self-tuned drive-train damper for utility-scale variable-speed wind turbines

This thesis describes the development of a procedure that tunes a wind turbine drivetrain damper (DTD) automatically. This procedure, when integrated into the controller of any utility-scale variable-speed wind turbine, will allow the turbine to autonomously and automatically tune its DTD on site. In practice this means that the effectiveness of the damper becomes independent on the accuracy of the model or the simulations used by the control engineers in order to tune the damper. This research is motivated by the fact that drive-train failures are still one of the biggest problems that stigmatises the wind turbine industry. The development of an automatically tuned DTD that alleviates the drive-train fatigue loads and thus increases the reliability and lifetime of the drive-train is thus considered very beneficial for the wind turbine industry. The procedure developed begins by running an experimental procedure to collect data that is then used to automatically system identify a linear model describing the drivetrain. Based on this model a single band-pass filter acting as a DTD is automatically tuned. This procedure is run for a number of times, and the resulting DTDs are compared in order to select the optimal one. The thesis demonstrates the effectiveness of the developed procedure and presents alternative procedures devised during research. Finally, insight into future work that could be performed is indicated in the last chapter of the thesis

Towards an Improved Gain Scheduling Predictive Control Strategy for a Solar Thermal Power Plant

This paper improves a recently proposed gain scheduling predictive control strategy for the ACUREX distributed solar collector field at the Plataforma Solar de Almería, in south-east Spain. Measured disturbances are an integral part of the plant and while simple classical, series and parallel, feedforward approaches have been proposed and used extensively in the literature, the proposed approach incorporates a feedforward systematically into the predictive control strategy by including the effects of the measured disturbances of the ACUREX plant into the predictions of future outputs. Models of the measured disturbances are estimated around a family of operating points directly from input-output data and using a subspace identification method while taking into account the frequency response of the plant. Input-output data are obtained from a validated nonlinear simulation model of the plant rather than the plant itself. The nonlinear simulation model is validated here against measured data obtained from the ACUREX plant and the effectiveness of the proposed control approach is evaluated in the same nonlinear simulation environment. The paper also considers related issues like the significance of sufficient modelling of the measured disturbances of the ACUREX plant and the impact of incorporating the expected future behaviour of a measured disturbance along a given prediction horizon, a theme which has received little attention in the literature