Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned

Model Prediction-Based Approach to Fault Tolerant Control with Applications

Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information\ud is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the\ud fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions

A Sliding Mode Multimodel Control for a Sensorless Photovoltaic System

In this work we will talk about a new control test using the sliding mode control with a nonlinear sliding mode observer, which are very solicited in tracking problems, for a sensorless photovoltaic panel. In this case, the panel system will has as a set point the sun position at every second during the day for a period of five years; then the tracker, using sliding mode multimodel controller and a sliding mode observer, will track these positions to make the sunrays orthogonal to the photovoltaic cell that produces more energy. After sunset, the tracker goes back to the initial position (which of sunrise). Experimental measurements show that this autonomic dual axis Sun Tracker increases the power production by over 40%

Adaptive backstepping control of induction motor powered by photovoltaic generator

This paper is aimed at addressing the design of an effective adaptive nonlinear control of a photovoltaic (PV) water pumping system powering a submersible induction motor and a centrifugal water pump. Four objectives are achieved using an adaptive Backstepping controller. First, it is applied to ensure maximum power point tracking, and uses the latter as a reference in regulation of the rotor speed to convert the maximum electrical power into maximum mechanical power. Second, the adaptive controller is synthesized to control motor rotor flux and restrict the magnetic circuit to its linear interval. Third, it is used to online estimate the rotor time-constant and the load torque disturbance estimation. Finally, this controller is employed to limit the stator currents to protect induction motor windings. Mathematical modelling of the main elements of the system is presented. A sliding mode rotor flux estimator is employed in the output feedback control of the whole system. DC-AC converter is controlled by pulse width modulation. The feasibility, the robustness and the effectiveness of the proposed adaptive nonlinear controller are evaluated through simulations in MATLAB/Simulink environment

Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview

Disturbance Observer has been one of the most widely used robust control tools since it was proposed in 1983. This paper introduces the origins of Disturbance Observer and presents a survey of the major results on Disturbance Observer-based robust control in the last thirty-five years. Furthermore, it explains the analysis and synthesis techniques of Disturbance Observer-based robust control for linear and nonlinear systems by using a unified framework. In the last section, this paper presents concluding remarks on Disturbance Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure

Control oriented concentrating solar power (CSP) plant model and its applications

Solar receivers in concentrating solar thermal power plants (CSP) undergo over 10,000 start-ups and shutdowns, and over 25,000 rapid rate of change in temperature on receivers due to cloud transients resulting in performance degradation and material fatigue in their expected lifetime of over 30 years. The research proposes to develop a three-level controller that uses multi-input-multi-output (MIMO) control technology to minimize the effect of these disturbances, improve plant performance, and extend plant life. The controller can be readily installed on any vendor supplied state-of-the-art control hardware.^ We propose a three-level controller architecture using multi-input-multi-output (MIMO) control for CSP plants that can be implemented on existing plants to improve performance, reliability, and extend the life of the plant. This architecture optimizes the performance on multiple time scalesreactive level (regulation to temperature set points), tactical level (adaptation of temperature set points), and strategic level (trading off fatigue life due to thermal cycling and current production). This controller unique to CSP plants operating at temperatures greater than 550°C, will make CSPs competitive with conventional power plants and contribute significantly towards the Sunshot goal of 0.06/kWh(e), while responding with agility to both market dynamics and changes in solar irradiance such as due to passing clouds. Moreover, our development of control software with performance guarantees will avoid early stage failures and permit smooth grid integration of the CSP power plants. The proposed controller can be implemented with existing control hardware infrastructure with little or no additional equipment.^ In the thesis, we demonstrate a dynamics model of CSP, of which different components are modelled with different time scales. We also show a real time control strategy of CSP control oriented model in steady state. Furthermore, we shown different controllers design for disturbance rejection and reference tracking to handle complex receiver dynamics under system disturbance and measurement noise. At last, we show different applications of this control oriented CSP model including life cycle enhancement and electricity load forecasting using both neural network and regression tree

The four-tank benchmark: a simple solution by embedded model control

The four-tank benchmark is a multivariate and nonlinear control problem which has been widely studied in the literature. Two pairs of tanks in series are supplied by two pumps. Under certain configurations, the Embedded Model Control approach provides a simple decoupled solution by separately controlling the two output tank levels and treating the input flow as a partly unknown disturbance. Neglected dynamics in a form of unknown delays both in sensors and actuator dynamics is considered. The core of the control unit is a discrete-time embedded model consisting of unknown disturbance dynamics and partly known nonlinear interactions. The embedded model is driven by the plant command and by a feedback vector which is retrieved from the model error. The feedback is capable of keeping updated the unknown disturbance prediction, ready to be cancelled by the control law. The control gains are tuned using two sets of closed-loop eigenvalues in order to trade-off between disturbance rejection and robust stability. Simulated runs under different tank interactions prove design effectiveness