State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch

This paper concerns state estimation problems in a mean field control setting. In a finite population model, the goal is to estimate the joint distribution of the population state and the state of a typical individual. The observation equations are a noisy measurement of the population. The general results are applied to demand dispatch for regulation of the power grid, based on randomized local control algorithms. In prior work by the authors it has been shown that local control can be carefully designed so that the aggregate of loads behaves as a controllable resource with accuracy matching or exceeding traditional sources of frequency regulation. The operational cost is nearly zero in many cases. The information exchange between grid and load is minimal, but it is assumed in the overall control architecture that the aggregate power consumption of loads is available to the grid operator. It is shown that the Kalman filter can be constructed to reduce these communication requirements,Comment: To appear, IEEE Trans. Auto. Control. Preliminary version appeared in the 54rd IEEE Conference on Decision and Control, 201

Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference

Design and Implementation of Smart Sensors with Capabilities of Process Fault Detection and Variable Prediction

A typical sensor consists of a sensing element and a transmitter. The major functions of a transmitter are limited to data acquisition and communication. The recently developed transmitters with ‘smart’ functions have been focused on easy setup/maintenance of the transmitter itself such as self-calibration and self-configuration. Recognizing the growing computational capabilities of microcontroller units (MCUs) used in these transmitters and underutilized computational resources, this thesis investigates the feasibility of adding additional functionalities to a transmitter to make it ‘smart’ without modifying its foot-print, nor adding supplementary hardware. Hence, a smart sensor is defined as sensing elements combined with a smart transmitter. The added functionalities enhance a smart sensor with respect to performing process fault detection and variable prediction. This thesis starts with literature review to identify the state-of-the-arts in this field and also determine potential industry needs for the added functionalities. Particular attentions have been paid to an existing commercial temperature transmitter named NCS-TT105 from Microcyber Corporation. Detailed examination has been made in its internal hardware architecture, software execution environment, and additional computational resources available for accommodating additional functions. Furthermore, the schemes of the algorithms for realizing process fault detection and variable prediction have been examined from both theoretical and feasibility perspectives to incorporate onboard NCS-TT105. An important body of the thesis is to implement additional functions in the MCUs of NCS-TT105 by allocating real-time execution of different tasks with assigned priorities in the real-time operating system (RTOS). The enhanced NCS-TT105 has gone through extensive evaluation on a physical process control test facility under various normal/fault conditions. The test results are satisfactory and design specifications have been achieved. To the best knowledge of the author, this is the first time that process fault detection and variable prediction have been implemented right onboard of a commercial transmitter. The enhanced smart transmitter is capable of providing the information of incipient faults in the process and future changes of critical process variables. It is believed that this is an initial step towards the realization of distributed intelligence in process control, where important decisions regarding the process can be made at a sensor level

Modeling, Identification and Control at Telemark University College

Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at Department of Electrical Engineering, Information Technology and Cybernetics

Modeling, Identification and Control at Telemark University College

Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at Department of Electrical Engineering, Information Technology and Cybernetics

A Study for Detection of Drift in Sensor Measurements

This study aims to develop methods for detection of drift in sensor measurements. The study consists of three major components; 1) residual generation, 2) statistical change detection, and 3) model building. To identify the statistical properties of the residuals and to utilize them for detection of the drift, a new method for estimation of the drift rate is proposed. The method formulates an augmented system matrix model and processes the model using a Kalman filter. An analytical method for estimation of the drift rate is also derived. A Hamiltonian approach is used for evaluation of the steady state covariance of the residuals. The steady state covariance and the estimated drift rate enable the existence of the drift in the measurements to be determined in a statistical way using the change detection algorithms. The statistical change detection algorithms process the residuals to determine the drift statistically. In the study, performance of the major algorithms, including the Exponentially Weighted Moving average (EWMA), Cumulative Sum (CUSUM) control chart, and Generalized Likelihood Ratio Test (GRLT), are investigated. A new method for detection of the change, named the Standardized Sum of the Innovation Test (SSIT), is also proposed. The statistical properties of the decision function of the SSIT are derived to set the decision threshold statistically. A method for estimation of the mean delay of the SSIT is also derived. The mean delay of the SSIT is shown in a demonstration and is the shortest of the change detection algorithms. For demonstration purposes, mathematical models of a pressurizer in a CANada Deuterium Uranium (CANDU) nuclear power plant are developed. The mathematical models in the form of nonlinear differential equations are verified by comparing the simulation results with those of the industry standard code known as CATHENA (Canadian Algorithm for Thermal Hydraulic Network Analysis). The developed algorithms have been successfully applied to the pressurizer model for detection and estimation of pressure sensor drifts. The results convincingly demonstrate the effectiveness of the proposed algorithms in the detection of the drift

METHODOLOGY FOR ON-LINE BATTERY HEALTH MONITORING

The growing demand for electric vehicles and renewable energy sources has increased the need for safe, reliable, and cost-effective energy-storage systems, many of which include batteries. The reliability and efficiency of these battery-based systems can be significantly improved using intelligent energy-management systems that effectively indicate battery health in real time. On-line monitoring can be difficult, however, because batteries are non-linear and time-varying systems whose characteristics depend on temperature, usage history, and other factors. The key metrics of interest in a battery are its remaining capacity and health. Most of the current methods require off-line measurement, and even the available on-line methods are only good in laboratory conditions. This thesis provides an enhanced streamlined framework for on-line monitoring. In this methodology, a non-intrusive test signal is superimposed upon a battery load which causes transient dynamics inside the battery. The resulting voltage and current are used as test data and the estimation is done in two parts. First, a non-linear least-squares routine is used to estimate the electrical parameters of a battery model. Second, a state-estimation algorithm is used to estimate the open-circuit voltage. Experimental results obtained at consistent temperatures demonstrate that the open-circuit voltage and parameter values together can combine to provide capacity and health measurements. This approach requires minimal hardware and could form the basis for a robust on-line monitoring system

Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management

This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR).Comment: Submitted to Sustainable Energy Grids and Networks (SEGAN). Accepted for publicatio

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science