Intelligent Coordination of Traditional Power Plants and Inverters Air Conditioners Controlled With Feedback-Corrected MPC in LFC

Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.</p

Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/2/rnc5188.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/1/rnc5188_am.pd

A Model-Based Holistic Power Management Framework: A Study on Shipboard Power Systems for Navy Applications

The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes’ theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied

Supervisory-plus-regulatory control design for efficient operation of industrial furnaces

A two-level system engineering design approach to integrated control and supervision of industrial multi-zone furnaces has been elaborated and tested. The application case study is the three-zone 25 MW RZS furnace plant at Skopje Steelworks. The integrated control and supervision design is based on combined use of general predictive control optimization of set-points and steady-state decoupling,at the upper level, and classical two-term laws with stady-state decouling, at the executive control level. This design technique exploits the intrinsic stability of thermal processes and makes use of constrained optimization, standard non-parametric time-domain process models, identified under operating conditions, using truncated k-time sequence matrices, controlled autoregressive moving average models. Digital implementations are sought within standard computer process control platform for practical engineering and maintenance reasons

Reference Governors: From Theory to Practice

Control systems that are subject to constraints due to physical limitations, hardware protection, or safety considerations have led to challenging control problems that have piqued the interest of control practitioners and theoreticians for many decades. In general, the design of constraint management schemes must meet several stringent requirements, for example: low computational burden, performance, recovery mechanisms from infeasibility conditions, robustness, and formulation simplicity. These requirements have been particularly difficult to meet for the following three classes of systems: stochastic systems, linear systems driven by unmodeled disturbances, and nonlinear systems. Hence, in this work, we develop three constraint management schemes, based on Reference Governor (RG), for these classes of systems. The first scheme, which is referred to as Stochastic RG, leverages the ideas of chance constraints to construct a Stochastic Robustly Invariant Maximal Output Admissible set (SR-MAS) in order to enforce constraints on stochastic systems. The second scheme, which is called Recovery RG (RRG), addresses the problem of recovery from infeasibility conditions by implementing a disturbance observer to update the MAS, and hence recover from constraint violations due to unmodeled disturbances. The third method addresses the problem of constraint satisfaction on nonlinear systems by decomposing the design of the constraint management strategy into two parts: enforcement at steady-state, and during transient. The former is achieved by using the forward and inverse steady-state characterization of the nonlinear system. The latter is achieved by implementing an RG-based approach, which employs a novel Robust Output Admissible Set (ROAS) that is computed using data obtained from the nonlinear system. Added to this, this dissertation includes a detailed literature review of existing constraint management schemes to compare and highlight advantages and disadvantages between them. Finally, all this study is supported by a systematic analysis, as well as numerical and experimental validation of the closed-loop systems performance on vehicle roll-over avoidance, turbocharged engine control, and inverted pendulum control problems

Developments in Stochastic Fuel Efficient Cruise Control and Constrained Control with Applications to Aircraft.

This dissertation presents contributions to fuel-efficient control of vehicle speed and constrained control with applications to aircraft. In the first part of this dissertation a stochastic approach to fuel-efficient vehicle speed control is developed. This approach encompasses stochastic modeling of road grade and traffic speed and uses the application of stochastic dynamic programming to generate vehicle speed control policies that are optimized for the trade-off between fuel consumption and travel time. It is shown that the policies lead to the emergence of time-varying vehicle speed patterns, often referred to as pulse and glide (PnG). Through simulations and experiments it is confirmed that these time-varying vehicle speed profiles are more fuel-efficient than driving at a comparable constant speed. A practical implementation strategy of these patterns is then developed and demonstrated. Also, several additional contributions are made to approaches for stochastic modeling of road grade and vehicle speed that include the use of Kullback-Liebler divergence and divergence rate and a stochastic jump-like model for the behavior of the road grade. In the second part of the dissertation, contributions to constrained control with applications to aircraft are described. Recoverable sets and integral safe sets of initial states of constrained closed-loop systems are introduced first and computational procedures of such sets based on linear discrete-time models are given. An approach to constrained flight planning based on chaining recoverable sets or integral safe sets is described and illustrated with a simulation example. Finally, two control schemes that exploit integral safe sets are proposed. The first scheme, referred to as the controller state governor (CSG), resets the controller state (typically an integrator) to enforce the constraints and enlarge the set of plant states that can be recovered without constraint violation. The second scheme, referred to as the controller state and reference governor (CSRG), combines the controller state governor with the reference governor control architecture and provides the capability of simultaneously modifying the reference command and the controller state to enforce the constraints. Theoretical results that characterize the response properties of both schemes are presented. Examples are reported that illustrate the operation of these schemes.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111399/1/kevinmcd_1.pd

A Novel Adaptive Neural Network Constrained Control for Multi-Area Interconnected Power System with Hybrid Energy Storage

This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control (LFC) applications. The HESS consists of a supercapacitor served as the main power source, and a fuel cell served as the auxiliary power source. Firstly, a Hammerstein-type neural network (HNN) is proposed to identify the HESS system, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Secondly, a feedforward neural network based on back-propagation training algorithm is designed to formulate the PID-type neural network (PIDNN), which is used for the adaptive control of HESS system. Meanwhile, a dynamic anti-windup signal is designed to solve the operational constraint of the HESS system. Then, an appropriate power reference signal for HESS can be generated. Thirdly, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd