Optimization of a PID Controller within a Dynamic Model of a Steam Rankine Cycle with Coupled Energy Storage

Fusion energy is an appealing option for future energy generation, but also presents unique design challenges. The UK Atomic Energy Authority is leading the Spherical Tokamak for Energy Production (STEP) programme to build a fusion power plant capable of net electricity generation. This work addresses the use of dynamic models in an optimization framework for the design of the thermal power generation cycle for STEP. The optimization of a proportional-integral-derivative controller regulating the power output of a steam Rankine cycle with a coupled thermal energy storage system is presented. A lumped-parameter dynamic model of the system has been implemented. The effectiveness of a controller design is evaluated by simulating the system under a perturbation to the power demand on the system. By minimizing the mean absolute power deviation, there is a reduction of 97 % compared to the initial controller design, as well as a reduction of 95 % in the maximum absolute power deviation and a faster return to setpoint. The optimized design does introduce more oscillations in the system, which are undesirable for control systems and are challenging for the optimization procedure

Low Power Dynamic Scheduling for Computing Systems

This paper considers energy-aware control for a computing system with two states: "active" and "idle." In the active state, the controller chooses to perform a single task using one of multiple task processing modes. The controller then saves energy by choosing an amount of time for the system to be idle. These decisions affect processing time, energy expenditure, and an abstract attribute vector that can be used to model other criteria of interest (such as processing quality or distortion). The goal is to optimize time average system performance. Applications of this model include a smart phone that makes energy-efficient computation and transmission decisions, a computer that processes tasks subject to rate, quality, and power constraints, and a smart grid energy manager that allocates resources in reaction to a time varying energy price. The solution methodology of this paper uses the theory of optimization for renewal systems developed in our previous work. This paper is written in tutorial form and develops the main concepts of the theory using several detailed examples. It also highlights the relationship between online dynamic optimization and linear fractional programming. Finally, it provides exercises to help the reader learn the main concepts and apply them to their own optimizations. This paper is an arxiv technical report, and is a preliminary version of material that will appear as a book chapter in an upcoming book on green communications and networking.Comment: 26 pages, 10 figures, single spac

Power Optimization of Electric Developments in Diesel Power Plant for the Electrical Energy Sources using Dynamic Programming Algorithm

The electricity need in the G4 Building at the State University ofMalang was more than 85 kVA. All electrical devices could beactivated; but when the energy source was inactive, all electricityrequirements were transferred to the diesel power plant (DPP).However, the electrical capacity of DPP was only 20 kVA;therefore, it was necessary to optimize the electrical power load sothat the DPP energy could be absorbed optimally using the roomscheduling and electrical devices priority systems. The DynamicProgramming Algorithm was embedded in the power optimizationsystem to help optimize the work. The power optimization prototypewas used to simulate the 1st floor of the G4 Building’s condition.The system consisted of a controller, a central controller, and auser interface. the controller comprised of a current sensor,microcontroller, and a relay. The central controller consisted ofRaspberry Pi 3 hardware that was installed as the server to answerthe HTTP request from the controller and user interface. The userinterface was displayed in a dynamic web to ease the user inmanaging the electrical devices and entering the room usageschedule. The power optimization system managed the electricalenergy from DPP by turning on the electrical devices according tothe priority value. The power optimization system tests were dividedinto six problems, of which each stage had an error value of 0%

Optimization of a PID Controller within a Dynamic Model of a Steam Rankine Cycle with Coupled Energy Storage

Fusion energy is an appealing option for future energy generation, but also presents unique design challenges. The UK Atomic Energy Authority is leading the Spherical Tokamak for Energy Production (STEP) programme to build a fusion power plant capable of net electricity generation. This work addresses the use of dynamic models in an optimization framework for the design of the thermal power generation cycle for STEP. The optimization of a proportional-integral-derivative controller regulating the power output of a steam Rankine cycle with a coupled thermal energy storage system is presented. A lumped-parameter dynamic model of the system has been implemented. The effectiveness of a controller design is evaluated by simulating the system under a perturbation to the power demand on the system. By minimizing the mean absolute power deviation, there is a reduction of 97 % compared to the initial controller design, as well as a reduction of 95 % in the maximum absolute power deviation and a faster return to setpoint. The optimized design does introduce more oscillations in the system, which are undesirable for control systems and are challenging for the optimization procedure

Dynamic modeling and optimal control of a positive buoyancy diving autonomous vehicle

The positive buoyancy diving autonomous vehicle combines the features of an Unmanned Surface Vessel (USV) and an Autonomous Underwater Vehicle (AUV) for marine measurement and monitoring. It can also be used to study reasonable and efficient positive buoyancy diving techniques for underwater robots. In order to study the optimization of low power consumption and high efficiency cruise motion of the positive buoyancy diving vehicle, its dynamic modeling has been established. The optimal cruising speed for low energy consumption of the positive buoyancy diving vehicle is determined by numerical simulation. The Linear Quadratic Regulator (LQR) controller is designed to optimize the dynamic error and the actuator energy consumption of the vehicle in order to achieve the optimal fixed depth tracking control of the positive buoyancy diving vehicle. The results demonstrate that the LQR controller has better performance than PID, and the system adjustment time of the LQR controller is reduced by approximately 56% relative to PID. The motion optimization control method proposed can improve the endurance of the positive buoyancy diving vehicle, and has a certain application value

Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency Control (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with UPFC (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended control method are proved by altering the parameters of the system from their nominal values and by the implementation of random loading in the system

Cyber-physical system based optimization framework for intelligent powertrain control

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulation-based parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark

Robust L2–L∞ control of uncertain differential linear repetitive processes

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdFor two-dimensional (2-D) systems, information propagates in two independent directions. 2-D systems are known to have both system-theoretical and applications interest, and the so-called linear repetitive processes (LRPs) are a distinct class of 2-D discrete linear systems. This paper is concerned with the problem of L2–L∞ (energy to peak) control for uncertain differential LRPs, where the parameter uncertainties are assumed to be norm-bounded. For an unstable LRP, our attention is focused on the design of an L2–L∞ static state feedback controller and an L2–L∞ dynamic output feedback controller, both of which guarantee the corresponding closed-loop LRPs to be stable along the pass and have a prescribed L2–L∞ performance. Sufficient conditions for the existence of such L2–L∞ controllers are proposed in terms of linear matrix inequalities (LMIs). The desired L2–L∞ dynamic output feedback controller can be found by solving a convex optimization problem. A numerical example is provided to demonstrate the effectiveness of the proposed controller design procedures.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm

This paper provides closed loop tuning of cascaded-tilted integral derivative controller (CC-TID) for load frequency control (LFC) of micro grid system. A micro grid system is the arrangement of distributed generation resources such as wind turbine generator (WTG), fuel cell (FC), aqua electrolyser (AE), diesel engine generator (DEG) and battery energy storage system (BESS). Different controllers such as proportional integral derivative (PID), two degree of freedom (2DOFPID), three degree of freedom (3DOFPID) and tilted integral derivative (TID) are used not only to sustain the disparity between real power generation and load demand but also accomplish zero steady state error to enrich the frequency and tie power regulations. The anticipated controller encompasses both the value of cascade (CC) and fractional order (FO) controls for better elimination of system instabilities. In the proposed CC-3DOFPID-TID controller, TID controller is castoff as a slave controller and 3DOFPID controller aided the role of dominant controller. The controlled parameters are optimized by adaptive symbiotic organism search (ASOS) algorithm for keen results of difficulties in LFC. To persist in ecosystem, symbiotic relations are predictable by organism through imitators. Further the dynamic behaviours of controller optimized by ASOS, teaching learning based optimization (TLBO) and differential evolution particle swarm optimization (DEPSO) are compared by extensive simulations in MATLAB/SIMULINK. Moreover the supremacy of proposed controller is performed through system dynamics comparison among PID, 2DOFPID, 3DOF-PID and CC-3DOFPID-TID controllers. Finally sensitivity of proposed controller has proven though random load perturbation

Optimal Design of PID Controller for Doubly-Fed Induction Generator-Based Wave Energy Conversion System Using Multi-Objective Particle Swarm Optimization

This paper presents the complete modeling and simulation of Wave Energy Conversion System (WECS) driven doubly-fed induction generator with a closed-loop vector control system. Two Pulse Width Modulated voltage source (PWM) converters for both rotor- and stator-side converters have been connected back to back between the rotor terminals and utility grid via common dc link. The closed-loop vector control system is normally controlled by a set of PID controllers which have an important influence on the system dynamic performance. This paper presents a Multi-objective optimal PID controller design of a doubly-fed induction generator (DFIG) wave energy system connected to the electrical grid using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). PSO and GA are used to optimize the controller parameters of both the rotor and grid-side converters to improve the transient operation of the DFIG wave energy system under a fault condition as compared with the conventional methods to design PID controllers