Advanced Real-time Control Research Articles

Disruption avoidance and investigation of the H-Mode density limit in ASDEX Upgrade

In recent years a strong effort has been made to investigate disruption avoidance schemes in order to aid the development of integrated operational scenarios for ITER. Within the EUROfusion programme the disruptive H-mode density limit (HDL) has been studied on the WPTE (Work Package Tokamak Exploitation) devices ASDEX Upgrade, TCV and JET. Advanced real-time control coupled with improved real-time diagnostics has enabled the routine disruption avoidance of the HDL. This allowed the systematic study of the influence of various plasma parameters on the onset and behavior of the HDL in regimes not easily accessible otherwise. The upper triangularity δtop is found to have a significant influence on the x-point radiator (XPR), which plays a major role for the evolution of the disruptive HDL. At high δtop the gas flow rate at which the onset of the XPR occurs is strongly reduced compared to low δtop . The reduction of δtop has proven to be an effective actuator for the HDL disruption avoidance on ASDEX Upgrade for highly shaped scenarios ( δtop>0.25 ). It is observed that the occurrence of the XPR and the H–L transition at the density limit are two separate events, the order of which depends on the applied auxiliary heating power. At sufficiently high heating power the XPR occurs before the H–L transition. Impurity seeding, used for divertor detachment, influences the onset and the dynamics of the XPR and the behavior of the HDL. The stable existence of the XPR, which is thought to be a requirement for detachment control in future devices, has also been observed without impurity seeding. The implementation of a robust and sustainable operational scenario, e.g. for ITER, requires the combination of continuous control and exception handling. For each disruption path the appropriate observers and actuators have to be validated in present devices. Automation of the dynamic pulse schedule has proven successful to scan the operational space of the HDL without disruption. Applying such a technique to ITER could reduce the machine risk induced by disruptions during commissioning. The methodology to develop physics-based observers, which indicate the entry into a disruption path well in time, and applying the appropriate action before the discharge becomes unstable has proven successful.

Energy Recovery Optimization by Means of a Turbine in a Pressure Regulation Node of a Real Water Network Through a Data-Driven Digital Twin

In recent years, various devices have been proposed for pressure regulation and energy recovery in water distribution and transport networks. To provide a real net benefit, they require a dedicated long-distance management system in order to carry on both hydraulic regulation and electricity production without direct human manual operations. This work presents a new proposal for the management of a pressure regulation system based on the PRS turbine. The proposal is applied to a real water distribution network, named Montescuro Ovest pipeline, at the San Giovannello station. The Real Time Control (RTC) logic currently applied at San Giovannello station is first presented and discussed. A new Advanced Real Time Control (ARTC) logic is then proposed, based on direct configuration of the turbine and the surrounding valves as computed by the solution of an optimization problem. In ARTC a digital twin, including the hydraulic model of the surrounding network, provides a one-to-one relationship between the configuration parameters and the state variables, i.e. flow rates and pressures. The digital twin model equations are continuously updated on the basis of the recorded measures. Besides providing almost identical performance to the current RTC logic in the current operational scenario, the improved ARTC is more robust, in that it guarantees better hydropower generation in modified operational scenarios, as shown in specific tests. The proposed methodology constitutes a new approach to regulating the valves in hydroelectric plants which are currently regulated with traditional automation algorithms.

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

RCCI combustion with renewable fuel mix – Tailoring operating parameters to minimize exhaust emissions

This article demonstrates a feasible approach towards minimizing negative impact of combustion, which comprises low net carbon emissions operation of internal combustion engines while achieving ultra-low engine-out exhaust emissions. Ultra-low emission operation was achieved by combining Reactivity Controlled Compression Ignition (RCCI) combustion concept and the use of an advanced fuel mix featuring low carbon life cycle emissions. This fuel mix consists of hydrotreated vegetable oil (HVO) as a representative of high reactivity renewable fuel and a methane rich (greater than95% by volume) natural gas (MRNG) being a model fuel representing biomethane. Comprehensive mapping and identification of optimal operating regions of a small-swept volume engine was performed using an experimental setup comprising advanced real-time acquisition and control systems, developed specifically for robust RCCI engine operation. Combustion process was tailored to comply with limits of mechanical stresses of engine components, favourable thermodynamic parameters and ultra-low engine-out emissions. Combustion and thus engine operation was optimized based on a sensitivity analysis of the rate of exhaust gas recirculation (EGR) and energy share supplied by MRNG, which are the main influencing parameters to control NOx and soot emissions in RCCI combustion concept. Experimental results demonstrate that it is possible to simultaneously achieve ultra-low engine-out emissions of NOx, PM and PN with an innovative fuel mix resulting in a significant decrease in life-cycle carbon emissions compared to conventional fuels on an account of higher CO and THC emissions, what is a known disadvantage of RCCI concept.

On-line monitoring of photosynthetic activity based on pH data to assess microalgae cultivation

Microalgae performance of outdoor cultivation systems is influenced by environmental and operating dynamics. Monitoring and control systems are needed to maximise biomass productivity and nutrient recovery. The goal of this work was to corroborate that pH data could be used to monitor microalgae performance by means of data from an outdoor membrane photobioreactor (MPBR) plant. In this system, microalgae photosynthetic activity was favoured over other physical and biological processes, so that the pH data dynamics was theoretically related to the microalgae carbon uptake rate (CUR). Short- and long-term continuous operations were tested to corroborate the relationship between the first derivate of pH data dynamics (pH’) and microalgae photosynthetic activity. Short-term operations showed a good correlation between gross pH’ values and MPBR performance. An indicator of the maximum daily average microalgae activity was assessed by a combination of on-line pH’ measurements obtained in the long-term and a microalgae growth kinetic model. Both indicators contributed to the development of advanced real-time monitoring and control systems to optimise microalgae cultivation technology.

Laboratory investigation on underground coal gasification technique with real-time analysis

Energy demand will continue rising in India due to increasing pressure of population; continuing urbanization; significant expansion of better living standards of the middle class peoples; electrification in more and more villages; and modernization of agriculture, infrastructure and manufacturing sectors. In India, coal is the most abundant and major fossil fuel which is going to play big role in meeting its energy demand. A major part of this requirement can be met by proper utilization of deep-seated coal. Underground coal gasification (UCG) process is a clean coal technology for utilization of unminable coal. In this article, a novel simple laboratory model of UCG is developed with advanced real-time control and monitoring. The experimental study is carried out on a high-ash single coal block for 48 h period. In this experiment, synthesis gases are produced by the gasification of coal block by using oxygen and steam as the main oxidising agents. Observation and analysis are made on the characteristic of produced gases, coal-tar, cavity-growth under variable process conditions. This laboratory-based experimental study will play a vital role for prediction on practically-difficult UCG process for successful operation in actual field by reducing its complexity.

Hardware acceleration of multibody simulations for real-time embedded applications

New products in the automotive and aerospace industries must provide increased energy efficiency and exceed previous performance, safety and reliability. To meet these expectations, the role of simulation continues to grow. Within this context, simulation models are used in real-time embedded applications such as advanced real-time control and virtual sensing. Both applications require the execution of simulation models in real-time on embedded hardware. The limited computational power of this hardware is, however, a major challenge in the adoption of model-based embedded applications. This research explores the use of multibody models for real-time embedded applications. It describes different techniques to accelerate parts or all of the multibody computations on ARM-based and/or FPGA-based hardware.

An adaptive PMU missing data recovery method

A high penetration of renewable energies into the modern grid creates randomness and uncertainties which require advanced real-time monitoring and control. Phasor measurement units (PMUs) and wide-area measurement systems (WAMS) show great promise for operation monitoring and stability enhancement due to characteristics of synchronization, rapidity, and accuracy. However, different levels of data loss issues can occur in practical applications as a result of varying conditions, including communication congestion, hardware failure, and transmission delay. Data loss issues can severely restrict such monitoring applications in power systems, and may even threaten the security of the grid. To address this problem, an adaptive PMU missing data recovery method is proposed in this paper. A data recovery method framework is proposed, in which the data is classified as either ambient or disturbance data, and recovered by different methods to achieve good performance efficiently. An approach based on decision tree is developed for identifying ambient and disturbance data. Then, an improved cubic spline interpolation based on the priority allocation strategy is proposed for ambient data loss, which can quickly and accurately recover ambient data. Simultaneously, a disturbance data recovery method based on singular value decomposition is presented. It can achieve disturbance data recovery accurately by a single channel of measurement. Finally, the feasibility and accuracy of the proposed methods are verified through simulation and hardware-based test platform fed by field recorded data. The simulation and testing results show this method can achieve data identification and recovery efficiently solely based on data, and that applying the proposed method to all aspects of the power system can provide superior PMU measurement guarantees.

Renewable energy storage and distribution scheduling for microgrids by exploiting recent developments in process network synthesis

Abstract In developing countries such as in Africa, rural areas are not always connected to the national or regional electric grid. As there is a huge potential for solar energy production, photovoltaics are established to supply electricity for operating critical infrastructures with battery systems and diesel generators installed for redundancy. These systems have the potential for further utilization. As a first step, overproduction of solar energy is offered to households due to simple control rules observing battery levels. This paper proposes an optimization method for energy allocation by process graphs or P-graphs. The method provides preliminary estimations on optimal periodical energy balances between energy producers, storage, and consumers of different priorities. These preliminary estimations serve as reconciliation parameters for advanced real-time control of energy distribution. Each step of transforming the practical problem of multiperiod energy distribution to the language of process network synthesis and then optionally to mathematical programming is detailed. To represent the problem adequately, the original formulation of process network synthesis is extended to take into account potential targets and intermediate entities that cannot be accumulated. The work aims to provide better utilization of renewable energy sources and storage. The microgrid profit and return of investment can also be improved by the proposed methodology.

Interactive kinetic façade: Improving visual comfort based on dynamic daylight and occupant's positions by 2D and 3D shape changes

Applying active occupant engagement into a responsive façade concept leads us to a transition from the façade regulatory function to the interactive phase. The Interactive facade has the capacity for hierarchically filtering daylight and real-time control, and preventing daylight discomfort. This research applied the combination of qualitative and quantitative methods for studying innovative daylight guide systems functions, relation with building forms and development of kinetic façade forms as advanced real-time daylight control. Literature referred to the responsive modular elements which can be adapted to dynamic daylight by continuously changing façade configurations. In particular, parametric decentralized façade's apertures interact with sun radiation based on relationship between external environment, interior space and occupant position as well. In this study, we develop a kinetic interactive façade with the capability to be transformed based on dynamic daylight and occupant position (functional scenario based) in order to meet visual comfort. Also, daylight parametric simulation investigates visual comfort performance provided by the kinetic façade forms through climate based daylight metrics. The simulation results prove high performance of the kinetic interactive facades for improving visual comfort regarding the base case. In particular, the three dimensional shape changes façade provides more visual comfort improvement than the two dimensional shape changes one regarding UDI, Exceed UDI and DGP metrics. Also, the results refer to multifunctional aspects of the three dimensional shape changes façade, as an advanced interactive daylighting system, which has a capability to control solar radiation in the facade ambient environment for preventing thermal discomfort.

Flood Mitigation through Optimal Operation of a Multi-Reservoir System by Using Model Predictive Control: A Case Study in Myanmar

Managing a multi-reservoir system is complicated, due to conflicting interests among various objectives. This study proposes an optimization-based approach for the operations of a multi-reservoir system. An advanced real-time control technique, Model Predictive Control (MPC), is adopted to control a multi-reservoir system with two control objectives, i.e., flood mitigation and water conservation. The case study area is the Sittaung River basin in Myanmar, where the current reservoir operating rule needs to be improved for a more effective operation. A comparison between an MPC-based operation and the current operation is presented by using performance indicators. The result shows a reduction of the system’s vulnerability by 0.9 percent using MPC. Due to the physical constraint of the reservoirs, it is impossible to completely eliminate the flood risk at Taungoo City during high inflow events. However, the results indicate that the potential flood risk can be mitigated by improving the current operating rule.

Tridiagonal Matrix Algorithm for Real-Time Simulation of a Two-Dimensional PEM Fuel Cell Model

This paper presents a novel two-dimensional real-time modeling approach for a proton exchange membrane fuel cell (PEMFC) based on a tridiagonal matrix algorithm (Thomas algorithm). The Thomas algorithm consists of a forward elimination and a backward substitution, its arithmetic complexity of computations being much lower than the Gaussian elimination. In order to use this advanced numerical solver, the differential equations of reactant gas convection and diffusion phenomena in serpentine channels are transformed into a tridiagonal equations system. In addition, a three-level bisection algorithm has been developed to solve spatial physical quantities distribution for electrochemical domain. The real-time computing methods developed in this paper are then implemented in C language for a fast execution time in a real-time processor. The proposed real-time model is experimentally validated using a 1.2 kW Ballard NEXA fuel cell system, and its practical feasibilities in advanced real-time control for PEMFC systems have been experimentally demonstrated in an RT-LAB real-time simulator.

Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

Abstract Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

Experimental validation of the Carbone–Mangialardi–Mantriota model of continuously variable transmissions

We present detailed experiments to validate the Carbone–Mangialardi–Mantriota model of continuously variable transmissions. Experimental tests were carried out in different working conditions for increasing values of the torque load. Steady-state conditions as well as shift manoeuvres were considered. A detailed comparison with the theoretical predictions of the Carbone–Mangialardi–Mantriota model shows good agreement with the experimental results. This is even more remarkable when it is considered that the Carbone–Mangialardi–Mantriota model is fully self-consistent and completely predictive. It does not need any parameter to be specifically tuned or measured. The results from the present research allows the Carbone–Mangialardi–Mantriota model to be definitely proposed as a very accurate and completely predictive model of the continuously variable transmission variator, and as a very promising tool to develop advanced real-time control of continuously variable transmissions for engineering applications.

Modeling chain continuously variable transmission for direct implementation in transmission control

Continuously variable transmissions (CVT) have been widely used in many different areas such as automotive, robotics, manufacturing and aerospace industry. In CVT drives, a properly designed control strategy is needed to ensure the precise control of the speed ratio, and a deep knowledge of the steady-state and transient behavior of the drive is necessary to this purpose. In the framework of belt and chain CVT drives, model-based approaches developed for this purpose are mainly of two types: continuous models and multibody models. Continuous models are much less costly from a computation point of view, while multibody models are usually believed to be more accurate. The aim of this paper is twofold: first the CMM continuum model [Carbone G., Mangialardi L., Mantriota G., ASME Journal of Mechanical Design, 127, 103–113 (2005)] is compared with a multibody model of the chain-CVT variator. Secondly, the CMM model is proposed for a fast and enhanced characterization of the shifting dynamics of chain CVT. The analysis shows that, except for dynamical effects due to the intermittent contact of the chain pins with the pulleys and caused by the polygonal action of the chain, the CMM and multibody models provide very similar results. Moreover, this study shows, by exploiting the CMM model, that the overall dynamics of the CVT can be described by a relatively simple first order nonlinear differential equation, which can be very easily implemented for CVT real-time control applications. The accuracy of such a simplified approach is then tested against some preliminary shifting experiments under torque load conditions. Results show a very good agreement between theoretical predictions and experimental outcomes, thus making this simplified approach a promising tool to develop advanced real-time control of CVT transmissions for automotive applications.

Operational flood control of a low-lying delta system using large time step Model Predictive Control

The safety of low-lying deltas is threatened not only by riverine flooding but by storm-induced coastal flooding as well. For the purpose of flood control, these deltas are mostly protected in a man-made environment, where dikes, dams and other adjustable infrastructures, such as gates, barriers and pumps are widely constructed. Instead of always reinforcing and heightening these structures, it is worth considering making the most of the existing infrastructure to reduce the damage and manage the delta in an operational and overall way. In this study, an advanced real-time control approach, Model Predictive Control, is proposed to operate these structures in the Dutch delta system (the Rhine–Meuse delta). The application covers non-linearity in the dynamic behavior of the water system and the structures. To deal with the non-linearity, a linearization scheme is applied which directly uses the gate height instead of the structure flow as the control variable. Given the fact that MPC needs to compute control actions in real-time, we address issues regarding computational time. A new large time step scheme is proposed in order to save computation time, in which different control variables can have different control time steps. Simulation experiments demonstrate that Model Predictive Control with the large time step setting is able to control a delta system better and much more efficiently than the conventional operational schemes.

Model reduction in model predictive control of combined water quantity and quality in open channels

Model predictive control (MPC) is an advanced real-time control technique that uses an internal model to predict the future system behavior and generates optimal control actions by solving an optimization problem. MPC has been more and more applied for controlling open water systems, especially open water channels. Most of the research however focuses on water quantity (water level) control. Since water quality management is recently attracting more attention, extending MPC on combined water quantity and quality management is a logical next step.In this paper, we study the application of complex models in MPC to control both water quantity and quality. However, because of the online optimization of MPC, the computational time becomes an issue. In order to reduce the computational time, a model reduction technique, Proper Orthogonal Decomposition (POD), is applied to reduce the model order. The method is tested on a Polder flushing case. The results show that POD can significantly reduce the model order for both water quantity and quality with high accuracy. The MPC using the reduced model performs well in controlling combined water quantity and quality in open water channels.

Sensor Issues and Requirements for Developing Real-Time Control for Plasma Spray Deposition

This paper discusses the requirements of sensors that could be used both for advanced real-time control as well as manual torch operator control of plasma spray processes, and reviews the critical issues associated with design and implementation of such sensors. The focus is on yttria-stabilized zirconia for thermal barrier coatings. The overarching requirement is that the sensor must capture the subset of particles that contributes most to coating properties and that the resulting measurements of these critical subdistributions must be aggregated in a manner that correlates best to the coating buildup. For deposition rate control, the focus of this work, experiments show that the mass flux of molten particles correlates better with coating thickness than bulk-average temperature or light intensity. However, measurement of molten mass flux for control requires sensors that are capable of sensing particle states at high rates from across a large portion of the full plume.

Multi-Agents Architecture for Scheduling and Control of Robotic Manufacturing Systems

This paper presents an innovative Multi-Agent approach related to an advanced real time control of flexible manufacturing systems. The proposed architecture is based on the paradigm of Distributed Intelligence and Multi-Agent Systems. The developed Multi-Agent prototype system integrates the following functions: Scheduling, dispatching, monitoring and error handling. A new negotiation protocol for manufacturing systems is presented in this paper. The purpose of this protocol is to assign dynamic operations to the resources of the Manufacturing System in order to accomplish the proposed tasks. This protocol is able to deal with exceptions.

Digital implementation of a magnetic suspension control system for laboratory experiments

In this paper, a digital control experiment using a magnetic suspension system is described. Although the results presented are for a quadratic optimal controller, the control system configuration used can cope with more advanced real-time control algorithms. Practical issues concerning parameter identification, computer simulation, discrete-time implementation, use of a digital signal processor based controller and filter design are addressed. Simulated and experimental responses are presented and analyzed. Finally, a detailed plan on reproducing the control experiment is included.