HIL-validation of an extremum seeking-based controller for advanced der management

Paradigm shifts in electricity generation are leading to more renewable and distributed energy resources (DER) on the grid. There is a strong interest to utilize these resources for various grid services, but the practicality of commanding multiple DER is often an obstacle to such approaches. In this article, we seek to validate through Hardware-in-the-Loop (HIL) simulation an extremum seeking (ES) based control scheme proposed in previous work. The method aggregates and coordinates multiple distributed controllers to offer transmission grid level services. The HIL validation uses a prototype real-time implementation of the controller's logic on distributed devices and photovoltaic (PV) inverters operating on a simulated utility distribution feeder. Several grid services, including load following and voltage regulation, were validated to demonstrate the deployment feasibility of the ES control approach on equipment already installed on the grid

Advancements in Real-Time Simulation of Power and Energy Systems

Modern power and energy systems are characterized by the wide integration of distributed generation, storage and electric vehicles, adoption of ICT solutions, and interconnection of different energy carriers and consumer engagement, posing new challenges and creating new opportunities. Advanced testing and validation methods are needed to efficiently validate power equipment and controls in the contemporary complex environment and support the transition to a cleaner and sustainable energy system. Real-time hardware-in-the-loop (HIL) simulation has proven to be an effective method for validating and de-risking power system equipment in highly realistic, flexible, and repeatable conditions. Controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are the two main HIL simulation methods used in industry and academia that contribute to system-level testing enhancement by exploiting the flexibility of digital simulations in testing actual controllers and power equipment. This book addresses recent advances in real-time HIL simulation in several domains (also in new and promising areas), including technique improvements to promote its wider use. It is composed of 14 papers dealing with advances in HIL testing of power electronic converters, power system protection, modeling for real-time digital simulation, co-simulation, geographically distributed HIL, and multiphysics HIL, among other topics

Advanced control and optimisation of DC-DC converters with application to low carbon technologies

Prompted by a desire to minimise losses between power sources and loads, the aim of this Thesis is to develop novel maximum power point tracking (MPPT) algorithms to allow for efficient power conversion within low carbon technologies. Such technologies include: thermoelectric generators (TEG), photovoltaic (PV) systems, fuel cells (FC) systems, wind turbines etc. MPPT can be efficiently achieved using extremum seeking control (ESC) also known as perturbation based extremum seeking control. The basic idea of an ESC is to search for an extrema in a closed loop fashion requiring only a minimum of a priori knowledge of the plant or system or a cost function. In recognition of problems that accompany ESC, such as limit cycles, convergence speed, and inability to search for global maximum in the presence local maxima this Thesis proposes novel schemes based on extensions of ESC. The first proposed scheme is a variance based switching extremum seeking control (VBS-ESC), which reduces the amplitude of the limit cycle oscillations. The second scheme proposed is a state dependent parameter extremum seeking control (SDP-ESC), which allows the exponential decay of the perturbation signal. Both the VBS-ESC and the SDP-ESC are universal adaptive control schemes that can be applied in the aforementioned systems. Both are suitable for local maxima search. The global maximum search scheme proposed in this Thesis is based on extensions of the SDP-ESC. Convergence to the global maximum is achieved by the use of a searching window mechanism which is capable of scanning all available maxima within operating range. The ability of the proposed scheme to converge to the global maximum is demonstrated through various examples. Through both simulation and experimental studies the benefit of the SDP-ESC has been consistently demonstrated

Contribution à la commande non linéaire robuste des systèmes d'alimentation en air des piles à combustible de type PEM

La pile à combustible (PàC) est un dispositif qui produit de l'électricité à partir d'une réaction chimique entre l'hydrogène et l'oxygène. Le système à PàC nécessite un certain nombre d'auxiliaires pour fonctionner. Pour cela, un système de commande est indispensable pour optimiser la performance de la PàC.Dans ce travail de thèse, nous nous sommes intéressés à trois types de problématiques de commande de la PàC. La première est celle de l'optimisation de la puissance délivrée par la PàC en contrôlant le rapport d'excès d'oxygène via le débit d'air du compresseur ; en prenant en compte les variations paramétriques, les incertitudes et les perturbations externes. Ce problème est résolu en utilisant la commande non-linéaire par mode glissant d'ordre 2. Deux types d'algorithme sont synthétisés, l'algorithme du mode glissant d'ordre 2 sous-optimal et l'algorithme du Super Twisting adaptatif. Les performances de ces lois de commande ont été validées grâce à un simulateur Hardware In Loop. La deuxième concerne la maximisation de la puissance nette fournie par la pile, tout en maintenant le fonctionnement du compresseur centrifuge dans sa zone nominale et tout en évitant le manque d'oxygène à la cathode, lors des variations rapides de charge. La solution proposée pour résoudre ce problème est un gestionnaire de charge qui consiste en un filtre à coefficient de filtrage ajustable. Deux approches d'ajustement de ce coefficient basées sur la technique de l'Extremum Seeking sont appliquées, comparées et validées expérimentalement. La troisième problématique abordée dans cette thèse est celle de la régulation de la différence de pression entre l'anode et la cathode, lors de variations de charge en présence de variations paramétriques et d'incertitudes. Une solution basée sur un contrôleur multi-variable par mode glissant d'ordre 2 associé à une étude de robustesse est proposée.The PEM fuel cell is a device which generates electricity from a chemical reaction between hydrogen and oxygen. The PEM fuel cell requires many ancillaries to operate the system. A control system is needed to optimize the performance of the PEMFC. This thesis is focused upon three specific control problems related to PEM fuel cell systems. The first problem is the control of the air (oxygen source) entering in the cathode side of fuel cell. The objective is to regulate the oxygen excess ratio in order to maintain the optimum net power output. This problem has been addressed using nonlinear second order sliding mode controllers, which are robust against parametric uncertainty and external disturbance. The SOSM controllers are based on two algorithms: sub-optimal and adaptive Super Twisting. Their performance is validated through Hardware In Loop simulation. The second problem is to maintain the centrifugal compressor in its operating zone, while avoiding the oxygen starvation in the cathode side during rapid load variations. The proposed solution to this problem is a load governor, which is similar to a variable bandwidth first order linear filter. Two adjustment algorithms have been applied for the bandwidth coefficient, based on the Extremum Seeking technique. Their performance has been validated experimentally. The third problem addressed in this thesis is the regulation of the pressure difference between the anode and the cathode during load variations. The control objective is achieved using second order sliding mode MIMO controller, which has been shown to be robust against parametric uncertainty and external disturbance.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

Intelligent traction motor control techniques for hybrid and electric vehicles

This thesis presents the research undertaken by the author within the field of intelligent traction motor control for Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) applications. A robust Fuzzy Logic (FL) based traction motor field-orientated control scheme is developed which can control multiple motor topologies and HEV/EV powertrain architectures without the need for re-tuning. This control scheme can aid in the development of an HEV/EV and for continuous control of the traction motor/s in the final production vehicle. An overcurrent-tolerant traction motor sizing strategy is developed to gauge if a prospective motor’s torque and thermal characteristics can fulfil a vehicle’s target dynamic and electrical objectives during the early development stages of an HEV/EV. An industrial case study is presented. An on-line reduced switching multilevel inverter control scheme is investigated which increases the inverter’s efficiency while maintaining acceptable levels of output waveform harmonic distortion. A FL based vehicle stability control system is developed that improves the controllability and stability of an HEV/EV during an emergency braking manoeuvre. This system requires minimal vehicle parameters to be used within the control system, is insensitive to variable vehicle parameters and can be tuned to meet a vehicle’s target dynamic objectives

Hourly Dispatching Wind-Solar Hybrid Power System with Battery-Supercapacitor Hybrid Energy Storage

This dissertation demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a specific dispatching horizon for an entire day utilizing a hybrid energy storage system (HESS) configured by batteries and supercapacitors. Here, wind speed and solar irradiance are predicted one hour ahead of time using a multilayer perceptron Artificial Neural Network (ANN), which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, multiple state of charge (SOC) controllers as a function of energy storage system (ESS) SOC are developed to accurately estimate the grid reference power (PGrid,ref) for each dispatching period. A low pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC), and the cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations. Besides, the optimum value of depth of discharge for ESS considering both cycling and calendar expenses has been investigated to optimize the life cycle cost of the ESS, which is vital for minimizing the cost of a dispatchable wind-solar power scheme. Finally, the proposed ESS control algorithm is verified by conducting control hardware-in-the loop (CHIL) experiments in a real-time digital simulator (RTDS) platform

Online Control of Automotive systems for improved Real-World Performance

[ES] La necesidad de mejorar el consumo de combustible y las emisiones de los sistemas propulsivos de automoción en condiciones reales de conducción es la base de esta tesis. Para ello, se exploran dos ejes: En primer lugar, el control de los sistemas de propulsión. El estado del arte de control en los sistemas propulsivos de automoción se basa en gran medida en el uso de técnicas de optimización que buscan las leyes de control que minimizan una función de coste en un conjunto de condiciones de operación denidas a priori. Estas leyes se almacenan en las ECUs de producción en forma de mapas de calibración de los diferentes actuadores del motor. Las incertidumbres asociadas al conjunto limitado de condiciones en el proceso de calibración dan lugar a un funcionamiento subóptimo del sistema de propulsión en condiciones de conducción real. Por lo tanto, en este trabajo se proponen métodos de control adaptativo que optimicen la gestión de la planta propulsiva a las condiciones esperadas de funcionamiento para un usuario y un caso determinado en lugar de a un conjunto genérico de condiciones. El segundo eje se reere a optimizar, en lugar de los parámetros de control del sistema propulsivo, la demanda de potencia de este, introduciendo al propio conductor en el bucle de control, sugiriéndole las acciones a tomar. En particular, este segundo eje se reere al control de la velocidad del vehículo (conocido popularmente como Eco-Driving en la literatura) en condiciones reales de conducción. Se proponen sistemas de aviso en tiempo real al conductor acerca de la velocidad óptima para minimizar el consumo del vehículo. Los métodos de control desarrollados para cada aplicación se describen en detalle en la tesis y se muestran ensayos experimentales de validación en los casos de estudio diseñados. Ambos ejes representan un problema de control óptimo, denido por un sistema dinámico, unas restricciones a cumplir y un coste a minimizar, en este sentido las herramientas desarrolladas en la tesis son comunes a los dos ejes: Un modelo de vehículo, una herramienta de predicción del ciclo de conducción y métodos de control óptimo (Programación Dinámica, Principio Mínimo de Pontryagin y Estrategia de Consumo Equivalente Mínimo). Dependiendo de la aplicación, los métodos desarrollados se implementaron en varios entornos experimentales: un motor térmico en sala de ensayos simulando el resto del vehículo, incluyendo el resto del sistema de propulsión híbrido y en un vehículo real. Los resultados muestran mejoras signicativas en el rendimiento del sistema de propulsión en términos de ahorro de combustible y emisiones en comparación con los métodos empleados en el estado del arte actual.[CA] La necessitat de millorar el consum de combustible i les emissions dels sistemes propulsius d'automoció en condicions reals de conducció és la base d'aquesta tesi. Per a això, s'exploren dos eixos: En primer lloc, el control dels sistemes de propulsió. L'estat de l'art de control en els sistemes propulsius d'automoció es basa en gran manera en l'ús de tècniques d'optimització que busquen les lleis de control que minimitzen una funció de cost en un conjunt de condicions d'operació denides a priori. Aquestes lleis s'emmagatzemen en les Ecus de producció en forma de mapes de calibratge dels diferents actuadors del motor. Les incerteses associades al conjunt limitat de condicions en el procés de calibratge donen lloc a un funcionament subòptim del sistema de propulsió en condicions de conducció real. Per tant, en aquest treball es proposen mètodes de control adaptatiu que optimitzen la gestió de la planta propulsiva a les condicions esperades de funcionament per a un usuari i un cas determinat en lloc d'un conjunt genèric de condicions. El segon eix es refereix a optimitzar, en lloc dels paràmetres de control del sistema propulsiu, la demanda de potència d'aquest, introduint al propi conductor en el bucle de control, suggerint-li les accions a prendre. En particular, aquest segon eix es refereix al control de la velocitat del vehicle (conegut popularment com Eco-*Driving en la literatura) en condicions reals de conducció. Es proposen sistemes d'avís en temps real al conductor sobre la velocitat òptima per a minimitzar el consum del vehicle. Els mètodes de control desenvolupats per a cada aplicació es descriuen detalladament en la tesi i es mostren assajos experimentals de validació en els casos d'estudi dissenyats. Tots dos eixos representen un problema de control òptim, denit per un sistema dinàmic, unes restriccions a complir i un cost a minimitzar, en aquest sentit les eines desenvolupades en la tesi són comunes als dos eixos: Un model de vehicle, una eina de predicció del cicle de conducció i mètodes de control òptim (Programació Dinàmica, Principi Mínim de *Pontryagin i Estratègia de Consum Equivalent Mínim). Depenent de l'aplicació, els mètodes desenvolupats es van implementar en diversos entorns experimentals: un motor tèrmic en sala d'assajos simulant la resta del vehicle, incloent la resta del sistema de propulsió híbrid i en un vehicle real. Els resultats mostren millores signicatives en el rendiment del sistema de propulsió en termes d'estalvi de combustible i emissions en comparació amb els mètodes emprats en l'estat de l'art actual.[EN] The need of improving the real-world fuel consumption and emission of automotive applications is the basis of this thesis. To this end, two verticals are explored: First is the online control of the powertrain systems. In state-of-the-art Optimal Control techniques (such as Dyanmic Programming, Pontryagins Minimum Principle, etc...) are extensively used to formulate the optimal control laws. These laws are stored in the production ECUs in the form of feedforward calibration maps. The unaccounted uncertainities related to the real-world during the powertrain calibration result in suboptimal operations of the powertrain in actual driving. Therefore, adaptive control methods are proposed in this work which, optimise the energy management of the conventional and the HEV powertrain control on real driving mission. The second vertical is regarding the vehicle speed control (popularly known as Eco-Driving in the literature) methods in real driving condition. In particular, speed advisory systems are proposed for real time application on a vehicle. The control methods developed for each application are described in details with their verication and validation on the designed case studies. Apart from the developed control methods, there are three tools that were developed and used at various stages of this thesis: A vehicle model, A driving cycle prediction tool and optimal control methods (dynamic programming, PMP and ECMS). Depending on the application, the developed methods were implemented on the Hardware-In-Loop Internal Combustion Engine testing setup or on a real vehicle. The results show signicant improvements in the performance of the powertrain in terms of fuel economy and emissions in comparison to the state-of-the-art methods.Pandey, V. (2021). Online Control of Automotive systems for improved Real-World Performance [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/173716TESI