Control integrated finite state machine design for photovoltaic module integrated converter

The operation state of photovoltaic Module Integrated Converter (MIC) is subjected to change due to different source and load conditions, while state-swap is usually implemented with flow chart based sequential controller in the past research. In this paper, the signatures for different operational states are evaluated and investigated, which lead to an effective control integrated finite state machine (CIFSM), providing real-time state-swap as fast as the local control loop. The proposed CIFSM is implemented digitally for a boost type MIC prototype and tested under a variety of load and source conditions. The test results prove the effectiveness of the proposed CIFSM design.

Control of cascaded DC-DC converter based hybrid battery energy storage systems - Part II:Lyapunov approach

A cascaded DC-DC boost converter is one of the ways to integrate hybrid battery types within a grid-tie inverter. Due to the presence of different battery parameters within the system such as, state-of-charge and/or capacity, a module based distributed power sharing strategy may be used. To implement this sharing strategy, the desired control reference for each module voltage/current control loop needs to be dynamically varied according to these battery parameters. This can cause stability problem within the cascaded converters due to relative battery parameter variations when using the conventional PI control approach. This paper proposes a new control method based on Lyapunov Functions to eliminate this issue. The proposed solution provides a global asymptotic stability at a module level avoiding any instability issue due to parameter variations. A detailed analysis and design of the nonlinear control structure are presented under the distributed sharing control. At last thorough experimental investigations are shown to prove the effectiveness of the proposed control under grid-tie conditions.

Control of cascaded DC-DC converter-based hybrid battery energy storage systems - Part I:stability Issue

There is an emerging application which uses a mixture of batteries within an energy storage system. These hybrid battery solutions may contain different battery types. A DC-side cascaded boost converters along with a module based distributed power sharing strategy has been proposed to cope with variations in battery parameters such as, state-of-charge and/or capacity. This power sharing strategy distributes the total power among the different battery modules according to these battery parameters. Each module controller consists of an outer voltage loop with an inner current loop where the desired control reference for each control loop needs to be dynamically varied according to battery parameters to undertake this sharing. As a result, the designed control bandwidth or stability margin of each module control loop may vary in a wide range which can cause a stability problem within the cascaded converter. This paper reports such a unique issue and thoroughly investigates the stability of the modular converter under the distributed sharing scheme. The paper shows that a cascaded PI control loop approach cannot guarantee the system stability throughout the operating conditions. A detailed analysis of the stability issue and the limitations of the conventional approach are highlighted. Finally in-depth experimental results are presented to prove the stability issue using a modular hybrid battery energy storage system prototype under various operating conditions.

Digital geometric-sequence control technique for bidirectional dual active bridge DC-DC converters used in future electric vehicles

Bidirectional DC-DC converters are widely used in different applications such as energy storage systems, Electric Vehicles (EVs), UPS, etc. In particular, future EVs require bidirectional power flow in order to integrate energy storage units into smart grids. These bidirectional power converters provide Grid to Vehicle (V2G)/ Vehicle to Grid (G2V) power flow capability for future EVs. Generally, there are two control loops used for bidirectional DC-DC converters: The inner current loop and The outer loop. The control of DAB converters used in EVs are proved to be challenging due to the wide range of operating conditions and non-linear behavior of the converter. In this thesis, the precise mathematical model of the converter is derived and non-linear control schemes are proposed for the control system of bidirectional DC-DC converters based on the derived model. The proposed inner current control technique is developed based on a novel Geometric-Sequence Control (GSC) approach. The proposed control technique offers significantly improved performance as compared to one for conventional control approaches. The proposed technique utilizes a simple control algorithm which saves on the computational resources. Therefore, it has higher reliability, which is essential in this application. Although, the proposed control technique is based on the mathematical model of the converter, its robustness against parameter uncertainties is proven. Three different control modes for charging the traction batteries in EVs are investigated in this thesis: the voltage mode control, the current mode control, and the power mode control. The outer loop control is determined by each of the three control modes. The structure of the outer control loop provides the current reference for the inner current loop. Comprehensive computer simulations have been conducted in order to evaluate the performance of the proposed control methods. In addition, the proposed control have been verified on a 3.3 kW experimental prototype. Simulation and experimental results show the superior performance of the proposed control techniques over the conventional ones.

Control Loop2

Control Loop for project management of MSc dissertation

Aplicação de dispositivos analógicos reconfiguráveis

Mestrado em Engenharia Electrotécnica e de Computadores

Coletores de distribuição e sua otimização hidráulica, aplicados a uma instalação de produção de água arrefecida em sistemas de AVAC ( Fórum Picoas)

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Fonte de carga de condensadores de alta tensão com compensação de fator potência

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica Industrial

Controller implementation using analog reconfigurable hardware (FPAA)

This Thesis has the main target to make a research about FPAA/dpASPs devices and technologies applied to control systems. These devices provide easy way to emulate analog circuits that can be reconfigurable by programming tools from manufactures and in case of dpASPs are able to be dynamically reconfigurable on the fly. It is described different kinds of technologies commercially available and also academic projects from researcher groups. These technologies are very recent and are in ramp up development to achieve a level of flexibility and integration to penetrate more easily the market. As occurs with CPLD/FPGAs, the FPAA/dpASPs technologies have the target to increase the productivity, reducing the development time and make easier future hardware reconfigurations reducing the costs. FPAA/dpAsps still have some limitations comparing with the classic analog circuits due to lower working frequencies and emulation of complex circuits that require more components inside the integrated circuit. However, they have great advantages in sensor signal condition, filter circuits and control systems. This thesis focuses practical implementations of these technologies to control system PID controllers. The result of the experiments confirms the efficacy of FPAA/dpASPs on signal condition and control systems.

Jatkuvatoimisen sellukeittimen säätöjen suorituskyvyn analysointi

Työssä tarkastellaan yleisiä menetelmiä säätöpiirien suorituskyvyn analysointiin ja sovelletaan niitä jatkuvatoimisen sellukeittimen säätöihin. Esitellyt menetelmät tarjoavat keinoja myös huonon säätötuloksen syyn selvittämiseen ja vinkkejä paremman suorituskyvyn saavuttamiseksi. Analyysissä edettiin top-down periaatteen mukaisesti lähtien liikkeelle keittimen tärkeimmästä säädöstä eli kappaluvun säädöstä. Sitten etsittiin tähän vaikuttavia tekijöitä mitatuista suureista. Seuraavaksi arvioitiin tärkeimmäksi katsotun tekijän (hakepinnankorkeus) säädön suorituskyky, jossa havaittiin parannettavaa. Lopuksi hakepinnankorkeuden säädön viritystämuutettiin ja tehtiin identifiointikoe säätörakenteen uudelleen järjestelyä varten.

SIMULATION OF AMB SUPPORTED ROTOR DURING DROP ON RETAINER BEARINGS

The active magnetic bearings present a new technology which has many advantages compared to traditional bearing designs. Active magnetic bearings, however, require retainer bearings order to prevent damages in the event of a component, power or a control loop failure. In the dropdown situation, when the rotor drops from the magnetic bearings to the retainer bearings, the design parameters of the retainer bearings have a significant influence on the behaviour of the rotor. In this study, the dynamics of an active magnetic bearings supported electric motor during rotor drop on retainer bearings is studied using a multibody simulation approach. Various design parameters of retainer bearings are studied using a simulation model while results are compared with those found in literature. The retainer bearings are modelled using a detailed ball bearing model, which accounts damping and stiffness properties, oil film and friction between races and rolling elements. The model of the ball bearings includes inertia description of rollingelements. The model of the magnetic bearing system contains unbalances of the rotor and stiffness and damping properties of support. In this study, a computationally efficient contact model between the rotor and the retainer bearings is proposed. In addition, this work introduces information for the design of physicalprototype and its retainer bearings.

Paperikoneen laatusäätöjärjestelmän uusinnan vaikutukset laatusäätöjen suorituskykyyn

Tutkimuksen kohteena olleen UPM-Kymmene Oyj Kajaanin tehtaan PK3:n laatusäätöjärjestelmä ja mittapalkki uusittiin, jolloin haluttiin selvittää uusinnan vaikutuksia laatusäätöjen suorituskykyyn ja paperin laatuun. Työn kirjallisessa osassa perehdyttiin paperinvalmistusprosessin osiin kyseisen sanomalehtipaperikoneen tapauksessa sekä keskeisimpiin paperin laatuominaisuuksiin liittyviin mittaus- ja säätölaitteisiin sekä niiden toimintaan. Seurattaviksi paperin laatusuureiksi valittiin neliömassa, kuivamassa, kosteus ja paksuus, jotka ovat sanomalehtipaperin tärkeimpiä online-mitattavia ominaisuuksia. Paperin laatusuureiden seurantaan käytetään erilaisia tunnuslukuja ja työkaluja, joita on esitelty tässä työssä. Laatusuureiden konesuuntaisen ja poikkisuuntaisen seurannan tunnusluvuksi valittiin yleisesti käytössä oleva 2σ-keskiarvohajonta. Säätöjen suorituskykyä seurattiin suorituskykykolmion ohjausmatkaindeksien (CTI) ja erosuureen integraalien (IAE) avulla. Kokeellisessa osassa kerättiin mittaustietoja sekä vanhan että uuden laatusäätöjärjestelmän aikana. Seurattavat ajotilanteet paperikoneella jaettiin stabiiliin ajoon ja muutostilanteisiin, jotka käsittävät katkot ja lajinvaihtotilanteet. Stabiilin ajon aikana selvitettiin laatusuureiden hajontojen ja säätöjen suorituskykyindeksien normaaleissa tasoissa tapahtuneet muutokset. Muutostilanteiden osalta haluttiin selvittää, nopeuttaako järjestelmäuusinta katkoista toipumista ja lajinvaihtoaikaa. Stabiilin ajon seurannasta saatujen tulosten perusteella neliömassan ja kuivamassan konesuuntaiset hajonnat kasvoivat järjestelmäuusinnan myötä, mutta kosteuden konesuuntaiset hajonnat pienenivät. Laatusuureiden poikkisuuntaisista hajonnoista neliömassan sekä kuivamassan hajonnat kasvoivat ja kosteuden sekä paksuuden hajonnat pienenivät joidenkin lajien osalta. Poikkisuuntaisten laatusuureiden, etenkin paksuuden, toipuminen katkon jälkeen nopeutui. Myös lajinvaihtoon kuluva aika lyheni poikkisuuntaisilla laatusuureilla. Muutostilanteiden konesuuntaisten hajontojen asettumisajat eivät juuri parantuneet.

Toimitusketjun mallinnuksen tarpeet ja rakenne LOCOMOTIVE projekstissa

Työn tavoitteena oli kuvata ja priorisoida toimitusketjun dynaamisen mallinnustyökalun vaatimukset, sekä muodostaa tämän pohjalta ohjelmistokehitystä tukeva oliomalli. Vaatimuksia selvitettiin teoreettisen tarkastelun, aiemmin toteutettujen kyselytutkimusten sekä viiden pilottitapauksen avulla. Toimitusketjun hallinta ei ole pelkästään materiaalivirtojen vaan myös näihin liittyvän informaation hallintaa. Holististen toimitusketjuongelmien mallintaminen edellyttää siis informaatiovirtojen ja niitä saatelevien ohjausmekanisemien mallintamista. Markkinoilla on selkeästi tilaa tukijärjestelmille, jotka mahdollistaisivat multidimensionaalisten - tuotto, aika, palvelu - toimitusketjuongelmien tarkastelun. Systeemidynamiikan teorian mukaisesti oliomallin lähtökohdaksi valittiin tärkeimpien takaisinkytkentäsilmukkojen mallinnus. Takaisinkytkentäsilmukoiden avulla kyetään mallintamaan kompleksisia systeemejä ajan suhteen. Mallinnetut toimitusketjujen takaisinkytkentäsilmukkat ovat operaatio-, ohjaus-, kysyntä- ja strategiasilmukka. Toimitusketjun ohjausmekanismien, sekä systeemidynamiikan perusteiden pohjalta mallinnustyökalun vaatimuksista muodostettiin oliomalli. Muodostettu oliomalli on Locomotiven - toimitusketjun mallinnustyökalun - perusta.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Neuartige Verfahren für die Überwachung und Diagnose von aktiv magnetgelagerten rotierenden Maschinen

Mit aktiven Magnetlagern ist es möglich, rotierende Körper durch magnetische Felder berührungsfrei zu lagern. Systembedingt sind bei aktiv magnetgelagerten Maschinen wesentliche Signale ohne zusätzlichen Aufwand an Messtechnik für Diagnoseaufgaben verfügbar. In der Arbeit wird ein Konzept entwickelt, das durch Verwendung der systeminhärenten Signale eine Diagnose magnetgelagerter rotierender Maschinen ermöglicht und somit neben einer kontinuierlichen Anlagenüberwachung eine schnelle Bewertung des Anlagenzustandes gestattet. Fehler können rechtzeitig und ursächlich in Art und Größe erkannt und entsprechende Gegenmaßnahmen eingeleitet werden. Anhand der erfassten Signale geschieht die Gewinnung von Merkmalen mit signal- und modellgestützten Verfahren. Für den Magnetlagerregelkreis erfolgen Untersuchungen zum Einsatz modellgestützter Parameteridentifikationsverfahren, deren Verwendbarkeit wird bei der Diagnose am Regler und Leistungsverstärker nachgewiesen. Unter Nutzung von Simulationsmodellen sowie durch Experimente an Versuchsständen werden die Merkmalsverläufe im normalen Referenzzustand und bei auftretenden Fehlern aufgenommen und die Ergebnisse in einer Wissensbasis abgelegt. Diese dient als Grundlage zur Festlegung von Grenzwerten und Regeln für die Überwachung des Systems und zur Erstellung wissensbasierter Diagnosemodelle. Bei der Überwachung werden die Merkmalsausprägungen auf das Überschreiten von Grenzwerten überprüft, Informationen über erkannte Fehler und Betriebszustände gebildet sowie gegebenenfalls Alarmmeldungen ausgegeben. Sich langsam anbahnende Fehler können durch die Berechnung der Merkmalstrends mit Hilfe der Regressionsanalyse erkannt werden. Über die bisher bei aktiven Magnetlagern übliche Überwachung von Grenzwerten hinaus erfolgt bei der Fehlerdiagnose eine Verknüpfung der extrahierten Merkmale zur Identifizierung und Lokalisierung auftretender Fehler. Die Diagnose geschieht mittels regelbasierter Fuzzy-Logik, dies gestattet die Einbeziehung von linguistischen Aussagen in Form von Expertenwissen sowie die Berücksichtigung von Unbestimmtheiten und ermöglicht damit eine Diagnose komplexer Systeme. Für Aktor-, Sensor- und Reglerfehler im Magnetlagerregelkreis sowie Fehler durch externe Kräfte und Unwuchten werden Diagnosemodelle erstellt und verifiziert. Es erfolgt der Nachweis, dass das entwickelte Diagnosekonzept mit beherrschbarem Rechenaufwand korrekte Diagnoseaussagen liefert. Durch Kaskadierung von Fuzzy-Logik-Modulen wird die Transparenz des Regelwerks gewahrt und die Abarbeitung der Regeln optimiert. Endresultat ist ein neuartiges hybrides Diagnosekonzept, welches signal- und modellgestützte Verfahren der Merkmalsgewinnung mit wissensbasierten Methoden der Fehlerdiagnose kombiniert. Das entwickelte Diagnosekonzept ist für die Anpassung an unterschiedliche Anforderungen und Anwendungen bei rotierenden Maschinen konzipiert.