Relative Rate Observer Self-Tuning of Fuzzy PID Virtual Inertia Control for An Islanded microgrid

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids\u27 dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC\u27s success in showing remarkable improvement in the microgrid\u27s dynamic response and enhancement of its stability. Moreover, it handles different contingency conditions successfully by giving the desired frequency support. Ant colony optimization (ACO) technique is used to find the optimal values of the RROSTF-PID based on VIC parameters. Furthermore, using MATLAB TM/Simulink, RROSTF-PID based on VIC response is compared to Optimal Fuzzy PID (OF-PID) based VIC, Fuzzy PID (F-PID) based VIC, PID-based VIC, conventional VIC responses, and the microgrid without VIC response under different operation conditions

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system

In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves fnding feed rate profles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefned concentration profles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defned. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.Fil: Fernández, Maria Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Ortiz, Oscar Alberto. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm

[EN] In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder¿Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.This work was supported by the Vanier Canada Graduate Scholarship, the Michael Smith Foreign Study Supplements Program from the Natural Sciences and Engineering Research Council of Canada and by the Ministerio de Economia y Competitividad (Spain), project DPI2015-71443-R. It was also supported by the Bourse Mobilite Etudiante from Ministere de l'Education du Quebec, the CEMF Claudette MacKay-Lassonde Graduate Engineering Ambassador Award and the SWAAC Bourseau merite pour etudiantes de cycles superieurs.Blondin, MJ.; Sanchís Saez, J.; Sicard, P.; Herrero Durá, JM. (2018). New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm. Applied Soft Computing. 62:216-229. https://doi.org/10.1016/j.asoc.2017.10.007S2162296

Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient

The Expected Hypervolume Improvement (EHVI) is a frequently used infill criterion in Multi-Objective Bayesian Global Optimization (MOBGO), due to its good ability to lead the exploration. Recently, the computational complexity of EHVI calculation is reduced to O(n log n) for both 2-D and 3-D cases. However, the optimizer in MOBGO still requires a significant amount of time, because the calculation of EHVI is carried out in each iteration and usually tens of thousands of the EHVI calculations are required. This paper derives a formula for the Expected Hypervolume Improvement Gradient (EHVIG) and proposes an efficient algorithm to calculate EHVIG. The new criterion (EHVIG) is utilized by two different strategies to improve the efficiency of the optimizer discussed in this paper. Firstly, it enables gradient ascent methods to be used in MOBGO. Moreover, since the EHVIG of an optimal solution should be a zero vector, it can be regarded as a stopping criterion in global optimization, e.g., in Evolution Strategies. Empirical experiments are performed on seven benchmark problems. The experimental results show that the second proposed strategy, using EHVIG as a stopping criterion for local search, can outperform the normal MOBGO on problems where the optimal solutions are located in the interior of the search space. For the ZDT series test problems, EHVIG still can perform better when gradient projection is applied.Algorithms and the Foundations of Software technolog

Intelligent controllers for vechicle suspension system using magnetorheological damper

Semi-active suspension control with magnetorheological (MR) damper is one of the most fascinating systems being studied in improving the vehicle ride comfort. This study aims to investigate the development of intelligent controllers for vehicle suspension system using MR damper, namely, the proportional-integral-derivative (PID) and fuzzy logic (FL) controllers optimized using particle swarm optimization (PSO), firefly algorithm (FA) and advanced firefly algorithm (AFA). Since the conventional optimization method always has a problem in identifying the optimum values and it is time consuming, the evolutionary algorithm is the best approach in replacing the conventional method as it is very efficient and consistent in exploring the values for every single space. The PSO and FA are among of the evolutionary algorithms which have been studied in this research. Nevertheless, the weakness of FA such as getting trapped into several local minima is an attractive area that has been focused more as a possible improvement during the evolutionary process. Thus, a new algorithm based on the improvement of the original FA was introduced to improve the solution quality of the FA. This algorithm is called advanced firefly algorithm. A parametric modelling technique known as Spencer model was proposed and employed to compute the dynamic behaviour of the MR damper system. The Spencer model was experimentally validated and conducted to capture the behaviour of the Lord RD-1005-3 MR damper with the same excitation input. A simulation of a semi-active suspension system was developed within MATLAB Simulink environment. The effectiveness of all control schemes were investigated in two major issues, namely the ability of the controller to reject the unwanted motion of the vehicle and to overcome the damping constraints. The result indicates that, the PID-AFA control scheme is more superior as compared to the PID-PSO, PID-FA, FL-PSO, FL-FA, FL-AFA and passive system with up to 27.1% and 19.1% reduction for sprung mass acceleration and sprung mass displacement, respectively. Finally, the performance of the proposed intelligent control schemes which are implemented experimentally on the developed quarter vehicle suspension test rig shows a good agreement with the results of the simulation study. The proposed control scheme of PID-AFA has reduced the sprung mass acceleration and sprung mass displacement over the FL-AFA and passive system up to 28.21% and 16.9%, respectively

Optimal Decentralized Load Frequency Control for Power System: A Mean-Field Team Approach

RÉSUMÉ Le problème de réglage fréquence-puissance (RFP) dans les réseaux électriques connaît un regain récent d’intérêt vu la pénétration de plus en plus importante dans ces réseaux de sources d’énergie renouvelable solaire ou éolienne, c’est à dire avec caractéristiques d’intermittence. En effet, la fréquence est un signal dont le comportement est sensible à tout déséquilibre entre génération et demande d’électricité, et son maintien dans un voisinage serré de sa valeur nominale (60 Hz en Amérique du Nord), est essentiel pour la stabilité du réseau. Le RFP vise à contrôler la puissance de sortie des générateurs en réponse aux changements de fréquence (dans le cas d’une zone unique) ainsi qu’à ceux des échanges d’énergie par rapport à leur valeur programmée dans les lignes de raccordement (dans le cas de zones multiples). Les techniques actuelles de RFP présentent un mélange de caractéristiques de centralisation et de décentralisation. Dans ce mémoire, nous souhaitons revisiter les algorithmes de RFP à la lumière des derniers développements de la théorie des équipes et jeux à champ moyens (mean field teams and mean field games), en exploitant le fait que le signal de fréquence global utilisé pour coordonner les générateurs est en réalité une moyenne pondérée des fréquences locales à un grand nombre de générateurs. Nous explorerons ainsi dans ce mémoire des approches de commande intégrale-proportionnelle avec structure de coût quadratique pour le RFP. Le problème de commande est formulé comme un problème d’équipe linéaire quadratique selon la structure d’information champ-moyen partagé, c’est-à-dire que chaque générateur observe son propre état (incluant 3 variables internes supposées mesurables) ainsi que le champ moyen consistant en une moyenne des états de tous les générateurs. La commande décentralisée correspond à la solution du problème d’équipe à champ moyen. Cette dernière est obtenue en résolvant 2 équations de Riccati dans le cas d’une zone isolée, l’une associée au générateur local et l’autre associée au champ moyen (ces équations deviennent des équations de Riccati couplées dans le cas d’un problème à 2 zones). Le mémoire est composé de deux parties dédiées respectivement à l’analyse de la commande pour une zone isolée, et celle associée à deux zones interconnectées. Dans la première partie, nous introduisons la théorie de l’équipe dans le contrôle RFP à zone unique. Il s’agit de problèmes de décision multi-agents dans lesquels tous les agents (générateurs individuels) partagent un coût commun [Mahajan et al., 2012]. L’approche de commande actuelle utilise une contrainte de taux de génération unique [Tan, 2010] pour contrôler le comportement de tous les agents/machines et la répartition de la charge se fait en se référant à la taille de la machine, ce qui est simple mais apriori un peu trop grossier.----------ABSTRACT Load frequency control or LFC is a fundamental mechanism for maintaining the stability of electric power systems. It aims at controlling the power output of generators in response to either changes in frequency (in a single area case) or in response to both changes in frequency and tie-line power interchange (in multi- area cases). Indeed frequency is a ubiquitous signal in power systems and its excursions away from its nominal value are indicative of imbalances between generation and load. Interest in LFC has come back to the fore in view of the challenges raised by increasing levels of penetration of renewable intermittent sources (Wind and solar energy). This situation creates frequent and important mismatches between system generation and system load, and thus create the need for more effective LFC schemes. The current set up is based on estimating a single integral control based power mismatch variable and redistributing a share of the correspondingly needed generation increase or decrease among units according to their power rating [Tan, 2010]. While this has proved to be a robust and algorithmically simple scheme, it is a rather rough approach, as it tends to ignore the particular current state of each generator when provided with a new set point. In order to allow more flexible and less aggressive control to each individual generator, normally only represented as a single aggregate unit, novel decentralized linear quadratic-proportional integral control methods for load frequency control respectively based on so-called mean field team theory (for single and two area systems) and mean field games ( for two area systems) are discussed in this thesis. The control problem is formulated as a linear quadratic (LQ) team problem under meanfield sharing (MFS) information structure, i.e., each generator observes its own state (3 state variables) and the mean field, that is in this context the average state of all generators if they are all identical, or the vector of class specific mean states in a non homogeneous multi-class situation. Also, following a team solution scheme developed in [Arabneydi and Mahajan, 2016], a separate mean field control term is a feedback on the vector of mean class specific individual states. The overall result is a decentralized control policy with coordination by the mean field term. The optimal solution is obtained by solving 2 Riccati equations, one for the local generator and another one associated with the mean field (this becomes instead a system of coupled Riccati equations in the subsequent mean field game game solution of the 2 area problem), for the full observation model