Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Strategic Technology Maturation and Insertion (STMI): a requirements guided, technology development optimization process

This research presents a Decision Support System (DSS) process solution to a problem faced by Program Managers (PMs) early in a system lifecycle, when potential technologies are evaluated for placement within a system design. The proposed process for evaluation and selection of technologies incorporates computer based Operational Research techniques which automate and optimize key portions of the decision process. This computerized process allows the PM to rapidly form the basis of a Strategic Technology Plan (STP) designed to manage, mature and insert the technologies into the system design baseline and identify potential follow-on incremental system improvements. This process is designated Strategic Technology Maturation and Insertion (STMI). Traditionally, to build this STP, the PM must juggle system performance, schedule, and cost issues and strike a balance of new and old technologies that can be fielded to meet the requirements of the customer. To complicate this juggling skill, the PM is typically confronted with a short time frame to evaluate hundreds of potential technology solutions with thousands of potential interacting combinations within the system design. Picking the best combination of new and established technologies, plus selecting the critical technologies needing maturation investment is a significant challenge. These early lifecycle decisions drive the entire system design, cost and schedule well into production The STMI process explores a formalized and repeatable DSS to allow PMs to systematically tackle the problems with technology evaluation, selection and maturation. It gives PMs a tool to compare and evaluate the entire design space of candidate technology performance, incorporate lifecycle costs as an optimizer for a best value system design, and generate input for a strategic plan to mature critical technologies. Four enabling concepts are described and brought together to form the basis of STMI: Requirements Engineering (RE), Value Engineering (VE), system optimization and Strategic Technology Planning (STP). STMI is then executed in three distinct stages: Pre-process preparation, process operation and optimization, and post-process analysis. A demonstration case study prepares and implements the proposed STMI process in a multi-system (macro) concept down select and a specific (micro) single system design that ties into the macro design level decision

Multi-objective optimal power resources planning of microgrids with high penetration of intermittent nature generation and modern storage systems

Microgrids are self-controlled entities at the distribution voltage level that interconnect distributed energy resources (DERs) with loads and can be operated in either grid-connected or islanded mode. This type of active distribution network has evolved as a powerful concept to guarantee a reliable, efficient and sustainable electricity delivery as part of the power systems of the future. However, benefits of microgrids, such as the ancillary services (AS) provision, are not possible to be properly exploited before traditional planning methodologies are updated. Therefore, in this doctoral thesis, a named Probabilistic Multi-objective Microgrid Planning methodology with two versions, POMMP and POMMP2, is proposed for effective decision-making on the optimal allocation of DERs and topology definition under the paradigm of microgrids with capacity for providing AS to the main power grid. The methodologies are defined to consider a mixed generation matrix with dispatchable and non-dispatchable technologies, as well as, distributed energy storage systems and both conventional and power-electronic-based operation configurations. The planning methodologies are formulated based on a so-called true-multi-objective optimization problem with a configurable set of three objective functions. Accordingly, the capacity to supply AS is optimally enhanced with the maximization of the available active residual power in grid-connected operation mode; the capital, maintenance, and operation costs of microgrid are minimized, while the revenues from the services provision and participation on liberalized markets are maximized in a cost function; and the active power losses in microgrid´s operation are minimized. Furthermore, a probabilistic technique based on the simulation of parameters from their probabilistic density function and Monte Carlo Simulation is adopted to model the stochastic behavior of the non-dispatchable renewable generation resources and load demand as the main sources of uncertainties in the planning of microgrids. Additionally, POMMP2 methodology particularly enhances the proposal in POMMP by modifying the methodology and optimization model to consider the optimal planning of microgrid's topology with the allocation of DERs simultaneously. In this case, the concept of networked microgrid is contemplated, and a novel holistic approach is proposed to include a multilevel graph-partitioning technique and subsequent iterative heuristic optimization for the optimal formation of clusters in the topology planning and DERs allocation process. This microgrid planning problem leads to a complex non-convex mixed-integer nonlinear optimization problem with multiple contradictory objective functions, decision variables, and diverse constraint conditions. Accordingly, the optimization problem in the proposed POMMP/POMMP2 methodologies is conceived to be solved using multi-objective population-based metaheuristics, which gives rise to the adaptation and performance assessment of two existing optimization algorithms, the well-known Non-dominated Sorting Genetic Algorithm II (NSGAII) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Furthermore, the analytic hierarchy process (AHP) is tested and proposed for the multi-criteria decision-making in the last step of the planning methodologies. The POMMP and POMMP2 methodologies are tested in a 69-bus and 37-bus medium voltage distribution network, respectively. Results show the benefits of an a posteriori decision making with the true-multi-objective approach as well as a time-dependent planning methodology. Furthermore, the results from a more comprehensive planning strategy in POMMP2 revealed the benefits of a holistic planning methodology, where different planning tasks are optimally and simultaneously addressed to offer better planning results.Las microrredes son entes autocontrolados que operan en media o baja tensión, interconectan REDs con las cargas y pueden ser operadas ya sea en modo conectado a la red o modo isla. Este tipo de red activa de distribución ha evolucionado como un concepto poderoso para garantizar un suministro de electricidad fiable, eficiente y sostenible como parte de los sistemas de energía del futuro. Sin embargo, para explotar los beneficios potenciales de las microrredes, tales como la prestación de servicios auxiliares (AS), primero es necesario formular apropiadas metodologías de planificación. En este sentido, en esta tesis doctoral, una metodología probabilística de planificación de microrredes con dos versiones, POMMP y POMMP2, es propuesta para la toma de decisiones efectiva en la asignación óptima de DERs y la definición de la topología de microrredes bajo el paradigma de una microrred con capacidad para proporcionar AS a la red principal. Las metodologías se definen para considerar una matriz de generación mixta con tecnologías despachables y no despachables, así como sistemas distribuidos para el almacenamiento de energía y la interconnección de recursos con o sin una interfaz basada en dispositivos de electrónica de potencia. Las metodologías de planificación se formulan sobre la base de un problema de optimización multiobjetivo verdadero con un conjunto configurable de tres funciones objetivo. Con estos se pretende optimizar la capacidad de suministro de AS con la maximización de la potencia activa residual disponible en modo conectado a la red; la minimización de los costos de capital, mantenimiento y funcionamiento de la microrred al tiempo que se maximizan los ingresos procedentes de la prestación de servicios y la participación en los mercados liberalizados; y la minimización de las pérdidas de energía activa en el funcionamiento de la microrred. Además, se adopta una técnica probabilística basada en la simulación de parámetros a partir de la función de densidad de probabilidad y el método de Monte Carlo para modelar el comportamiento estocástico de los recursos de generación renovable no despachables. Adicionalmente,la POMMP2 mejora la propuesta de POMMP modificando la metodología y el modelo de optimización para considerar simultáneamente la planificación óptima de la topología de la microrred con la asignación de DERs. Así pues, se considera el concepto de microrredes interconectadas en red y se propone un novedoso enfoque holístico que incluye una técnica de partición de gráficos multinivel y optimización iterativa heurística para la formación óptima de clusters para el planeamiento de la topología y asignación de DERs. Este problema de planificación de microrredes da lugar a un complejo problema de optimización mixto, no lineal, no convexos y con múltiples funciones objetivo contradictorias, variables de decisión y diversas condiciones de restricción. Por consiguiente, el problema de optimización en las metodologías POMMP/POMMP2 se concibe para ser resuelto utilizando técnicas multiobjetivo de optimización metaheurísticas basadas en población, lo cual da lugar a la adaptación y evaluación del rendimiento de dos algoritmos de optimización existentes, el conocido Non-dominated Sorting Genetic Algorithm II (NSGAII) y el Evolutionary Algorithm Based on Decomposition (MOEA/D). Además, se ha probado y propuesto el uso de la técnica de proceso analítico jerárquico (AHP) para la toma de decisiones multicriterio en el último paso de las metodologías de planificación. Las metodologías POMMP/POMMP2 son probadas en una red de distribución de media tensión de 69 y 37 buses, respectivamente. Los resultados muestran los beneficios de la toma de decisiones a posteriori con el enfoque de optimización multiobjetivo verdadero, así como una metodología de planificación dependiente del tiempo. Además, los resultados de la estrategia de planificación con POMMP2 revelan los beneficios de una metodología de planificación holística, en la que las diferentes tareas de planificación se abordan de manera óptima y simultánea para ofrecer mejores resultados de planificación.Línea de investigación: Planificación de redes inteligentes We thank to the Administrative Department of Science, Technology and Innovation - Colciencias, Colombia, for the granted National Doctoral funding program - 647Doctorad

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Controller Design and Optimization for Rotor System Supported by Active Magnetic Bearings

Active Magnetic Bearings (AMBs) have been receiving increased attention in industry because of the advantages (contact-free, oil-free, etc.,) that they display in comparison with conventional bearings. They are used extensively in rotor system applications, especially in conditions where conventional bearing systems fail. Most AMBs are controlled by Proportional-Integral-Derivative (PID)-controllers. Controller design for AMB systems by means of hand tuning is time-consuming and requires expert knowledge. In order to avoid this situation and reduce the effort to tune the controller, multi-objective optimization with genetic algorithm is introduced to design and optimize the AMB controllers. In the optimization, criteria both in time and frequency domain are considered. A hierarchical fitness function evaluation procedure is used to accelerate the optimization process and to increase the probability of convergence. This evaluation procedure guides the optimizer to locate the small feasible region resulting mainly from the requirement for stability of control system. Another strategy to reduce the number of optimization parameters is developed, which is based on a sensitivity analysis of the controller parameters. This strategy reduces directly the complexity of the optimization problem and accelerates the optimization process. Controller designs for two AMB systems are considered in this thesis. Based on the introduced and presented hierarchical evaluation strategy, the controller design for the first AMB system is obtained without specific requirements related to initial solutions. The optimal controller design is applied to a test rig with a flexible rotor supported by AMBs. The results show that the introduced optimization procedure realizes the desired results of the controlled system’s behavior. The maximal speed of 15000 rpm is reached. The second AMB system is designed for a turbo-compressor. The introduced parameter reduction strategy is applied for the controller design of this AMB system. The controller design is optimized in the search space around an initial solution. Optimization results show the efficiency of the introduced strategy.Aufgrund vieler Vorteile (wie z. B. Kontaktfreiheit, Ölfreiheit) gegenüber konventionellen Lagern etablieren sich aktive Magnetlager zunehmend in der Industrie. Aktive Magnetlager werden zum großen Teil in Rotorsystemen verwendet, wo konventionelle Öllager für die Anwendung versagen. PID-Regler werden häufig für Magnetlager verwendet. Die Auslegung des Reglers wird durch manuelle Einstellung (trial and error) bestimmt und ist sehr zeitaufwendig. Zudem bedarf es spezieller Fachkenntnisse zur Einstellung. Um diese Situation zu vermeiden und den Aufwand für die Reglerauslegung zu reduzieren, wird die Mehrzieloptimierung mit Genetischen Algorithmen in der vorliegenden Arbeit zur Optimierung des Reglerentwurfs eingesetzt. In der Optimierung werden die Zielfunktionen sowohl im Zeit- wie auch im Frequenzbereich definiert. Um den Optimierungsprozess zu beschleunigen und die Wahrscheinlichkeit der Konvergenz der Optimierung zu erhöhen, wird eine hierarchische Struktur zur Bewertung der Zielfunktionen eingeführt. Dies hilft dem Optimierer bei der Lokalisierung des kleinen zulässigen Bereichs, der im Wesentlichen aus der Anforderung an die Stabilität des Magnetlagersystems resultiert. Desweitern wird eine Strategie zur Reduzierung der Optimierungsparameter entwickelt, die auf der Sensitivitätsanalyse der Reglerparameter basiert. Diese Strategie reduziert die Komplexität des Optimierungsproblems und führt zu einer Beschleunigung des Optimierungsprozesses. In der vorliegenden Arbeit wird der Reglerentwurf von zwei Magnetlagersystemen berücksichtigt. Mit Hilfe der eingeführten Strategie zur Bewertung der Zielfunktionen, werden die Reglerparameter von dem ersten Magnetlagersystem bestimmt bzw. optimiert, ohne dass irgendeine Information über die Anfangslösung erforderlich ist. Der optimale Reglerentwurf wird dann in einem Versuchstand implementiert, in dem eine elastische Welle durch zwei Magnetlager gelagert ist. Die Versuchsergebnisse zeigen, dass das gewünschte dynamische Verhalten des geregelten Magnetlagersystems durch die Optimierung erzielt wird. Die maximal zulässige Drehzahl (15000 rpm) des Versuchsstandes wird mit dem optimalen Regler ohne Probleme erreicht. Als zweites Beispiel wird der Reglerentwurf eines magnetgelagerten Rotorsystems eines Turboverdichters betrachtet. In der Reglerauslegung wird die vorgeschlagene Optimierungsstrategie mit Hilfe von Parameterreduktion verwendet. Die optimale Lösung wird lokal in der Nähe einer Anfangslösung gesucht. Die Optimierungsergebnisse zeigen die Effizienz der Optimierungsstrategie