Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.Вступ. В електроенергетичній системі потужність синхронних генераторів змінюється внаслідок збурень або різких змін навантаження. Ці зміни в потужності серйозно впливають на стабільність енергетичної системи та якість електроенергії. Синхронний генератор оснащений автоматичним регулятором напруги для підтримання напруги на його клемах на рівні номінальної напруги. Декілька методів управління використовуються для поліпшення реакції системи автоматичного регулятора напруги, однак пропорційний інтегральний похідний контролер (PID-контролер) є найбільш часто використовуваним контролером, але його параметри вимагають оптимізації. Новизна. У цій роботі хаотична послідовність, заснована на логістичній схемі, гібридизується за допомогою оптимізації рою частинок, щоб знайти оптимальні параметри PID для системи автоматичного регулятора напруги. Ініціалізація на основі хаотичної послідовності логістичної схеми та найкращий глобальний вибір дозволяють алгоритму вийти із локальної мінімальної стагнації та покращити швидкість збіжності, що дає найкращі оптимальні параметри. Мета. Основною метою запропонованого підходу є поліпшення перехідної реакції системи автоматичного регулятора напруги шляхом мінімізації максимального перевищення, часу встановлення, часу наростання та пікових значень напруги на клемах і усунення помилки у стаціонарного стані. Методи. У процесі настройки параметрів техніку оптимізації рою хаотичних частинок кілька разів пропускали через запропоновану гібридну цільову функцію, яка враховує переваги двох найбільш часто використовуваних цільових функцій з мінімальною кількістю ітерацій,і знайдено оптимальне значення коефіцієнту підсилення PID. Запропонований алгоритм порівнюється з сучасними метаевристичними алгоритмами, включаючи звичайну оптимізацію рою частинок, вдосконалений алгоритм нирок та інші. Результати. Для оцінки ефективності об'єднуються характеристики інтеграла у часі, помноженого на похибки у квадраті, та цільових функцій Цве-Лі Гейнга. Крім того, проводяться аналіз у часовій області, аналіз у частотної області та аналіз стійкості, щоб показати кращу ефективність запропонованого алгоритму. Результат показує, що автоматичний регулятор напруги, налаштований на хаотичну оптимізацію рою частинок, заснований на поліпшенні виходу PID в перевищеннях,часі налаштування та значенні функції перевищує на 14,41 %, 37,91 %, 1,73 % нещодавно запропонований нирковий алгоритм та на 43,55 %, 44,5 %, 16,67 % перевищує звичайні алгоритми оптимізації рою частинок. Поліпшення перехідної реакції ще більше покращує стабільність автоматичного регулятора напруги для систем електроенергетики

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

COPERITE-computer-aided Tool for Power Engineering Research, Instruction, Training and Education

A graphics-oriented, primarily PC-based tool for education, research, and training in power engineering is introduced. The tool, called COPERITE, has all user interfaces resident on an IBM-386 microcomputer. Menus and windows are used for the interface, and attractive graphical representations and displays are included. Application programs that are interfaced are power flow, contingency analysis, economic dispatch, security-constrained dispatch, system stability, and fault analysis. These programs are executed on a VAX 8800 computer mainly for speed of execution. Information exchange between the PC and the VAX is made through an Ethernet connection which is transparent to the user. Results of execution show on the graphical front-end accessible to the user. COPERITE has a powerful network editor with the capabilities of adding, deleting, moving, and finding symbols with a graphics cursor. Provisions are present for building and using artificial intelligence techniques for system operation enhancement

Investigation of Advanced Engine Cooling Systems - Optimization and Nonlinear Control

Advanced automotive engine cooling systems can positively impact the performance, fuel economy, and reliability of internal combustion engines. A smart engine cooling system typically features multiple real time computer controlled actuators: a three way linear smart valve, a variable speed coolant pump, and electric radiator fan(s). In this dissertation, several innovative comprehensive nonlinear control and optimization operation strategies for the next generation smart cooling application will be analyzed. First, the optimal control has been investigated to minimize the electric energy usage of radiator fan matrix. A detailed mathematical model of the radiator fan(s) matrix operation and the forced convection heat transfer process was developed to establish a mixed integer nonlinear programming problem. An interior points approach was introduced to solve the energy consumption minimization problem. A series of laboratory tests have been conducted with different fan configurations and rotational shaft speed combinations, with the objective to cool a thermal loaded engine. Both the mathematical approach and the laboratory test results demonstrated the effectiveness of similar control strategies. Based on the tests data and mathematical analysis, an optimization control strategy reduced the fan matrix power consumption by up to 67%. Second, a series of experimental laboratory tests were implemented to investigate the contributions of each electro-mechanical device in automotive thermal management system. The test results established a basis for several key operating conclusions. The smart valve and variable speed pump impacted the engine temperature by adjusting the heat transfer rate between the engine and the radiator through coolant redirection and/or coolant flow rate. On the other hand, the radiator fan(s) operation affects the engine\u27s temperature by modifying the heat rejection rate of the radiator which can influence the entire cooling system. In addition, the smart valve\u27s operation changes the engine\u27s temperature magnitude the greatest amount followed by the radiator fan(s) and the coolant pump. Furthermore, from a power consumption aspect, the radiator fan(s) consumes the most engine power in comparison to the two other actuators. Third, a Lyapunov based nonlinear control strategy for the radiator fan matrix was studied to accommodate transient engine temperature tracking at heavy heat load. A reduced order mathematical model established a basis for the closed-loop real time feedback system. Representative numerical and experimental tests demonstrated that the advanced control strategy can regulate the engine temperature tracking error within 0.12°C and compensate the unknown heat load. The nonlinear controller provided superior performance in terms of power consumption and temperature tracking as evident by the reduced magnitude when compared to a classical proportional integral with lookup table based controller and a bang bang controller. Fourth, a nonlinear adaptive multiple-input and multiple-output (NAMIMO) controller to operate the smart valve and radiator fans has been presented. This controller regulates the engine temperature while compensating for unknown wide range heat loads and ram air effects. A nonlinear adaptive backstepping (NAB) control strategy and a state flow (SF) control law were introduced for comparisons. The test results indicated that the NAMIMO successfully regulated the engine temperature to a desired value (tracking error, |e|\u3c0.5°C, at steady state) subject to various working conditions. In contrast, the NAB control law consumes the least radiator fan power but demonstrated a larger average temperature tracking error (40% greater than the NAMIMO controller), a longer response time (34% greater than the NAMIMO controller), and defected when the heat load was low. Lastly, the SF controller, characterized by greater oscillation and electrical power consumption (18.9% greater than the NAMIMO controller), was easy to realize and maintained the engine temperature to within |e|\u3c5°C. An important aspect of engineering research is the knowledge gained from learning materials to fully understand the thermal management. As part of the dissertation, advanced three-dimensional (3D) visualization and virtual reality (VR) technology based engineering education methods has been studied. A series of computer aided design (CAD) models with storyboards have been created to provide a step to step guide for developing the learning modules. The topics include automotive, aerospace, and manufacturing. The center for aviation and automotive technological education using virtual e-schools (CA2VES) at Clemson University has developed a comprehensive e-learning system integrated with eBooks, mini video lectures, 3D virtual reality technologies, and online assessments as supplementary materials to engineering education