Optimal Tuning Of PI Controller Research Articles

Intelligent hybrid technique for cascaded multilevel inverter based three phase grid-tie hybrid power system: a WPSNN technique

This dissertation introduces an intelligent hybrid technique for the design of modelling and controller of the three-phase hybrid network connection system based on CMI. The hybrid technique is the Recurrent Neural Network (RNN) assisted by Wolf Pack Search (WPS) and therefore it is known as WPSNN technique. Here, the three stage CMI control scheme is considered as two stages and these are analyzed with the help of the proposed intelligent controller. In first stage, the WPS is developed to demonstrating the PV total and wind voltages and the WPS algorithm of the second stage assisted to RNN is used to extracting the reference currents of network. In first stage, input of WPS algorithm is PV and wind reference voltages and the gain parameters is generated as the output for optimal tuning of PI controller. In the second phase, the RNN is trained with on line process; the output of RNN is given to the WPS algorithm. It provides the corresponding tuning parameters to achieve the network current. To regulate the dc link voltage and the extraction of network current is developed with established technique. CMI based on the established technique is synchronizes the firing work relationship and reduces the modulation load. By then, the established technique is evaluated in MATLAB/Simulink working stage and the execution is assessed to existing techniques.

Optimal tuning of PI controller using genetic algorithm for wind turbine application

Nowadays, wind turbine energy has an increased importance in electrical power applications since when it is considered as an essential inexhaustible and broadly available energy resource. An aerogenerator is a device that transforms a part of the kinetic energy of the wind into available mechanical energy on a transmission shaft, and then into electrical energy through a generator, which is in our case a dual power asynchronous machine. An important characteristic of a wind turbine is that the avail, able maximum power is provided only in a single given operating point, called Maximum Power Point. Many classical methods and controllers have been widely developed and implemented to track the maximum power point. Among drawbacks of a classical PI controller is that its parameters are not constant, these conventional control laws may be are insufficient because they are not robust, especially when the accuracy requirements and other dynamic characteristics of the system are strict. The new idea in this paper is to introduce the Genetic Algorithms theory into the controlstrategy that used inthe conversion chain of the wind turbine, in order to improve stability. Simulation results approve that the application of Genetic Algorithms to the PI regulator, minimize or eliminate the drawbacks of the classical PI regulator, and greatly promote the efficiency and stability of the wind turbine systems.

LQR Based Optimal Tuning of PI Controller for a Conical Tank Level Process

The liquid level control in conical tank is tough task owing to variable cross-sectional area. The replica of the conical tank is recognized using standard step test technique and is approximated to be FOPTD model. In this paper, PSO based LQR-PI controller has been wished-for conical tank system. The PI controller gains are formulated as the optimal state-feedback gains. A PSO has been accustomed optimally to discover the weighting matrices, related to the various optimum state- feedback regulator design whereas minimizing ISE and the controller effort. The performance of the proposed PSO based LQR-PI controller is contrasted with those of GA-LQR-PI and LQR-PI controllers.

Метод оптимального налаштування пі-регуляторів із врахуванням обмежень

Анотація . Розроблено метод оптимального налаштування ПІ-регуляторів при врахуванні обмежень. Метод ґрунтується на зведенні задачі налаштування до задачі безумовної оптимізації. Для цього було запропоновано спеціальний комплексний критерій, який характеризується бажаними топологічними властивостями. Останні отримані за допомогою вагових коефіцієнтів, що враховують вимоги щодо стійкості системи, обмежень та комплексу критеріїв, які відображають небажані показники регулювання. Наперед задані топологічні властивості критерію дали змогу знайти його глобальний. У роботі для цієї мети була використана модифікація методу рою часточок ME - PSO , яка характеризується переініціалізацією рою при умові його стагнації. Для оцінки ефективності використання методу були виконані розрахунки коефіцієнтів ПІ-регулятора при обмеженні за перерегулюванням (прийнято, що воно повинно бути нульовим). Критерієм оптимізації була швидкодія регулювання. У результаті отримано, що для чотирьох тестових передаточних функцій (другого та третього порядків; одна передаточна функція відповідає об’єкту регулювання із часовою затримкою) швидкодія регулювання значно збільшується при забезпеченні обмеження за перерегулюванням. Отримані результати порівнювались із тими, що відповідають регуляторам, що налаштовані із використанням інших (відомих у інженерній практиці) методів налаштування ПІ-регуляторів (порівнювані методи дають змогу отримати нульове перерегулювання). Крім того, аналіз якості автоматичного регулювання був проведений із використанням показників: середньоінтегральної похибки та середньоінтегрального регулювання. Розроблений підхід доволі універсальний. Він може використовуватись для синтезу оптимальних систем регулювання у тому числі і нелінійних. Ключові слова: регулятор, оптимізація, критерій, обмеження, налаштування, метод рою часточок

Optimal Tuning of PI Controllers for DFIG-Based Wind Energy System using Self-adaptive Differential Evolution Algorithm

Optimal Tuning of PI Controllers for DFIG-Based Wind Energy System using Self-adaptive Differential Evolution Algorithm

Firefly algorithm-based load frequency controller design of a two area system composing of PV grid and thermal generator

In this paper, firefly algorithm (FA) for optimal tuning of PI controllers for load frequency control of hybrid system composing of photovoltaic (PV) system and thermal generator is introduced. Also, maximum power point tracking of PV is considered in the design process. The block diagram of the hybrid system is performed. To robustly tune the parameters of controllers, a time domain-based objective function is established which is solved by the FA. Simulation results are presented to show the improved performance of the suggested FA-based controllers compared with genetic algorithm (GA). These results show that the proposed controllers present better performance over GA in terms of settling times and different indices.

Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm

In this paper, firefly algorithm (FA) for optimal tuning of PI controllers for load frequency control of hybrid system composing of photovoltaic (PV) system and thermal generator is introduced. Also, maximum power point tracking of PV is considered in the design process. The block diagram of the hybrid system is performed. To robustly tune the parameters of controllers, a time-domain-based objective function is established which is solved by the FA. Simulation results are presented to show the improved performance of the suggested FA-based controllers compared with genetic algorithm (GA). These results show that the proposed controllers present better performance over GA in terms of settling times and different indices.

Dynamic modelling and small signal stability analysis of voltage source converter-based HVDC system

In this paper, the modelling of the entire VSC-HVDC system is presented based on d-q axis decomposition and state space modelling is formulated for analysis. Eigenvalues analysis is used for optimal tuning of PI controller based on PSO technique. The system is simulated under most of the frequently occurring faults and abnormal operating conditions such as three-phase fault, parameter variation, frequency variation and change in active and reactive power, change in short circuit ratio and bus voltage reduction and change in converter parameters through MATLAB simulation. The results are presented for the proposed controller with comparison to conventional PI controller under various fault condition.

Load frequency controller design via BAT algorithm for nonlinear interconnected power system

Abstract BAT algorithm is proposed in this paper for optimal tuning of PI controllers for load frequency controller (LFC) design. The problem of robustly tuning of PI based LFC design is formulated as an optimization problem according to time domain objective function that is solved by BAT algorithm to find the most optimistic results. To demonstrate the effectiveness of the proposed method, a two-area interconnected power system is considered as a tested system. To ensure robustness of the proposed control strategy to stabilize frequency oscillations, the design process takes a wide range of operating conditions and system nonlinearities into account. The simulation results are given to detect the superiority of BAT algorithm over Simulated Annealing (SA) in tuning PI controller parameters through different indices. Results evaluation show that the proposed algorithm achieves good robust performance for wide range of system parameters and load changes compared with SA.

Optimal Tuning of PI Controller for Full-Order Flux Observer of Induction Motor Using the Immune Genetic Algorithm

This paper presents a new method to tune the parameters of the adaptation PI controller of full-order flux observer. The method employs an Immune Genetic Algorithm (IGA) based optimization routine that can be implemented off-line. A novel fitness function is designed to assess both the estimation accuracy and the noise sensitivity of the rotor speed estimation system when each antibodys parameters are employed. The diversity of population is guaranteed by the evaluating of the antibody similarities function. The Roulette-wheel selection is used to choose the parents and large mutation probability is adopted to prevent the evolution from prematurity. The simulation results verify that the IGA has better performance in convergence speed and computation efficiency compared to the traditional GA.

Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT

In this paper, an artificial intelligence method particle swarm optimization (PSO) algorithm is presented for determining the optimal PI controller parameters for the indirect control active and reactive power of doubly fed induction generator (DFIG) to ensure a maximum power point tracking of a wind energy conversion system. A digital simulation is used in conjunction with the PSO algorithm to determine the optimum parameters of the PI controller. Integral time absolute error, integral absolute error and integral square error performance indices are considered to satisfy the required criteria in output active and reactive power of a DFIG. From the simulation results it is observed that the PI controller designed with PSO yields better results when compared to the traditional method in terms of performance index.

Charged System Search Algorithms for Optimal Tuning of PI Controllers

This paper suggests the application of Charged System Search (CSS) algorithms to the optimal tuning of PI controllers. The presentation is focused on a class of second-order processes with an integral component and variable parameters. The CSS algorithms solve the optimization problems which aim the minimization of objective functions expressed as the integral of squared control error augmented by the integral of squared output sensitivity functions. The expressions of the output sensitivity functions are derived from the state sensitivity models of the control systems with respect to process gain variations. The optimal tuning of PI controllers is proposed such that to ensure a reduced sensitivity with respect to process gain variations. A case study is included to illustrate the application of the new CSS algorithms and to compare it with a Particle Swarm Optimization and a Gravitational Search Algorithm.