Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy

Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind power generator.Наиболее распространенные методы построения мультиклассовой классификации заключаются в определении набора двоичных классификаторов и их объединении. В данной статье предложена машина опорных векторов с классификатором выходных кодов исправления ошибок(ECOC-SVM) с целью классифицировать и характеризовать такие нарушения качества электроэнергии, как гармонические искажения, падение напряжения и скачок напряжения, включая генератор ветровых электростанций в системах передачи электроэнергии. Сначала выполняется анализ потока несимметричной нагрузки трех фаз для расчета разностных характеристик электрической сети, уровней напряжения, активной и реактивной мощности. После этого дискретное вейвлет-преобразование объединяется с вероятностной моделью ECOC-SVM для построения классификатора. Наконец, ECOC-SVM классифицирует и идентифицирует тип возмущения в соответствии с отклонением энергии дискретного вейвлет-преобразования. Предложенный метод дает удовлетворительную точность 99,2% по сравнению с хорошо известными методами и показывает, что каждое нарушение качества электроэнергии имеет определенные отклонения от чисто синусоидальной формы волны, что способствует распознаванию и определению типа возмущения, генерируемого ветровым генератором

Optimal Power Flow Solution of the Algerian Electrical Network using Differential Evolution Algorithm

This paper presents solution of optimal power flow (OPF) problem of a power system via differential evolution (DE) algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, static VAR compensator (SVC) parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static VAR compensators (SVC). To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA

Simultaneous allocation of multiple distributed generation and capacitors in radial network using genetic-salp swarm algorithm

In recent years, the problem of allocation of distributed generation and capacitors banks has received special attention from many utilities and researchers. The present paper deals with single and simultaneous placement of dispersed generation and capacitors banks in radial distribution network with different load levels: light, medium and peak using genetic-salp swarm algorithm. The developed genetic-salp swarm algorithm (GA-SSA) hybrid optimization takes the system input variables of radial distribution network to find the optimal solutions to maximize the benefits of their installation with minimum cost to minimize the active and reactive power losses and improve the voltage profile. The validation of the proposed hybrid genetic-salp swarm algorithm was carried out on IEEE 34-bus test systems and real Algerian distributed network of Djanet (far south of Algeria) with 112-bus. The numerical results endorse the ability of the proposed algorithm to achieve a better results with higher accuracy compared to the result obtained by salp swarm algorithm, genetic algorithm, particle swarm optimization and the hybrid particle swarm optimization algorithms.В последние годы задача размещения распределенной генерации и батарей конденсаторов привлекает особое внимание многих организаций и исследователей. В данной работе рассмотрены отдельное и совместное размещение распределенной генерации и батарей конденсаторов в радиальной распределительной сети при различных уровнях нагрузки: слабом, среднем и пиковом с использованием алгоритма генетического роя сальпов (genetic-salp swarm algorithm). Разработанный алгоритм гибридной оптимизации генетического роя сальпов (GA-SSA) использует системные входные переменные радиальной распределительной сети для поиска оптимальных решений с целью максимизации преимуществ их установки с минимальными затратами для минимизации потерь активной и реактивной мощности и улучшения профиля напряжения. Тестирование предложенного алгоритма гибридной оптимизации генетического роя сальпов было проведено на экспериментальных 34-шинных системах IEEE и реальной 112-шиной алжирской распределенной сети Джанета (крайний юг Алжира). Численные результаты подтверждают способность предложенного алгоритма достигать лучших результатов с большей точностью по сравнению с результатом, полученным методом роя сальпов, генетическим алгоритмом, оптимизацией роя частиц и алгоритмами гибридной оптимизации роя частиц

2nd INTERNATIONAL CONFERENCE ON ELECTRICAL SYSTEMS (ICES 2006) 8-10 May 2006,

The 2nd International Conference on Electrical Systems (ICES 2006) was held 810 May 2006 at Larbi Ben M’Hidi University, Oum El-Bouaghi, Algeria. This conference provides opportunities for professional engineers, particularly young engineers, from both industry and academia to share ideas, explore recent developments, current practices and future trends in all aspects of electrical systems and related fields. ICES 2006 was of similar standing to the previous conference (PCSE’05) by the high quality of the presentations, the technical content of the papers, and the number of delegates attending. As in PCSE’05, it had a broad theme, covering all aspects of electrical power engineering, and was attended by academics, researchers, consultants and members of the manufacturing and electrical Supply industries. During the sessions, 86 papers selected from 300 uploads from 13 countries were debated

Particle Swarm Optimization Applied to the Economic Dispatch Problem

This paper presents solution of optimal power flow (OPF) problem of a power system via a simple particle swarm optimization (PSO) algorithm. The objective is to minimize the fuel cost and keep the power outputs of generators, bus voltages, shunt capacitors/reactors and transformers tap-setting in their secure limits.The effectiveness of PSO was compared to that of OPF by MATPOWER. The potential and superiority of PSO have been demonstrated through the results of IEEE 30-bus syste

SLIME MOULD ALGORITHM FOR PRACTICAL OPTIMAL POWER FLOW SOLUTIONS INCORPORATING STOCHASTIC WIND POWER AND STATIC VAR COMPENSATOR DEVICE

Purpose. This paper proposes the application procedure of a new metaheuristic technique in a practical electrical power system to solve optimal power flow problems, this technique namely the slime mould algorithm (SMA) which is inspired by the swarming behavior and morphology of slime mould in nature. This study aims to test and verify the effectiveness of the proposed algorithm to get good solutions for optimal power flow problems by incorporating stochastic wind power generation and static VAR compensators devices. In this context, different cases are considered in order to minimize the total generation cost, reduction of active power losses as well as improving voltage profile. Methodology. The objective function of our problem is considered to be the minimum the total costs of conventional power generation and stochastic wind power generation with satisfying the power system constraints. The stochastic wind power function considers the penalty cost due to the underestimation and the reserve cost due to the overestimation of available wind power. In this work, the function of Weibull probability density is used to model and characterize the distributions of wind speed. Practical value. The proposed algorithm was examined on the IEEE-30 bus system and a large Algerian electrical test system with 114 buses. In the cases with the objective is to minimize the conventional power generation, the achieved results in both of the testing power systems showed that the slime mould algorithm performs better than other existing optimization techniques. Additionally, the achieved results with incorporating the wind power and static VAR compensator devices illustrate the effectiveness and performances of the proposed algorithm compared to the ant lion optimizer algorithm in terms of convergence to the global optimal solution.Мета. У статті пропонується процедура застосування нового метаеврістіческого методу в реальній електроенергетичній системі для розв’язання задач оптимального потоку енергії, а саме алгоритму слизової цвілі, який заснований на поведінці рою і морфології слизової цвілі в природі. Дане дослідження спрямоване на тестування і перевірку ефективності запропонованого алгоритму для отримання хороших рішень для проблем оптимального потоку потужності шляхом включення пристроїв стохастичною вітрової генерації і статичних компенсаторів VAR. У зв'язку з цим, розглядаються різні випадки, щоб мінімізувати загальну вартість генерації, знизити втрати активної потужності і поліпшити профіль напруги. Методологія. В якості цільової функції завдання розглядається мінімальна сукупна вартість традиційної генерації електроенергії і стохастичної вітрової генерації при задоволенні обмежень енергосистеми. Стохастична функція енергії вітру враховує величини штрафів через недооцінку і резервні витрати через завищену оцінку доступної вітрової енергії. У даній роботі функція щільності ймовірності Вейбулла використовується для моделювання і характеристики розподілів швидкості вітру. Практична цінність. Запропонований алгоритм був перевірений на системі шин IEEE-30 і великий алжирської тестовій енергосистемі зі 114 шинами. У випадках, коли мета полягає в тому, щоб звести до мінімуму традиційне вироблення електроенергії, досягнуті результати в обох тестових енергосистемах показали, що алгоритм слизової цвілі функціонує краще, ніж інші існуючі методи оптимізації. Крім того, досягнуті результати з використанням вітрової енергії і статичного компенсатора VAR ілюструють ефективність і продуктивність запропонованого алгоритму в порівнянні з алгоритмом оптимізатора мурашиних левів з точки зору збіжності до глобального оптимального рішення

Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow

International audienceUnder critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the optimal power flow (OPF) with non-smooth cost function and under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning designed based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the proposed algorithm tested on IEEE 30-Bus, 26- Bus and IEEE 118-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, PSO, MTS, MDE and ACO) and with two robust simulation packages: PSAT and MATPOWER. The results show that the proposed approach can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time

A combined methodology of H∞ fuzzy tracking control and virtual reference model for a PMSM

The aim of this paper is to present a new fuzzy tracking strategy for a permanent magnet synchronous machine (PMSM) by using Takagi-Sugeno models (T-S). A feedback-based fuzzy control with h-infinity tracking performance and a concept of virtual reference model are combined to develop a fuzzy tracking controller capable to track a reference signal and ensure a minimum effect of disturbance on the PMSM system. First, a T-S fuzzy model is used to represent the PMSM nonlinear system with disturbance. Next, an integral fuzzy tracking control based on the concept of virtual desired variables (VDVs) is formulated to simplify the design of the virtual reference model and the control law. Finally, based on this concept, a two-stage design procedure is developed: i) determine the VDVs from the nonlinear system output equation and generalized kinematics constraints ii) calculate the feedback controller gains by solving a set of linear matrix inequalities (LMIs). Simulation results are provided to demonstrate the validity and the effectiveness of the proposed method

Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch

International audienceThis paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems. The proposed approach combines practical experience extracted from global database formulated in fuzzy rules to adjust dynamically the three parameters associated to PSO mechanism search. The adaptive PSO executed in parallel based in decomposed network procedure as a local search to explore the search space very effectively. The robustness of the proposed modified PSO tested on 40 generating units with prohibited zones and compared with recent hybrid global optimization methods. The results show that the proposed approach can converge to the near solution and obtain a competitive solution with a reasonable time compared with recent previous approaches