Fuzzy Inference System for VOLT/VAR control in distribution substations in isolated power systems

This paper presents a fuzzy inference system for voltage/reactive power control in distribution substations. The purpose is go forward to automation distribution and its implementation in isolated power systems where control capabilities are limited and it is common using the same applications as in continental power systems. This means that lot of functionalities do not apply and computational burden generates high response times. A fuzzy controller, with logic guidelines embedded based upon heuristic rules resulting from operators at dispatch control center past experience, has been designed. Working as an on-line tool, it has been tested under real conditions and it has managed the operation during a whole day in a distribution substation. Within the limits of control capabilities of the system, the controller maintained successfully an acceptable voltage profile, power factor values over 0,98 and it has ostensibly improved the performance given by an optimal power flow based automation system

A FUZZY LOGIC CONTROLLER FOR STABILITY VOLTAGE AND MAXIMUM ENERGY EXTRACTION FOR FIXED SPEED WIND POWER GENERATION SYSTEMS

This paper introduces two advantages of a fuzzy logic controller for the first is stability voltage and the second is maximum energy extraction for fixed speed wind power generation systems. Maximum energy extraction is determined by thetip speedratio (TSR) and the power factor of a wind turbine in order to capture the maximum efficiency and control the speed of turbine in order to control voltage stability.While the TSRand power factor were taken as output variables we propose control the tip speed of generator in surrounding the value of maximum TSR. The proposedmodel produced fuzzy logic controller can keep the voltage stability at maximum TSR and nearest. The results indicate that we can do two task (power max extraction and stability voltage) in one design

Modeling and comparison of IP and fuzzy-pi regulators of speed control of DFIM for supply of power to the electrical network

This paper deals with a comparison between a fuzzy logic controller and a  conventional IP controller utilized for speed control with a direct stator flux orientation control of a doubly fed induction. The effectiveness of the proposed control strategy is  measured under diverse operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation. Results obtained from simulation indicte that the fuzzy logic controller is more robust than a conventional IP controller against parameter variation and uncertainty, and is less sensitive to external load torque disturbance with a fast dynamic response; the stator side power factor is controlled at unity level. Then, an intelligent artificial fuzzy control of a wind energy system based on DFAM for supply of power to the electrical network. Its simulated performances are then compared to those of a classical PI controller. Specifically fuzzy systems are created to overcome the disadvantages of fuzzy systems. Results obtained in Matlab/Simulink environment show that the fuzzy control is more robust, have superior  dynamic performance and hence found to be a suitable replacement of the conventional PI controller for the high performance drive applications.Key words: Doubly fed asynchronous machine (DFAM); Field oriented control; Fuzzy control, Fuzzy PI controller, conventional IP controller

Fuzzy inference system for integrated VVC in isolated power systems

This paper presents a fuzzy inference system for integrated volt/var control (VVC) in distribution substations. The purpose is go forward to automation distribution applying conservation voltage reduction (CVR) in isolated power systems where control capabilities are limited. A fuzzy controller has been designed. Working as an on-line tool, it has been tested under real conditions and it has managed the operation during a whole day in a distribution substation. Within the limits of control capabilities of the system, the controller maintained successfully an acceptable voltage profile, power factor values over 0,98 and it has ostensibly improved the performance given by an optimal power flow based automation system. CVR savings during the test are evaluated and the aim to integrate it in the VVC is presented.Comment: arXiv admin note: substantial text overlap with arXiv:1401.163

Performance Improvement of Hybrid System Based DFIG-Wind/PV/Batteries Connected To DC And AC Grid By Applying Intelligent Control

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow and providing a unit power factor by imposing a zero reactive power. An Energy Management Algorithm (EMA) is developed to maintain energy balance, meet the DC load demand, mitigate fluctuations caused by weather condition variations (wind speed and solar irradiance), and minimize battery overcharge and deep discharge. To test the proposed hybrid microgrid system operation, the different parts of the system are modeled, the wind turbine associated to the DFIG, the photovoltaic system as well as the battery storage system. Furthermore, the associated power converters with their control strategies are also presented. Global system simulation, using MATLAB/Simulink, is carried out to validate the effectiveness of both EMA and control techniques. The obtained results show significant reduction of active/reactive power ripples and THD by about 64%, 72%, and 50%, respectively. The EMA ability to manage the energy flow, produced and requested by the load. The THD rate of all injected currents is less than 4%, meaning that the proposed controls will increase the used equipments’ life span, minimize their maintenance and then reduce the hybrid power system cost

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PD

TLBO tuned a novel robust fuzzy control structure for LFC of a hybrid power system with photovoltaic source

This study proposes a new fuzzy logic control (FLC) design-based I controller plus a Fuzzy Cascade FOPI-FOPD (I + F C FOPI-FOPD) for load frequency control (LFC) in power systems. The structure of this design offers a satisfactory level of reliability as well as excellent robustness performance. The proposed fuzzy design is employed in a hybrid dual area power system based on a photovoltaic renewable energy plant in area one and a thermal generation unit in area two. In order to achieve the best possible dynamic performance of the proposed structure, the teaching learning-based optimization (TLBO) algorithm is suggested to optimally tune the scaling factor gains of the proposed fuzzy configuration. The superiority of the suggested fuzzy control design is investigated by conducting a comparative study between this design and a previously applied PI-based firefly algorithm. Simulation results revealed that the fuzzy logic controller introduced in this study is reliable and superior, and appropriately handled the problem of frequency variation

A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

© 2017 Elsevier Ltd Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center

AMELIORATE DIRECT POWER CONTROL OF STANDALONE WIND ENERGY GENERATION SYSTEM BASED ON PERMANENT MAGNET SYNCHRONOUS GENERATOR BY USING FUZZY LOGIC CONTROL

Purpose. Electricity is a basic energy for life and its consumption increased so we need the discovery of new sources of energy such as wind energy .for this ameliorate the quality of generated wind energy by using the intelligent artificial control, this control is made to optimize the performance of three-phase PWM rectifier working. Methodology. These strategies are based on the direct control of the instantaneous power, namely: the control direct power control (DPC) with classic PI regulator and direct power control with fuzzy logic regulator. The fuzzy characterized by its ability to deal with the imprecise, the uncertain has been exploited to construct a fuzzy voltage regulator. The simulation of these methods was implemented using Matlab/Simulink. Results. A comparison with the results obtained by the classic PI showed the improvement in dynamic performance. This makes the fuzzy controller an acceptable choice for systems requiring quick, precise adjustments and less sensitive to outside disturbances. Originality. The proposed this control strategy using for to obtain a performance adjustment of the DC bus voltage and sinusoidal currents on the network side. Practical value. Fuzzy logic is proven to be effective in terms of reducing the harmonic distortion rate of the currents absorbed, correct adjustment of the active and reactive power and DC voltage and unit power factor operation.Мета. Електроенергія є основною енергією для життя, і її споживання збільшується, тому нам необхідно відкриття нових джерел енергії, таких як енергія вітру. Для поліпшення якості енергії вітру, що генерується за допомогою управління на основі штучного інтелекту, таке управління призначене для оптимізації продуктивності роботи трифазного ШІМ випрямляча. Методологія. Дані стратегії засновані на прямому управлінні миттєвою потужністю, а саме: пряме управління потужністю з класичним ПІ-регулятором і пряме управління потужністю регулятором з нечіткою логікою. Нечіткість, що характеризується її здатністю справлятися з неточністю, невизначеністю, була використана для створення нечіткого регулятора напруги. Моделювання цих методів було реалізовано за допомогою Matlab/Simulink. Отримані результати. Порівняння з результатами, отриманими за допомогою класичного ПІ-регулятора, показало поліпшення динамічних характеристик. Це робить нечіткий контролер прийнятним вибором для систем, що вимагають швидкої і точної настройки і менш чутливих до зовнішніх перешкод. Оригінальність. Запропоновано стратегію управління, що використовує для отримання регулювання продуктивності напруги шини постійного струму і синусоїдальні струми на стороні мережі. Практична цінність. Доведено, що нечітка логіка ефективна з точки зору зниження коефіцієнта гармонійних спотворень поглинаються струмів, коректного регулювання активної і реактивної потужності і постійної напруги, а також коефіцієнта потужності роботи блоку.

An Adaptive Soft Handover Scheme Using Fuzzy Load Balancing for WCDMA Systems

In cellular systems, user distribution variations can cause load imbalance between cells. Embedding a load balancing strategy within the handover scheme means that ensuing traffic congestion can be alleviated by dynamically reallocating load between neighbouring cells. An adaptive soft handover scheme for multimedia cellular communication systems is proposed in this paper, that considers both the cell load factors as well as the pilot channel signal-to-interference-and-noise-ratio (SINR) for soft handovers. By using fuzzy principles, the soft handover thresholds and time hysteresis are adapted dependent upon the loads of the neighbouring cells. Simulation results show that the new algorithm provides improved system performance in terms of a more evenly distributed load, lower blocking probabilities and higher throughput