PI Fuzzy Logic Research Articles

Improving photovoltaic energy harvesting systems with hybrid fuzzy logic-PI MPPT optimized by PSO under normal and partial shading conditions

Improving photovoltaic energy harvesting systems with hybrid fuzzy logic-PI MPPT optimized by PSO under normal and partial shading conditions

Fuzzy Based Quick Acting DC Link Voltage Controller for Three Phases of D-Statcom Connected to the Microgrid

The microgrid systems are the combinations of solar photovoltaic, wind energy, fuel cell and battery systems, as well as conventional generators for backup. The micro grid system barriers that are encountered for their integration to the network. In power systems, voltage stability and power quality are major problems. Due to fluctuations in dc link voltage, changes in load may have an effect on the compensation. This work offers a fuzzy logic-based supervisory approach to enhance the performance of the dc link transient. The controllers employ proportional and integral gain changes with fuzzy logic PI controllers integrated with distribution static synchronous compensator (D-STATCOM). These changes occur in the transient response phase right after a load fluctuation. In order to verify the suggested controller, mathematical equations are given to compute the gains of the traditional controller using fuzzy-logic PI fast-acting dc-link voltage controllers for obtaining similar quick transient reaction. Fuzzy logic PI controller enhancements that employ greater sampling during the transient period and expert knowledge of system behavior enhance the controller's performance. Compared to a standard PI controller, a dc link error reduces the capacitor voltage during a load shift. Utilizing the suggested method and the outcomes of MATLAB simulations, it will be demonstrated that efficiency and the voltage's quick settling time.

Optimal design of adaptive model predictive control based on improved GWO for autonomous vehicle considering system vision uncertainty

The tuning issue of the parameters and the system uncertainty represent big challenges in most engineering applications. In this regard, a new tuning approach is developed for adaptive model predictive control (AMPC) of autonomous vehicles (AVs). The proposed control strategy tackles the uncertainty issue of the vision system due to the variation of the velocity, road curvature, time delay, and look ahead distance. The proposed AMPC is designed depending on a new improved grey wolf optimizer (IGWO) algorithm with different learning procedures and fitness-distance balance (FDB) approach to equilibrium the exploration and exploitation manners of the original GWO. This development of GWO based on FDB can increase the capability of the GWO for global search and avoid trapping in local states. Furthermore, the proposed IGWO does not require more adjustable parameters that can enhance the convergence to the best global solution rapidly. The new IGWO is compared with recent optimization algorithms such as the mayfly optimization algorithm (MA), chimp optimization algorithm (COA), dynamic arithmetic optimization algorithm (DAOA), whale optimization algorithm (WOA), jaya algorithm (JA), archimedes optimization algorithm (AOA), and equilibrium optimizer (EO) as well as the original GWO. Besides, the performance of the designed AMPC is compared with the fuzzy logic proportional integral (FLPI) controller and the traditional MPC. Various test scenarios are performed to assert the robustness and effectiveness of the designed AMPC against the variation of the velocity, road curvature, time delay, and look ahead distance. The results emphasize that the proposed AMPC can perform the best damping response with an overshoot of around 1.3 m and a settling time of around 1.12 s less than the FLPI controller and the traditional MPC.

Grid Secondary Frequency Control by Fuzzy PI–PD Controller

This paper develops a new approach for controlling the frequency in a two-area power system using a fuzzy PI PD controller. Frequency stabilization is one of the important factors for any power system to operate efficiently and reliably. The fluctuating characteristics of RES, like solar PV and wind energy, due to changing atmospheric conditions, require additional reserves to compensate for fluctuations in generated power. With growing application of EVs and their charging requirement, charting huge loads may add to the power grid and lead to stability problems if not properly regulated. This paper coordinates EV charging into the secondary frequency regulation, aided by an enhanced fuzzy controller designed for dealing with fluctuating imbalances between electricity demand and generation. In this context, a control technique is suggested that incorporates the flexibility of fuzzy logic with the accurate PI and PD methods for control. The controller continuously monitors changes in frequency at each node in the power system network. The fuzzy logic works on uncertain input data in terms of prescribed linguistic variables and rules that allow it to achieve a reasoned output. The difference between the desired and actual frequencies alters the control variable through the PI component of the controller, while the PD component considers the rate of change of this error to make the system's response time better. The effectiveness of the fuzzy PI PD controller is proven through detailed simulation experiments under different operational conditions and disturbances. It can be rundown that the controller efficiently reduces these frequency deviations, which guarantees smooth and reliable operation of the two-area power system network. In addition, it can be seen that the controller has robust performance but is rather reactive to dynamical changes in system parameters. Case studies are included in the research to test the performance of the proposed control approach in reducing frequency deviations; simulation findings always prove its efficacy. This paper therefore presents a complex method of frequency regulation in power systems using fuzzy logic and combined PI PD control to deal with problems caused by RES integration and EV charging. The proposed technique enhances the frequency stability in order to enhance both the reliability and efficiency of power grid operations due to the ever-changing energy demands and conditions.

An Improved Power Quality in a Renewable Energy-based Microgrid System Using Adaptive Hybrid UPQC Control Strategy

Due to its ability to integrate renewable energy, improve energy efficiency, and fortify the power system's resilience, microgrids are widely used as regional energy systems. But these advantages do have certain drawbacks in terms of control and Power Quality (PQ). In order to guarantee the proper operation of equipment that is connected and the system's general health, PQ is crucial in microgrids. For microgrids to operate successfully, governing stratagems in concurrence with front-line power electronics devices bid a firm context to knob PQ challenges like Voltage Sag and Swell, Source/Grid current harmonics, Voltage imbalances, Active and Reactive power compensation etc. Multifunctional systems that can integrate clean energy generation and as well as enhance PQ are necessary to meet the demands of complex loads that have the capability of operating in the situation of an unavailable network grid and power electronics devices, which necessitates the need for clean energy. Hence, this paper provides enactment of an adaptive hybrid control strategy based on an Adaptive Leaky Least Mean Square (AL_LMS) algorithm combined with Fuzzy Logic (FL) to the Unified Power Quality Conditioner (UPQC) in a Solar-PV energy based Microgrid system in improving microgrid power quality. The concert of UPQC is assessed by the conventional PI controller and Fuzzy Logic control with MATLAB/SIMULINK software platform, simulation results are conversed with proportionate studies in improving the PQ by minimizing Total Harmonic Distortion (THD), the Voltage Sag & Swell and the result comparison shows the effectiveness of the Fuzzy Logic in coordination with the AL_LMS algorithm resulting improved power quality within the IEEE Power Quality-519 Standards.

Modified Fuzzy Logic PI Speed Controller with Scheduling Boundaries of Integral Time Constant for PMSM Drive

Modified Fuzzy Logic PI Speed Controller with Scheduling Boundaries of Integral Time Constant for PMSM Drive

Development of Powered Semi-Active Ankle-Foot Prosthetic with Fuzzy Logic-PI Controller

Development of Powered Semi-Active Ankle-Foot Prosthetic with Fuzzy Logic-PI Controller

Fuzzy Logic PI controller based Direct Torque control of a Self-Excited Induction Generator through a three-level Rectifier

The main goal of this work is to control the terminal voltage of a Self-excitation induction generator (SEIG) that supplies autonomous load and is driven by a variable speed wind turbine. More advanced strategies of control have been suggested and applied these through a two-level converter. However, in this paper, we propose to study a modified DTC approach which based on the use of three-level converter (NPC structure) and instead of the traditional PI controller, we use a PI fuzzy controller to reduce torque and flux ripples, the induction generator's stator flux and electromagnetic torque are controlled using voltage space vector selection. The proposed control strategy has to maintain constant DC bus voltage regardless of load or wind speed variations. Finally, it should be noted that this control accounts for the induction generator's magnetic saturation phenomenon.

Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter

The power circuit of AC voltage controller capable of operating at a leading, lagging, and unity power factor is studied by a lot of researchers in the literature. Circuits working with high switching frequency are known as power factor correctors (PFCs). The single-phase boost converter has become the most popular topology for power factor correction (PFC) in general purpose power supplies. Power factor correction circuit provides conventional benefits to electric power systems. The benefits are the reduction of power factor penalty and utility bill and power loss. Therefore, a boost converter power factor correction scheme is presented in this paper. A PI, fuzzy logic PI and fractional order PI (FOPI) controllers are used to fix an active shaping of input current of the circuit and to improve the power factor. The controller parameters (coefficients) are optimized using the Particle Swarm Optimization (PSO) algorithm. Average current mode control (ACMC) method is used in the circuit. The converter circuit consists of a single-phase bridge rectifier, boost converter, transformer and load. A mathematical model of the plant is required to design the PI controller. A model for power factor correction circuit is formed in MATLAB/Simulink toolbox and a filter is designed to reduce THD value. The proposed model is simulated using a combination of PI, fuzzy logic and FOPI controllers. The control scheme is applied to 600 Watt PFC boost converter to get 400 Volt DC output voltage and 0.99 power factor. The input voltage is 230 VRMS with 50 Hz. The combination of FOPI and PI controller has the best solution to control the power factor according to PI and fuzzy controllers.

DESIGN OF A VARIABLE GAIN NONLINEAR FUZZY CONTROLLER AND PERFORMANCE ENHANCEMENT DUE TO GAIN VARIATION

In this paper, variable gain nonlinear PD and PI fuzzy logic controllers are designed and the effect of the variable gain characteristic of these controllers is analyzed to show its contribution in enhancing the performance of the closed loop system over a conventional linear PID controller. Simulation results and time domain performance characteristics show how these fuzzy controllers outperform the conventional PID controller when used to control a nonlinear plant and a plant that has time delay.

Adaptive Controller PI-Fuzzy Logic Speed for Brushless DC Motor Drive Supplied by PEMFC Cell Optimized by P&O

Brushless Direct Current (BLDC) motors have recently gained momentum. In this study, a fuel cell stack, namely, a Proton-Exchange Membrane Fuel Cell (PEMFC), one of the promising renewable energy technologies, is chosen for a brushless DC motor. To improve the performance of PEMFC, an efficient maximum power point tracking (MPPT) algorithm was applied to the DC/DC boost converter. To this end, the perturbation and observation (P&O) algorithm were developed. This work proposes an adaptive controller proportional-integral (PI)-fuzzy logic speed for the BLDC. To evaluate its performance, the proposed controller was simulated under several conditions: load disturbance and reference speed variation. This controller is analyzed and compared with the classical PI controller. Therefore, the control performance parameters, such as rise time, settling time, steady-state error, and overshoot, were determined and compared. This system is analyzed and simulated using MATLAB/Simulink software.

Hybrid Control and Energy Management of a Residential System Integrating Vehicle-to-Home Technology

Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environmentally friendly and more affordable solutions for consumers. This article discusses the integration of PV and EV in a residential system to meet the requirements of residential loads taking into account the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control model has been proposed to control the residential system. The combined PI-Fuzzy logic controller is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A rule-based energy management strategy has been proposed to control the flow of energy between the components of the residential system. The suggested energy management system (EMS) covers every scenario that might occur. Whether the EV is linked to the home or not, and also takes into account the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle (H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the conditions related to the EV. All possible scenarios are tested and validated. The simulation results show that the proposed EMS is a reliable solution that can reduce the power grid intervention.

Ensuring energy balance for sudden demand changes in smart grids

With the increasing diversity in the use of electrical energy and the inclusion of renewable electricity power plants in the system, more efficient and more controllable electrical power systems are provided by smart grid technologies. Load balance based on pricing in electrical power systems; It is a strategy that takes into account customer demands, enables customers to use electrical energy at the most attractive price, and provides suppliers with the desired profit, that is, in summary, it coordinates the efficient operation of energy systems. In this study; While load balance is created with smart grid technology, demand-supply-price balance is achieved by using an optimum Fuzzy Logic PI controller within a closed-loop electric power control system model. Within the scope of the study, the simulation results obtained by applying 6 different demand scenarios to the power system model consisting of different types of generation resources were given graphically and analyzed. The different demand scenarios applied include difficult demand conditions such as possible sudden increase or decreases in demand in the electrical power system.

A robust fuzzy logic PI controller for solar system battery charging

This article discusses a design of a photovoltaic (PV) system that allow charging a battery under variable climatic circumstances. The system under study is composed of two DC-DC converters a boost and Buck converter. The first one is used to extract the maximum power from the PV array through a novel variable step size P&O based MPPT offering a high tracking accuracy compared to classical approaches such PO and INC. The second converters aim at regulating the output voltage and current that feeds the battery using a robust optimal PI (O-PI) which has a faster time response and high accuracy compared to classical PI and PID controllers. The overall system and thecontrol strategies are tested and validated in MATLAB/Simulink environment. The simulations results show the effectiveness and the robustness of the system.

Optimizing BLDC motor drive performance using particle swarm algorithm-tuned fuzzy logic controller

A brushless DC (BLDC) motor is synchronous motor with trapezoidal/square wave counter-electromotive force, which is a typical example of highly coupled nonlinear systems. In industrial control, BLDC motor drive usually uses proportional–integral (PI) controller to control the speed, but it is very difficult to adjust the scale factors. In this study, we present a particle swarm algorithm-tuned fuzzy logic-PI (PF-PI) controller applied to the speed control system. The objective of this paper is to optimally tune the PI controller parameters to obtain the best drive response. The scale factors are optimized using particle swarm optimized-PI (P-PI) controller and PF-PI controller. The three performance indicators integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are used to measure the effectiveness of PF-PI controller optimization. The results show that the optimal torque ripple and speed response curves are obtained by using ITAE as the performance indicator. The conclusions demonstrate that the proposed method provides superior dynamic performance for BLDC motor.HighlightsIn terms of research content, we propose a new PF-PI controller driven control system based on the traditional BLDC speed control system, and the applicability of three performance indicators on the controller is discussed.In terms of research method, we compare the no-load start, variable speed and sudden addition disturbance load start capabilities of P-PI controller and PF-PI controller, and verify the fast and robustness of PF-PI controller.In the research significance, the PI controller structure is improved and the dynamic performance of BLDC speed control system is enhanced.

Photovoltaic emulator using error adjustment fuzzy logic proportional-integral controller

The photovoltaic (PV) technology has been increasingly used in our energy generation. Therefore, it is essential to have a good PV testing facility during the development process. The PV emulator (PVE) is a voltage or current source that mimic the current-voltage characteristic as a PV module that requires proper control strategy to work. The resistance feedback method (RFM) control strategy has many good attributes, except the transient response, which is caused by the proportional-integral (PI) controller. This paper proposed a new fuzzy logic PI (FLPI) controller to improve the transient performance of the RFM PVE. It is based on the error adjustment method that founded on the transient state and load of the PVE. The performance of the proposed PVE is compared with the original PVE that used RFM with the PI controller. The finding of the research shows that the transient performance of the proposed PVE has improved 2.3 times compared to the original PVE without affecting its accuracy.

Performance Analysis of Buck Boost and Cuk converters for Solar PV Applications using PI controller and Fuzzy Logic Controller

Abstract: In this paper, analysis of DC-DC converters Buck-Boost and Cuk converter by using PI Controller and Fuzzy Logic controller. These converters are mainly used in solar PV applications and it is explained. In this paper mainly MATLAB/ Simulink software is used for the compare the performance of both. the design is mainly compared with the proportional integrated controller versus FLC. The main objective of this work to reduce the overshoot of these types of converters. The buck boost and cuk converter are simulated for different Loads and their output voltage is settle at desired voltage The performance of buck-boost and cuk converter are simulated using Matlab/Simulink and comparison study is performed. The complete control system has been developed analysed and validation by the simulation. Keywords: Buck-Boost Converter, Cuk Converter, MATLAB/ Simulink, Solar PV and Fuzzy Logic Converter

Fuzzy based MPPT Control System in Grid Connected Solar Plant

The increase in demand for the electricity system is increasing. Thus, enviromentally friendly technologies such as new and renewable energy are needed, one of which is a power plant with Photovoltaic as the main component. Solar power plant has enviromentally friendly properties. The design of this solar power plant uses Fuzzy Logic Controller and PI Controller methods, where both methods are used to find the maximum power peak point used for fast charges and MPPT, which is simulted using MATLAB/Simulink. The things presented in developing the generating system using these two methods are solar radiation intensity, temperature and Photovoltaic module. The model developed will make it possible to examine the characteristics of solar power plant and predict the amount of energy generated by solar panels in a particular location and compare the effectiveness of Fuzzy Logic and PI methods. The results shows that Fuzzy method produces a boost converter output voltage in the range of 600 V with result that are still not good and less stable, but the irradiation of 100 W/m2 voltage is stable, while the boost converter output voltage controller by the PI method is very good and stable in all conditions. Radiation and voltage are also in the range of 600 V.

Increase Stability and Efficiency in PV-Battery-Grid Systems Using PSO Algorithm

In this article, the meta-heuristic algorithm PSO with PI fuzzy logic controller is proposed to develop a new control strategy of bidirectional converter (CSBC), in order to improve the stability and increase the efficiency of energy flow exchange in grid-connected photovoltaic generator (PVG) Battery hybrid system. The proposed command aims to satisfy the DC Motor load demand, manages the power flows from different parts, injects surplus energy into the grid as and when need, ensure charge-discharge battery operation even under the fluctuating condition of power generation, and stabilize the DC bus voltage. This new control has been placed in the network topology, which consists of a PVG Photovoltaic Generator, grid-connected DC/AC converter, storage battery and DC motor load. A bidirectional buck-boost converter is used for optimum exploit of power from PVG along with battery charging/discharging control, and for feeding DC motor load. The effectiveness of the proposed control scheme is demonstrated by using MATLAB / Simulink program. The results obtained, show that the proposed control provided the system with the stability of Vdc voltage, and contributed to improving the speed produced by the DC motor load. The results have been compared with the conventional control.

Supercapacitors based energy storage system for mitigating solar photovoltaic output power fluctuations

PurposeNon-linear power–voltage characteristics of solar cell and frequently changing output due to variation in solar irradiance caused by movement of clouds are the major issues need to be considered in photovoltaic (PV) penetration to maintain the power quality of the grid. It is important for a PV module to always function at its maximum available power point to increase the efficiency and to maintain the grid stability. A possible solution to mitigate these generation fluctuations is the use of an electric double-layer capacitor or supercapacitor energy storage device, which is an efficient storage device for power smoothing applications. This study aims to propose a power smoothing control approach to smoothen out the output power variations of a solar PV system using a supercapacitor energy storage device.Design/methodology/approachTo extract the maximum possible power from a PV panel, there are several maximum power points tracking (MPPT) algorithms developed in literature. Fuzzy logic controller-MPPT method is used in this work as it is a very efficient and popular technique which responds quickly under varying ecological conditions, reduced computational complexity and does not depend on any system constraints. Fuzzy logic-based MPPT controller by Boost DC–DC converter is developed for operating the PV panels at available maximum power point. Fuzzy logic-proportional integral (PI) charge controller is implemented by Buck–Boost converter to provide the constant current and suitable voltage for supercapacitor and to achieve better power smoothing. PI charge controller is preferred in this work as it offers better outcomes and is very easy to implement.FindingsSimulation results conclude that the proposed power smoothing control approach can efficiently smooth out the power variations under variable irradiance and temperature situations. To confirm the accurateness of the proposed system, it is validated for poly-crystalline PV module and comparison of results is done by using different case study with and without the use of an energy storage system under change in irradiance condition. The proposed system is developed and examined on MATLAB/Simulink environment.Originality/valueThe performance comparison between PV power output with and without the use of a supercapacitor energy storage device under different Case Studies shows that the improved performance in smoothing of power output was achieved with the use of a supercapacitor energy storage device.