Variable structure TITO fuzzy-logic controller implementation for a solar air-conditioning system

The design and implementation of a Two-Input/Two-Output (TITO) variable structure fuzzy-logic controller for a solar-powered air-conditioning system is described in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. The first affects the temperature in the generator of the solar air-conditioner, while the second, the pressure in the power loop. The difficulty of Multi-Input/Multi-Output (MIMO) systems control is how to overcome the coupling effects among each degree of freedom. First, a traditional fuzzy-controller has been designed, its output being one of the components of the control signal for each DC motor driver. Secondly, according to the characteristics of the system’s dynamics coupling, an appropriate coupling fuzzy-controller (CFC) is incorporated into a traditional fuzzy-controller (TFC) to compensate for the dynamic coupling among each degree of freedom. This control strategy simplifies the implementation problem of fuzzy control, but can also improve the control performance. This mixed fuzzy controller (MFC) can effectively improve the coupling effects of the systems, and this control strategy is easy to design and implement. Experimental results from the implemented system are presented.     

Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control

Currently, the grid-connected large PV farms are extensively installed in power systems. Nevertheless, in addition to the load change, the intermittent power output of PV farms may lead to the serious problem of the system frequency fluctuation. To handle this problem, this paper proposes a new design of Sugeno fuzzy logic controller based on particle swarm optimization (PSO-SFLC) of intelligent PV farms for the frequency stabilization in a multi-area interconnected power system. To handle various scenarios, the frequency deviations and solar insolations are used as input signals of the PSO-SFLC. The output signal of the PSO-SFLC is a command signal for adjusting PV output power. The output power of PV is controlled by the PSO-SFLC to meet the load demand so that the system frequency fluctuation can be suppressed. Without the difficulty of trial and error, the optimal input and output membership functions, and control rules of PSO-SFLC are automatically achieved by PSO. Simulation study in a three-area loop interconnected power system with large PV farms elucidates that the frequency stabilizing performance and robustness of the PV equipped with the PSO-SFLC is much superior to that of the PV with the SFLC and the PV with the maximum power point tracking control in scenarios with various solar insolations and loading conditions.     

Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter

In this study, a maximum power point tracking DC–DC quadratic boost converter for high conversion ratio required applications is proposed. The proposed system consists of a quadratic boost converter with high step-up ratio and fuzzy logic based maximum power point tracking controller. The fuzzy logic based maximum power point tracking algorithm is used to generate the converter reference signal, and the change in PV power and the change in PV voltage are selected as fuzzy variables. Determined membership functions and fuzzy rules which are design to track the maximum power point of the PV system generates the output signal of the fuzzy logic controller's output. It is seen from MATLAB/Simulink simulation and experimental results that the quadratic boost converter provides high step-up function with robustness and stability. In addition, this process is achieved with low duty cycle ratio when compared to the traditional boost converter. Furthermore, simulation and experimental results have validated that the proposed system has fast response, and it is suitable for rapidly changing atmospheric conditions. The steady state maximum power point tracking efficiency of the proposed system is obtained as 99.10%. Besides, the output power oscillation of the converter, which is a major problem of the maximum power point trackers, is also reduced.     

Optimization of fuzzy-based MPPT controller via metaheuristic techniques for stand-alone PV systems

Due to the alteration of power-voltage characteristics of solar module output under multiple environmental conditions such as solar irradiation and ambient temperature, these systems hardly function at maximum power point (MPP). However, maximum power point tracking (MPPT) plays a significant role in their efficiency. On the other hand, solar module characteristics are extremely nonlinear and their slope on either side of MPP is asymmetric. Thus using a nonlinear control method which has the potential of adapting the operating point of the system to MPP seems useful. This has motivated authors to present MPPT method which maximizes PV's output power by tracking MPP continuously. In the present study, a fuzzy logic controller (FLC) is presented for MPPT in photovoltaic systems. Four optimization algorithms are presented in this paper for optimizing fuzzy membership functions (MFs) and generating proper duty cycle for MPPT. The presented algorithms include: Teaching Learning Based Optimization (TLBO), Firefly Algorithm (FFA), Biogeography based optimization (BBO), and Particle Swarm Optimization (PSO), which are all described and simulated. Finally, to validate performance of the proposed optimized FLC, it is compared with other algorithms such as symmetrical fuzzy logic controller (SFLC) and conventional Perturbation and Observation (P&O). According to the simulation results, P&O algorithm shows significant oscillations, energy loss, and in some cases, it cannot obtain MPP. Simulation results also indicate that TLBO and FFA based asymmetric fuzzy MFs not only increase MPPT convergence speed but also enhance tracking accuracy in comparison with symmetric fuzzy MFs and asymmetric fuzzy MFs based on BBO and PSO.     

Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system

This paper proposes a methodology of designing a Maximum Power Point Tracking (MPPT) controller for photovoltaic systems (PV) using a Fuzzy Gain Scheduling of Proportional-Integral-Derivative (PID) type controller (FGS-PID) with adaptation of scaling factors (SF) for the input signals of FGS. The proposed adaptive FGS-PID method is based on a two-level control system architecture, which combines the advantages of fuzzy logic and conventional PID control. The initial values of the PID's gains are determined by the Ziegler–Nichols tuning method. During transient and steady states, the PID's gains are adapted by the FGS-PID to damp out the transient oscillations, to reduce settling time and to guarantee system stability and accuracy. Also, the conditioned input signals of the FGS-PID are tuned dynamically by gain factors which are based on fuzzy logic system (FLS). The FLS is characterized by a set of fuzzy rules which are fuzzy conditional statements expressing the relationship between inputs (error and change of error) and outputs. This approach creates an adaptive MPPT controller and achieves better overall system performance. The simulation results demonstrate the effectiveness of the proposed adaptive FGS-PID and show that this approach can achieve a good maximum power operation under any conditions such as different levels of solar radiation and PV cell temperature for varying PV sources. Compared to conventional methods (PID, perturb and observe method P&O), this method shows a considerable high tracking performance.     

Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. There are two ways to increase the efficiency of PV power generation system. The first is to develop materials offering high conversion efficiency at low cost. The second is to operate PV systems optimally. However, the PV system can be optimally operated only at a specific output voltage and its output power fluctuates under intermittent weather conditions. Moreover, it is very difficult to test the performance of a maximum-power point tracking (MPPT) controller under the same weather condition during the development process and also the field testing is costly and time consuming. This paper presents a novel real-time simulation technique of PV generation system by using dSPACE real-time interface system. The proposed system includes Artificial Neural Network (ANN) and fuzzy logic controller scheme using polar information. This type of fuzzy logic rules is implemented for the first time to operate the PV module at optimum operating point. ANN is utilized to determine the optimum operating voltage for monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies.     

Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptive fuzzy logic to control a switch of a boost converter. Adaptive fuzzy logic controllers provide attractive features such as fast response, good performance. In addition, adaptive fuzzy logic controllers can also change the fuzzy parameter for improving the control system. The single phase inverter uses predictive current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. Both conventional fuzzy logic controller and adaptive fuzzy logic controller are simulated and implemented to evaluate performance. Simulation and experimental results are provided for both controllers under the same atmospheric condition. From the simulation and experimental results, the adaptive fuzzy logic controller can deliver more power than the conventional fuzzy logic controller.     

Modeling and simulation of three phase multilevel inverter for grid connected photovoltaic systems

This paper presents a control for a three phase five-level neutral clamped inverter (NPC) for grid connected PV system. The maximum power point tracking (MPPT) is capable of extracting maximum power from the PV array connected to each DC link voltage level. The MPPT algorithm is solved by fuzzy logic controller. The fuzzy MPPT is integrated with the inverter so that a DC–DC converter is not needed and the output shows accurate and fast response. A digital PI current control algorithm is used to remain the current injected into the grid sinusoidal and to achieve high dynamic performance with low total harmonic distortion (THD). The validity of the system is verified through MATLAB/Simulink and the results are compared with three phase three-level grid connected NPC inverter in terms of THD.     

Enhancement of micro-grid performance during islanding mode using storage batteries and new fuzzy logic pitch angle controller

Power system deregulation, shortage of transmission capacities and needing to reduce green house gas have led to increase interesting in distributed generations (DGs) especially renewable sources. This study developed a complete model able to analysis and simulates in details the transient dynamic performance of the Micro-Grid (MG) during and subsequent islanding process. Wind speed fluctuations cause high fluctuations in output power of wind turbine which lead to fluctuations of frequency and voltages of the MG during the islanding mode. In this paper a new fuzzy logic pitch angle controller is proposed to smooth the output power of wind turbine to reduce MG frequency and voltage fluctuations during the islanding mode. The proposed fuzzy logic pitch controller is compared with the conventional PI pitch angle controller which usually used for wind turbine power control. Results proved the effectiveness of the proposed fuzzy controller in improvement of the MG performance. Also, this paper proposed using storage batteries technique to reduce the frequency deviation and fluctuations originated from wind power solar power fluctuations. Results indicate that the storage batteries technique is superior than fuzzy logic pitch controller in reducing frequency deviation, but with more expensive than the fuzzy controller. All models and controllers are built using Matlab® Simulink® environment.     

Wind power smoothing using fuzzy logic pitch controller and energy capacitor system for improvement Micro-Grid performance in islanding mode

The need of reducing CO₂ emissions in electricity generation field for solving global warming problems has led to increase interest in Micro-Grid (MG) especially the one with renewable sources such as solar and wind generations. Wind speed fluctuations cause high fluctuations in output power of wind turbine which cause fluctuations in fr and voltages of the MG in the islanding mode and originate stability problems. In this study, a new fuzzy logic pitch controller and an energy storage ultra capacitor are proposed and developed to smooth the output power of wind turbine and enhance MG's performance in islanding mode. These two proposed controllers are compared with the conventional PI pitch controller, which is usually used to control wind generation system when the wind speed exceeds a rated value. Obtained results proved that our two proposed strategies are effective for the MG performance improvement during islanding mode. All models and controllers are developed using Matlab^® Simulink^® environment.     

A fuzzy logic pitch angle controller for power system stabilization

In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active‐stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large‐signal control of active‐stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set‐up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short‐circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non‐linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. Copyright © 2006 John Wiley &Sons, Ltd.     

太阳能光伏发电最大功率跟踪控制器的研究

针对目前太阳能发电系统效率低、铅酸蓄电池使用寿命短等问题,利用微控制器MC9S08QG8设计一种太阳能控制器。该控制器采用升降压式DC/DC转换电路、利用电压扰动法实现最大功率点跟踪,使太阳能电池始终保持最大功率输出;控制器还能实时测量蓄电池的端电压,对蓄电池进行充放电保护。该控制器软硬件结合、可靠性高,提高了太阳能发电系统的效率,延长了蓄电池的使用寿命。     

A microcontroller-based stand-alone photovoltaic power system for residential appliances

In this study, a stand-alone photovoltaic power system was designed and implemented to operate residential ac-powered appliances such as fluorescent lambs, fans etc. Sun-tracker is implemented for improved efficiency of the system by keeping the solar module perpendicular to the sun's incoming rays. The charge method is realized with closed-loop current control of buck-boost dc–dc converter. The proposed system also uses a voltage source type PWM inverter to convert DC voltage from battery storage to supply AC loads. In the PWM method used, selected harmonics are eliminated with the smallest number of switching and an improvement in the system efficiency by reducing switching losses and providing ease of filtering on the inverter output is obtained. Charge controller and PWM inverter systems have been realized by using PIC16F873 microcontrollers. An experimental system was implemented to demonstrate the effectiveness of the proposed system. Simulation and experimental results are given to verify the system's efficiency.     

Adaptive fuzzy control for wind-diesel weak power systems

This work is concerned with the development of an adaptive fuzzy logic controller for a wind-diesel system composed of a stall regulated wind turbine with an induction generator connected to an AC busbar in parallel with a diesel generator set having a synchronous generator. In this work we propose to use an adaptive network based inference system (ANFIS) in order to generate fuzzy membership functions and control rules for the controller. A feedback linearized proportional integral controller is used to provide the required expert knowledge. A controller design process is identified; it consists of generating input-output data pairs to identify the control variables range and initial fuzzy memberships, and then to tune or adapt them using an ANFIS network structure. The controller inputs are the frequency error and its integral for the governor part of the controller, and the voltage and frequency errors for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented     

Neuro-fuzzy control for autonomous wind–diesel systems using biomass

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed into a diesel engine. The controller inputs are the engine speed error and its derivative for the governor part of the controller, and the voltage error and its derivative for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by tuning the fuzzy logic controllers, optimal time domain performance of the autonomous wind–diesel system can be achieved in a wide range of operating conditions compared to fixed-parameter fuzzy logic controllers and PID controllers.     

Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode

This paper suggests improved control strategies using Fuzzy Gain Scheduling of Proportional-Integral-Derivative (FGS-PID) controller for a hybrid Photovoltaic (PV) and Battery Energy Storage (BES) system under different weather conditions. The proposed scheme is implemented using a two-level control system structure, combining the benefits of the PID as well as the fuzzy logic controller (FLC) for maximum power point tracking (MPPT). Ziegler-Nichols tuning method is also employed to select the initial values of PID gains. Within the period of steady-states and transients, FGS-PID adopts the gains to ensure the stability of the control scheme. It also damps out transient fluctuations and reduces settling time. Also, BES could be employed to provide a stable and reliable power from the output of PV sources to loads. It can enhance the performance of the entire power system during the grid-connected mode. The simulation results under Matlab/Simulink show that the suggested control strategies are robustness, fast transient response and proper steady-state performance in the grid-connected mode in comparison other presented methods.     

Energy management and control of a hybrid water pumping system with storage

This paper aims to define a control and management strategy for water pumping system which would be powered by a hybrid PV/diesel generator system with battery storage. The particularity of the proposed power management method is to ensure the water volume in need and to maximize the use of PV generator while limiting the use of the diesel generator. In order to capture the maximum power from PV generator, a fuzzy logic maximum power point tracking controller is applied. On the other hand, a PI regulator is used with a boost converter in order to adapt the voltage of the battery bank to the DC bus. The water flow of the pump is also controlled. The developed power management and control strategy has been implemented using SIMPOWER toolbox in Matlab/Simulink. The obtained satisfying simulation results prove the efficiency of the proposed solution that assures continuous supply of water and electricity.     

Evaluation of a proper controller performance for maximum-power point tracking of a stand-alone PV system

In this paper the implementation of a suggested stand-alone PV system, for maximum-power point tracking (MPPT), is carried out. Also, this paper presents a comparative study, through experimental work, between the conventional PI controller and the fuzzy logic controller (FLC) under different atmospheric conditions. The implemented system with both the PI controller and the FLC gives a good maximum-power operation of the PV array, but the tracking capability for different optimum operating points is better and faster for the case of using the FLC compared to the case of using the PI controller.     

Daylight illuminance control with fuzzy logic

The purpose is to take full advantage of daylight for inside illumination. The inside illuminance and luminous efficacy of the available solar radiation were analyzed. The paper deals with the controlled dynamic illuminance response of built environment in real-time conditions. The aim is controlled functioning of the roller blind as a regulation device to assure the desired inside illuminance with smooth roller blind moving. Automatic illuminance control based on fuzzy logic is realized on a test chamber with an opening on the south side. The development and design of the fuzzy controller for the corresponding positioning of the roller blind with the available solar radiation as external disturbance is the subject of this paper.     

模糊滑模自适应控制及其在电厂热工过程中的应用

通过一种新学习算法的导出,并结合模糊逻辑系统中的模糊基函数,给出了一种带有通用规则库的模糊滑模自适应控制器。这种控制器能够在线自动调整参数,能对热工过程中的大惯性大迟延对象进行有效的控制。当对象参数变化或存在较大扰动的情况下,仍能保持很好的控制品质。该方法已被用于某电厂主汽压力控制系统。仿真结果表明:该算法具有良好的控制品质和很强的鲁棒性。图4表1参7