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.     

A novel self-tuning type-2 fuzzy maximum power point tracking technique for efficiency enhancement of fuel cell based battery chargers

Given the uncertainties associated with proton-exchange membrane fuel cell systems and relatively low efficiency of the fuel cell stacks for low-power applications, designing a high-efficiency maximum power point tracking (MPPT) controller for the fuel cell electric vehicles is an important and also technically challenging issue. For this purpose, in this article, aiming to develop a high-efficiency and low cost battery charger, a novel self-tuning type-2 fuzzy MPPT controller is presented. The main task of the controller is to provide the better performance and regulate the switching duty cycle of the used power converter under the system's uncertainty conditions in order to dynamically extract the maximum power from the fuel cell system and maintain the battery at its highest possible state of charge while protecting it from overcharging. For the sake of computational efficiency, an improved invasive weed optimization algorithm, called elitist invasive weed optimization (EIWO), is also presented to tune the type-2 fuzzy set parameters, whose improvement is demanding due to the limited human experience and knowledge. All data processing and simulations are conducted in the MATLAB software. Finally, the performance of the proposed MPPT controller is examined through using experimental tests with a prototype device.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

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.     

A new RBFN based MPPT controller for grid-connected PEMFC system with high step-up three-phase IBC

Extraction of maximum power from a proton exchange membrane fuel cell (PEMFC) power source is necessary for its economical and optimal utilization. In this paper, a neural network based maximum power point tracking (MPPT) controller is proposed for the grid-connected PEMFC system. Radial basis function network (RBFN) algorithm is implemented in the neural network controller to extract the maximum power from PEMFC. A high step-up three-phase interleaved boost converter (IBC) is also designed in order to reduce the current ripples coming out from the PEMFC. Interleaving technique provides high power capability and reduces the voltage stress on the power semiconductor devices. The performance analysis of the proposed RBFN MPPT controller is analyzed in MATLAB/Simulink platform for both standalone as well as for the grid-connected PEMFC system.     

A novel MPPT controller based PEMFC system for electric vehicle applications with interleaved SEPIC converter

The Proton Exchange Membrane Fuel Cells (PEMFCs) are one of the most effective and optimistic renewable energy source and they are extensively used in automotive applications. In the past, many researchers focused on solving the issues of extracting maximum power from fuel cell, controlling the speed and reducing the torque ripple of Brushless DC (BLDC) motor for fuel cell based Electric Vehicle (EV) systems. However, it is challenging to fine-tuning the gain parameters in the existing works MPPT approach and extracting maximum amount of energy. Additionally, it has limitations like unregulated voltage, problems of large overshoot, slow tracking speed, output power fluctuation, computational complexity and intricate modeling. Thus, the proposed work aims to create a revolutionary methodology called Unified Firefly Ersatz Neural Network (UFENN) - Maximum Power Point Tracking (MPPT). The UFENN is a kind of optimization-based machine learning technique that was created for efficiently optimizing the parameters to extract the maximum energy from the fuel cells. Furthermore, in order to control the output voltage with the least amount of power loss, an Interleaved SEPIC converter is also used in this work. During performance analysis, an extensive simulation results have been taken for validating the results of the proposed scheme by using various evaluation indicators.     

Brushless DC motor drives supplied by PV power system based on Z-source inverter and FL-IC MPPT controller

This paper discusses operation performance of a water pumping system consist of a brushless dc (BLDC) motor coupled a centrifugal pump and accompanying a Z-source inverter (ZSI) fed by a photovoltaic (PV) array, to be improved. Despite conventional double-stage power converters, this paper proposes utilizing a single-stage ZSI to extract the maximum power of the PV array and supply the BLDC motor simultaneously. Utilizing the ZSI provides some inherent advantages such as high efficiency and low cost, which is very promising for PV systems due to its novel voltage buck/boost capability. In addition, in order to precisely perform the maximum power point tracking (MPPT) of the PV array the fuzzy logic-incremental conductance (FL-IC) MPPT scheme is proposed. The proposed FL-IC MPPT scheme provides enough modification to the conventional IC method to enjoy an appropriate variable step size MPPT control signal for the ZSI. Moreover, direct torque control (DTC) is found more effective in comparison with hysteresis current control with current shaping to drive the BLDC motor, because it benefits from faster torque response, reduced torque ripple, less sensitivity to parameters variations, and simple implementation. In the mean time, due to the frequently variations of the PV power generation; delivered mechanical power to the centrifugal pump is variable. Thus, the BLDC motor should be driven with variable reference speed. In order to improve the speed transient response of the BLDC motor and enhance the energy saving aspect of the system, it should enjoy a high quality dynamic response characteristic. Therefore, to realize these purposes, particle swarm optimization (PSO) has been proposed to regulate the proportional-integral-derivative (PID) parameters of the BLDC motor speed controller. The system configuration, operation principle and control methods are presented in detail. Finally, the proposed system was simulated in different operation conditions of the PV array by computer simulations and the effectiveness of the proposed control strategies has been validated by comparative studies and simulation results.     

A comparative study of maximum power point tracking methods for a photovoltaic-based water pumping system

In this paper the performance of the proposed fuzzy-based maximum power point tracking (MPPT) is investigated and compared with incremental conductance and constant voltage controller for a photovoltaic (PV) pumping system. A fuzzy logic controller with a mamdani inference engine using only nine rules is designed to track the optimum power point. An induction motor has been used to drive the centrifugal pump. The system performance is analysed for different weather conditions. A detailed comparative study presenting the merits and demerits of each technique is also presented in order to develop a relative relationship. Simulation results obtained indicate better performance of the fuzzy-based MPPT algorithm for the PV pumping system.     

Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations

This paper analyses the operation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the SPV modules by changing the duty ratio of the boost converter. The duty ratio of the boost converter is calculated for a given solar irradiance and temperature condition by a closed-loop control scheme. The ANFIS is trained to generate maximum power corresponding to the given solar irradiance level and temperature. The response of the ANFIS-based control system is highly precise and offers an extremely fast response. The response time is seen as nearly 1 ms for fast varying cell temperature and 6 ms for fast varying solar irradiance. The simulation is done for fast-changing solar irradiance and temperature conditions. The response of the proposed controller is also presented.     

THD reduction with reactive power compensation for fuzzy logic DVR based solar PV grid connected system

Dynamic voltage restorer (DVR) is used to protect sensitive loads from voltage disturbances of the distribution generation (DG) system. In this paper, a new control approach for the 200 kW solar photovoltaic grid connected system with perturb and observe maximum power point tracking (MPPT) technique is implemented. Power quality improvement with comparison is conducted during fault with proportional integral (PI) and artificial intelligence-based fuzzy logic controlled DVR. MPPT tracks the actual variable DC link voltage while deriving the maximum power from a photovoltaic array and maintains DC link voltage constant by changing modulation index of the converter. Simulation results during fault show that the fuzzy logic based DVR scheme demonstrates simultaneous exchange of active and reactive power with less total harmonic distortion (THD) present in voltage source converter (VSC) current and grid current with fast tracking of optimum operating point at unity power factor. Standards (IEEE-519/1547), stipulates that the current with THD greater than 5% cannot be injected into the grid by any distributed generation source. Simulation results and validations of MPPT technique and operation of fuzzy logic controlled DVR demonstrate the effectiveness of the proposed control schemes.     

Doubly-fed induction generator drive based WECS using fuzzy logic controller

The purpose of this paper is to improve the control performance of the variable speed, constant frequency doubly-fed induction generator in the wind turbine generation system by using fuzzy logic controllers. The control of the rotor-side converter is realized by stator flux oriented control, whereas the control of the grid-side converter is performed by a control strategy based on grid voltage orientation to maintain the DC-link voltage stability. An intelligent fuzzy inference system is proposed as an alternative of the conventional proportional and integral (PI) controller to overcome any disturbance, such as fast wind speed variation, short grid voltage fault, parameter variations and so on. Five fuzzy logic controllers are used in the rotor side converter (RSC) for maximum power point tracking (MPPT) algorithm, active and reactive power control loops, and another two fuzzy logic controllers for direct and quadratic rotor currents components control loops. The performances have been tested on 1.5 MW doubly-fed induction generator (DFIG) in a Matlab/Simulink software environment.     

Performance evaluation of fuel cell fed electric vehicle system with reconfigured quadratic boost converter

The performance evaluation of 1.26 kW fuel cell fed electric vehicle system with reconfigured Quadratic Boost Converter along with the neural network based maximum power point tracking algorithm is presented in this paper. The acceptance of EV in modern society is relevant for the creation of pollution free environment. The main reason for creation of excessive pollution is transportation by the mode of roadways, with the own internal combustion engines by using crude oil as primary energy source. In this paper, a 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC) fed electric vehicle is designed in MATLAB/Simulink environment. To integrate PEMFC to brushless DC (BLDC) motor are configured Quadratic Boost Converter is designed for high static converter voltage gain. The performance of the proposed EV system is analysed with perturb and observer method and neural network based MPPT control techniques and obtained results are compared at different fuel cell input temperature conditions with respect to different time periods.     

A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

The fuel cell has been regarded as one of the most promising renewable energy technologies for various applications such as distributed power generation, transportation, portable power source, and automobile. The output power of a fuel cell is affected by operational parameters such as cell temperature, oxygen partial pressure, and hydrogen partial pressure. This paper deal with a two-stage grid-connected PEMFC system. In the first stage, a fuzzy logic controller is proposed to track the maximum power generated by PEMFC by using a boost converter. In the second stage, a multi-objective FSC-MPC two-step prediction to control of 3L-NPC is proposed. The use of Finite Control Set Model Predictive Control (FSC-MPC) can significantly improve the control performance of a three-level NPC (3L-NPC) inverter. Furthermore, this method uses the inverter's discrete behavior to find optimal switching states that minimize the cost function. The suggested model predictive control method for the 3L-NPC inverter is based on a multi-objective cost function that is meant to regulate inverter currents, dc-link voltage balance, and minimize the number of switch states. The performance of the proposed MO–FSC–MPCTS controlled 3L-NPC inverter is simulated with MATLAB/Simulink. The results show that the suggested method ensures MPP tracking and injecting the current into the grid with a 2% THD.     

Adaptive maximum power point tracking control of fuel cell power plants

A fuel cell's output power depends nonlinearly on the applied current or voltage, and there exists a unique maximum power point (MPP). This paper reports a first attempt to trace MPPs by an extremum seeking controller. The locus of MPPs varies nonlinearly with the unpredictable variations in the fuel cell's operation conditions. Thus, a maximum power point tracking (MPPT) controller is needed to continuously deliver the highest possible power to the load when variations in operation conditions occur. A two-loop cascade controller with an intermediate converter is designed to operate fuel cell power plants at their MPPs. The outer loop uses an adaptive extremum seeking algorithm to estimate the real-time MPP, and then gives the estimated value to the inner loop as the set-point, at which the inner loop forces the fuel cell to operate. The proposed MPPT control system provides a simple and robust control law that can keep the fuel cell working at MPPs in real time. Simulation shows that this control approach can yield satisfactory results in terms of robustness toward variations in fuel cell operation conditions.     

Modeling and control of photovoltaic and fuel cell based alternative power systems

Photovoltaic (PV) systems and fuel cells (FCs) represent interesting solutions as being alternative power sources with high performance and low emission. This work presents a modeling and control study of two power generators; photovoltaic array and fuel cell based systems. An MPPT approach to optimize the PV system performances is proposed. The PV system consists of a PV array connected to a DC-DC buck converter and a resistive load. A maximum power point tracker controller is required to extract the maximum generated power. Based on Incremental Conductance (INC) principle, the idea of the proposed control is to use a Fuzzy Logic Controller (FLC) that allows the choice of the duty cycle step size which is used to be fixed in conventional MPPT algorithms. The variable step is computed according to the value of the PV power-voltage characteristic slope. The second working system comprises a controlled DC-DC converter fed by a proton exchange membrane fuel cell (PEMFC) and supplies a DC bus. The mathematical model of the PEMFC system is given. The converter duty cycle is adjusted in order to regulate the DC bus voltage. Obtained simulation results validate the control algorithms for both of studied power systems.     

How fuzzy logic can improve PEM fuel cell MPPT performances?

This paper addresses the development of new variable step size fuzzy based MPPT controller. In this study, the fuzzy logic approach is firstly used to auto-scale the variable step size of the Incremental Conductance (IC) MPPT controller. Secondly, the proposed variable step size fuzzy based MPPT controller is used to track the output power of the PEM fuel cell system composed of 7 kW fuel cell supplying a 50Ω resistive load via a DC-DC boost converter controlled using the proposed MPPT. The proposed variable step size fuzzy-based MPPT controller is compared to the conventional fixed step size IC, the variable step size IC and the fuzzy scaled variable step size IC MPPTs using the implemented Matlab/Simulink PEM Fuel Cell power system model. Comparative simulation results between the four studied MPPTs show better performances for the proposed fuzzy based variable step size MPPT in term of: response time reduction between 3.6% and 82.35%; overshoot reduction between 34.55% and 100%; and ripple reduction between 70.93% and 100%, improving as consequence the fuel cell lifetime affected by high current ripple.     

Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power?voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.     

Maximum power point tracking supported proton exchange membrane fuel cell based intelligent dynamic voltage restorer

In modern era, electrical power utilities are more concerned about power quality. In this endeavour, dynamic voltage restorer (DVR) provides adequate support to the system. Accordingly, the present work illustrates intelligent hybrid control mechanism for DVR. Artificial neural network (ANN) is incorporated to obtain real-time optimal gains under distinct voltage situations. Closed loop type 2 fuzzy logic (CLT2-FL) is also realized in order to assist the ANN supported unit concomitantly. In order to enhance the potential of the present DVR, a CLT2-FL controlled maximum power point tracking (MPPT) based proton exchange membrane fuel cell arrangement is also explored in the present study. CLT2-FL module is adopted in DC-DC converter topology to provide simultaneous supply to the loads at different and regulated voltage levels. Consequently, the results are evaluated and compared to the state-of-the-arts which unveil the efficacy of the implemented controller against the oddity seen in the voltage waveform, thereby, exhibiting better voltage regulation and less harmonics. The effectiveness of the implemented MPPT unit from the viewpoints of convergence speed and oscillations is also established.     

Implementation of photovoltaic water pumping system with MPPT controls

To increase the output efficiency of a photovoltaic (PV) system, it is important to apply an efficient maximum power point tracking (MPPT) technique. This paper describes the analysis, the design and the experimental implementation of the tracking methods for a stand-alone PV system, using two approaches. The first one is the constant voltage (CV) MPPT method based on the optimum voltage, which was deduced experimentally, and considered as a reference value to extract the optimum power. The second one is the increment conductance (Inc-Cond) MPPT method based on the calculation of the power derivative extracted by the installation. The output controller can adjust the duty ratio to the optimum value. This optimum duty ratio is the input of a DC/DC boost converter which feeds a set of Moto-pump via a DC/AC inverter. This paper presents the details of the two approaches implemented, based on the system performance characteristics. Contributions are made in several aspects of the system, including converter design, system simulation, controller programming, and experimental setup. The MPPT control algorithms implemented extract the maximum power point (MPP), with satisfactory performance and without steady-state oscillation. MATLAB/Simulink and dSpace DS1104 are used to conduct studies and implement algorithms. The two proposed methods have been validated by implementing the performance of the PV pumping systems installed on the roof of the research laboratory in INSAT Tunisia. Experimental results verify the feasibility and the improved functionality of the system.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.