Incremental Conductance Maximum Power Point Tracking Research Articles

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

AbstractThis study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.

PERFORMANCE ANALYSIS ON PMSG BASED WIND GENERATION SYSTEM INTERFACED MULTILEVEL CONVERTER WITH ARTIFICIAL INTELLIGENCE TECHNIQUE

This paper focuses on the development of ANN based MPPT interfaced Permanent magnet synchronous generator (PMSG) for wind energy conversion system (WECS). There are many drawbacks to conventional energy sources, such as higher fossil fuel prices, damaging to the environment, a shortage of resources, and increased pollution. Due to the unpredictable and arbitrary nature of wind energy, it is crucial to introduce several control strategies in order to maximize its efficiency. In this work, Levenberg algorithms are developed for Artificial Intelligence Technique using MPPT algorithms for PMSG-based wind energy conversion systems. The proposed system consists of 3 kW PMSG integrated WECS, DC to DC boost converter, multilevel inverter and different loads. The DC to DC converter is connected to the multilevel inverter through a DC link capacitor. The inverter power is fed to the grid through a step-down transformer. MPPT controllers are used in the standalone wind energy conversion system. The goal of this research is to develop an MPPT controller using Levenberg-based Artificial Intelligence Technique for wind energy conversion systems. With the proposed Levenberg-based Artificial Intelligence Technique, MPPT uses voltage and current as input variables for DC to DC boost converter duty ratios. The Artificial Intelligence Technique of MPPT controller for standalone wind energy conversion systems with various different input/output conditions is modeled and simulated in MATLAB/ Simulink environment. The performance of the Artificial Intelligence Technique-based MPPT controller versus the conventional incremental conductance MPPT controller. Simulation results show that for a wide range of input wind speed, Artificial Intelligence Technique based MPPT controller shows improved performance than the conventional MPPT at various operating conditions. The response of the developed Levenberg based Artificial Intelligence Technique based MPPT algorithms for PMSG based wind energy conversion system in MATLAB/ Simulink environment to investigate the robustness of the developed control strategy and validate the reliability and stability under different input/output conditions for grid connected wind generation system.

A New Hybrid MPPT Based on Incremental Conductance-Integral Backstepping Controller Applied to a PV System under Fast-Changing Operating Conditions

Maximum power point tracking (MPPT) is becoming more and more important in the optimization of photovoltaic systems. Several MPPT algorithms and nonlinear controllers have been developed for improving the energy yield of PV systems. On the one hand, most of the conventional algorithms such as the incremental conductance (INC) demonstrate a good affinity for the maximum power point (MPP) but often fail to ensure acceptable stability and robustness of the PV system against fast-changing operating conditions. On the other hand, the MPPT nonlinear controllers can palliate the robust limitations of the algorithms. However, most of these controllers rely on expensive solar irradiance measurement systems or complex and relatively less accurate methods to seek the maximum power voltage. In this paper, we propose a new hybrid MPPT based on the incremental conductance algorithm and the integral backstepping controller. The hybrid scheme exploits the benefits of the INC algorithm in seeking the maximum power voltage and feeds a nonlinear integral backstepping controller whose stability was ensured by the Lyapunov theory. Therefore, in terms of characteristics, the overall system is a blend of the MPP-seeking potential of the INC and the nonlinear and robust potentials of the integral backstepping controller (IBSC). It was noted that the hybrid system successfully palliates the conventional limitations of the isolated INC and relieves the PV system from the expensive burden of solar irradiance measurement. The proposed hybrid system increased the operational efficiency of the PV system to 99.94% and was found better than the INC MPPT algorithm and 8 other recently published MPPT methods. An extended validation under experimental environmental conditions showed that the hybrid system is approximately four times faster than the INC in tracking the maximum power with better energy yield than the latter.

Improved variable-step incremental conductance MPPT algorithm applied to photovoltaic storage systems

For the traditional PV maximum power tracking control method in terms of tracking speed and stability, proposed an improved variable-step incremental conductance method. The method first speeds up the tracking speed by the constant voltage coefficient method to quickly locate around the MPP(Maximum power point,MPP) and dynamically adjusts the step size using the inverse tangent function value when the operating point is located to the left of the maximum power point, and dynamically adjusts the step size using the exponential function value when the operating point is located to the right. Given the characteristics of photovoltaic power generation, it usually constitutes a light storage system with an energy storage battery. To verify the effectiveness, the simulation experiments are carried out in Matlab/Simulink to build a photovoltaic energy storage system model, which contains an energy storage battery. Results indicate that the proposed new method achieves the optimization of tracking precision and speed according to the nature of the function and can make the energy storage battery charge and discharge stably, which verifies the practicality of the proposed improved algorithm.

Implementation of FPGA Based MPPT Techniques for Grid-Connected PV System

Global energy demand is growing rapidly owing to industrial growth and urbanization. Alternative energy sources are driven by limited reserves and rapid depletion of conventional energy sources (e.g., fossil fuels).Solar photovoltaic (PV), as a source of electricity, has grown in popularity over the last few decades because of their clean, noise-free, low-maintenance, and abundant availability of solar energy. There are two types of maximum power point tracking (MPPT) techniques: classical and evolutionary algorithm-based techniques. Precise and less complex perturb and observe (P&O) and incremental conductance (INC) approaches are extensively employed among classical techniques. This study used a field-programmable gate array (FPGA)-based hardware arrangement for a grid-connected photovoltaic (PV) system. The PV panels, MPPT controllers, and battery management systems are all components of the proposed system. In the developed hardware prototype, various modes of operation of the grid-connected PV system were examined using P&O and incremental conductance MPPT approaches.

Critical Performance Comparison Between Single-Stage and Two-Stage Incremental Conductance MPPT Algorithms for DC/DC Boost-Converter Applied in PV Systems

Operating with maximum power point tracking (MPPT) strategy is the key functions of the solar energy management system in solar PV deployment. Therefore, in this paper, the strengths and weaknesses of the MPPT based on an incremental conductance (IC) algorithm are analyzed. Furthermore, assessments in terms of performance of two MPPT-IC techniques are presented. More specifically, a two-stage optimized MPPT-IC algorithm in this study is developed and implemented for isolated solar PV system with the conventional single-stage MPPT-IC algorithm. Both MPPT-IC methods are analyzed and compared based on their tracking efficiencies, steady state error and transient speed responses. For the case of the two-mode MPPT-IC algorithm, significant modification is carried-out by combining the conventional IC and modified constant voltage methods. As a result, a better performance under both sunny and cloudy weather scenarios respectively is achieved with higher tracking efficiencies than the conventional IC algorithm. The two stage-algorithm not only rapidly tracks the maximum power point (MPP) under dynamic weather conditions, but also significantly reduces the fluctuations in power around the MPP when subjected to fast and slow speed’s change of irradiations.

Performance improvement of maximum power point tracking for photovoltaic system using grasshopper optimization algorithm based ANFIS under different conditions

ObjectivePower systems have been accommodating solar energy to a large extent, particularly when it comes to photovoltaic (PV) systems. In this regard, a number of parameters, including temperature and solar irradiance, have a significant impact on the power output of such systems. Maximum power point tracking (MPPT) strategies have been devised and deployed in PV systems as a successful remedy to boost efficiency in response to varying climate parameters. Accordingly, a promoted MPPT strategy is designed in this paper deploying the incremental conductance (INC) method optimized by employing the grasshopper optimization algorithm (GOA)-based adaptive neuro-fuzzy inference system (ANFIS). MethodsThis technique includes two stages: specifying the optimal values of voltage by deploying the GOA taking into consideration various scenarios of temperature and solar irradiance. Afterward, the ANFIS would give the optimal value of the voltage by using solar irradiance on PV panels. It is noteworthy that the INC technique would initialize at this value and seek the maximum power point (MPP). One of the merits associated with using the ANFIS-based INC technique is the smaller dataset needed for the training purpose. In this regard, in case the ANFIS does not specify the definite point, the INC approach would do it. ResultSimulations are performed to validate the usefulness of the suggested GOA-ANFIS-based INC MPPT technique and compared with conventional MPPT schemes.

Adaptive control-based Isolated bi-directional converter for G2V& V2G charging with integration of the renewable energy source

In this research paper, Adaptive control-based Isolated bi-directional converter for modernized electric vehicle system is introduced with control on charge and discharge of the electric vehicle (EV) battery as per the reference value. The EV battery is operated in two modes, such as charge (Grid-to-Vehicle) mode and discharge (vehicle-to-grid) mode. In charging mode, constant current and constant voltage (CCCV) control is adopted with control on charging voltage and current. Whereas in discharging mode, constant current (CC) control is adopted for control on discharge current from battery into the grid. The grid is also interconnected to PV system for renewable energy power sharing. In both the control techniques, ANFIS is integrated for better performance of the battery during charge and discharge conditions. The ANFIS takes input from error generated by comparison of reference and measured value and produces the required duty ratio value. The training of the fuzzy structure is done using data generated from the PI controller input and output. The training uses hybrid algorithm for adapting the data points considered from the PI controller. The EV battery is charged using the conventional AC grid and the renewable energy source in G2V mode, and the EV battery and renewable energy sources are used to supply power to the AC load in the V2G mode, which reducing the load demand on the AC grid. The DC-Link voltage and power to the AC load are kept constant in both modes of operation by using a synchronous reference frame controlled VSC and an ANFIS controlled dual active full bridge converter. The incremental conductance MPPT algorithm is used to control the Photo Voltaic Array (PVA). The proposed work is implemented using MATLAB/Simulink and HIL performance of the proposal system is examined in the RT-LAB OP-5700 real time simulator environment.

Inverter based implementation of maximum power point techniques

The utilization of renewable energy sources is being pushed by both greater environmental consciousness and expanding demand. Recently, solar photovoltaic technology has found increased use for a broader range of applications. This may be ascribed to solar energy's extensive availability as well as its long-term viability and low cost. According to the global photovoltaic (PV) industry, 594 gigawatts (GW) of PV capacity were installed in 2019, with the objective of replacing conventional source-based generating facilities. The major problem in PV production, however, is identifying the maximum power point tracking (MPPT) systems that are currently in use to compute peak output. For 1240 W PV power plants, this article compares perturb & observe MPPT approaches with incremental conductance MPPT techniques. The MATLAB Simulink program was utilized to conduct the study, which was based on many factors such as voltage, current, and output power under various weather conditions. When these MPPT algorithms are applied to solar trackers, the efficiency, reaction time, and steady-state oscillations all improve.

Maximum power point tracking based on self-adaptive neural network combined with incremental conductance

ABSTRACT This article proposes a hybrid method that combines the self-adaptive neural network (SANN) and incremental conductance (IC) algorithm for maximum power point tracking (MPPT) in a photovoltaic (PV) system. The main objective of the proposed algorithm is to extract the maximum power of solar panels quickly and faster than the recent artificial neural network (ANN)-IC MPPT algorithm. Compared to the previous ANN-IC algorithm, a minimum number of data sets were used to obtain the GMP without compromising accuracy. Furthermore, comparison demonstrates that the suggested SANN-IC provides high efficiency, reliability, rapid tracking, and less response time tracking of GMP than w.r.t. all state-of-the-art techniques and ANN-IC method. The simulation is done with the help of MATLAB/SIMULINK. This is the first article about the performance of the hybrid MPPT technique tested by a sun simulator. The benefit of this hybrid method is that it reduces the search space of ANN, and hence, convergence speed is significantly increased. Training time and computing power are optimized by altering the number of epochs and requiring less memory. The experimental result showed the best convergence time and less oscillation than the ANN-IC method. Hence, the overall performance of SANN-IC in a steady-state condition with a partial shading condition meets the motive of the work.

PV FED Interleaved Boost Converter for BLDC Motor

Abstract: The generation of solar energy and its conversion techniques gained greater importance due to increase use of electricity. The power generation scheme is more striking due to its advantages among all the Renewable Energy Sources. This project introduces a PV fed interleaved boost converter for BLDC motor, which is a simple, cost effective and efficient brushless DC (BLDC) motor drive for solar photovoltaic (SPV) array system. A interleaved boost converter is used in order to extract the maximum available power from the SPV array. This control algorithm eliminates phase current sensors and adapts a fundamental frequency switching of the voltage source inverter (VSI) hence avoids the power losses due to high frequency switching. In speed control of BLDC motor no additional circuitry or control is used. An appropriate control of interleaved boost converter is done by the incremental conductance maximum power point tracking (INC-MPPT) algorithm which offers soft starting of the BLDC motor. The proposed system is designed and modelled at steady state conditions of practical operating conditions is demonstrated through simulation results using MATLAB/ Simulink.

A new tuning rule for stabilized integrator controller to enhance the indirect control of incremental conductance MPPT algorithm: Simulation and practical implementation

The Incremental Conductance (IncCond) algorithm based on direct control mode is one of the highest Maximum Power Point Tracking (MPPT) algorithms ensuring multiple performances compared to some other classical methods. Unfortunately, these performances can deteriorate, especially in the case of sudden changes in weather conditions and load variation., Preventing these drawbacks will form the basis of a new IncCond-MPPT scheme based on indirect control mode in which a smooth electrical power response generated by photovoltaic (PV) panels is well ensured, regardless of quick changes in solar irradiance, absolute temperature and load variation. The proposed MPPT approach uses two stages of control. The first one is used to provide the reference PV-module voltage. The second control loop is formulated as a transfer function that is then controlled using an appropriate Integral controller. Its integral gain is well tuned by applying the Routh’s stability criterion on the characteristic equation of the obtained closed loop transfer function. This allows providing the proposed new guideline, which represents the main contribution of this paper. The proposed IncCond-MPPT scheme is compared with the standard one in terms of MPP tracking accuracy and electrical power ratio, which exceeds 97 %, where quick changes of weather conditions and load variations are considered.

Performance Comparison of FLC MPPT with P & O and Incremental Conductance MPPT For standalone PV System

Abstract: Solar energy is one of the most important renewable energy sources. Photovoltaic power is one of the main ways of solar energy utilization, has the characteristics of environmentally-friendly and non-polluting, application range is very wide, has now been used in communication, transportation and other fields, has a place in the national economy energy system. However, the use of the solar energy is deeply influenced by the terrain, topography, position, weather and any other natural conditions, and has the problem about intermittence, sunlight direction and intensity changing along with the time, so it requires a higher request of collecting and using solar energy. In this paper, Fuzzy Logic Control (FLC)based MPPT is adopted for extraction of maximum power from PV panel and its performance is compared with P & O and InC algorithm for tracking a MPP on the V-I characteristic of the solar PV panel. The simulation results can be validated the effectiveness of the proposed work under various atmospheric conditions using Matlab Simulink software.

Implementation of Modified Incremental Conductance MPPT A lgorithm in Grid Connected PV System Under Dynamic Climatic Conditions

Background: The main goal of the grid-connected Photo Voltaic (PV) system is to extract maximum power from the array with reduced loss by using an appropriate MPPT method. Methods: A boost converter topology with linear INCMPPT algorithm is used to provide maximum power of 100 kW from the solar PV array to the grid. The integral regulator in the modified algorithm minimizes the error and guarantees exact control of the duty cycle of the DC-DC converter to produce constant DC voltage 500V with a minimum error of 2V. This proposed methodology perfectly matches with the optimal design of the DC-AC converter (inverter) for grid integration. A three-level IGBT-based voltage source inverter and filters are designed to supply pure AC voltage to the existing 100 kW grid. Detailed simulation work is completed using MATLAB/ Simulink software to prove the effectiveness of the proposed algorithm under various temperature (25-50◦C) and irradiance (200-1000W/m2) levels. Findings: The proposed MPPT algorithm is simple and effective in reducing the error required for the duty cycle correction from 0.45 to 0.518 and eliminating the oscillations at MPP. By using this methodology, the output voltage of the boost converter is adjusted to match the reference value. The simulation results reveal the better performance of the grid-connected PV system with the modified incremental conductance MPPT method. This modified INC MPPT algorithm exactly controls the duty cycle of the boost converter to provide better settling time and low ripples in the grid parameters. Novelty: This article provides a simple and effective improved incremental conductance (INC) MPPT algorithm to minimize the tracking error (1.5%). No additional tuning and selection of parameters, computational burden and memory are required for the implementation of the proposed control algorithm. Keywords: Gridconnected photovoltaic (PV) system; Maximum power point tracking (MPPT); Voltage Source Inverter (VSI); Boost converter; Incremental conductance (INC)

Hybrid Renewable Energy Generation Through Incremental Conductance Mppt

The relevance of electricity generation from renewable energy sources is growing every day in the current global energy environment. The scarcity of fossil fuels and the environmental risks connected with traditional power producing methods are the main reasons behind this. The major sources of non-conventional energy are wind and solar which can be harnessed easily. A new system design for hybrid photovoltaic and wind-power generation is introduced within this study. A Modified M.P.P.T. has been proposed to strengthen productivity of this system. The proposed approach employs the Incremental Conductance (IC) MPPT technique. Under varied climatic conditions (Solar irradiance & Temperature), IC is utilized to determine the optimum voltage output of a photo voltaic generator (P.V.G.) within the photo voltaic system (P.V.) structure. The Incremental Conductance is utilized to manage the converter’s technology having boosting function. The P.M.S.G. is used to determine the maximum voltage output for varied wind flow rates in wind turbine system. Simulations are conducted in Matlab2019b to test efficacy of the proposed MPPT. The proposed scheme's effectiveness can be supported with simulation results.

Hybrid Renewable Energy Generation Through Incremental Conductance Mppt

The relevance of electricity generation from renewable energy sources is growing every day in the current global energy environment. The scarcity of fossil fuels and the environmental risks connected with traditional power producing methods are the main reasons behind this. The major sources of non-conventional energy are wind and solar which can be harnessed easily. A new system design for hybrid photovoltaic and wind-power generation is introduced within this study. A Modified M.P.P.T. has been proposed to strengthen productivity of this system. The proposed approach employs the Incremental Conductance (IC) MPPT technique. Under varied climatic conditions (Solar irradiance & Temperature), IC is utilized to determine the optimum voltage output of a photo voltaic generator (P.V.G.) within the photo voltaic system (P.V.) structure. The Incremental Conductance is utilized to manage the converter’s technology having boosting function. The P.M.S.G. is used to determine the maximum voltage output for varied wind flow rates in wind turbine system. Simulations are conducted in Matlab2019b to test efficacy of the proposed MPPT. The proposed scheme’s effectiveness can be supported with simulation results.

PV combined ZSI-PAPF with special fuzzy logic controller for power quality improvement

The main aim of this paper is to reduce the current harmonics and reactive power compensation by using PV fed parallel active power filter connected by a Z-source inverter (ZSI) topology. This ZSI based PAPF capable of handling buck and boost operation without the need of extra DC-DC converter. The reference current extraction of this filter is based on modified PLL-SRF technique. Slightly changed incremental conductance MPPT is used to track maximum power. A Special type of Fuzzy logic controller is used to control DC link voltage and adaptive HCC is used to generate gate pulse generation. The propose system is tested by using MATLAB/Simulink. The performances show that the proposed PV fed PAPF performs good under a non sinusoidal system with nonlinear load system.

Artificial Neural Network Assisted Variable Step Size Incremental Conductance MPPT Method with Adaptive Scaling Factor

In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.

ARTIFICIAL INTELLIGENCE TECHNIQUE BASED MPPT CONTROLLER FOR STANDALONE SOLAR ENERGY CONVERSION SYSTEM

This paper presents the development and performance analysis of Artificial Intelligence Technique based MPPT controller for a standalone solar energy conversion system. The proposed system consists of 2.5 kW PV array, DC to DC boost converter, inverter and different load. The DC to DC Converter connected to Inverter through DC link capacitor. The inverter power fed to the load through step down transformer. The standalone solar energy conversion system consists of MPPT controller. In this research Artificial Intelligence Technique based MPPT controller on Levenberg algorithm developed. For the proposed Artificial Intelligence Technique based MPPT takes two variables as PV voltage and PV current as an input and output the duty ratio for DC to DC boost converter. The Artificial Intelligence Technique based MPPT controller for standalone solar energy conversion system with various load is modeled and simulated in MATLAB/ Simulink environment. Performance of both the Artificial Intelligence Technique based MPPT controller compared with conventional incremental conductance MPPT. Simulation results show that for a wide range of input irradiance, Artificial Intelligence Technique based MPPT controller shows improved performance than the conventional incremental conductance MPPT with at various operating conditions.

Optimization for a Photovoltaic Pumping System Using Indirect Field Oriented Control of Induction Motor

Due to the increase in electricity and diesel costs, solar photovoltaic pumping systems have become a good solution, especially in rural areas. This work presents a standalone photovoltaic (PV) water pumping system (PVWPS) driven by an induction motor without energy storage to improve the pumping system’s performance. First, a comparison is made between two types: perturb and observe (P&O) method and incremental conductance (INC) MPPT method with a variable step size that is automatically adjusted. Studying these two techniques helps to understand which one can result in a system with less oscillation and greater efficiency when tracking the maximum power point from the PV panel under sudden irradiation conditions. This MPPT works on the operating duty cycle of the boost converter. Then, that converter combines with a voltage source inverter (VSI) to convert DC power to AC power. Second, we use indirect field-oriented control (IRFOC), which drives the three-phase of an induction motor in turn to run the centrifugal pump. The simulation results of this work were obtained using the MATLAB Simulink platform.