Incremental Conductance Maximum Power Point Tracking Research Articles

Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.

Design and implementation of microcontroller-based solar charge controller using modified incremental conductance MPPT algorithm

This paper presents the modeling, design, and implementation of a rapid prototyping low-power solar charge controller with maximum power point tracking (MPPT). The implemented circuit consists of a 60 W photovoltaic (PV) module, a buck converter with an MPPT controller, and a 13.5V-48Ah battery. The performance of the solar charge controller is increased by operating the PV module at the maximum power point (MPP) using a modified incremental conductance (IC) MPPT algorithm. While the traditional IC MPPT approach requires a substantial amount of code and algorithmic steps to keep the PV module at the MPP, the proposed IC achieves the same process with a reduced number of lines of code, owing to its optimized algorithmic approach. By adjusting the duty cycle of the generated Pulse Width Modulation (PWM) signal, maximum power transfer from the PV module is attained during the operation of the battery charging process. The simulation model is configured and tested in Matlab/Simulink environment under different solar data (1000 W/m2, 500 W/m2, 800 W/m2) with constant temperature (25 °C). To validate the simulations, experimental studies are conducted using the developed rapid prototype with the 32-bit embedded microcontroller in the laboratory. According to the simulation and laboratory results, the maximum power tracking performance of the traditional method was found to be 95.51%, while the proposed method achieved a ratio of 96.59% with less steady-state oscillation. The average tracking efficiency has increased by 1.13%. The proposed IC tracks the MPP more accurately and provides maximum available power for battery charging at different solar radiations compared to the traditional IC approach. For low-powered electric devices, the proposed system can be used to provide a charging infrastructure solution.

Improved Incremental Conductance MPPT Technique Designed to Addressing Drift Problem in a Photovoltaic System

Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.

Particle swarm optimization for enhanced maximum power point tracking: design and implementation in Proteus

This study introduces a photovoltaic (PV) system model tailored for PV design, incorporating a particle swarm optimization (PSO) MPPT technique to achieve optimal efficiency, swift responsiveness, and cost-effectiveness. To initiate, a PV module model is formulated within Proteus using SPICE coding. Subsequently, an experimental test setup is deployed to authenticate and validate the model. Following this, a PSO-based MPPT algorithm is proposed, which overcomes the limitations of conventional perturb and observe (P&O) and incremental conductance MPPT methods, notably reducing the reliance on mathematical divisions. To substantiate the effectiveness of the proposed approach, both methodologies are implemented on an affordable Arduino Uno platform utilizing the simulated PV module model. The outcomes highlight that the PSO-based MPPT algorithm excels in terms of rapid response (0.09 s), minimal steady-state oscillation, and an impressive 99 percent efficiency.

Modelling and performance analysis of improved incremental conductance MPPT technique for water pumping system

Utilization of Solar Photo Voltaic Cell (SPVC) has significantly increased in residential, commercial, and industrial due to freely available. From the power-voltage (P–V) curve, fluctuation has been revealed over an hour because of varying irradiance, and temperature of sun rays. Maximum power point tracking (MPPT) has been essential to track power and maximize the output from SPVC constantly. In this study, a novel Improved Incremental Conductance (IIC) MPPT technique is proposed, and integrated with a boost converter to take out the maximum power from SPVC. The output power has been provided to a brushless DC motor (BLDC). Pulse Width Modulation (PWM) commutation system has been employed to control the BLDC motor through the 3-level bridge converter. The proposed study has been implemented in MATLAB/Simulink. The complete analysis has been examined at different irradiation conditions. Power consumption, loss, and efficiency have been estimated. Simulation results have been compared with conventional incremental conductance (IC). The proposed IIC MPPT has an improved 5% of tracking efficiency than conventional MPPT techniques. The results show that the effectiveness of the proposed IIC technique has attained a steady state in varying irradiation levels. In spite of the low irradiation level of 250 W/m2, the system remains effective.

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

BLDC Motor Driven Solar PV Array Fed Water Pumping System Employing Zeta Converter

Abstract—This paper proposes a simple, cost effective and efficient brushless DC (BLDC) motor drive for solar photovoltaic (SPV) array fed water pumping system. A zeta converter is utilized in order to extract the maximum available power from the SPV array. The proposed control algorithm eliminates phase current sensors and adapts a fundamental frequency switching of the voltage source inverter (VSI), thus avoiding the power losses INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (IJSREM) VOLUME: 07 ISSUE: 09 | SEPTEMBER - 2023 SJIF RATING: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM25857 | Page 2 due to high frequency switching. No additional control or circuitry is used for speed control of the BLDC motor. The speed is controlled through a variable DC link voltage of VSI. An appropriate control of zeta converter through the incremental conductance maximum power point tracking (INC-MPPT) algorithm offers soft starting of the BLDC motor. The proposed water pumping system is designed and modeled such that the performance is not affected under dynamic conditions. The suitability of proposed system at practical operating conditions is demonstrated through simulation results using MATLAB/ Simulink followed by an experimental validation.

A Hybrid Solar Photovoltaic and Wind Turbine Power Generation for Stand-Alone System with IoT-Based Monitoring and MPPT Control

The goal of this effort is to monitor and manage a hybrid stand-alone photovoltaic (PV) and wind energy system (WES) using the Internet of Things (IoT). The suggested hybrid system uses Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) and Perturb and Observe (P&O)-based Sliding Mode Control (SMC) approaches. The cost-effective Arduino Uno board in Matlab is used to analyze the P&O and INC MPPT methods. Energy from renewable sources is stored in a battery, and the output voltage of each source is monitored using the blynk app and GSM 800c module. Simulation results validate the controller’s operation, and the performance of both MPPT algorithms is compared. Experimental outcomes demonstrate the effectiveness of the IoT-based controller and its characteristic functionalities. Overall, this proposed hybrid PV and WES configuration offers advantages such as reduced human resources, cost-effectiveness, time savings, enhanced reliability, and improved data collection and control capabilities.

A current-source DC-AC converter and control strategy for grid-connected PV applications

This paper presents a two-stage current-source DC-AC converter for grid-connected PV applications which is composed of an input step-up stage, followed by a step-down stage and an unfolding inverter. A decentralized control strategy of the DC-DC stage allows maximizing the renewable energy harvest using an Incremental Conductance MPPT algorithm and synthesizing an output current to be injected into the grid with low harmonic distortion. Double-loop PI controllers are used for the boost stage. The DC bus voltage of the buck stage is regulated using a PI controller, and an inner Proportional-Resonant (PR) controller tracks a sinusoidal reference. The PR controller proposed in this paper, includes a reduced number of resonant stages meeting the energy quality required by standards, which results in good stability margins. Finally, a SOGI-FLL algorithm synchronizes the inverter operation with the grid. Experimental results show an excellent dynamic response of the system, and the injected current complies with the IEEE Std. 1547–2018 specifications regarding harmonic content using a control law with a low computational burden.

Comparison of queen honey bee colony migration with various MPPTs on photovoltaic system under shaded conditions

Shaded conditions cause a decrease in the performance of photovoltaic (PV) systems. In this situation, the power versus voltage curve shows two maximum power points, namely local (LMPP) and global (GMPP). The main challenge for extracting the maximum power from a PV system during shading conditions is the existence of a false maximum or LMPP along with a true maximum or GMPP. Traditional maximum power point tracking (MPPT) has faced hurdles in overcoming the situation. Therefore, this paper describes the implementation of Queen Honey Bee Migration (or QHBM for short) to track GMPP of PV systems, which called QHBM MPPT. The highlight of this paper is the simulation results of QHBM MPPT on PV systems under various shading conditions.
 We implemented QHBM MPPT on a boost converter installed on a 1200 Wp PV system. We conducted a simulation using MATLAB® with five scenarios which aim to show the various shadows that PV systems might encounter in reality. The MPPT QHBM is tested repeatedly and then the average value is taken to measure performance in MPP tracking. The average value is used to calculate tracking efficiency, number of iteration or convergence time. We also compared QHBM with other methods, namely incremental conductance (IC) and Particle Swarm Optimization (PSO). The results obtained show that the QHBM and PSO MPPTs outperform the IC MPPT in terms of efficiency, convergence time and the number of iterations. IC MPPTs oscillate under shading conditions since no knowledge of GMPP. Both PSO and QHBM MPPTs know GMPP from scouts or particles, respectively. Therefore, PSO and QHBM MPPTs are better than IC MPPT in various shading cases

A novel topology for power quality improvement using EPO incremental conductance MPPT controller for SPV system with 51-level inverter

A novel topology for power quality improvement using EPO incremental conductance MPPT controller for SPV system with 51-level inverter

A Comparative Analysis of series and parallel topologies of Perturb and Observe (P&O) and Incremental Conductance (InC) MPPT Algorithms for Photovoltaic System

Photovoltaic (PV) is considered one of the most important sources of energy in the world, owing to its inherent advantages of being cheap, environmental friendliness, and energy-efficient. To increase the output power of the solar PV system, it is most important to enforce it to operate at the highest possible maximum power point tracking (MPPT) algorithm lead an important role in optimization the performance of a photovoltaic (PV) generation systems. This paper compares two widely used MPPT algorithms, namely the Perturb and Observe (P&O) method and the Incremental Conductance (IC) method, known for their low cost and eases of realization. The algorithms were evaluated based on important parameters such as voltage, current, and power output for different combinations, with and without shading. The performance of the photovoltaic array was evaluated using the MATLAB Simulink toolbox. The simulation results demonstrate that the IC method can rapidly detect the maximum power point (MPP) when irradiance changes suddenly, with faster convergence and higher accuracy than the P&O algorithm. Simulations are performed to test the controller's capability oftracking the MPP when sudden variations in weather condition

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

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.