PV Maximum Power Point Tracking Research Articles

Application of circle search algorithm for solar PV maximum power point tracking under complex partial shading conditions

Solar photovoltaic (SPV) energy exhibits a potential role in the world for generating electricity and avoiding the block out in the power system. However, the hardest task in the PV system is to track the global maxima power (GPP) instead of local peak power (LPP) under partial shading conditions (PSC). To track the GPP, a new circle search algorithm (CSA) is implemented in this paper and is executed in MATLAB/SIMULINK environment. This method is examined at various irradiation conditions and compared their performance with other studied methods in terms of tracking time, settling time and efficiency. The simulation outcome of the CSA method efficiencies 99.96% in case 1, 99.74% in case 2, and 99.94% in case 3. For checking the robustness and stability of the system statistical analysis was done on the proposed methods. It is found that the suggested method exhibits higher performance than the other methods.

A PV MPPT control method based on async-PSO and INC algorithm under shading condition

For the time being, solar energy has received considerable attention and development on account of its distinct advantages, such as rich reserves and no geographical restrictions. Nevertheless, in practical applications, the photovoltaic module is easily affected by external environments, which gives rise to a decrease in photovoltaic power. The maximum power point tracking (MPPT) technology for PV power system is an effective method to elevate the efficacy of photovoltaic electricity conversion. The frequently used control methods include the perturb and observe (P&O) algorithm and the incremental conductance (INC) method, and so forth; these methods vary tremendously in terms of the required parameters, algorithm complexity, tracking speed, tracking accuracy, hardware requirements etc. This work puts forth a MPPT control method on the basis of Async-PSO and INC algorithm to achieve a better performance in the MPPT. To reflect the change of light amplitude and temperature in a day, the temperature varies from 25℃ to 60℃ and irradiance from 450W/m2 to 900W/m2 . An extensively used mono-crystalline silicon PV module with 240W was considered as the research object to compare the capability of the recommended MPPT control method. MATLAB/Simulink software was adopted to model and simulate the algorithm. Aside from that, comprehensive comparisons were made with other MPPT methods to test and verify the recommended algorithm has significantly improved the tracking speed and accuracy at the maximum power point with smaller oscillations under various conditions.

A comparative study between deterministic and two meta-heuristic algorithms for solar PV MPPT control under partial shading conditions

• Under partial shading conditions, adequate tuning of firefly and particle swarm optimization maximum power point tracking algorithms converge to the global maximum power point irrespectively of the starting voltage value. • Under partial shading conditions, there is no guarantee that perturb and observe algorithm will converge to the global maximum power point even for different starting voltage values. • The sensitivities of the parameters of firefly algorithm in improving the performance of the algorithm are investigated to find the best possible control parameters. • Firefly algorithm based maximum power point tracking performed slightly well than particle swarm optimization based maximum power point tracking for most irradiance patterns investigated in this paper. During partial shading conditions (PSC), the PV modules in a solar PV array become reverse biased and act as a load, resulting in hotspot issues. This can significantly decrease the efficiency of the solar PV. The general way to deal with PSC is to connect bypass diodes across the non-shaded PV module. However, this will alter the uniform characteristics of the PV array, resulting in multiple power peaks i.e., a multi modal landscape. The conventional gradient-based Perturb and Observe (PnO) algorithm generally used for maximum power point tracking (MPPT) in solar PV is ineffective in finding the maximum power during PSC because it is prone to converge to local optimum due to its nature of searching in a multimodal landscape. However, in this article an attempt is made to show how the PnO behaves in a multimodal landscape considering different starting points. Robust stochastic algorithms that are based on a population of search agents are used to guarantee convergence to the global MPP. In this paper, the maximum power point under PSC was tracked using two meta-heuristic algorithms namely, particle swarm optimization (PSO) and the firefly algorithm (FA). The performances of these algorithms in tracking the maximum power point are evaluated. The efficiency, standard deviation (STD), and root mean square error (RMSE) of the PSO and FA algorithms are compared to those of the PnO algorithm. The PSO was found to have the lowest RMSE, and the FA had the lowest STD. The efficiency of the PSO and FA were relatively the same. Simulation results show that PSO and FA-based MPPT algorithms can efficiently track the global maximum power point (GMPP) irrespectively of the starting points. On the other hand, PnO is shown to be unreliable under PSC even with different starting points.

Review of PV array modelling, configuration and MPPT techniques

ABSTRACT This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation

The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with $6\times3$ PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.

Robust configuration and intelligent MPPT control for building integrated photovoltaic system based on extreme learning machine

Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy.

PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions

PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions

An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking

Aiming at the large error of the traditional constant control method in predicting the maximum power of solar UAV, this paper proposed an improved mind evolutionary algorithm combined with BP neural network (BPNN), in which the improved mind evolutionary algorithm optimizes the BPNN. The optimized model is used to predict the voltage at the maximum power of the panel in the UAV. The constant voltage parameter based on the conventional constant pressure control algorithm is replaced by this value. At the same time, a new control simulation model of constant voltage solar panel maximum power tracking based on the improved mind evolution algorithm optimize BPNN was built. At last, the algorithm was simulated and validated in the MATLAB/Simulink environment. The simulation results show that this algorithm is better than simply using BPNN and genetic algorithm optimize BPNN stability and higher accuracy.

Coordinated control of distributed generation system with improved generalised enhanced phase locked loop control algorithm

Here, a distributed generation system (DGS) is proposed that allows the different distributed energy resources to operate in coordination with others to provide the electricity in rural areas. Two individual controls are used in the DGS: (i) solar maximum power point tracking (MPPT) and the battery protection control, and (ii) voltage source converter (VSC) control. The proposed VSC control algorithm is based on an improved generalised filter algorithm for power quality improvement of the DGS. This control strategy effectively regulates the active power flow and provides the harmonics mitigation, reactive power compensation, and load balancing in the DGS with low steady-state error and fast dynamic response under high impulsive noise with DC-offset component. The solar MPPT control is designed on the basis of battery state of charge (SOC). In this control, the solar PV array operates in limited power point tracking (LPPT) after the storage battery surpasses the maximum SOC limit. In LPPT control, the solar PV MPPT operates in the voltage source operating region of PV array characteristic, thereby, reducing the power generation and avoids the overcharging of the storage battery. The proposed DGS performance is validated through test results under dynamics and steady-state conditions.

Development and validation of a real time flow control integrated MPPT charger for solar PV applications of vanadium redox flow battery

In this paper a real time flow control integrated solar PV Maximum Power Point Tracking (MPPT) charge controller for Vanadium Redox Flow battery (VRFB) is developed and its performance is demonstrated under practical dynamic insolation profiles. Unlike the conventional Lead acid and Li-ion batteries the major challenge in designing the solar MPPT charger for VRFB lies in simultaneous control of charging current and corresponding flow rate for maintaining the maximum overall system efficiency of the VRFB besides achieving the maximum charging efficiency. In this work, the usual Perturb & Observe (P&O) algorithm for designing MPPT charger for conventional batteries has been significantly modified in case of VRFB charge controller by including the real time control of flow rate along with the charging current. The proposed charging algorithm has been validated by a practical 1 kW 6 h VRFB system operation. The modified MPPT based three stage constant current constant voltage (CC-CV) charging topology is found to be the most efficient for VRFB charging from solar PV. Solar MPPT charging of VRFB with a constant flow rate leads to premature thermal shut down of the charge controller resulting incomplete charging of VRFB which is prevented by the use of dynamic flow control integrated solar PV MPPT charging. Two practical case studies of sunny weather and cloudy weather conditions have been adopted for validation of the integrated MPPT charge controller. The proposed VRFB charging system is a generalised one and thus can be very useful for large scale VRFB application in solar PV power systems.

A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions

Mounting demand for energy and accumulation of hazardous nuclear wastes has invited the world to reduce their addiction to conventional power generation. Having established its global potential to replace fossil fuels, solar PV is increasingly installed worldwide. Yet PV is ill-starred due to its non-linear characteristics and hence, PV systems employ Maximum Power Point (MPP) controllers. Even though enough research progress is kept at the forefront in the MPP research area, the necessity to improvise the existing methods becomes mandatory to improve the energy conversion efficiency. Hence, in this paper, a global maximum power point tracking (GMPPT) algorithm based on Leader Particle Swarm Optimization (LPSO) is proposed for PV system. Apart from the conventional PSO, exclusive mutations strategies are employed to obtain the global best leader that helps the algorithm to differentiate between local and global MPPs. The simulation results are validated under numerous test conditions in which partial shading conditions are analyzed over a wide extent and in validation, the results of LPSO method is compared with PSO and P&O methods as well. Interestingly LPSO method has an inherent exploration and exploitation quality that made it to produce hasty converge within 0.5s under any shade conditions. Acknowledging to the promise shown in simulation, the mutation based LPSO method has managed to excel even in hardware experimentation which in turn justifies its suitability for MPPT application.

A New Modified Artificial Neural Network Based MPPT Controller for the Improved Performance of an Asynchronous Motor Drive

Objectives: To improve the performance of asynchronous motor drive, the proposed Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) controller has been used to fed the asynchronous motor drive with the obtained output voltage and currents of PV MPPT. Methods and Analysis: DC-DC boost converter and space vector modulation technique inverter are used to provide the required supply to the load. The proposed ANN based MPPT improves the system efficiency even at abnormal weather conditions. Findings: Solar energy is an important alternative out of the various renewable energy sources. On an average the sunshine hour in India is about 6hrs per day also the sun shines in India is about 9 months in a year. To generate electricity from the sun, the Solar Photo Voltaic (SPV) modules are used. The SPV comes in various power outputs to meet the load requirements. Maximization of power from a solar photo voltaic module is a special case to increase the efficiency of the system. The proposed artificial neural network (ANN) based MPPT controller is used to track the maximum power. DC-DC boost converter and space vector modulation technique inverter are used to provide the required supply to the load. The proposed ANN based MPPT improves the system efficiency even at abnormal weather conditions. Torque and current ripple contents have been reduced to a large extent with the help of proposed ANN based MPPT for an asynchronous motor drive. Also the better performance of an asynchronous motor drive is analyzed by the comparison of existed conventional and proposed MPPT controller using Matlab-simulation results. Improvement: Improvements in torque and current ripple and better speed performances are clearly analyzed with the help of practical validations. And also few of the observations are carried out and tabulated.

Research on Control Strategy of PV MPPT and Grid-Connected Single Phase Converter Based on Two Stage Topology

This paper studied the control strategy of single-phase two-stage grid-connected photovoltaic generation system. The two-stage topology includes the boost converter and single phase inverter. The function of maximum power point tracking (MPPT) is achieved in the boost circuit by IC (Increment Conductance) method. The inverter circuit adopted the double-closed loop control, a DC voltage outer loop control for power balance with PI correction and an AC current inner loop control for feedback designed to ensure the current quality and stabilize the DC voltage. The inner loop used the novel quasi-Proportional-Resonant (PR) controller, which has a better steady tracking characteristics and stronger anti-interference ability to the disturbance. Finally, a simulation system is built with Matlab/Simulink in this paper to verify the validity of the MPPT algorithm and the quasi-PR control strategy.Keywords：Photovoltaic Generation System;Maximum Power Point Tracking; Grid-connected Inverter; quasi-Proportional-Resonant (PR) controller

배터리 내장형 초소형 태양광 장치용 PV MPPT 및 충방전 제어 알고리즘

To increase the efficiency of the photovoltaic, almost photovoltaic appliances are controlled by Maximum Power Point Tracking(MPPT). Existing most of the PV MPPT techniques have used power which multiplies sensed output current and voltage of the solar cell. However, these algorithms are unnecessarily complicated and too expensive for small and compact system. The other hand, the proposed MPPT technique is only one sensing of the MPPT converter’s output current, so there is no need to insert another sensors of battery side. Therefore, this algorithm is simpler compared to the traditional approach and is suitable for low power solar system. Further, the novel proper charge/discharge algorithm for the battery with PV MPPT is developed. In this algorithm, there is CC battery charge mode and load discharge mode of the PV cell & battery dual. Also we design current control to regulate allowable current during the battery charging. The proposed algorithm will be applicable to battery included solar cell applications like solar lantern and solar remote control car. Finally, the proposed method has been verified with computer simulation.

Modeling of Maximum Power Point Tracking Controller for Solar Power System

In this paper, a Maximum Power Point Tracking (MPPT) controller for solar power system is modeled using MATLAB Simulink. The model consists of PV module, buck converter, and MPPT controller. The contribution of the work is in the modeling of buck converter using equation model approach rather than circuit model one. The buck converter model is developed using equation model that allowing the input voltage of the converter, i.e. output voltage of PV is changed by varying the duty cycle, so that the maximum power point could be tracked when the environmental changes. From the experiment, the developed model comforms with the circuit model provided by MATLAB Simulink Power Simulation. Furher, the simulation results show that the developed model performs well in tracking the maximum power point (MPP) of the PV module using Perturb and Observe (P&O) Algorithm .

PV Maximum Power‐Point Tracking by Using Artificial Neural Network

In this paper, using artificial neural network (ANN) for tracking of maximum power point is discussed. Error back propagation method is used in order to train neural network. Neural network has advantages of fast and precisely tracking of maximum power point. In this method neural network is used to specify the reference voltage of maximum power point under different atmospheric conditions. By properly controling of dc‐dc boost converter, tracking of maximum power point is feasible. To verify theory analysis, simulation result is obtained by using MATLAB/SIMULINK.

MPPT Control and Simulation of PV Power Systems

Maximum Power Point Tracking (MPPT) is an important means to improve efficiency of PV power systems. The simulation model achieving MPPT of independence PV power system, BOOST circuit as the core, is established. The model can simulate the implementation of PV arrays and MPPT without exact characteristics and parameters of the internal circuit. The simulation results show that the closed-loop system can track the maximum power point of PV and work stably in the maximum power point. It can fast track to the corresponding maximum power point in the case of environment changes. The requirements of steady and accurate of auto follower system are achieved.

On the testing, characterization, and evaluation of PV inverters and dynamic MPPT performance under real varying operating conditions

AbstractThe increasing number of photovoltaic inverters that are coming on to the PV market stresses the need to carry out a dynamic characterization of these elements and their maximum power point tracking (MPPT) algorithms under real operating conditions. In order to make these conditions repeatable at the laboratory, PV array simulators are used. However, actual simulators, including the commercial simulators, recreate only a single or small set of PV array characteristic curves in which quite commonly theoretical calculations are included in order to simulate irradiance and temperature artificial variations. This is far from being a recreation of the real and long dynamic behavior of a PV array or generator. The testing and evaluation of the performance of PV inverters and MPPT algorithms has to be carried out when the PV system moves dynamically according to real operating conditions, including processes such as rapidly changing atmospheric conditions, partial shadows, dawn, and nightfall. This paper tries to contribute to the analysis of this problem by means of an electronic system that both measures the real evolution of the characteristic curves of PV arrays at outdoor operation and then recreates them at the laboratory to test PV inverters. This way the equipment can highlight the different performances of PV inverters and MPPT techniques when they operate under real operating conditions. As an example, two commercial inverters are tested and analyzed under the recreated behavior of a PV generator during 2 singular days that include processes of partial shading and fast irradiance variations. Copyright © 2007 John Wiley & Sons, Ltd.