Enhancing grid connected photovoltaic systems performance using a bio-inspired MFB algorithm for efficient maximum power point tracking

Novel fast and high accuracy maximum power point tracking method for hybrid photovoltaic/fuel cell energy conversion systems

In this study, a novel and fast maximum power point tracking (MPPT) algorithm for a photovoltaic generation system is proposed. The main idea is to remove the random number in the voltage calculation equation of the conventional cuckoo search method. The advantages of the proposed method include 1) fast maximum power point (MPP) tracking speed and high MPP tracking accuracy under both uniform insolation and partially shaded conditions (PSCs), 2) simple MPPT structure, and 3) consistent solution can be achieved with only three particles and only one parameter is required to be tuned. In order to validate the effectiveness and correctness of the proposed method, both simulation and experiments are carried out on a 300 W prototyping circuit. According to the experimental results, the proposed MPPT method can improve the tracking time by 46.42% and 11.76% comparing to conventional perturb and observe (P&O) and variable-step P&O technique, respectively. In addition, the proposed method can successfully track the global MPP in 249 of 252 different PSC patterns. The tracking accuracies under both uniform irradiance and PSC conditions are all higher than 99.8%.

In the future solar energy will become a very important green energy. Maximum power point (MPP) tracking (MPPT) technology is widely used in solar photovoltaic (PV) systems to generate peak power for PV arrays that depend on solar irradiation and ambient temperature. Literature[1] shows that the maximum power point tracking (MPPT) technology can improve the efficiency of photoelectric conversion by more than 20% and the economic cost is relatively low. Based on PROTUES, the output characteristics of PV arrays under different shadow conditions are simulated and the rule between local maximum power point and open circuit voltage is obtained. Among all the MPPT strategies, the incremental conductance (INC) algorithm and perturb and observe (P&O) algorithm are widely used due to the high tracking accuracy at steady state and easy implementation.In this paper, a novel adaptive variable step size INC MPPT method is proposed according to the above rule. This algorithm not only has the advantage of INC, but also can automatically adjust the step size to track the maximum power point of PV arrays. Compared with the adaptive perturb and observe (P&O) algorithm, the proposed approach can effectively improve the MPPT steady-state response speed and accuracy simultaneously. The theoretical analysis and design principle of the proposed algorithm are presented in this paper. The simulation results show that the algorithm can accurately track the maximum power point (MPP) with shadow. The average tracking time is only 0.13 seconds, and the power tracking efficiency reaches 98%.

Neural-network-based maximum power point tracking methods for photovoltaic systems operating under fast changing environments

A multi-input single inductor dual-output boost converter for multi-junction photo-voltaic (MJ-PV) energy harvesting is presented. The fast maximum power point (MPP) tracking of each photo-voltaic (PV) sub-cell for irradiation change and the high MPP tracking (MPPT) efficiency are obtained using a dual-loop MPPT control. The implemented discontinuous conduction mode (DCM) of converter operation minimizes cross-regulation among MJ-PV sub-cells. A dual-output path provides regulation at load and stores the extra harvested energy to a battery. A prototype board with the proposed system is implemented. The achieved peak power efficiency is 83% and the MPPT efficiency is 95%. This system can also be used for cell-level parallel-connected PV solar system.

The integration of photovoltaic (PV) modules into dc microgrids relies on the capabilities of maximum power point (MPP) tracking and output voltage regulation (OVR). Under partial shading or mismatches between PV submodules, accurate global MPP tracking and efficient OVR are challenging processes. For global MPP tracking, the distributed MPP tracking is a potential solution but comes at the expense of increased system complexity. For the OVR, operating the PV module in its current source region would result in rather high power losses in the converter circuit and, thus, in increased heat accumulation. The existence of multiple current source regions in the mismatched PV characteristics complicates the control design. The novel digital controller for module integrated converters developed here supports the effective integration of mismatched and partially shaded PV modules while employing a minimal number of sensors. The proposed double-stage global MPP tracking algorithm realizes fast and accurate MPP tracking with neither periodic scanning nor oscillations around the optimum. For the OVR, the algorithm targets the reduction of the converter power losses through effective allocation of the PV operating point. A prototype of the control is realized as a proof of concept.

For the optimal operation of photovoltaic system, The MPPT (Maximum Power Point Tracking) control unit is an essential part for the photovoltaic system. In addition to the protection function, this command ensures the continuation of the maximum power point (MPPT) and allows the PV generator to deliver its maximum power regardless of the variation in climatic conditions (sunshine and) temperature). This work intends to provide an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and the P&O algorithm. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB/ Simulink, we used in The proposed ANN two inputs which are irradiation and ambient temperature, and one output is the optimum voltage of the PV system. The proposed ANN was analyzed under different irradiation conditions. The response of the proposed ANN for MPPT controllers found to be lesser oscillation at MPP and faster tracking response compared with the P&O algorithm. Comparisons of MPPT with P&O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.

<span>The main components of a Stand-Alone Photovoltaic (SAPV) system consists of PV array, DC-DC converter, load and the maximum power point tracking (MPPT) control algorithm. MPPT algorithm was used for extracting maximum available power from PV module under a particular environmental condition by controlling the duty ratio of DC-DC converter. Based on maximum power transfer theorem, by changing the duty cycle, the load resistance as seen by the source is varied and matched with the internal resistance of PV module at maximum power point (MPP) so as to transfer the maximum power. Under sudden changes in solar irradiance, the selection of MPPT algorithm’s sampling time (T<sub>S_MPPT</sub>) is very much depends on two main components of the converter circuit namely; inductor and capacitor. As the value of these components increases, the settling time of the transient response for PV voltage and current will also increase linearly. Consequently, T<sub>S_MPPT </sub>needs to be increased for accurate MPPT and therefore reduce the tracking speed. This work presents a design considerations of DC-DC Boost Converter used in SAPV system for fast and accurate MPPT algorithm. The conventional Hill Climbing (HC) algorithm has been applied to track the MPP when subjected to sudden changes in solar irradiance. By selecting the optimum value of the converter circuit components, a fast and accurate MPPT especially during sudden changes in irradiance has been realized.</span>

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.

Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems

The Maximum Power Point Tracking (MPPT) is a very important function in a Solar Photovoltaic (SPV) system. While previous research has been focussed on optimizing the performance of the MPPT, there is further scope to improve upon the MPPT efficiency without compromising on the complexity of the MPPT technique in terms of the algorithm and hardware requirements. The research work presented in this paper aims to address this gap. The paper presents two novel MPPT schemes which are the proposed Perturb and Observe (P&O) and proposed Incremental Conductance (IC) methods based on two-step control and direct duty ratio perturbation. The proposed techniques are efficient, computationally less complex and hardware minimal than previous study in this field. For verification, simulation has been performed for extensive irradiation profiles of Standard Test Conditions (STC), rapidly changing and gradually changing insolation conditions which are representative of the boundary cases. Results of the proposed MPPT methods are compared with that of conventional MPPT methods. The results show that proposed MPPT schemes have excellent tracking efficiency and dynamic response with respect to previous research.

태양전지의 출력 특성은 비선형이고 온도와 일사량에 많은 영향을 받는다. 최대전력점 추종 제어는 태양광 발전의 출력을 최대로 하기 위해 사용되는 제어기법이다. 태양광발전 시스템의 출력 및 효율을 증가시키기 위해 더욱 우수한 최대전력점 추종 제어기법이 필요하다. 본 논문에서는 태양광 발전 시스템의 효율을 개선하기 위해 일사량을 고려한 새로운 최대전력점 추종 제어 알고리즘을 제시한다. 제시한 알고리즘은 종래의 P&O방법과 CV 방법을 혼합한 것이며, PSIM 시뮬레이터를 통하여 종래의 MPPT 알고리즘과 다양한 일사량 조건에서 성능시험을 비교하였다.<BR> 제시한 알고리즘은 종래의 알고리즘에 비해 출력의 자려진동없이 다양한 일사량에서 우수한 성능을 나타냈다. 이로서 본 논문에서 제시한 HB 방법의 최대전력점 추종 제어 알고리즘의 타당성을 입증하였다.

This paper proposes a novel linearized maximum power point tracking (MPPT) method for a photovoltaic system. This method involves first the linearization of the photovoltaic (PV) module and the DC-DC converter. Hence, some of the difficulty level in switching control due to highly non-linear and time-variant characteristics of PV module is removed. Further, for an efficient maximum power point tracking (MPPT) performance of the PV system, the MPPT operation can be accomplished efficiently with a simple PI-controller. The proposed linearized MPPT method has been tested using one mono-crystalline (BP350) and other multi-crystalline (SSI-M6-205) solar panels. The solar panels have been tested at varying environmental conditions like solar radiations of 200 to 1000 watt/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and temperatures of -25 to 50°C considering various practical operating regions of the solar module. The effectiveness of the proposed linearized maximum power point tracking method has been validated by experimental results of Opal-RT.

This paper proposes a reduced sensor-based nonlinear maximum power point tracking (MPPT) controller for grid-integrated photovoltaic (PV) systems operating under rapidly changing climatic conditions. Unlike conventional approaches that require costly irradiance sensors, the proposed method employs a mathematical irradiance estimation model and a radial basis function neural network to generate optimal reference voltages, which are then enforced by a backstepping nonlinear controller. This two-stage design enables fast and robust MPPT while maintaining DC-link stability and grid power quality. The controller was validated on a 100 kW MATLAB/Simulink-based grid-tied PV system with a DC–DC boost converter and inverter. Under step changes in irradiance, the system tracked the new MPP in as little as 7 ms, while restoring DC-link stability (500 V) within 42 ms. Under continuously varying conditions, it maintained synchronization with the grid and achieved a total harmonic distortion (THD) below 0.1%. Comparative results against Perturb & Observe (P&O), Improved Differential Evolution (IDE), and Particle Swarm Optimization (PSO) demonstrated that the proposed method achieved the highest PV-side power yield (80.41 kW vs. 79.71 kW for P&O, 73.44 kW for IDE, and 59.34 kW for PSO), the highest grid-side active power delivery (78.69 kW vs. 77.98 kW, 72.14 kW, and 58.29 kW respectively), and the lowest integral absolute error of DC-link voltage (IAE = 10.6156). These results confirm that the controller provides faster convergence, improved voltage regulation, and superior grid stability compared to state-of-the-art MPPT methods, making it a promising solution for real-world deployment in large-scale PV systems.

This paper presents the comparative analysis of most commonly used Maximum Power Point Tracking (MPPT) techniques viz Open Circuit Voltage (OCV), Perturb and Observe (PnO) and Incremental Conductance (INC) methods that are capable of extracting maximum power from the PV generation system with Soft Switched Interleaved Flyback(SSIFB) converter. The OCV technique is an indirect MPPT method that tracks the Maximum Power Point (MPP) using empirical data or mathematical expressions with numerical corrections and approximations. The direct methods such as PnO and INC techniques measure the actual instantaneous values of PV voltage and PV current to seek the MPP. The steady state performance of each of the MPPT algorithms on PV-SSIFB system under different solar irradiations is compared in terms of accuracy, MPPT efficiency and tracking speed. MATLAB/SIMULINK software is used to simulate and investigate the suitability and limitations of the PV - SSIFB system implemented with MPPT algorithm. The simulation results prove that the OCV MPPT method provides an effective MPP tracking at low irradiations and INC MPPT method offers the better steady state performance at medium and higher irradiations.