A firefly optimized fast extreme learning machine based maximum power point tracking for stability analysis of microgrid with two stage photovoltaic generation system

Photovoltaic (PV) modules subject to partial shading (PS) or inhomogeneous irradiation drastically decrease the PV string power output. An algorithm composed of maximum voltage point tracking (MVPT) and maximum power point (MPP) tracking (MPPT) is presented to restore part of the power loss caused by PS. The MVPT can simultaneously drive the operating point of series PV modules toward their respective MPPs and guarantee power peak uniqueness. The MVPT is performed by means of a controllable current transformer (CCT) disposed at the terminal of each PV module permitting a compatible current in the series path of a PV string. The CCT output current can be regulated using a dependent current source according to the MPPT algorithm. An appropriate CCT current determined by the MPPT algorithm can avert undue CCT activation and consequently reduces the switching and copper losses of the converter circuit. Experimental results validate that the proposed strategy can balance the PV modules' output current subject to PS and facilitate convergence to the theoretical global MPP.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

The power output of photovoltaic (PV) cells is influenced by the environmental conditions, particularly by solar irradiation level. The irradiation level of light falling on all the cells in the module or the PV modules in an array is uneven under the cloudy weather conditions; this produces multiple peaks in the output of PV modules. It is essential to have a model of PV module in which the partial shading effect can be analyzed so that an effective Maximum Power Point Tracking (MPPT) system can be implemented for a PV system. This paper presents the modeling and performance analysis of PV modules under partial shaded conditions. A generalized PV module model is developed in MATLAB based on the mathematical equation of PV cell. The model is simulated for analysis of partially shaded cells in the PV module and also for partial shaded modules in the PV string for various weather conditions. The obtained results are satisfactory and confirm the accuracy of the developed PV module model. The developed model can be used for simulation and analysis of any PV modules under partially shaded conditions and also for development of PV power system with MPPT.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

The main aim of this paper is to propose an integrated solar mesh photovoltaic energy generating system (SPV) which includes an SPV, DC-DC boost converter and voltage source converter (VSC), inductors interface, corrugated filter, and three-phase system that would provide various linear and non-linear applications. The currents of the reference grid are forecasted with the power balancing theory to assess the three branches of the voltage source converter (VSC). The hypothesized SPV energy-producing system offers unitary reactive factor and power allowance for voltage control in a three-phase 3φ distribution system. A DC PI voltage regulator governs the VSC's intermediate circuit tension (proportional integral), The maximum power point tracking (MPPT) with an ace in power point is accomplished with the DC-DC boost converter within a two-stage Solar photo voltaic power producing system. The network-connected SPV electrical generation system is designed and analyzed in MATLAB.

A novel population based maximum point tracking algorithm to overcome partial shading issues in solar photovoltaic technology

Photovoltaic (PV) systems are increasingly used as a sustainable energy source, but their efficiency is significantly affected by environmental factors such as shading and temperature variations. This study proposes a novel Maximum Power Point Tracking (MPPT) algorithm based on the fusion of Support Vector Regression (SVR) and Incremental Conductance (INC) techniques, designed to enhance the performance of PV systems under partial shading conditions. The SVR model is employed to predict the power output of the PV system based on voltage and current measurements, leveraging its capability to handle non-linear relationships with high accuracy. The INC method is then integrated with the SVR predictions to dynamically adjust the operating point of the PV array, ensuring rapid and precise tracking of the maximum power point (MPP). The algorithm was implemented in MATLAB, and comprehensive simulations were conducted to evaluate its performance. Simulation results demonstrate that the SVR-INC fusion algorithm significantly improves the accuracy and stability of MPPT under varying shading conditions. The algorithm effectively reduces the tracking time to the MPP and minimizes power losses caused by partial shading. Compared to traditional MPPT methods, the proposed algorithm shows enhanced robustness and faster convergence, making it highly suitable for practical applications in real-world PV systems. Furthermore, the integration of SVR with INC provides a robust framework for dealing with the complexities of $P V$ power generation. The algorithm’s ability to adapt to changing environmental conditions ensures that the PV system operates at its maximum efficiency. The study also discusses the optimization of SVR parameters and the importance of datadriven approaches in enhancing MPPT performance. In conclusion, the proposed SVR-INC fusion MPPT algorithm offers a promising solution for improving the efficiency and reliability of PV systems under partial shading. The findings highlight the potential of this approach for future advancements in PV technology, contributing to the development of more efficient and resilient solar energy systems..

Various peaks in the Power vs. Voltage (P-V) characteristic curve of Photovoltaic (PV) arrays appear due to the effects of partial shading. Conventional Maximum Power Point Trackers (MPPTs) may be unable to track the Global Peak (GP) among the Local Peaks (LP). For tracking the GP under partial shading conditions it is important to develop special MPPTs. In commercially available MPPTs, blind scanning of the P-V curve is performed in order to find the GP. This type of scanning takes various seconds during which the array is not operated at the MPP. In this paper an MPPT algorithm is proposed which quickly takes the operating point of a PV to the GP under uniform and partial shading conditions. The presence of partial shading and the number of peaks is determined from the operating voltage of the modules. The voltage at which the peaks in the P-V curve are located is also determined from the module voltages.

Photovoltaic (PV) modules are devices that transform photon energy into electrical energy. The output power of the PV modules is influenced by the intensity of solar radiation and the ambient temperature. Non-uniform shading can cause variations in the extent of sunlight absorbed by PV modules, resulting in a decrease in power output. Maximum power point tracking (MPPT) techniques are employed to optimize the power output of PV modules by operating them at their maximum power point (MPP). The main objective of the study is to investigate the performance analysis of Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) MPPT strategies in uniform and partially shaded conditions with equally and unequally different irradiance differences. Simulation studies were conducted on the PV circuit model using Matlab/Simulink, and the results were evaluated. MPPT algorithms are compared based on their tracking efficiency and convergence speed when solar radiation conditions vary. The findings of the simulation indicate that the P&O is unable to determine global MPP and gets trapped in one of the local MPPs. However, the PSO is very effective in tracking MPP under different partial shading patterns with more than 96% tracking efficiency. In the first partial shading configuration where the sunlight intensity of the PV modules is uniformly distributed, the PSO technique has reduced steady-state oscillations around the MPP. However, the P&O technique demonstrates superior response time and convergence speed in comparison to the PSO technique.

This paper focuses on the performance evaluation of grid-connected photovoltaic (PV) inverters in terms of static maximum power point tracking (MPPT) efficiency. A simple test-bench set-up is proposed to accurately analyze the behavior of commercial inverters applied to PV plants under partial shading conditions. The system performs a real-time monitoring of the operating point of the PV plant during the MPPT and compares the measured data to a traced I-V curve with the aim of distinguishing the dc power losses induced by shading from those ascribable to MPPT failures. A commercial inverter is tested for a PV string under sunny and partial shading conditions as a case study.

K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems

With the increasing penetration of renewable energy such as photovoltaic (PV) in the power system, the grid shows the characteristics of low inertia and low short circuit ratio (SCR), which seriously deteriorates the anti-interference ability of the system. The output characteristic of the PV generation system is determined by the system connecting converter (SCC). In order to protect the PV system from system disturbance while trying to provide necessary support during system contingency, how to select the appropriate control strategy of the SCC to become a crucial problem that has received much attention. In this paper, a hysteretic switching control strategy with the combining of the vector current control (VCC) and power synchronization control (PSC) is proposed to provide continuously operating capability of the PV generation system during the system strength continuous and large-scale change. Firstly, two PV SCC control strategies, VCC and PSC, are analyzed under different system strengths by small-signal stability analysis. Then, based on the obtained critical short circuit ratio (CSCR), a hysteretic switching control strategy is developed to realize a smooth switch between the VCC and PSC during the system strength change. A PV generation system connected to the modified IEEE 33 bus system is established in PSCAD/EMTDC to verify the effectiveness of the proposed hysteretic switching control strategy. The simulation results indicate that the application of the hysteretic switching strategy proposed in this paper can keep the PV generation system work stability during the system contingency.

The proposed work deals with the implementation of a control approach for an active power transfer between solar photovoltaic (PV) array and grid/ load alongwith power quality improvement by eliminating harmonics and compensating reactive power required by the load in the distribution system. The PV array is directly connected across the DC link of a voltage source converter (VSC). The power is transferred to the grid/ load through VSC, which performs the function of distribution static compensator (DSTATCOM) by injecting compensating currents in the proposed system. The PV array is operated at maximum power by employing the usage of perturb and observe (P&O) maximum power point tracking (MPPT) approach in order to extract the available maximum power from the solar PV array. A feed-forward term for the solar PV array power, is incorporated for the enhancement of dynamic response. The prominence of the control algorithm lies in the efficient switching of VSC, by generating reference grid currents, which are obtained through indirect current control technique. The recursive digital filter is utilized for processing the load currents and extracting the active power component of the load currents. Therefore, the active power component of load currents obtained, are used for generating the reference grid currents in the proposed system. A prototype of the system is developed in the laboratory and its performance is studied for varying loads, changing solar insolation, and voltage swell and voltage sag conditions.

The study was intended to eliminate the noise in three-dimensional transvaginal ultrasound (3D-TVS) images and improve the diagnostic accuracy in intrauterine adhesion (IUA). The extreme learning machine (ELM) algorithm was introduced first for statement. One hundred and thirty cases of suspected IUA patients were taken as the research subjects. The denoising effects of ELM algorithm were evaluated in terms of mean square errors (MSE), peak signal-to-noise ratio (PSNR), and running time, and its diagnostic efficiency of IUA was identified from precise, specificity, and sensitivity. Furthermore, the support vector machine (SVM) algorithm was introduced for comparison. It was found that the MSE and PSNR of the ELM algorithm were 0.0021 and 64.5, respectively, and its average operation time was 11.22 ± 0.89s, that the MSE values of SVM algorithm and ELM algorithm were 0.0045 and 0.0021 and the PSNR values were 52.3 and 64.5, respectively, and that the average running time of SVM algorithm was 16.35 ± 1.33s, and the average running time of ELM algorithm was 11.22 ± 0.89s, superior to the SVM algorithm in denoising effects. Moreover, the ELM algorithm showed excellent diagnostic efficiency for patients with various degrees of IUA. In conclusion, ELM can effectively eliminate noise in 3D-TVS images and demonstrates excellent diagnostic efficiency on IUA, which is worthy of clinical application.

Objectives: Climatic perturbation and environmental factors affect solar power generation. When these solar panels are connected to the grid it causes power quality issues such as voltage harmonics, voltage distortion, etc. These problems are addressed in this paper using a distributed MPPT algorithm. Methods: Multilevel inverters have made remarkable contributions to renewable energy, high voltage, and other high-power applications. Hence, a single-stage Cascaded Seven-Level Modular Multilevel Converter (CSLMMC) is used to address the issues. Zero Sequence Voltage (ZSV) control is proposed for producing balanced power during varying irradiance conditions. The proposed method is simulated using MATLAB SIMULINK software. Partial shading is conquered by using distributed MPPT. Findings: The simulation of the proposed system is performed for a three-phase system. The three-phase Cascaded Modular Multilevel Converter is connected to the grid through two loads which are controlled by a Zero Sequence Voltage Controller. Power quality parameters such as current, harmonics, and power are analyzed for the proposed system. Novelty: THD analysis for a five-level cascaded H bridge converter and the proposed systems are compared and presented. Keywords: Photo voltaic, Cascaded seven-level modular multilevel converter, Zero sequence control, Maximum power point tracking, Voltage Source Converter