Leaky-Least-Logarithmic-Absolute-Difference-Based Control Algorithm and Learning-Based InC MPPT Technique for Grid-Integrated PV System

Abstract

This work introduces a novel Leaky Least Logarithmic Absolute Difference (LLLAD) based control algorithm and Learning based Incremental Conductance (LIC) MPPT (Maximum Power Point Tracking) algorithm, for grid-integrated solar PV (Photovoltaic) system. Here, a three-phase topology of grid-integrated PV system is implemented, with the nonlinear/linear loads. Proposed LIC technique is an improved form of an Incremental Conductance (InC) algorithm, where inherent problems of traditional InC technique like steady-state oscillations, slow dynamic responses and fixed step size issues, are successfully mitigated. The prime objective of proposed LLLAD control is to meet the active power requirement of the loads from generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonics filtering. Moreover, when solar irradiation is zero, then the DC link capacitor and VSC, act as DSTATCOM (Distribution Static Compensator), which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype, in solar insolation variation conditions, unbalanced loading, as well as in different grid disturbances such as over and under-voltage, phase imbalance, harmonics distortion in the grid voltage etc. Test results have met the objectives of proposed work and parameters are under the permissible limit, according to the IEEE-519 standard.

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

“…However, elevated oscillations are introduced in search of the best solutions. Kumar et al 35 developed the Leaky Least Logarithmic Absolute Difference (LLLAD) approach that was integrated with a learning‐based L&C MPPT algorithm, which is used in the grid‐connected PV system. However, the operational burden is very low, but the process of the LLLAD control is not suitable for the transient condition because the grid is changed in accordance with the frequency.…”

An Adaptive Model Predictive Controller for Current Sensorless MPPT in PV Systems

“…Using equations (3) to (14) directly for MPPT implementation would require multiple sensors for the PV voltage, PV current, and the output voltage. Without loss of generality, the load in the expressions (3)-(14) could represent a model-based expression for any load-side connected component, as discussed in [38].…”

“…the system reaches the MPP [13]. These techniques may exhibit large output power oscillations around the MPP and slow settling time in response to step changes [14].…”

An Adaptive Resistance Perturbation Based MPPT Algorithm for Photovoltaic Applications

“…Though the tracking efficiency is improved but tracking speed and accuracy is still challenging to track the new mpp, at sudden varying irradiance condition. Some researchers focused on mathematical based theorem for mppt algorithms [16][17][18][19][20][21][22]. In [16], the authors modified a conventional INC algorithm by applying a residual theorem; here the fluctuation around the MPP have been minimized to apply a minimum residue values of algorithm by setting a proper gain value of controller.…”