Comparison between Kalman filter and incremental conductance algorithm for optimizing photovoltaic energy

Abstract

The purpose of this paper is to present a performance comparison between two maximum power point tracking algorithms. These two algorithms are incremental conductance (INC) which is an improved version of the perturb and observe algorithm, and the second algorithm is the Kalman filter applied to a photovoltaic system. In this work, a photovoltaic panel is modeled in PSIM tool; a Boost converter controlled by the maximum power point tracker is put between the PV panel and the load. Then the two algorithms are implemented by using C language and C block provided by PSIM tool. Next, several tests under stable and variable environmental conditions are made for the two algorithms, and results show a better performance of the Kalman filter compared to the INC in terms of response time, efficiency and steady-state oscillations.

Analyses of MPPT Algorithms in Real Test Conditions

“…The rule base used to find the output is shown in the table of Fig. 5, where the reference voltage for the PI controller (Vref) is calculated based on (8). In the defuzzification stage, the fuzzy logic controller output is converted to a controller variable.…”

“…Extensive research has been done during last decade. Dozens of algorithms have been investigated [4,5], from the simplest ones [6] to the most complex [7,8]. Many studies are dedicated to comparative analysis.…”

Contribution to the Optimization of Energy Withdrawn from a PV Panel Using An Embedded System

“…The first set is called "the time update" or also "the prediction state"; it is composed of two equations. The first equation is used to project the state ahead [108], [114]. − A is a constant that depends on the system in which the Kalman filter is used; it is the state transition model that is applied to the previous state.…”

Enhancement of Photovoltaic Pressurized Irrigation System Based on Hybrid Kalman Fuzzy

“…The Kalman filter estimates the MPP voltage while the FLC controls the DC converter to achieve MPP based on the error voltage between the MPP voltage estimate and the actual PV voltage. The The Kalman filter has efficient computation and supports the estimation of the present, past, and future states [19,20]. This filter can also estimate when the system's character being modeled is unknown.…”