Determination of the maximum power point in a photovoltaic panel using Kalman Filter on the environment PSIM

Abstract

the objective of this document is the determination of the maximum power point using the best suited algorithm on the environment Psim. The photovoltaic panel will be modelled by a diode and two resistances, the first on will be put in parallel, the second one will be put in series. The output of this model will be composed of the current, voltage, and the power. We added a DC-DC boost converter which will adapt the impedance in order to be always on the maximum power point, to track this maximum power point we added another block in which we implemented an algorithm to get to this point as fast as possible without needing lot of resources. And to get to that objective, we used an adapted version of the Kalman Filter.

Contribution to the Optimization of Energy Withdrawn from a PV Panel Using An Embedded System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

“…The second set of equation is called "the measurement update" or also the "correction state" and it is used to correct the value predicted during "the time update" step. This set of equations is constituted from three equations presented below [108]:…”

“…First, we compute the Kalman gain: − C is a constant that depends on the system in which the Kalman filter is used; it is the observation model which maps the true state space into the observed space. These two steps "the time update" and "the measurement update" are repeated during each iteration which causes noise to reduce and the error covariance to become zero [108].…”

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

“…The second equation is used to project the error covariance ahead (Aoune et al 2016): where Q is the process noise covariance. It is a covariance matrix associated with the noise in states; it is generally constructed intuitively, but there are some points that need to be regarded choosing it.…”

“…These two steps "the time update" and "the measurement update" are repeated during each iteration which causes noise to reduce and the error covariance to become zero (Aoune et al 2016).…”