This method showed better efficiencies during rapid irradiance changes than P&O (98.78% compared to 93.31%). However, the additional perturbation stage effectively divides \(T_{MPPT}\). The division occurs because several perturbations need to be made before making a control decision. In [20; 21], the authors propose an algorithm based on the traditional P&O (dp-P&O) with an additional power measurement stage in the middle of each perturbation period (\(T_{MPPT}/2\)) to calculate the direction in irradiance changes. Since MPPT does not perform any action between perturbation periods, it presumes that any observed power change is exclusively due to external factors. Although, experimental data showed that the algorithm would still suffer some degree of drifting from MPP. Traditional MPPT algorithms either wait for the system to reach a steady-state before measuring power changes or compute an average of numerous measurements during \(T_{MPPT}\) to remove ripple components. Nonetheless, methods that consider natural oscillations occurring in the converters show good dynamic tracking [5; 22; 23; 24]. It has been observed that the continuous oscillations around the MPPT produced by these schemescan decrease steady-state efficiency. Furthermore, the added complexity makes them more challenging to implement than compared with traditional MPPTs. This study presents an improvement to the classical P&O that prevents MPPT from drifting during fast irradiance changes and steady-state oscillations around the MPP by calculating power derivatives during transitory states. Using an additional measurement in the PV curve, the proposed algorithm computes the derivative between the previous operating point and the maximum overshoot peak (\(dp_{1}\)) and between the maximum overshoot and the final operating point (\(dp_{2}\)). Because of this, the proposed method is referred to as transient differentiation MPPT (Td-MPPT). When the PV system has underdamped behavior, this algorithm only needs one perturbation around the MPP in each direction instead of the two required by traditional methods. Because of these enhancements, the simulation results showed that efficiency increased compared to the classical P&O and dP-P&O. Furthermore, the performance is compared to the algorithm in [21] to better illustrate differences between these two approaches. Even though this method addresses tracking a local MPPT such as P&O, it can be combined with different techniques such as the fractional short circuit (FSC) [17], the genetic algorithm (GA) [25], artificial bee colony (ABC) [26], particle swarm optimization (PSO) [27], grey wolf optimization (GWO) [28], and enhanced leader particle swarm optimization (ELPSO) [29] to operate under partial shading conditions. This work is structured as follows: Section two presents classic P&O and dp-P&O operations. In section three, the reasoning behind the Td-MPPT algorithm and how it calculates the direction of the next perturbation is explained in detail. Then, simulation results of the proposed algorithm and comparison with the method presented in [21] and the traditional P&O are evaluated to demonstrate the added value of the proposed approach. A performance comparison for the three algorithms is carried out for global efficiency during the tests and different irradiance rates of change. Efficiency is calculated according to the definition proposed in the EN50530:2010 standard [30]. Lastly, the conclusions are presented.