This paper presents a detailed analysis of the two most well-known hill-climbing maximum power point tracking (MPPT) algorithms: the perturb-and-observe (P&O) and incremental conductance (INC). The purpose of the analysis is to clarify some common misconceptions in the literature regarding these two trackers, therefore helping the selection process for a suitable MPPT for both researchers and industry. The two methods are thoroughly analyzed both from a mathematical and practical implementation point of view. Their mathematical analysis reveals that there is no difference between the two. This has been confirmed by experimental tests according to the EN 50530 standard, resulting in a deviation between their efficiencies of 0.13% in dynamic and as low as 0.02% under static conditions. The results show that despite the common opinion in the literature, the P&O and INC are equivalent.

The high variability of solar irradiance, originated by moving clouds, causes fluctuations in Photovoltaic (PV) power generation, and can negatively impact the grid stability. For this reason, grid codes have incorporated ramp-rate limitations for the injected PV power. Energy Storage Systems (ESS) coordinated by ramp-rate (RR) control algorithms are often applied for mitigating these power fluctuations to the grid. These algorithms generate a power reference to the ESS that opposes the PV fluctuations, reducing them to an acceptable value. Despite their common use, few performance comparisons between the different methods have been presented, especially from a battery status perspective. This is highly important, as different smoothing methods may require the battery to operate at different regimes (i.e., number of cycles and cycles deepness), which directly relates to the battery lifetime performance. This paper intends to fill this gap by analyzing the different methods under the same irradiance profile, and evaluating their capability to limit the RR and maintain the battery State of Charge (SOC) at the end of the day. Moreover, an analysis into the ESS capacity requirements for each of the methods is quantified. Finally, an analysis of the battery cycles and its deepness is performed based on the well-established rainflow cycle counting method.

An acceleration model based on the Peck equation was applied to power performance of crystalline silicon cell modules as a function of time and of temperature and humidity, which are the two main environmental stress factors that promote potential-induced degradation (PID). This model was derived from module power degradation data obtained semicontinuously and statistically by in-situ dark current-voltage measurements in an environmental chamber. The modeling enables prediction of degradation rates and times as functions of temperature and humidity. Power degradation could be modeled linearly as a function of time to the second power; additionally, we found that the quantity of electric charge transferred from the active cell circuit to ground during the stress test is approximately linear with time. Therefore, the power loss could be linearized as a function of coulombs squared. With this result, we observed that when the module face was completely grounded with a condensed phase conductor, leakage current exceeded the anticipated corresponding degradation rate relative to the other tests performed in damp heat.

This article proposes an improved maximum power point tracking (MPPT) method that features a simple design, and improved efficiency in fast-changing irradiance conditions. The method uses three consecutive measurements and compares the power difference between each two consecutive samples, furthermore the voltage variation between the last two successive samples is observed. According to the obtained result of these comparisons, the algorithm applies the suitable action either increasing or decreasing the voltage. This simple concept allows easy implementation and reduces the implementation cost and calculation burden. Second, the method has a prompt tracking response during fast changes in solar irradiance (e.g., due to passing clouds). The proposed method is validated through experimental tests using solar irradiance profiles according to the EN50530 standard and is compared to the classical Perturb and Observe method. The experimental results show that the proposed MPPT effectively identifies the change in solar irradiance, and maintains high tracking efficiency even in fast-changing conditions.