Under partial shading conditions (PSCs), photovoltaic (PV) system characteristics vary and may have multiple power peaks. Conventional maximum power point tracking (MPPT) methods are unable to track the global peak. In addition, it takes a considerable time to reach the maximum power point (MPP). To address these issues, this paper proposes an improved hybrid MPPT method using the conventional evolutional algorithms, i.e., Particle Swarm Optimization (PSO) and Differential Evaluation (DE). The main feature of the proposed hybrid MPPT method is the advantage of one method compensates for shortcomings of the other method. Furthermore, the algorithm is simple and rapid. It can be easily implemented on a low-cost microcontroller. To evaluate the performance of the proposed method, MATLAB simulations are carried out under different PSCc. Experimental verifications are conducted using a boost converter setup, an ET-M53695 panel and a TMS320F28335 DSP. Finally, the simulation and hardware results are compared to those from the PSO and DE methods. The superiority of the hybrid method over PSO and DE methods is highlighted through the results.

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this article brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

This paper aims to present a novel control strategy for Modular Multilevel Converters (MMC) based on differential flatness theory (DFT), in which instantaneous active and reactive power values are considered as the flat outputs. To this purpose, a mathematical model of the MMC taking into account dynamics of the ac-side current and the dc-side voltage of the converter is derived in a d-q reference frame. Using this model, the flat outputs-based dynamic model of MMC is obtained to reach the initial value of the proposed controller inputs. In order to mitigate the negative effects of the input disturbance, model errors, and system uncertainties on the operating performance of the MMC, the integral-proportional terms of the flat output errors are added to the initial inputs. This can be achieved through defining a control Lyapunov function which can ensure the stability of the MMC under various operating points. Moreover, the small-signal linearization method is applied to the proposed flat output-based model to separately evaluate the variation effects of controller inputs on flat outputs. The proficiency of the proposed method is researched via MATLAB simulation. Simulation results highlight the capability of the proposed controller in both steady-state and transient conditions in maintaining MMC currents and voltages, through managing active and reactive power.

Accurate modeling of photovoltaic (PV) modules is required to predict performance of PV systems in various climatic conditions, often far different than manufacturer specifications. Snowfall during cold months reduces output of PV modules. As the application of PV systems is increasing in cold areas, it is vitally important to address this issue through an appropriate method capable of estimating PV performance due to snow effect. This paper proposes a novel PV modeling approach that can represent instantaneous electrical characteristics of PV modules in the presence of uniform snow coverage. The proposed model utilizes the Bouguer-Lambert Law to estimate the level of insolation reaching surface of snow-covered PV cells. This is achieved by introducing an extinction coefficient which depends on the snow properties. To study the efficiency of PV cells at low insolation levels, a two-diode equivalent circuit model is employed. The simulation results of the proposed model are validated with experimental measurements from field tests for different commercial PV modules as well as real data collected by the SCADA system of a 12-MW grid-connected PV farm. Good agreement was observed between power generation results estimated from the proposed model and those obtained experimentally on snow-covered PV systems. This model would be helpful for researchers and PV systems developers in cold regions.

This paper proposes a high performance shadetolerant maximum power point tracking (STMPPT) technique for DC-DC converter stage of photovoltaic (PV) applications. The average current-mode control (ACMC) is utilized to regulate the PV array current using two feedback control loops. The currentmode control is a superior scheme in control of DC-DC power electronic converters. The proposed STMPPT technique operates in two modes. The ACMC with the perturb and observe (P&O) MPPT algorithm functions in a local MPPT (LMPPT) mode under normal irradiance condition. When the PV array is likely to be partially shaded, a global MPPT (GMPPT) subroutine effectively scans the PV profile to optimize the PV system operation. This is achieved by implementing simple innovations to the ACMC-based P&O algorithm. The innovations benefit from useful observations of I-V characteristics. The idea behind using the I-V characteristics is to significantly reduce the search space, make the algorithm independent of shading conditions and PV array configuration, and inherently recognize the occurrence of partial shading conditions (PSCs). The proposed STMPPT technique enables very fast and reliable tracking of global maximum power point (GMPP). In addition, it can stably work under dynamic environmental change without losing correct sense of tracking direction. Its simplicity and independency would offer a viable solution for PV converter products. Simulation and experimental performance assessments are presented under different operating conditions that could happen in outdoor PV installations.

With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms.