Dynamic reconfiguration of photovoltaic arrays is one of the effective ways to decrease partial shading effects. In this paper, by using auxiliary modules and after a suitable fixed reconfiguration, an optimizer and economic method based on dynamic reconfiguration is presented. In this method, Auxiliary modules are arranged next to the photovoltaic array and replaced with shaded modules to maximize the energy delivery. The best connection between the auxiliary modules and the array is determined by an optimal decision process. The objective function for this decision process is energy delivery of the solar array in shadow conditions which is maximized by the genetic algorithm. Significant improvement in the output power of the photovoltaic array and smaller number of switches than the other dynamic reconfiguration methods are the main advantages of the proposed method. Benefits and effectiveness of this method are compared with other recently dynamic configuration approaches, and the results confirm power enhancement of the photovoltaic arrays in various shadow patterns.

Grid-connected inverters are crucial in transmitting power from distributed production systems and renewable sources to the grid. However, these inverters often generate current harmonics due to high-frequency switching and DC link ripple. To address these issues, various filters, including the LCL filter, are employed. This paper focuses on improving the power quality of grid-connected photovoltaic arrays using LCL filters, primarily through a current sensor and virtual impedance shaping. The paper divides the output impedance of the photovoltaic array power optimization system into an active and passive part. It neutralizes the active component by introducing a series of virtual impedances and counteracts its adverse effects with parallel virtual impedance. The design process for both series and parallel virtual impedance is elaborated, and the system's sensitivity is thoroughly analyzed. To validate the proposed approach, extensive simulations have been carried out using MATLAB software. The simulations demonstrate the robust and precise performance of the control system in effectively injecting the maximum power generated by the photovoltaic array into the grid. Additionally, they showcase the high-quality current being injected into the grid and the system's capacity to maintain stability, even in a weak network environment.

Nowadays, Distributed Generation (DG), especially PV systems as a new source of power absorbed a high percentage of investment. Fault Detection and analysis in solar photovoltaic (PV) arrays are important issues to increase reliability, efficiency and safety in PV arrays. Due to PV’s non-linear characteristics, current-limiting nature, high fault impedances, low irradiance conditions, the PV grounding schemes, or inverter condition and protection system weakness, faults in PV arrays are not properly recognized. Therefore, to fill this protection gap, machine learning techniques has been proposed for fault detection based on PV array voltage and current measurements and irradiance and temperature in a grid connected 17.6 kw photovoltaic system.To determine the type and classification of the faults, choosing the best method of classification with high accuracy and finding suitable feature in commercial-scale photovoltaic arrays, are important issues that has not been done so far. The input data for using Bayesian and K-Nearest Neighbor Methods are the simulation results of different defined classes of the line to line and open circuit faults by various temperature and irradiance. The results have shown that using the suggested classification system is very successful in the detection and classification of faults in an array string.

Nowadays, Distributed Generation (DG), especially PV systems as a new source of power absorbed a high percentage of investment. Fault Detection and analysis in solar photovoltaic (PV) arrays are important issues to increase reliability, efficiency and safety in PV arrays. Due to PV’s non-linear characteristics, current-limiting nature, high fault impedances, low irradiance conditions, the PV grounding schemes, or inverter condition and protection system weakness, faults in PV arrays are not properly recognized. Therefore, to fill this protection gap, machine learning techniques has been proposed for fault detection based on PV array voltage and current measurements and irradiance and temperature in a grid connected 17.6 kw photovoltaic system.To determine the type and classification of the faults, choosing the best method of classification with high accuracy and finding suitable feature in commercial-scale photovoltaic arrays, are important issues that has not been done so far. The input data for using Bayesian and K-Nearest Neighbor Methods are the simulation results of different defined classes of the line to line and open circuit faults by various temperature and irradiance. The results have shown that using the suggested classification system is very successful in the detection and classification of faults in an array string.

Dust deposition on the photovoltaic (PV) arrays surface may cause physical damages, weakens the sun's radiation, and increases the panel temperature that results in the reduction of the electrical efficiency of the panel. PV array cleaning is engaged with issues such as water and energy consumption, and long operating time. An efficient mechanical cleaner improves the cleaning operations compared to the manual operation. In this study, development of a mechanical cleaning machine for photovoltaic arrays surface was carried out. The machine was evaluated using two factors including: the equivalent speed ratio of the brush to the cleaning platform, and the type of detergent at three levels, respectively. The tests were arranged at completely randomized design with three replications. The results showed that the effect of equivalent speed ratio of the brush to the cleaning platform on dust removal was significant (p < 0.05). Also, the effect of all studied factors on output power was significant (p < 0.05). It was revealed that the cleaning machine can increase the amount of power in each 280-Watt panel by up to 40 Watt and the efficiency up to 4%.

A zero-emission photovoltaic system is critical for efficient energy supply and environmental protection. Because of the intermittent nature of solar insolation and temperature, the maximum power point tracking controller for the photovoltaic system has become complicated. At each intermittent condition, the photovoltaic array has only one operating point with maximum power output. Therefore, a nature-inspired algorithm-based controller is needed to determine the actual maximum operating point at different environmental conditions; thus, it can increase the overall efficiency of the photovoltaic system. This paper proposes a novel maximum power point tracking controller based on a hurricane optimization algorithm hybridised with chaos to improve power tracking in photovoltaic systems. The proposed hybrid algorithm is created by combining chaotic search behaviour with the standard hurricane optimization algorithm in order to increase efficiency. The proposed controller generates the optimal duty cycle for controlling the DC-DC boost converter by tracing the exact maximum operating point on a regular basis. The simulation results show that the proposed HOA -chaos-based MPPT controller outperforms INC, P\& O, PSO and HOA based controllers in terms of efficiency and control.

Many conventional incremental conductance (INC) methods are applied for maximum power point tracking (MPPT) of photovoltaic arrays. Where, the optimization step size determines the speed of MPPT. Fast tracking could be achieved with bigger increments but the system might not operate properly at the MPP and might become oscillated at this point; therefore, there is a trade-off between the time needed to reach the MPP and the oscillation error. This article is to present an adaptive optimization step size in the INC to improve solar array performance. To adjust the MPP in the photovoltaic (PV) operation point, brain emotional learning based intelligent controller (BELBIC) is applied as an adaptive optimization step size in the INC. This would considerably increase the system's accuracy. The effectiveness of this proposed method is verified by comparing its simulation and experimental results with the conventional methods in different operating conditions.

Grid-connected inverters are considered vital elements for effectively connecting renewable energy sources and distributed generation system applications. Ripple-induced current harmonics in DC link and high switching frequency are the disadvantages of grid-connected inverters that are reduced by LCL filters. However, the intrinsic resonance in the LCL filter leads to instability of the power transmission system. As a result, suitable damping is essential for removing resonance in the LCL filters. The contribution of this paper is to improve the quality of injectable power of LCL filter-based grid-connected photovoltaic array. For this contribution, the stability of the grid-connected inverter has been investigated using active damping method, and maximum power point tracking (MPPT) for the PV array has been performed. The capacitor voltage feedforward active damping method considering computational delay is presented in this paper. By using the inverter-side current feedback beside this method, the proposed control maintains the system's low-frequency specifications independent of the grid impedance changes. It provides high harmonic rejection capability without additional compensators. Also, the number of sensors is decreased due to the alternative measurement of the capacitor voltage instead of the grid voltage for the phase lock loop (PLL). Meanwhile, maximum power point tracking is implemented using the incremental conductance (IC) technique in the boost converter. In addition, a simple and suitable computational method for designing LCL filter parameters is presented, and the system’s sensitivity is analyzed. Finally, the simulation has been implemented in MATLAB software that indicates the accurate performance of the control system in injecting the maximum power of the photovoltaic array into the grid and the highly desirable quality of the injectable current to the grid.

The primary objective of the proposed work is the design of a Hybrid Teaching Learning-based Horse Herd Optimization Algorithm regulated Fractional Order Tilt Derivative Acceleration with Filter (TLBO-HHOA regulated FOTDAF) controller for enhanced performance and enhanced devaluation of harmonic components of the grid-connected photovoltaic system. The solar photovoltaic system incorporates constituents such as a photovoltaic array, interleaved fractional order boost converter (IFOBC), Reduced Switch Multilevel Inverter (RSMI), and TLBO-HHOA regulated FOTDAF controller. IFOBC is preferred over boost converter because of its low ripple voltage, faster transient response, high efficiency, low duty cycle, reduced EMC, and improved reliability and stability. In this control strategy, the control logic is formulated by using a Tilt Integral Derivative Controller (TIDC), whose control parameters are considered as a function of the error to improve the robustness. The validation, better performance, and superiority of TLBO-HHOA regulated FOTDAF are established by comparative result analysis using modern controllers. This study implements TLBO-HHOA-regulated FOTDAF and applies Support Vector Pulse Width Modulation (SVPWM) technique. The proposed model managed to achieve improvements in overall system response and reduced harmonic distortions as well as better accuracy, improved stability, improved robustness, and better capabilities to handle system uncertainties.

Compared to multistage grid-connected photovoltaic system (GCPVS), complexity in monitoring and controlling scheme in a single-stage GCPVS is much increased as maximum power point tracking and power sharing with the grid need to be considered simultaneously in the same stage. An efficient technique, for performing aforementioned functions simultaneously, in a single stage system by adjustment of voltage phasor of a voltage source inverter is presented. Active and reactive power flow from inverter is regulated by adjustment of amplitude and phase angle of inverter output voltage with respect to grid voltage. An algorithm is also developed for automatic active and reactive powers sharing for the intended applications in the single stage GCPVSs. A typical 2.8 kWp, 368 V (DC) nominal photovoltaic array based GCPVS is modeled and simulated to analyze proper load matching for different ambient and grid voltage conditions. Performances like efficiency of the inverter, total harmonic distortion of inverter output voltage and current are studied with the variation of ambient conditions and modulation index of sinusoidal pulse width modulation of the IGBT based inverter. Simulation results have been included to show the feasibility of the proposed technique.