Photovoltaic String Research Articles

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

Comparative Study of Voltage Equalizing Circuits for Photovoltaic String

Partial shading is one of the inevitable condition that photovoltaic (PV) strings encounter. When photovoltaic strings are partially shaded, numerous peaks appear on their power versus voltage (P-V) characteristic curve, significantly reducing their ability to deliver power. This paper presents, three power electronics topologies that permit voltage equalization by charge redistribution between the PV modules wired in series. Three popular topologies, namely; switched-capacitor (SC), Buck-boost switched capacitor (BBSC) and PWM with switched capacitor (SC-PWM) converters have been examined. Complete experimentation have been done using MATLAB/Simulink environment. A comparative analysis have been done under various shading conditions. The findings demonstrate that the converters under consideration can improve the performance of string under partial shading.

Fault Diagnosis of Photovoltaic Strings Based on SiamMN Networks and Panoramic I-V Features

Fault Diagnosis of Photovoltaic Strings Based on SiamMN Networks and Panoramic I-V Features

New Global Maximum Power Point Tracking Technique Based on Indirect PV Array Voltage Control for Photovoltaic String Inverters With Reduced Number of Sensors

New Global Maximum Power Point Tracking Technique Based on Indirect PV Array Voltage Control for Photovoltaic String Inverters With Reduced Number of Sensors

Analysis of Design Requirements and Optimization Possibilities of Partial Power Converter for Photovoltaic String Applications in DC Microgrids

Analysis of Design Requirements and Optimization Possibilities of Partial Power Converter for Photovoltaic String Applications in DC Microgrids

Study and analysis of partial shading effect on power production of a photovoltaic string controlled by three different MPPT techniques: P&O, PSO and ANN

Partial shading occurs when some of the solar panels are exposed to reduced irradiation. Partial shading can lead to creating peaks and troughs in power production. The goal of this study is to compare the effect of partial shading on the capacity of maximum power point tracking (MPPT) methods, to find the global maximum power point. To this end, the study focuses on performance simulation and discussion of Perturb and Observe (P&O), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN) controls. Analysing the three MPPT controller's results, in terms of accuracy, the ANN and PSO controls showed high performance. On the other hand, the P\&O control showed lower accuracy, particularly under partial shading. For the speed of reaction, the P&O and ANN controls proved to be the fastest, while the PSO control showed a slightly longer response time. However, it is important to note that ANN approach presents added complexity in terms of conception.

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

The solar photovoltaic (PV) energy generation technology has grown rapidly. Herein, a novel continuous‐time Lyapunov‐based model reference adaptive control (LB‐MRAC) technique is suggested for determining the duty cycle of boost converter to maintain maximum power point (MPP) of the solar PV panel. For ensuring the fast‐tracking speed, the reference voltage of, respectively, MPP is calculated using perturb and observe (PO) and input to the MRAC technique. Using MATLAB/Simulink, the performances of the LB‐MRAC maximum power point tracking are comparatively analyzed with PO, incremental conductance, and adaptive neuro‐fuzzy inference system under diverse testing modes such as stand‐alone, partial shading, grid integrated, and real time. The proposed technique tracks MPP in just 3.7 ms having tracking efficacy between 99.15% and 99.59% under highly fluctuating irradiance and temperature with load uncertainties. In partial shading mode, four patterns (3 × 1 PV string) are devised and determined where that the suggested approach monitors the global MPP in merely 0.04 s with the highest power tracked and the least amount of shading loss. Next, the efficacy is checked in a three‐phase 50 kW grid‐integrated mode under realistic weather. Finally, the performance of the suggested continuous‐time LB‐MRAC technique is experimentally corroborated employing OPAL‐RT(OP4510) in the real‐time mode.

Implementation of String Inverters for 50 MW Large Scale Solar Power Plant in Malaysia

The growth of large-scale solar photovoltaic (LSSPV) power plant site development in Malaysia has increased the demand for the reliability and potentiality of the system in achieving efficient monitoring, operation, and maintenance for the plants. A configuration of string inverters is applied in a 50 MW large-scale solar photovoltaic installation because of its flexibility and scalable technological advancements. This installation demonstrates that the plant can optimise the power output and monitor the performance of the system up to the string level with a cost-efficient implementation. The output from each photovoltaic (PV) string provides a guarantee of better maximum power point tracking (MPPT) capability. Instead of having shorter DC wires compared to the central inverter, this system offers the potentiality of monitoring the solar PV module at a string level, which also affords scalability for future expansion. The tests conducted verify that the string inverter configuration serves as a reliable outcome for grid code test (GCT) requirements. Furthermore, the string inverters provide a potential approach to achieving maximum power generation in different climate conditions, thus reducing the need for manpower in operation and maintenance activities. The string system offers a feasible implementation of effective configurations and decarbonisation for LSSPV in Malaysia without frequent shutdowns of plant generation due to defects or damage to PV equipment and modules.

Comparing solar inverter design rules to subhourly solar resource simulations

The input of a solar inverter depends on multiple factors: the solar resource, weather conditions, and control strategies. Traditional design calculations specify the maximum current either as 125% of the rated module current or as the maximum 3 h average current from hourly simulations over a typical year, neglecting extreme irradiance conditions: cloud enhancement events that usually last minutes. Inverter power-limiting control strategies usually prevent extreme events to cause strong currents at the inverter, but in some cases, they can fail, leading to high currents. In this study, we aim to report how frequent and strong these high currents could be. We use 10 years of 1 min data from seven stations across the United States to estimate the photovoltaic string output through modeling the short-circuit current Isc, and the maximum-power point current Imp, and compare them to traditional inverter design values. We consider different configurations: minutely to hourly resolution; 5 min to 3 h averaging time intervals; monofacial and bifacial modules (with a case of enhanced albedo); and 3 fixed-tilt angles and horizontal single-axis tracking. The bifacial modules with enhanced albedo lead to the highest currents for 1 min data, exceeding 3 h averages by 53% for Isc and 38% for Imp. The 3 h average maxima surpass the conservative 125% design rule for bifacial modules. Inverter ratings at either a 200% of the rated current or 1.55 times the 3 h maximum could withstand all events regardless of control strategies. In summary, for some locations it is prudent to compare current design rules to subhourly simulations to guarantee the fault-free operation of solar PV plants.

Small-Signal Model of the NPC + GCC Multilevel Transformerless Inverter in Single-Phase Photovoltaic Power Systems

Photovoltaic transformerless inverters are very efficient and economical options for solar-power generation. The absence of the isolation transformer improves the converters’ efficiency, but high-frequency voltage to ground can appear in the photovoltaic string poles. The high capacitance to ground of the photovoltaic generator leads to undesirable high-leakage currents. Using half-bridge topologies dramatically reduces the leakage to ground, and using a multilevel half-bridge inverters improves the output quality compared with classical inverters. The neutral point clamped + generation control circuit (NPC + GCC) topology is a multilevel single-phase transformerless inverter capable of tracking the maximum power point of two photovoltaic sources at the same time. This paper presents the control structure and the dynamic modeling of the NPC + GCC inverter. The pulse-width modulated (PWM) switch model in continuous conduction mode (CCM) was used to obtain the small-signal model of the two switching converters that make up the inverter. The resulting dynamic model was used to quantify the stability margins of both converters’ current and voltage loops.

Differential power processing architecture to increase energy harvesting of photovoltaic systems under permanent mismatch

Differential power processing (DPP) architectures are a prominent solution for mitigating undesirable issues caused by mismatch that often occurs in photovoltaic (PV) strings. In this scenario, the association of DPP converters in parallel with PV strings is a common approach for enhancing energy harvesting. However, most topologies presented so far focus on temporary mismatch caused by partial shading conditions only, with little attention given to permanent mismatch due to the connection of modules with distinct ratings. Given the above, this article presents a novel DPP topology capable of handling both temporary and permanent mismatch in PV strings. Important advantages include lower component count; reduced voltage stresses on the active switches; operation under zero-current switching (ZCS) condition, with a direct impact on the converter efficiency; and greater design flexibility in terms of a modular solution that can be implemented at the PV module level. Experimental tests on a string composed of six modules are presented and the results are thoroughly discussed to validate the theoretical assumptions. It is effectively demonstrated that the architecture allows for increasing the power extracted from the string under two severe operating conditions.

Comparison of harmonic emission in LV side of a large grid connected PV power plant

The use of solar photovoltaic (PV) in distribution networks has increased considerably in recent years. Although they have many advantages, PV systems can also result in complex power quality issues in distribution networks, like harmonic distortion, that can interfere with loads and controllers. Harmonic emission from different inverters and their aggregation is thus of interest. In this work, harmonic emission of two PV string inverters and two substations operating in a large PV power plant is presented. The distribution of the full range of current harmonics is analyzed, as well as the correlation between the values of each harmonic and the power level produced, by the inverter or substation, and the correlation between voltage and current harmonics. Significant differences are observed in the harmonic emission of the inverters, despite being under the same operating loading and grid conditions. The results obtained for the substations are in line to those for the inverters. This study was performed in a grid connected 12 MW PV power plant operating in Europe.

Stability Analysis for DC-Link Voltage Controller Design in Single-Stage Single-Phase Grid-Connected PV Inverters

The dc-link voltage control is vitally important to ensure the operation of photovoltaic (PV) system at the maximum power voltage, where its performance affects the power quality injected into the grid. In this work, we propose a method, based on the Lyapunov function, for investigating the control system stability, during the design of a nonlinear dc-link voltage controller for single-stage single-phase grid-connected PV inverters. Furthermore, we demonstrate analytically that the non-linearity of the PV string's power-voltage (P-V) curve leads to system instability. Consequently, this article proposes an adaptive dc-link voltage controller, built from the P-V curve, as an application in order to establish a stability criterion. Simulations and experimental validations have been carried out on a grid-connected single-stage single-phase PV inverter test platform. The results confirm the feasibility of the analytical study developed in this article, as well as the proposed dc-link voltage controller with and without the stability analysis.

Susceptibility to polarization type potential induced degradation in commercial bifacial p‐PERC PV modules

AbstractPotential induced degradation (PID) is a reliability issue affecting photovoltaic (PV) modules, mainly when PV strings operate under high voltages in hot/humid conditions. Polarization‐type PID (PID‐p) has been known to decrease module performance quickly. PID‐p can be reduced or recovered under the light in some cases, but this effect, as expected, would be less pronounced on the rear side of bifacial PV modules receiving lower irradiance. As bifacial PV modules are projected to dominate the PV market within the next 10 years, it is crucial to understand the PID‐p issue in bifacial modules better. In this study, we performed indoor PID testing to induce PID‐p on 14 commercial bifacial p‐PERC modules with three different module constructions from three manufacturers. Four rounds (+ve and −ve polarities for front and rear sides) of PID testing are done at 25°C, 54% relative humidity (RH) for 168 h using the aluminum foil method. Each module side (front cell side and back cell side) is tested individually under both negative and positive voltage bias. The results show that the highest degradation of 32% in maximum power (Pmax) at standard test conditions (1000 W/m2) and 51% at low irradiance (200 W/m2) has been observed in some cases. Recovery under sunlight is also done, and outcomes show a near‐complete recovery in Pmax. This study presents an extensive experimental methodology and a detailed analysis to systematically and simultaneously/sequentially evaluate multiple construction types of bifacial modules to the PID‐p susceptibility and recovery.

Type-2 fuzzy-logic based control of photovoltaic-hydrogen production systems

Green hydrogen will play an important role for sustainable development, it can be considered as a promising solution for energy transition. In this paper, direct and indirect-coupled configuration of hydrogen production system consisted of Photovoltaic (PV) source and electrolyzer stack are investigated. A study on power loss and efficiency in PV-electrolyzer system is conducted for both cases. Optimal matching conditions in directly coupled hydrogen system can be achieved by modifying the configuration of series/parallel cells in electrolyzer stack or PV generator. However, the energy transfer can be improved by incorporating a buck converter between PV string and electrolyzer stack. In this article, the type-2 fuzzy inference system is proposed to control the operating point of PV string by adjusting the duty cycle of control signal of buck converter. A comparison with classical MPPT methods is carried out in order to verify the efficiency of suggested technique. It has been found that the indirect-coupled hydrogen system can achieve higher energy transfer rate by ensuring continuous extracting of maximum power of PV source under various operating conditions. The obtained results, under Matlab/Simulink environment, demonstrated a satisfactory performance of type-2 fuzzy logic controller in both transient and permanent regimes. We obtain a minimal power loss value (less than 8.1%) in directly-coupled hydrogen production system, in addition a satisfactory value of efficiency (more than 93%) has been obtained.

A virtual bus parallel differential power processing configuration for photovoltaic applications

Photovoltaic (PV) systems are often exposed to mismatch caused by partial shading, different mounting angles, dust accumulation, cell degradation, and so on. This paper proposes a novel parallel differential power processing (P-DPP) configuration to minimize mismatch-related losses among PV strings. The proposed configuration, called PV to Virtual Bus P-DPP, uses a virtual bus as an input for all P-DPP converters. Since the virtual bus voltage can be selected lower than the DC Bus voltage, components’ voltage rating can be reduced. An essential feature of the proposed configuration is the ability of the converters to generate both positive and negative output voltage. Therefore, a bidirectional flyback converter connected to a bridgeless converter is proposed as the P-DPP converter. To find the MPP of each PV string, the Perturb and Observe (P&O) algorithm is implemented. Moreover, a proportional–integral feedback controller controls the virtual bus voltage through the central converter. The benefits of the proposed configuration are discussed, and the operation of the proposed structure is further verified through simulations with the software PLECS.

Voltage mapping and local defects identification in solar cells using non-contact method

Electroluminescence, infrared imaging, and current–voltage curve techniques are used to detect and map underperforming cells in a photovoltaic module or the modules in a photovoltaic string. In this work, we present a non-contact electrostatic voltmeter technique to detect and map the underperforming spots in a cell and the cells in a module. This non-contact technique directly maps the charged surface voltage (671 mV) of the superstrate glass, and it had an 11-mV difference with the voltmeter values (660 mV). Another data set of voltage values obtained by electroluminescence images conversion into a voltage map showed a difference of 7 mV with the non-contact voltmeter values. The direct voltage values obtained at various good and poor-performing spots of the cells using this technique are 3.69 V and 3.79 V and are validated using the voltage values obtained in electroluminescence analysis and the difference in voltage obtained by the two techniques is determined to be less than 2%. In this work, we combine the strengths of two complementary techniques of the electrostatic voltmeter (strength: quantitative) and electroluminescence (strength: spatial mapping) to obtain a quantitative spatial mapping of defects. Furthermore, this work is extendable to detect the poor-performing modules in solar PV power plants.

An Algorithm for Emulating Photovoltaic Strings With Dynamic Partial Shadowing Capability: A Practical Study

An Algorithm for Emulating Photovoltaic Strings With Dynamic Partial Shadowing Capability: A Practical Study

A generalized gradual fault diagnosis using photovoltaic plant reflectometry profile

This paper presents a generalized gradual fault diagnosis technique for photovoltaic (PV) plants using photovoltaic plant reflectometry profile (PPRP). PV plants can suffer from many types of defects; the most critical are the electrical ones that may appear either after a long or a short period of time. Many fault detection techniques are used depending on fault types, and reflectometry is one of the most efficient. Nevertheless, it has its limitations, such as location detection of the first fault only within a PV string, in addition to not being able to identify the fault type. This paper presents a mathematical model of the reflectometry technique, named hereafter the photovoltaic plant reflectometry profile (PPRP), that enhances its performance by enabling it to detect and locate multiple faults on a N-PV string, in addition to detecting any impedance mismatches, even the smallest ones, between a good PV string and one with faulty modules. Simulations were conducted to evaluate the accuracy of the PPRP analytical model considering a 7-PV string and its effectiveness to detect the expected locations of the faulty modules, and results show that the simulated reflected signal matches exactly the expected PPRP using the developed mathematical model.

Fault diagnosis of photovoltaic strings by using machine learning‐based stacking classifier

AbstractPhotovoltaic (PV) modules are prone to short circuits, open circuits, cracks, which can bring serious harmful effects. It is difficult to establish the corresponding PV fault models to diagnose the status of PV strings. The paper proposes a machine learning‐based stacking classifier (MLSC) for accurate fault diagnosis of PV strings. Specifically, for the operating state of PV modules, the parameter sensitivity algorithm is used to analyze the impact of characteristic factors on the characteristics of PV modules. Then based on the characteristic factors (irradiance, temperature, current, and power), MLSC is proposed to realize the accurate fault diagnosis of PV strings. This structure of MLSC is to integrate all kinds of classifiers by stacking to play the characteristics of each classifier. It combines the characteristics of various types of machine learning algorithms to improve the overall classification effect by using the advantages of each classifier. Finally, experiments reveal that MLSC improves the accuracy of PV fault diagnosis.