Grid-connected Photovoltaic System Research Articles

Unsupervised fault detection approach based on depth auto-encoder for photovoltaic power generation system

Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems.

Voltage Error Estimation-Based Fault Diagnosis Method for Three- phase PWM-inverter in Grid-connected Photovoltaic Power System Without any Over-rating

Inverter is an essential component of a grid connected PV system. In fact, an open-switch fault in this power converter could result an important system malfunction and consequently leads to system disconnection. In this context, this paper focuses on the fault-tolerant control of PWM-inverter with redundancy leg for grid-connected PV system. In addition, a fast method of fault detection and compensation is used to maintain the continuous operation of the converter when there is a half-leg open-circuit fault (IGBT+ anti-parallel diode) on one of PWM-inverter legs. Therefore, it can detect this type of fault and compensate it in less than 50 μs by using a time criterion instead of voltage criterion. Also, the fuzzy logic technique is employed to control the active and reactive power injected into the network without exceeding the whole system power capacity limits. Note that, in this study, the active and reactive power references of the chosen operating point (the reactive-to-active power ratio: tgφ = Q/P = 0.19) are based on experimental tests. Simulation results show the feasibility and the effectiveness of the proposed fault diagnosis method in terms of fault detection and service continuity in presence of a half-leg open-circuit fault in PWM-inverter without any over-rating of the PV system components or instability.

Techno-economic assessment of grid-connected residential photovoltaic systems considering overvoltage-induced generation curtailment

Grid-connected residential photovoltaic (PV) systems are continuously installed in worldwide communities, predominantly to reduce electricity bills. However, the rapid growth of distributed PV systems inevitably causes overvoltage in distribution networks. Conventionally, PV curtailment is implemented in a grid-connected inverter to prevent the system from overvoltage. Nevertheless, previous studies have failed to consider PV curtailment caused by overvoltage when conducting techno-economic assessments. Therefore, this study conducts a comprehensive techno-economic assessment of grid-connected residential PV systems, considering overvoltage-induced generation curtailment. A volt-watt curtailment strategy is designed based on a droop characteristic. Simulation studies are conducted using real-world load profiles and electricity tariffs retrieved from the Metropolitan Electricity Authority in Thailand. Furthermore, a typical low-voltage distribution feeder in Bangkok, Thailand, is employed using Thailand’s grid code. Simulation results indicate that the optimal PV size could be reduced by up to 58.33% (depending on the numbers and locations of PV systems) when considering overvoltage-induced generation curtailment, resulting in a profitability reduction of 21.34%. It is concluded that considering overvoltage-induced generation curtailment can accurately determine the PV size, preventing oversizing and overvoltage.

Control strategy for current limitation and maximum capacity utilization of grid connected PV inverter under unbalanced grid conditions

Under grid voltage sags, over current protection and exploiting the maximum capacity of the inverter are the two main goals of grid-connected PV inverters. To facilitate low-voltage ride-through (LVRT), it is imperative to ensure that inverter currents are sinusoidal and remain within permissible limits throughout the inverter operation. An improved LVRT control strategy for a two-stage three-phase grid-connected PV system is presented here to address these challenges. To provide over current limitation as well as to ensure maximum exploitation of the inverter capacity, a control strategy is proposed, and performance the strategy is evaluated based on the three generation scenarios on a 2-kW grid connected PV system. An active power curtailment (APC) loop is activated only in high power generation scenario to limit the current’s amplitude below the inverter’s rated current. The superior performance of the proposed strategy is established by comparison with two recent LVRT control strategies. The proposed method not only injects necessary active and reactive power but also minimizes overcurrent with increased exploitation of the inverter’s capacity under unbalanced grid voltage sag.

New Upgrade to Improve Operation of Conventional Grid-Connected Photovoltaic Systems

The incorporation of distributed generation with photovoltaic systems entails a drawback associated with intermittency in the generation capacity due to variations in the solar resource. In general, this aspect limits the level of penetration that this resource can have without producing an appreciable impact on the quality of the electrical supply. With the intention of reducing its intermittency, this paper presents the characterization of a methodology for maximizing grid-connected PV system operation under low-solar-radiation conditions. A new concept of a hybrid system based on a constant current source and capable of integrating different sources into a conventional grid-connected PV system is presented. Results of an experimental characterization of a low-voltage grid–PV system connection with a DC/DC converter for constant-current source application are shown in zero and non-zero radiation conditions. The results obtained demonstrate that the proposed integration method works efficiently without causing appreciable effects on the parameters that define the quality of the electrical supply. In this way, it is possible to efficiently incorporate another source of energy, taking advantage of the characteristics of the GCPVS without further interventions in the system. It is expected that this topology could help to integrate other generation and/or storage technologies into already existing PV systems, opening a wide field of research in the PV systems area.

Improve The Conversion Efficiency and Electrical Output of The Solar PV System by Developing and Implementing a Cooling System

The performance of the grid-connected solar photovoltaic (PV) system in the present study is improved using cooling technology. This technology reduces the temperature of solar PV modules and thus increases their efficiency and lifespan. The current solar PV system has been installed in northern Baghdad in the city of Taji. This study included improving electrical power, conversion efficiency, and performance ratio. All of these improvements are achieved by cooling using water. The performance ratio (PR) and efficiency maximum values of the reference (unimproved) and improved solar PV modules are at 10:10 am at 88.30% & 96.30% and 13.4% & 14.6%, respectively. Improved and reference PV modules maximum solar radiation values occur at 12:00 pm at 1.91 W and 1.566 W, respectively. The maximum solar radiation values of  the reference and improved PV modules are at 12:00 pm at 922.9 W/m2 and 1012.22 W/m2, respectively. The maximum daily gain of the power and solar irradiance are 22% and 10%, respectively. The significant innovation in the study is the successful performance optimization of the grid-connected solar PV system. Using different equations to study performance by following different behaviors. Along with improving and studying CIGS PV module performance.

A Comparative Analysis of Various Simulation Software for grid-connected Residential Building: A Case Study at Jeddah, Saudi Arabia

Saudi Arabia receives significant energy daily from solar radiation, making it one of the best places to use solar energy to meet its energy needs. Grid connected Photovoltaic (PV) systems provide the electrical energy available from the solar radiation into the grid. Different simulation tools are currently available used to assess the performance of solar PV systems. The present study was conducted to design and evaluate the performance of 15 kW grid-connected solar PV system under climatic conditions of in Jeddah, Saudi Arabia. Two simulation models, Helioscope and PVsyst were used in the analysis and the results were then compared. The annual energy production, specific production, energy injected into grid, and annual performance ratio (PR) are evaluated by the software’s around a whole year at the same climatic conditions. Results showed that the maximum annual solar irradiation in May was 2212 kWh/m2. The PR based on PVsyst simulation (78.04 %) was higher than that of the Helioscope simulation (74.8%) by about 4.15%. The maximum energy production in May was about 6.1 kWh. The energy production from the PVsyst and Helioscope is 28.98 and 25.9 MWh/year with 289.8 and 259.13 MWh/year energy injected into the grid, respectively.

A novel learning approach for short-term photovoltaic power forecasting - A review and case studies

Integrating photovoltaic power into the power system can offer significant economic and environmental benefits. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the current power system's planning and operation. Accurate photovoltaic power generation forecasting is crucial for delivering high-quality electric energy to consumers and increasing system reliability.In this study, a multi-stage approach is proposed. In the first stage, three decomposition techniques (Iterative Filtering decomposition, Variational Mode Decomposition, and Wavelet Packet Decomposition) are employed for time series decomposition. Then, for each Intrinsic Mode Function (IMF) component resulting from the decomposition block, five machine learning and three deep learning algorithms are utilized, serving as local forecasting models. In the final forecast phase, the best forecasting result for each regressor is selected during the reconstruction phase. Two years of photovoltaic power data recorded in three grid-connected photovoltaic systems installed in South Algeria were utilized for training and testing the proposed forecasting models. Upon comprehensive analysis and examination of the outcomes, the proposed method exhibits the lowest normalized Root Mean Square Error values across all forecast horizons and monitoring stations. Particularly, for forecasting steps at time intervals +1, +3, and +5, the proposed method attains an average normalized Root Mean Square Error, showcasing its efficacy: 0.709%, 2.097%, and 3.241% for station 1; 1.147%, 3.546%, and 5.347% for station 2; and 0.922%, 2.158%, and 4.539% for station 3. The experimental results underscore the superiority of our approach over conventional regression algorithms, thus substantiating its prowess in delivering robust and competitive performance outcomes.

Harmonics Assessment of Distribution Transformer with Photovoltaic Integration and Unbalanced Loads

Harmonic distortion is one of the monumental power quality problems in an electrical power system. It frequently originated from non-linear loads generating non-sinusoidal steady state waveforms, which may lead to various undesirable effects on distribution transformers such as increased losses, overheating and lifetime reduction. In previous works, the harmonic current contributions from non-linear loads from consumers have been appointed as the main contributor of harmonic issues in the distribution transformers. However, since the integrations of renewable energy as a distributed generation (DG) have exponentially increasing in recent years, the impact of solar photovoltaic (PV) system implementation into distribution system on amplifying additional harmonic contents also needs to be addressed. Therefore, this paper conducts a comparative analysis of voltage and current harmonic profiles within the distribution transformer in three-phase distribution network and three-phase grid-connected photovoltaic (GCPV) system, with both subjected to supply unbalanced non-linear loads. The simulation is carried out using MATLAB Simulink 2021a software and the results are compared according to standards. As a result, the distribution transformer in the GCPV system is proven to contain higher levels of voltage and current harmonic distortion compared to a normal distribution grid.

Model-Based Algorithm for Flexible Power Point Tracking for Photovoltaic Participation in Primary Frequency Regulation

Grid-connected photovoltaic (PV) systems are commonly designed for maximum energy production. However, as their presence grows, revised grid regulations increasingly require these systems to partially adhere to the primary frequency regulation (PFR) by reducing their power production when the grid frequency exceeds the nominal value. Nevertheless, full participation in PFR of PV systems, without battery storage, would require operating with a dedicated active power reserve to be utilized in the event of underfrequency conditions. This paper presents a model-based (MB) flexible power point tracking (FPPT) algorithm for PV systems. Results of a microgrid simulation show that the proposed algorithm enables a PV system with a nominal power of 100 kW to maintain a 20 kW active power reserve under various irradiation and temperature conditions, with a relative error lower than 3%. Furthermore, a simulation of the PV system assisting in primary frequency regulation is presented. Thanks to the proposed strategy, when employing the algorithm, the frequency deviation is reduced both in the event of underfrequency and overfrequency conditions. By enabling operation with a constant active power reserve, as well as a quick adjustment of the produced active power, in the event of frequency imbalance, the proposed MB algorithm is an interesting candidate for achieving complete PFR capabilities of PV systems.

LCL-T Module Reduction of Offshore Photovoltaic Power Generation System Based on Current Feedback-routh Approximation Method

The rapid development of offshore photovoltaic power generation has attracted more and more attention. Aiming at the problem of poor output power quality caused by the randomness and intermittentness of offshore photovoltaic power generation, LCL filters and isolation transformers are widely used on the AC side of photovoltaic power generation systems to improve the power quality of Grid-connected. Although this method has a good suppression effect on harmonics and DC components, its higher order will result in a larger amount of calculation, especially in the simulation of large-scale photovoltaic grid-connected systems, the operating speed will be too slow. In order to increase the simulation rate while ensuring the quality of the output power, the high order LCL-T (LCL filter and isolation transformer) module of the photovoltaic power generation system is reduced in order. When the transformer is not saturated, the LCL-T module reduction method based on the weighted current feedback method is adopted, and the purpose of simplification is achieved through the zero-pole cancellation of the transfer function; When the transformer is saturated, the LCL-T module reduction method based on the Routh approximation method is adopted, Use advanced mathematics formulas to reduce the order of high-order system equivalent processing. The PSCAD/EMTDC simulation software verifies the off-grid characteristics and simulation rate of the reduced-order model.

The Influence of Solar Irradiance and Temperature on The Pll-Synchronized Inverter Within Grid-Connected PV Systems

This study assesses the influence of solar irradiance and temperature on αβPLL synchronized inverters by investigating their intricate  relationship within grid-connected PV systems. The intermittent nature of the DC side of these systems can significantly impact their  performance. However, the extent of this impact remains uncertain when employing the stationary reference frame PLL (αβPLL)  algorithm for synchronization with the utility grid. Employing simulation in MATLAB 2020a and a Model Based Design approach in the  Simscape toolbox of SIMULINK, we conducted this investigation through two test scenarios: one with constant solar irradiance and  varying temperature, and the other with varying solar irradiance and constant temperature. Our results indicate that fluctuations in solar  irradiance and temperature have minimal effects on the output parameters of grid-connected PV systems when synchronized with the  stationary reference frame PLL (αβPLL) algorithm. This conclusion is supported by the total harmonic distortion values, which all fall  within the specified limits of IEEE Standard 929- 2000. These findings underscore the potential of αßPLL synchronization as a promising  technique, capable of effectively synchronizing grid inverters even in the presence of DC input distortion. This proficiency in grid  integration holds promise for enhancing the integration of renewable energy sources into the grid, thereby bolstering power availability  for household distribution. 

TWO STAGE THREE PHASE GRID CONNECTED PHOTOVOLTAIC SYSTEM

The new age of electricity generation is renewable energy. There is no other room, but to use renewable resources in energy generation to make the planet safer, and sustainable for the future. Renewable resources come in a variety of ways, but solar energy is by far the most practical. These days, solar panels are widely used due to their ease of use, accessibility, and efficiency in producing power. This Project mainly represents the simulation of compact design of a grid-tied solar system for energy production. Several fundamental characteristics, including temperature, irradiance, voltage, and current, affect the efficiency of a solar panel. Hence sudden change in any of these parameters leads drop in power and voltage. The precept aim is to improve the output voltage of PV array and boost converter there by reducing the harmonic content in grid current. The main points discussed here are the MPP tracking algorithm, the synchronization of the inverter and the connection to the grid. Tracking the dc voltage and current allows MPP calculation which gives the inverter to function efficiently. Key Words:- Grid Connected PV Systems, DC-DC Boost Converter, Inverter, Harmonic Filter, MPPT and Phase Locked Loop(PLL).

An integrated skip convolutional network with residual learning and feature extraction for point and interval prediction of solar radiation

Spinning reserve based on solar radiation prediction can ensure the secure operation of large-scale grid-connected photovoltaic systems, but irrational spinning reserve can lead to substantial economic losses and even grid collapse. Therefore, it is imperative to identify characteristic patterns of solar radiation and establish an effective solar radiation prediction model. However, existing hybrid models often struggle to efficiently recognize and leverage input features, compromising the robustness of the model. To address this limitation, this study proposes an integrated skip-convolutional network with residual learning and feature extraction (InSCNet), which enhances the capability to represent information data and thereby improves the stability and accuracy of solar radiation prediction by employing a series of feature extraction and prediction blocks with residual learning. InSCNet includes a sophisticated feature extraction module to effectively address the underutilization of feature information, and it incorporates residual learning, skip-convolution, and recursive networks in the prediction module, reducing the risk of gradient vanishing or gradient explosion while enhancing prediction accuracy. Additionally, an interval estimation method with adaptively chosen distributions is introduced, which extends the prediction interval estimation method. The proposed InSCNet is rigorously evaluated using datasets from three major cities in Pakistan. Experimental results demonstrate that InSCNet outperforms existing solutions in both point and interval predictions.

Enhanced FOPID Controller Based Cascaded Inverter for Grid-Connected Photovoltaic System

An enhanced controller is proposed to investigate grid-connected photovoltaic systems employing cascaded two-level inverters. The primary focus is on optimizing power output, achieved through the development, modeling, and testing of photovoltaic systems using the suggested upgraded controller. This advanced controller, specifically a Fractional Order PID (FOPID) controller, incorporates the Ant-Lion Optimizer (ALO) algorithm for enhanced performance compared to conventional controllers. The FOPID controller’s reliability is emphasized, and further improvements are pursued by optimizing gain parameters through the prescribed method. Power supply under both schemes is examined, and the controller’s performance in extracting maximum power under specified operating conditions is deemed satisfactory. To validate the proposed approach, practical implementation is conducted using the MATLAB/Simulink platform. The effectiveness of proposed and conventional methodologies for analyzing the id and iq currents is assessed. A comparison is drawn between the suggested approach and current methods such as the base controller, GSA, and ABC methodologies. In comparison to the existing methods, the suggested FOPID controller demonstrates superior performance in maintaining the DC link voltage, achieving an efficiency of 94%, while the GSA method reaches 91.5%, the ABC method achieves 91%, and the base controller achieves 90%.

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, deep digital twins (DDT) with information gain stacking sparse autoencoders (IGSSAEs). Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes information gain ratio to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a stacked sparse autoencoder fault classification network is designed, incorporating multi-level feature compression to enhance the model’s stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method’s effectiveness with a diagnostic accuracy of 98.4%.

A novel dynamic/adaptive K-nearest neighbor model for the prediction of solar photovoltaic systems’ performance

The inherent variability in solar Photovoltaic (PV) systems motivates developing dynamic and adaptive data-driven models capable of accurately predicting the systems’ performance. A dynamic/adaptive K-Nearest Neighbor (K-NN) model to estimate the conversion efficiency of a solar PV system is proposed in this work. The proposed model estimates the conversion efficiency of a test pattern based on three steps: (i) identifying the similar historical operational conditions experienced by the system, (ii) assigning a weight to each condition based on its similarity to the test pattern at hand, and (iii) computing the weighted average conversion efficiency of the test pattern. Thus, the proposed model is adaptive and dynamic as the nearest neighbors and the associated weights vary from one test pattern to another. The proposed model is applied to a 7.98 kWp rooftop grid-connected solar PV system at the Hashemite University in Jordan. Its effectiveness is evaluated by resorting to the Mean Square Error (MSE), Accuracy, Coefficient of Determination (R2), and Adjusted R2 (R2adjusted), and comparing the results with the traditional K-NN and other techniques adequately developed in the same context, such as Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Multiple Linear Regression (MLR). Results show that the proposed model exhibited superior performance across all metrics, with a performance gain reaching up to 45.2 % and 6.8 % for the MSE and R2adjusted, respectively, compared to the base MLR. Further, the simplicity and computational efficiency of the proposed model validate its practical deployment in predicting PV system performance.

Power quality improvement in grid-connected photovoltaic system under partial shading condition

ABSTRACT Solar systems are becoming widely used renewable energy source since photovoltaic cell generates high power output. However, during partial shading conditions (PSC), the existing MPPT techniques failed to determine the actual maximum point of the photovoltaic (PV) cell, and the photovoltaic system is stuck at a local maximum thus output is gradually reduced. The Optimized Grid-Connected Photovoltaic system aims to extract the most power from the solar array under partial shading conditions. It uses an optimisation algorithm named Sovereign Butterfly Optimization Algorithm and a sequence controller to optimize power value selection. The voltage harmonics and phase delay cause the system to be in an unbalanced state, so the proposed model uses an anomaly obliteration filter fusion technique in which the Butterworth filter is utilised for reducing voltage harmonics and removing phase delay completely by avoiding negative sequence by converting three-phase current to dq0 frames. To increase the overall power quality in the photovoltaic system without synchronization problems, a Subsystem Synchronization Control Strategy is applied which effectively synchronizes the active and non-active currents injected into the grid. Simulated results using the proposed model showed high tracking accuracy of 99.23% and minimum power oscillation of 2.53%, thus enhancing the overall power quality.

Power quality assessment and compliance of grid-connected PV systems in low voltage networks using microinverters

AbstractSolar PV has experienced unprecedented growth in the last decade, with the most significant additions being utility-scale solar PV. The role of grid inverters is very critical in feeding power from distributed sources into the grid. With the increasing growth of grid-tied solar PV systems (both rooftop and large-scale), the awareness of power quality issues has risen with new regulations and standards to ensure the stability of the power grid. The power quality of microinverters has been investigated under steady solar irradiation and PV power source and also under real outdoor conditions in compliance with the accepted solar PV integration requirements. The current total harmonic distortion (THD) measured for the studied microinverter under outdoor conditions far exceeded the current THD for the study under steady indoor conditions and was beyond the accepted standard. However, the voltage THD outputs for the two studied cases were in good agreement with the grid codes. The voltage and current THD for the 400 Wm−2 (60 Wp) and the 1000 Wm−2 (146 Wp) scenarios under the steady solar irradiation (solar PV power) were 2.24%, 13%, and 2.27, 6.93%, respectively. The voltage and current THDs for the outdoor study were 2.03% and 14.28% for Solarex (pc-Si module), 1.94%, and 27.43% for Juta (mc-Si modules), and 1.97% and 33.6% for Dunasolar (a-Si glass module). Results showed a strong correlation between the intermittence of solar radiation and the current THD. 67%, 54%, and 37% of the recorded power factor for Dunasolar, Juta, and Solarex modules, respectively, exceeded the limits prescribed by the standards.

Application of optimized photovoltaic grid-connected control system based on modular multilevel converters

Photovoltaic power generation is a promising method for generating electricity with a wide range of applications and development potential. It primarily utilizes solar energy and offers sustainable development, green environmental benefits, and abundant solar energy resources. However, there are many external factors that can affect the output characteristics of Photovoltaic cells and the effectiveness of the grid-connected control system. This study describes the introduction of Modular Multilevel Converter (MMC) technology into photovoltaic power generation systems to improve power generation efficiency. It proposes optimizing and improving the technology by adjusting the temperature and magnitude of lighting and combining traditional algorithms to propose a composite control algorithm. The photovoltaic power generation system employs the modular multi-level converter technology to enhance power generation efficiency alongside optimization and improvement. The temperature and size of light are regulated alongside the traditional algorithm to introduce the composite control algorithm. The improved composite algorithm surpasses the traditional one after experimental comparison of the results. The testing of a model photovoltaic power grid-connected system shows that the combination of modular multi-level converter technology and a photovoltaic grid-connected system, incorporating composite proportional integral control and quasi-proportional resonant control algorithms, yields improved results and feasibility. With rationality and effective control. The simulation results show that at 0.5 s, the light intensity suddenly increases from 750 to 1000 W/m2, and the direct-current voltage suddenly increases for a short time, but then decreases rapidly and finally returns to a stable level close to the rated voltage. From this, it can be seen that when the light intensity continues to change, the voltage value on the direct-current bus side of this MMC grid-tied photovoltaic system can still be maintained close to the rated value, ensuring the operational stability of the entire system. Sensibly and effectively controlled. The implementation of MMC technology in photovoltaic power generation systems enhances power generation efficiency, whilst simultaneously supporting the advancement of photovoltaic power generation and contributing towards environmental protection in the long term.