Large-scale Photovoltaic Plants Research Articles

Novel risk index integrating practical operation limits enhances probabilistic contingency ranking for large-scale photovoltaic plant planning

This research addresses the pressing need for heightened grid security amid increasing uncertainties in photovoltaic PV generation. The research problem lies in the limitations of conventional contingency analysis metrics, failing to adequately consider both contingency occurrences and uncertainties inherent in PV generation. In response, a comprehensive algorithm is proposed that introduces a novel severity function framework, enhancing traditional contingency ranking metrics. This approach incorporates grid remedial actions and refines line and bus voltage classification by considering available correction time, aiming to offer a more robust security assessment. Motivated by the imperative to address uncertainty in PV generation, the proposed work builds on established analysis tools. A probabilistic load flow algorithm manages PV generation uncertainties, utilizing historical data for contingency incidence uncertainty. Additionally, a probabilistic model for PV plants integrates historical solar data, deriving hourly probability density functions to meet grid code requirements, including reactive power considerations. The justification for this work lies in the algorithm's demonstrated efficacy, validated on the IEEE 14-bus network. Results highlight its ability to identify risks associated with line overloading and bus voltage breaches. Comparative evaluations underscore proper coupling buses for security, favoring distributed capacity to mitigate line overloading risks. The study's key results emphasize voltage risk amplification with reactive power omission, stressing the significance of compensation strategies. This research addresses a critical problem, presenting a comprehensive algorithmic solution to enhance grid security amidst uncertainties in PV integration. Findings offer valuable insights for strategically interaction between large scale PV plants and electrical grid, contributing to an improved grid security paradigm in a dynamic and uncertain energy model.

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning.

With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.

A comprehensive analysis based on GIS-AHP to minimise the social and environmental impact of the installation of large-scale photovoltaic plants in south Spain

This article aims to propose a methodology to assess and prioritise the territorial factors considered when selecting the location of large-scale photovoltaic plants. It therefore seeks to answer the current social conflict problem regarding their installation, particularly in the rural context. The territory zoning is conducted from a holistic perspective, taking into account its social and environmental impact.Therefore, a series of analysis and restriction parameters are established and the Analytic Hierarchy Process by Geographic Information Systems is used. Those criteria are superimposed on the landscape of the Jimena Depression. Located in the south-west of Spain, it is coastal landscape countryside where energy companies currently exert great pressure to expedite applications for photovoltaic projects.A mapping characterization is thus produced that determines which areas should be considered excluded, along with the zones of greater or lesser environmental and social awareness. Assessing this zoning, along with the energy demands and the location of the existing plant projects leads to a series of questions to be considered in the planning of those municipalities. They respond to the need to conduct an overall study, and ones on land protection, the cumulative impact regulation, and on implementing social measures to offset the changes caused.

Energy and exergy analysis of a 20-MW grid-connected PV plant operating under harsh climatic conditions

Abstract Large grid-connected photovoltaic (PV) plants are increasingly being installed around the world, including in harsh desert climates. Evaluating their performance can help improve the design and operation of PV systems. This study performed an energy and exergy analysis of a 20-MW grid-connected PV plant under desert climatic conditions in southern Algeria over a period of 1 year. The PV plant was divided into two 10-MW subsystems. Energy analysis was performed using actual irradiation, power output, wind speed and ambient temperature data. The annual average energy efficiency of the plant and subsystems was 10.82%, 10.95% and 10.69%, respectively. Solar radiation had the most significant impact (80% determination coefficient) on thermal exergy loss. The exergy efficiency of the plant was lower than the literature values, likely due to the harsh desert conditions. The comprehensive energy and exergy analysis provides insights into the performance of large-scale PV plants in desert climates. The results can help guide the system design and operation improvements for such conditions. Regular cleaning and cooling could improve performance.

Infrared thermography monitoring of solar photovoltaic systems: A comparison between UAV and aircraft remote sensing platforms

The continuous increase in the number and scale of solar photovoltaic power plants requires the implementation of reliable diagnostic tools for fault detection. With the recent advances in low-weight, high-precision, and fast-response thermal cameras, along with professional aerial platforms, aerial infrared thermography (aIRT) is currently the most popular method for non-destructive, fast, and relatively inexpensive monitoring of photovoltaic (PV) power plants. Typically, aerial inspections are conducted using a UAV (drone) equipped with a thermal camera to produce a report indicating detected thermal defects, typically associated with failures. The main purpose of this paper was to compare the thermographic results for two different PV plants provided by two remote sensing-based approaches: the classical UAV-mounted thermal camera survey and the inspection by high-speed thermal cameras mounted on an airplane. The post-processing of thermal patterns showed good agreement between the results provided by the two aerial platforms, with an overlap of thermal anomalies detected up to 98%. An economic analysis demonstrated that, while airplane surveys incur higher costs (compared to UAV surveys) due to the hire of vehicles and more expensive instrumentation, they, on the other hand, require a reduced amount of time and may be more convenient when inspecting large-scale PV plants or multiple PV plants located within a close area.

Comprehensive ground coverage analysis of large-scale fixed-tilt bifacial photovoltaic plants

In the design of large-scale photovoltaic plants, decisions on array pitch and tilt play an essential role in life-cycle economics. These design issues have been studied extensively in traditional monofacial systems. However, bifacial systems, which are in increasing demand, are being studied and face challenges in system modeling, particularly in characterizing rear irradiance. Studies considering bifacial panels in plant layouts are recent and must be extensive. Existing studies are based on computationally intensive software and are limited to specific configurations. This study presents the development and validation of an analytical model of a multirow bifacial photovoltaic plant against Bifacial Radiance and DUET ray-tracing software. Comprehensive ground coverage and array tilt analyses are carried out for all ranges of configuration parameters (clearance height, albedo, collector length, bifaciality, or yield loss) and latitudes of up to 60° using this model. The results of extensive simulations were used to train the artificial neural networks. These mathematical architectures allow easy and fast execution of the proposed model with a slight accuracy loss and are provided in the supplementary material. In-depth analysis and developed tools will facilitate decision-making to obtain photovoltaic layouts more efficiently in terms of energy and economy for large-scale bifacial plants.

Numerical model study on influences of photovoltaic plants on local microclimate

The construction of large-scale photovoltaic plants will inevitably change the local environment. We consider that the influences of such increasing large-scale photovoltaic installations on radiation, temperature, wind profile and other factors within the region can be described in a layer of photovoltaic canopy. This paper improves the UCRC-Solar model for photovoltaic plants, with reference to the calculation of sensible heat flux of the underlying forest surface. Model verification was done with measured data from the Red Rock photovoltaic plant, Arizona, USA. In this study, it indicates that photovoltaic plant has locally generated heat island effects in daytime. The calculation of local microclimate changes before and after the construction of the photovoltaic plants and, the impact of photovoltaic coverage finds an increase in the temperature daily range and a reduction in the ground temperature by about 5 °C. Furthermore, a 15 % increase in photovoltaic coverage increased the daily 3 m air temperature difference by 0.55 °C and reduced the ground temperature by 3.6 °C in the day and 1.1 °C at night, respectively. In the future, rain and snow processes would be considered to further explore the microclimate impact of photovoltaic plants.

Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

Land suitability evaluation of large-scale photovoltaic plants using structural equation models

Land suitability evaluation is vital for large-scale photovoltaic (PV) plant construction. However, subjectivity in previous methods affects result reliability. We propose a new method using structural equation models to develop PV suitability indices, and demonstrate its validity in Zhejiang Province, China. By combining theoretic design with data mining technique, our method measures the structured paths between the overall index and evaluation indicators, exhibiting good model fit, flexible and objective weighting. Our results suggest that the spatial distribution of targeted PV plants based on suitability indices largely differs from that of observed ones. Most suitable areas for PV construction in Zhejiang Province are situated along the eastern coast, northern plain and river networks. The estimated PV technical potential for areas with and above moderate suitability is approximately 1,600 TWh/year, equivalent to 830 million tons/year of carbon emission reduction. Our study provides PV development strategies for similar areas in China and beyond.

Cloud-edge collaborated dust deposition degree monitoring for distributed photovoltaic systems

After installation, the long-term efficient operation dominates the profitability of photovoltaic (PV) generation systems. Unlike large-scale PV plants, distributed PV systems are relatively dispersed, and belong to different owners, whose main obstacle for maximizing the profit is the lack of professional maintenance. This paper therefore proposes a cloud-edge collaborated dust deposition degree monitoring scheme for distributed PV systems without employing additional equipment, sensors or meteorological data, which just assumes the operational and historical data of all distributed PV systems in the same area to give the guidance whether the distributed PV system needs cleaning. In implementation, temporal and interactive models are established based on the operation characteristics of distributed PV systems, whose overall classification accuracy could reach more than 98%. In addition, in order to improve the adaptivity of monitoring scheme, this paper proposes a data-based random grouping method, which avoids the model mismatch caused by the addition or withdrawal of PV arrays. Finally, experimental verifications are carried out to prove the effectiveness of the presented scheme.© 2017 Elsevier Inc. All rights reserved.

A novel method for the site selection of large-scale PV farms by using AHP and GIS: A case study in İzmir, Türkiye

Effective and sustainable climate-friendly policies to reduce carbon dioxide emissions in line with the 2020 European Green Deal are necessary. Accordingly, renewable energies assumed a critical role, rendering the site selection of these systems very crucial. The present study proposes a novel approach to the site selection of large-scale photovoltaic (PV) plants using a combination of analytic hierarchy process (AHP) and geographic information system (GIS). In the study, the weights of criteria used for selecting solar PV panels are adjusted according to the installed capacity of the PV plant. The land of cost is included as a criterion in the AHP for the first time in PV plant site selection. Besides, a novel method called optimality-based site growing (OBSG) is introduced to further analyze the suitable sites obtained from GIS simulations and to determine the most suitable locations of PV farms. The proposed method is demonstrated with a case study of Türkiye, and the results show that the method effectively determines the most suitable locations for large-scale PV plants.

The impact of photovoltaic power plants on surface energy budget based on an ecohydrological model

Solar photovoltaic (PV) generation has become the major type of solar energy utilization as the world's energy demand grows. However, the layout of large-scale public PV plants changes the original underlying conditions and has an impact on the local surface energy budget and distribution. Based on the ecohydrological model T&C, taking the fixed utility-scale PV plants as the typical object proposes a PV module, and the T&C-PV model is developed to simulate the impact of PV plants on the regional surface energy budget. Compared with the original T&C model, a substantially improvement of the simulation of energy flux in the PV plant paved underlying surface has been achieved in the T&C-PV model. This model is applied to PV power plants in Xinjiang Province, China, and the results show that the sensible heat flux increased by 12% and the latent heat flux decreased by 25%, with the percentage of transpiration decreasing from 90.1% to 74.5% and the percentage of evaporation increasing from 9.9% to 25.5% after the PV power plants are set up.

Harmonic Resonance Suppression With Inductive Power Filtering Method: Case Study of Large- Scale Photovoltaic Plant in China

Taking actual engineering as a case study, this article provides a novel scheme of applying the inductive power filtering method (IPFM) to resolve the harmonic resonance issues in large-scale photovoltaic (PV) plant. By using IPFM's special structure and dual zero-impedance design, the impedance network of large-scale PV plant is reshaped while the performance of power filters is improved, so that the harmonic resonance due to the interaction between the inverters and the power grid is suppressed. First, the topology and components of the IPFM-based large-scale PV plant are introduced. The three-phase mathematical model in harmonic domain is then established. Based on the deduced transfer matrix, the simplified circuit of the studied large-scale PV plant is obtain-ed. Moreover, the IPFM's resonance damping mechanisms on resonant frequency shift and harmonic amplification mitigation are analyzed. Both simulation and engineering tests verify the feasibility of the proposal.

Image registration based fault localization in panoramas of mountain-mounted PV plants

Large-scale photovoltaic (PV) plants have been widely distributed in the mountains. However, the complex geographical environment and the irregular arrangement of PV strings bring great challenges to maintenance. In this paper, a novel scheme combining an improved YOLOv5s model and image registration algorithms is proposed to detect and locate module faults in mountain-mounted PV plants (MPVs) via aerial dual-light videos and panoramas. The improved YOLOv5s used to detect faults in IR images becomes more lightweight owing to the integration of MobileNetV3 and ECA-Net. To realize fault localization in panoramas of MPVs, image registration algorithms are adopted. IR and visual images are simultaneously extracted frame by frame from dual-light videos. Faults detected in the IR image are first mapped to the corresponding visual image by the homography (H1) matrix. To map faults from the visual image to the panorama, another registration between the visual image and panorama is achieved. Multi-scale matching between the visual image and the panorama is first performed to reduce the area of registration. The conversion of grayscale probability density function (GPDF) of the visual image and the adoption of AKAZE feature detection algorithm make the homography (H2) matrix used for registration more accurate. A MPV with a power generation of 212.85 kW is used for testing. The precision and recall of fault detection are 88.31% and 91.67%, respectively. The accuracy of locating faults to the corresponding modules is 85.3%, and the recall of locating is 94.12%. Experimental results show that the proposed scheme can effectively identify faults and accurately locate faults in MPVs.

A layer-2 solution for inspecting large-scale photovoltaic arrays through aerial LWIR multiview photogrammetry and deep learning: A hybrid data-centric and model-centric approach

Defective components within solar photovoltaic (PV) arrays overheat, resulting in particular temperature patterns under the long-wave thermal infrared (LWIR) spectrum. The detection and on-field localization of these patterns is of paramount aid to the operations and maintenance of PV installations. In this context, we develop a two-layer end-to-end inspection solution for the detection, quantification and on-field localization of overheated regions on PV arrays from LWIR UAV imagery. Layer 1 generates a georeferenced orthomosaic of the inspected site via a Structure from Motion-MultiView Stereo (SfM-MVS) photogrammetric acquisition/post-processing workflow. Layer 2 is a tile-based deep semantic segmentation stage that extracts and quantifies the affected regions from the generated orthomosaic. We collect aerial images from 103 PV sites, comprising approximately 342000 modules. After a SfM-MVS workflow, we produce and annotate 7910 orthorectified unique affected tiles, posteriorly augmented to prepare the state-of-the-art dataset in terms of size and representativeness. Through a training/cross-validation and test process, we investigate the implementation of 9 models in the segmentation process: FCN, U-Net, FPN, DeepLab, LinkNet, DANet, CFNet, ACFNet and TransU-Net, each of which experimented with 2 backbones: ResNet50 and DenseNet121. The models feature efficient encoder-to-decoder feature map transfers, pyramidal feature recognition, spatial and channel attention, feature co-occurrence, class center as well as vision transformers. The best performance is achieved by FPN-DenseNet121, with a mean mIoU of 93.44% and an F1-score of 96.39% on our test set. The two-layer solution takes the best of the data-centric and model-centric paradigms, alongside addressing the limitations of conventional inspection procedures. It is put into a concrete application framework, where it provides a pixel-based and a tile-based quantification of the affected regions within a PV plant. The results are promising, and the selected model can be deployed efficiently for extensive aerial monitoring of large-scale PV plants.

Carbon capture and utilization: More than hiding CO2 for some time

Carbon capture and utilization: More than hiding CO2 for some time

A New Cloud-Based IoT Solution for Soiling Ratio Measurement of PV Systems Using Artificial Neural Network

Solar energy is considered the most abundant form of energy available on earth. However, the efficiency of photovoltaic (PV) panels is greatly reduced due to the accumulation of dust particles on the surface of PV panels. The optimization of the cleaning cycles of a PV power plant through condition monitoring of PV panels is crucial for its optimal performance. Specialized equipment and weather stations are deployed for large-scale PV plants to monitor the amount of soil accumulated on panel surface. However, not much focus is given to small- and medium-scale PV plants, where the costs associated with specialized weather stations cannot be justified. To overcome this hurdle, a cost-effective and scalable solution is required. Therefore, a new centralized cloud-based solar conversion recovery system (SCRS) is proposed in this research work. The proposed system utilizes the Internet of Things (IoT) and cloud-based centralized architecture, which allows users to remotely monitor the amount of soiling on PV panels, regardless of the scale. To improve scalability and cost-effectiveness, the proposed system uses low-cost sensors and an artificial neural network (ANN) to reduce the amount of hardware required for a soiling station. Multiple ANN models with different numbers of neurons in hidden layers were tested and compared to determine the most suitable model. The selected ANN model was trained using the data collected from an experimental setup. After training the ANN model, the mean squared error (MSE) value of 0.0117 was achieved. Additionally, the adjusted R-squared (R2) value of 0.905 was attained on the test data. Furthermore, data is transmitted from soiling station to the cloud server wirelessly using a message queuing telemetry transport (MQTT) lightweight communication protocol over Wi-Fi network. Therefore, SCRS depicts a complete wireless sensor network eliminating the need for extra wiring. The average percentage error in the soiling ratio estimation was found to be 4.33%.

48-Pulse Voltage-Source Converter Based on Three-Level Neutral Point Clamp Converters for Solar Photovoltaic Plant

The demand for high-power converters for use in large-scale solar photovoltaic (PV) plants increases as the plant size continues to grow. The multipulse voltage-source converter (VSC) configurations are the viable option for high-power solar PV converter applications because they improve conversion efficiency due to low switching losses and allow direct integration with a medium-voltage (MV) grid. In this article, a 48-pulse VSC based on the fundamental frequency switching (FFS) three-level neutral point clamp (NPC) converter with a start–delta transformer configuration is used for large-scale solar PV plant application. It is also used as a PV-static synchronous compensator (STATCOM) to provide the required reactive power support to the grid to regulate the voltage. The solar PV plant configuration for a 100-MW capacity is modeled in MATLAB and implemented in the real-time OPAL-RT platform to verify its design and controls. The harmonics, static, and dynamic performances are demonstrated in detail.

Enhancing static voltage stability of a power system in the presence of large-scale PV plants using a battery energy storage control scheme by the probabilistic technique

This paper aims to investigate the impact of large-scale photovoltaic plant (LS-PVP) controllers on the voltage stability (VS) of a power system. Besides, a new control strategy is proposed for the large-scale battery energy storage system (LS-BESS) to enhance a power system integrated with LS-PVPs, especially VS. To this end, a probabilistic technique, based on the stochastic nature of LS-PVPs and loads, is employed to accurately evaluate the impact of LS-PVPs on the system. Next, a new coupling between the optimization algorithm and the probabilistic technique is proposed to determine the optimal number, location, and capacity of the LS-BESSs by the probabilistic particle swarm optimization (PPSO) algorithm. The objective function is then used to simultaneously maximize the total VS and minimize the total power losses, line loading, as well as investment costs of LS-BESSs. Furthermore, technical performance indices (TPIs) are used for a more comprehensive investigation to compare the proposed solution to the synchronous generators. Finally, several disturbances occurring in the Nordic system are used to validate the effectiveness of the proposed approach, using the Monte Carlo simulation (MCS) method. According to the results, when a fault occurs, a system integrated with LS-PVPs in the stressed area does not operate better than synchronous generators. Therefore, an advanced control method is required to reduce undesirable effects of LS-PVPs. Using the proposed solution, voltage instability was prevented, LS-BESS investment costs were minimized, and negative effects of LS-PVPs on the system were reduced. Besides, costs required for expanding the transmission network as well as communication between controllers and reactive power compensation devices were reduced.