Large-scale Photovoltaic Plants Research Articles

Transient Stability Analysis of Large-Scale PV Penetration on Power Systems

Due to the growing demand on the electricity and global warming issues, many countries increased their attention to explore in renewable means of energy production to integrated into conventional power systems as an alternative to conventional generation methods. Among the alternative renewable energy sources, the solar PV system today is an established source of renewable energy due to simple, fast developing of PV technology and dramatic drop of the cost. The difference in the characteristics of the power generation between generators and solar photovoltaic energy systems may make changing and affecting regarding to the integrity, reliability, security and stability of the electrical power system. This research will investigate the potential effects and benefits of connecting of large-scale photovoltaic (LSP) systems to the conventional power grid in terms of transient stability analysis under three cases, increasing the system load, compensation of the power shortage from the synchronous generator (SG) with LSP plant and the replacement of SG by LSP plant. The different level of the LSP penetration are considered in some assumed cases to examine the variation of the rotor angle and bus voltage after a three phase fault, This research deals with a representation of a real network (Duba – Wajh grid) as a case study by using the PowerFactory program (DIgSILENT 2019). The thesis will focus on monitoring and observing the bus voltage magnitude, rotor angle, active and reactive power. The results of the transient stability analysis have been plotted and presented in time domain. The simulation results exhibit favorable impacts of increased PV penetration on transient stability performance.

Modelling and robust control of digitally controlled large‐scale PV plants

Large-scale photovoltaic (PV) plants may be destabilised by the large grid impedance introduced by transformers and long transmission lines. When considering the digital control delay of grid-connected inverters, the problem will become more serious. In this study, the equivalent impedance model of digitally controlled large-scale PV plants is established, considering that the values of the parameters of the LCL filter follow Gaussian distributions. Then, the system stability can be analysed conveniently, and the stability analysis shows that the digital control delay will make the system robust to large grid impedance worse. To enhance the stability-robustness of the system, an H ∞ robust controller is proposed in this study. By properly selecting the weighting functions, the desired tracking error performance and robustness are achieved on the basis of the mixed-sensitivity optimisation control design. Compared with the traditional dual current loop control strategy, the H ∞ robust controller improves significantly the system stability-robustness against large grid impedance. Finally, simulation and experimental tests are carried out in this study, and the results demonstrate the effectiveness of the proposed H ∞ robust controller.

Enabling Utility-Scale Solar PV Plants for Electromechanical Oscillation Damping

This paper presents a new control method to enable large-scale solar photovoltaic (PV) plants to damp electromechanical oscillations. The proposed step-down modulation (SDM) control method is based on active power modulation, and it does not require curtailment as in other approaches. After an oscillation event is detected, the PV panel voltage is controlled to transiently deviate the power from its maximum power point (MPP), this power margin is used to modulate active power until the oscillation event is mitigated. Then, the SDM control restores the PV power to its MPP, and it is reset to operate for the next event. The control design, panel voltage strategy, and implementation is tested in a two-area system. A comparison of the SDM control with a curtailment-based PV damping control is also presented, using a test case of the 179-bus WECC system with six large-scale PV plants, showing the improved damping capability of the proposed method.

Multi-criteria decision-making model for assessment of large photovoltaic farms in Brazil

The decision of the location for the installation of photovoltaic power plants is a critical issue for large-scale projects due to climatic aspects, the proximity to other facilities, and the presence of protected environmental areas. The objective of this study is to propose a model capable of indicating the best location for the implantation of large-scale photovoltaic plants. According to the proposed modeling, the relevant factors were identified to be considered in the choice of the ideal place, which were combined with the GIS-MCDM tools. The analysis of the areas was processed by the gvSIG software, using AHP methods for the weighting of the factors and TOPSIS for ranking the alternatives. In application of this model, a total of 453 areas were identified that could receive a large photovoltaic installation, equivalent to 1823.35 km2 of the study area. Among these study areas, according to our model rating, 67.23% of them are inadequate, 0.52% is good, 12.34% are very good, and only 19.91% are excellent. Since the areas considered excellent are concentrated in the extreme south of the analyzed region, a place of low index of slope and near the substation. In order to validate the quality of the solution obtained by the proposed model was applied a detailed analysis with two scenarios. In the first one are used weights equal to all the criteria and in the second, the MAUT method was applied to rank the alternatives. The modeling proposed in this paper combining with GIS-MCDM is applicable to similar investigations in other regions and also to evaluate the spatial adequacy of other renewable energy technologies.

An Unmanned Inspection System for Multiple Defects Detection in Photovoltaic Plants

Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the efficient and reliable operation of large-scale photovoltaic plants. This article presents an algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle. The proposed solution is composed of three stages to efficiently and accurately analyze various forms of module defects. First, the Kirsch operator is employed to identify the anomalous regions, which can significantly reduce the computational complexity, and error rate. Afterward, a deep convolutional neural network is adopted to extract defect features. Finally, a multiple classification support vector machine is developed to facilitate the defects detection decision-making. The proposed solution is extensively evaluated by the comprehensive dataset collected from real-world solar photovoltaic plants. The experimental results clearly demonstrate the effectiveness of our solution for photovoltaic modules diagnosis with multiple visible defects.

Cost Effective Offline Reconfiguration for Large-Scale Non-Uniformly Aging Photovoltaic Arrays Efficiency Enhancement

In the past decades, a huge number of large-scale photovoltaic (PV) plants have been constructed all around the world. However, the capital and operational costs can be high, which limits their widespread applications. Because the installed PV modules often operate in harsh environments (i.e. storm, high temperature, dust, hail, etc.), non-uniform aging phenomena of PV modules cannot be avoided and it impacts adversely on the performance of PV plants, especially in the middle and late periods of their service life. This paper develops an offline PV module reconfiguration strategy for the non-uniform aging PV array to mitigate this effect. In order to maximize the economic benefit, electricity price and labor cost are considered in the offline reconfiguration. The Branch-and-Bound based optimization algorithm is proposed to find the highest economic benefit. In order to verify the proposed algorithm, MATLAB software-based modelling and simulation have been performed in four case studies, in which a typical 42kW PV array with 7 × 20 connection is employed in a testing benchmark, and the manpower cost and electricity price in USA, UK, China and Turkey are considered in the case study. The results demonstrate that the economic benefit from a non-uniformly aged PV array is further enhanced with the proposed reconfiguration method.

Understanding and accounting for the effect of exchange rate fluctuations on global learning rates

Learning rates are a central concept in energy system models and integrated assessment models, as they allow researchers to project the future costs of new technologies and to optimize energy system costs. Here we argue that exchange rate fluctuations are an important, but thus far overlooked, determinant of the learning-rate variance observed in the literature. We explore how empirically observed global learning rates depend on where technologies are installed and which currency is used to calculate the learning rate. Using global data of large-scale photovoltaic (≥5 MW) plants, we show that the currency choice can result in learning-rate differences of up to 16 percentage points. We then introduce an adjustment factor to correct for the effect of exchange rate and market focus fluctuations and discuss the implications of our findings for innovation scholars, energy modellers and decision makers. Learning rates are a measure of reduction in costs of energy from technologies such as solar photovoltaics. These are often estimated internationally with all monetary figures converted to a single currency, often US dollars. Lilliestam et al. show that such conversions can significantly affect the learning rate estimates.

A Review for Solar Panel Fire Accident Prevention in Large-Scale PV Applications

Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas.

Time series forecasting of solar power generation for large-scale photovoltaic plants

Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 h of operation data taken from a 20 MW grid-connected PV station in China.

A Novel Frequency Support Control Method for PV Plants Using Tracking LQR

Growing level of Photovoltaic (PV) generation challenges the conventional generation control in power systems, as the current PV systems do not possess inertia or governor system. However, a supervisory control can be adopted for such PV systems to provide frequency support. In this article, a novel controller for large-scale PV plants is proposed for this purpose. Different than other approaches which only focus on emulating droop and inertial response of local PV, the proposed approach uses a tracking linear quadratic regulator (LQR) based controller to help the system frequency effectively track that of a designed reference system with given inertia and droop constants. To design the LQR, an unknown input observer (UIO) is adopted to estimate the system states, as well as the unknown disturbance. We also introduce a small-signal PV system model which is suitable for designing frequency support controls. The model is validated against the detailed nonlinear PV system model, and the effectiveness of the proposed controller is demonstrated using a standard test system. Test results also show that the proposed method achieves the desired frequency response more effectively than the alternative method from literature, while the later method is liable to over-compensation of frequency response at the price of requiring more power reserve in PV.

An approach for fault detection and location in solar PV systems

The task of fault detection and diagnosis in large-scale photovoltaic (PV) plants is expected to be a major challenge as more and more plants with increasingly large capacities continue to come into existence. To maintain safety, reliability, and productivity of large-scale PV plants it is essential to develop approaches that allow automatic detection and location of any mal-operation among thousands of PV modules. This paper proposes an approach to detect PV plant faults through the generation of fault indicator signals called ‘residuals’ for each string and the comparison of residuals with a threshold value. Furthermore, a regression-based approach is proposed to estimate fault location as a function of fault current and irradiance level measurements. The proposed approach is demonstrated by specifically focusing on intra-string line-line faults. Various line-line fault case studies are verified through simulations and validated on an experimental setup in a solar PV plant. Initially the approach is developed for a fixed size PV array, but subsequently extensions of the proposed approach to any array size are developed and presented. The generalisation potential of the approach is demonstrated through successful validation on multiple PV array configurations.

Measured and Calculated DC Arc-Flash Incident Energy in a Large-Scale Photovoltaic Plant

There is a growing amount of high-power dc equipment being deployed, including solar photovoltaic (PV) plants, but very few studies have measured and quantified dc arc-flash risks. Most dc arc-flash literature is based on the theory and, when their theoretical calculations are applied to real-world conditions, can significantly deviate from actual measured values. For PV, the non-linearity of the power source (i.e., PV modules) creates an additional level of uncertainty for theoretical approaches. This paper quantifies the thermal hazards from arc flash in a real-world 1000-kWdc PV plant by measuring arc current, arc voltage, and incident energy (IE) and then compares results against existing IE models. The study reveals that none of the available models adequately represent real-world hazards. It is also found that a PV source acts as a sustainable constant-current source during an arc-flash event. This paper is an important first step toward developing an improved model that more accurately assesses dc arc-flash risk in a PV system.

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46–13.46%.

Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM

Photovoltaic (PV) solar power generation is always associated with uncertainties due to weather parameters intermittency. This poses difficulties in grid management as solar penetration rate rise continuously. Thus, accurate Photovoltaic (PV) power prediction is required for the successful integration of solar energy into the power grid, and short-term forecasting (minutes-1 day ahead) is significant for real-time power dispatching. Day-ahead power output time-series forecasting methods are proposed in this paper, in which ideal weather type and non-ideal weather types have been separately discussed. For ideal weather conditions, a forecasting method is proposed based on meteorology data of next day for ideal weather condition, using long short term memory (LSTM) networks. For non-ideal weather conditions, time-series relevance and specific non-ideal weather type characteristic are considered in LSTM model by introducing adjacent day time-series and typical weather type information. Specifically, daily total power, which is obtained by discrete grey model (DGM), is regarded as input variables and applied to correct power output time-series prediction. Prediction performance comparison between proposed methods with traditional algorithms reveal that the RMSE accuracy of forecasting methods based on LSTM networks can reach 4.62% for ideal weather condition. For non-ideal weather condition, the dynamic characteristic is effectively described by proposed methods and the proposed methods obtained superior prediction accuracy.

A Dual-Active-Clamp Quasi-Resonant Isolated Boost Converter for PV Integration to Medium-Voltage DC Grids

With the development of renewable energy generation, medium-voltage dc collection in large-scale photovoltaic (PV) plants has become a research focus with less losses and less system complexity. Maximum power point tracking control of PV panels requires converters with a large input voltage range and a stable output, among which active-clamp isolated boost converters qualify for PV applications, due to their high and variable step-up ratio. In this paper, an improved dual-active-clamp quasi-resonant isolated boost converter is proposed to increase the efficiency and decrease the voltage pressure across switches. The proposed topology has an additional resonant branch with a small resonant capacitor on top of the conventional active-clamp isolated boost converters, where the capacitor resonates with the leakage inductor of the transformer to improve the voltage and current waveforms during switching transients. The proposed converter achieves soft switching of all the switches at both turn-on and turn-off transients, while retaining good voltage regulation ability. Additionally, the induced voltage spikes due to the parasitic inductors in the conventional topology are suppressed well. Discussions on detailed steady-state operation, zero-voltage switching (ZVS) regions, and design considerations are given to show the advantages of the proposed converter. Simulation and experimental results are presented to verify the expected performances.

Probability risk assessment of island operation event for large scale photovoltaic plant

ABSTRACTOriginal methodology for the calculation of island operation event occurrence possibility is proposed for the photovoltaic power plants connected to medium voltage grid. Within the methodology description, event fault tree with number of pairs of basic initial events is discussed, where probability for the basic events are defined and flow chart for the proposed calculation is given. The novelty in the approach for probability calculation includes also proposal for development of frequency distribution functions for concerned power generation and consumption variables which have to be modelled for the probability calculations, for which the triangular distributions curves have been chosen. Being boundary conditions for definition of non-detected island event, borders of non-detection zones are defined for the common island detection protection functions: over frequency and under frequency, over voltage and under voltage and rate of change of frequency functions. Comparison of the island event probability is presented on case study example, with conclusion that standard voltage and frequency protection alone are not satisfying, but ROCOF protection could be suitable, subject to particular local grid characteristic and calculative verification of probability.

Study of joint temporal-spatial distribution of array output for large-scale photovoltaic plant and its fault diagnosis application

The temporal-spatial distribution of photovoltaic (PV) array output in large-scale PV plant has following features: (i) In the DC (direct current) side of PV plant, the outputs from different arrays display a substantial correlation with each other. (ii) The dynamic difference for a PV array is covered by the random fluctuation of PV output. (iii) It is difficult to explain the occurrence and evolution process of PV faults with operation data only. Currently, most fault diagnosis methods for the PV arrays do not take advantage of the temporal-spatial distribution information contained in the operation data. To solve these above problem, a new fault diagnosis method using the spatio-temporal distribution characteristics of photovoltaic array output is proposed. Here, the temporal fluctuation and spatial distribution characteristics of PV array output under different fault conditions were analyzed. The spatio-temporal composite function is constructed, and then used to set fuzzy rules for fault diagnosis of the PV array. Finally, an example is used to verify the effectiveness of the proposed method, and the results show that the method can describe the characters of spatial and temporal distribution of PV output under faults conditions, and it can effectively classify different faults.

Operation of a Grid-Tied Cascaded Multilevel Converter Based on a Forward Solid-State Transformer Under Unbalanced PV Power Generation

The application of cascaded multilevel converters in large-scale photovoltaic plants connected to the utility grid has many advantages, such as the absence of sinusoidal filters and line-frequency transformers. Improved overall performance under partial shading conditions is possible due to the distribution of arrays among a large number of maximum power point trackers. In this situation, although the cells operate at different power levels, the three-phase currents injected into the point of common coupling must be balanced. A suitable control technique is required to achieve this goal. This paper presents a complete algorithm to handle three-phase power balance and single-phase power harvesting simultaneously. Simulation results assuming a medium-voltage system connected to a typical utility grid illustrate the dynamic and steady-state performance capability of the proposed strategy. Experimental results of a low-voltage, solid-state transformer-based, three-cell prototype are also included.

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models.

Real-Time Coordinated Voltage Control of PV Inverters and Energy Storage for Weak Networks With High PV Penetration

There are more large-scale photovoltaic (PV) plants being established in rural areas due to availability of low-priced land. However, distribution grids in such areas traditionally have feeders with low X/R ratios, which makes the independent reactive power compensation method less effective on voltage regulation. Consequently, upstream step voltage regulator (SVR) may suffer from excessive tap operations with PV-induced fast voltage fluctuations. Although a battery energy storage system (BESS) can successfully smooth PV generation, frequent charge/discharge will substantially affect its cost effectiveness. In this paper, a real-time method is designed to coordinate PV inverters and BESS for voltage regulation. To keep up with fast fluctuations of PV power, this method will be executed in each 5 s control cycle. In addition, charging/discharging power of BESS is adaptively retuned by an active adjustment method in order to avoid BESS premature energy exhaustion in a long run. Finally, through a voltage margin control scheme, the upstream SVR and downstream PV inverters and BESS are coordinated for voltage regulation without any communication. This research is validated via a real-time digital simulator MatLab cosimulation platform, and it will provide valuable insights and applicable strategies to both utilities and PV owners for large-scale PV farm integration into rural networks.