Performance Of Solar Plants Research Articles

Using Machine Learning for Performance Classification and Early Fault Detection in Solar Systems

The steady increase in the world’s population has directly influenced global climate change, resulting in catastrophic environmental consequences. This has created an immediate need for scientists from interdisciplinary domains like clean technology innovation in solar energy and computer science to join in the effort to save the world for future generations. As such, the United Nations has set a goal to ensure global access to affordable, sustainable, and clean energy. As a leading influential G20 economy, Saudi Arabia has recently established the Green Saudi initiative to align with the UN goal for enhancing the use of green energy. However, research in this area is sparse and greater effort is still required. This work is among the first to address the issue of enhancing and expanding the use of clean energy by means of studying the data collected from solar plants around Saudi. We used machine learning-based methods to assess the energy output performance of solar plants and employed the collected data to train the models to make early detection of faults. Our models achieved the highest performance at an accuracy score of 98.85% and 0.98 weighted F-score using the J48 model trained on a publicly available dataset of 874 instances collected from 26 different sites across Saudi. We anticipate that the findings of this work to serve as testbed to facilitate further research in this area and enhance the early fault detection in solar energy stations.

Reliability based modeling of the performance of solar plants with multistate PV modules

Solar energy is widely used as a renewable energy source in the world. Photovoltaic modules are the main components of a photovoltaic system to generate the solar power from the solar radiation. The photovoltaic modules may have multistate working conditions and different performance levels depending on the solar radiation. Each component can be in different states, namely, complete failure, partial working, and perfect functioning. In this study, we present a model for solar power systems with PV modules having various levels of operational performance. We develop a reliability model for the system's power regarding the $m$ threshold value that is the minimum required total performance level for the system. This model reflects the performance levels of PV modules and working probabilities of modules. The problem is considered under different conditions regarding the dependency of two types of multistate PV modules. Two numerical examples are also conducted to evaluate the reliability and power generated by two solar plants located in two different regions. Beta and Weibull distributions are used for the numerical calculations to differ solar radiation regimes in the regions.

The water cost effect of hybrid-parallel condensing systems in the thermo-economical performance of solar tower plants

The importance of considering the water price in the analysis of the impact of dry versus hybrid condensing systems in the thermo economical performance of solar tower plants was demonstrated in this work. The dry condensing system consists of several induced-draft air-cooled condenser cells (ACCs) and the hybrid system consists of a parallel system where the condensing steam is split between the ACCs and a surface steam condenser where circulating water is cooled in a wet mechanical-draft cooling tower. The influence of the operating parameters of either the dry or wet cooling systems on the cooling load and fan power consumption were studied. Then, for a given condensing system (a system with a defined number of installed ACCs units and cooling tower units) and given the dry-air and wet-bulb air temperatures, the operating parameters were optimized to maximize the revenues of the power plant. This optimization depends on the water-to-electricity price ratio R , showing that at low ambient temperature when this ratio increases it is not profitable to turn on the cooling towers since the water cost is not counterbalanced by the higher cycle efficiency obtained with the lower condensation temperature. Finally, the annual operation and the LCOE and NPV of the CSP plant located in Dunhuang were analyzed for both dry and hybrid condensing systems with different number of ACCs and wet towers, showing that the most cost-effective configuration is the 16 ACCs with 3 wet cooling towers for water-to-electricity price ratio R = 4 ($/m 3 )/($/kWh e ) and R = 5 ($/m 3 )/($/kWh e ), but for R = 10 ($/m 3 )/($/kWh e ), the best option is with only 2 wet towers. • The thermo-economical behavior of dry and hybrid condensing systems is compared. • The annual operation analysis of a real solar tower plant is presented. • The condensing systems operation parameters were optimized to maximize the revenues. • The water cost effect in the thermo-economical performance of the solar plant is shown.

The design of technical requirements in public solar auctions: Evidence from India

Solar technology diffuses across the globe as countries transition from fossil to renewable energy. Little solar-specific experience and capacity in newly adopting countries can result in technical failures and lower solar plant performance. This contributes to making the investment in solar plants in newcomer countries risky and may undermine political targets of solar energy deployment. One solution suggested by international organizations is for policymakers in adopting countries to include international quality standards as technical requirements in public auctions. Here, we develop a conceptual framework on how international quality standards could help build a solar sector. As a case study, we analyze the explanatory factors of technical requirements in 100 public auctions of utility-scale solar photovoltaic plants carried out in India between 2013 and 2019. Our findings suggest that more international quality standards are required in auctions in which the government rather than a private actor ultimately carries the commercial risk. On the other hand, local content requirements and attracting foreign investors do not correlate with technical requirements. We argue that using minimal quality standards is unlikely to promote local technological catch-up or attract long-term foreign investments but transfers the techno-commercial risk from the government to the private sector.

IoT based smart solar energy monitoring systems

Today our society needs more energy for day-to-day activities due to rapid globalization and industrialization. In order to minimize the stress and dependency on fossil fuel, the most sustainable way is to harness suns energy. Solar energy is characterized by low cost, environment friendly, does not require frequent maintenance and most importantly, negligible maintenance cost. However, there is a requirement of monitoring solar installation in order to maximize the output power by setting real-time angles with the suns position. This can be easily done by the adoption of IoT technologies. The Internet of Things is often utilized in the measurement of solar energy for efficiency. It's also utilized to maintain the solar plant's health. The cost of renewable energy technology is decreasing throughout the globe, which encourages the construction of large-scale solar plants. Automation of plant observation on such a large scale of preparatory deployments requires sophisticated systems reliant on Internet connections since the majority of field units are situated in isolated, remote locations and therefore are not overseen from a central office. The project is based on the use of the most up-to-date, cost-effective method for remotely monitoring a solar plant performance by the inclusion of IoT. It can assist with plant maintenance, problem diagnostics, and real-time monitoring.

PV Fault Detection Using Positive Unlabeled Learning

Solar array management and photovoltaic (PV) fault detection is critical for optimal and robust performance of solar plants. PV faults cause substantial power reduction along with health and fire hazards. Traditional machine learning solutions require large, labeled datasets which are often expensive and/or difficult to obtain. This data can be location and sensor specific, noisy, and resource intensive. In this paper, we develop and demonstrate new semi supervised solutions for PV fault detection. More specifically, we demonstrate that a little-known area of semi-supervised machine learning called positive unlabeled learning can effectively learn solar fault detection models using only a fraction of the labeled data required by traditional techniques. We further introduce a new feedback enhanced positive unlabeled learning algorithm that can increase model accuracy and performance in situations such as solar fault detection when few sensor features are available. Using these algorithms, we create a positive unlabeled solar fault detection model that can match and even exceed the performance of a fully supervised fault classifier using only 5% of the total labeled data.

Flexible Electricity Dispatch of an Integrated Solar Combined Cycle through Thermal Energy Storage and Hydrogen Production

In this work, the flexible operation of an Integrated Solar Combined Cycle (ISCC) power plant has been optimized considering two different energy storage approaches. The objective of this proposal is to meet variable users’ grid demand for an extended period at the lowest cost of electricity. Medium temperature thermal energy storage (TES) and hydrogen generation configurations have been analyzed from a techno-economic point of view. Results found from annual solar plant performance indicate that molten salts storage solution is preferable based on the lower levelized cost of electricity (0.122 USD/kWh compared to 0.158 USD/kWh from the hydrogen generation case) due to the lower conversion efficiencies of hydrogen plant components. However, the hydrogen plant configuration exceeded, in terms of plant availability and grid demand coverage, as fewer design constraints resulted in a total demand coverage of 2155 h per year. It was also found that grid demand curves from industrial countries limit the deployment of medium-temperature TES systems coupled to ISCC power plants, since their typical demand curves are characterized by lower power demand around solar noon when solar radiation is higher. In such scenarios, the Brayton turbine design is constrained by noon grid demand, which limits the solar field and receiver thermal power design.

Extended exergy analysis of a solar driven water production plant via reverse osmosis

• Novel small scale solar desalination plant provides a solution for water scarcity. • Proposed desalination plant using solar energy studied via extended exergy analysis. • Extended exergy analysis evaluates solar plant performance and sustainability. • Labor and capital exergy equivalents are 42.68 MJ/workhour and 12.09 MJ/Euro. • Products having much higher specific exergy content could be produced. Water scarcity and contamination of available water are becoming one of the most complex problems worldwide and they compromise economic development, global sustainability, and human supply, among others. Water is required in almost all human activities and also for planet equilibrium in terms of biodiversity. In this research a new home scaled water desalination plant, using a reverse osmosis technology and driven with solar energy is analyzed via extended exergy analysis, which is one of the most accurate methods for evaluating both energy performance and sustainability. The proposed system, that is presented and analyzed, consists of a solar concentrator having 1000 concentrating rate, a Photon Enhanced Thermionic Emitter, a Dye-Sensitized Solar Cell for the electricity generation and a Reverse Osmosis Desalination Plant (RODP) for water purification using the produced electricity. This system allows water desalination in small scale plants requiring low density energy sources and can produce sustainable water for multiples uses, such as domestic use or agricultural, among others. The most important results are labor and capital exergy equivalents, 42.68 MJ/workhour and 12.09 MJ/Euro , respectively, and an exergy destruction of 389.700 GJ for all the system annually. The proposed technology can be extended and used in different locations and the Extended Exergy Analysis can be used as a powerful tool for both design and optimization.

Solar System Array by Fuzzy Logic based on MPPT Algorithm

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm.

Supercritical CO2 Mixtures for Advanced Brayton Power Cycles in Line-Focusing Solar Power Plants

This work quantifies the impact of using sCO2-mixtures (s-CO2/He, s-CO2/Kr, s-CO2/H2S, s-CO2/CH4, s-CO2/C2H6, s-CO2/C3H8, s-CO2/C4H8, s-CO2/C4H10, s-CO2/C5H10, s-CO2/C5H12 and s-CO2/C6H6) as the working fluid in the supercritical CO2 recompression Brayton cycle coupled with line-focusing solar power plants (with parabolic trough collectors (PTC) or linear Fresnel (LF)). Design parameters assessed are the solar plant performance at the design point, heat exchange dimensions, solar field aperture area, and cost variations in relation with admixtures mole fraction. The adopted methodology for the plant performance calculation is setting a constant heat recuperator total conductance (UAtotal). The main conclusion of this work is that the power cycle thermodynamic efficiency improves by about 3–4%, on a scale comparable to increasing the turbine inlet temperature when the cycle utilizes the mentioned sCO2-mixtures as the working fluid. On one hand, the substances He, Kr, CH4, and C2H6 reduce the critical temperature to approximately 273.15 K; in this scenario, the thermal efficiency is improved from 49% to 53% with pure s-CO2. This solution is very suitable for concentrated solar power plants coupled to s-CO2 Brayton power cycles (CSP-sCO2) with night sky cooling. On the other hand, when adopting an air-cooled heat exchanger (dry-cooling) as the ultimate heat sink, the critical temperatures studied at compressor inlet are from 318.15 K to 333.15 K, for this scenario other substances (C3H8, C4H8, C4H10, C5H10, C5H12 and C6H6) were analyzed. Thermodynamic results confirmed that the Brayton cycle efficiency also increased by about 3–4%. Since the ambient temperature variation plays an important role in solar power plants with dry-cooling systems, a CIT sensitivity analysis was also conducted, which constitutes the first approach to defining the optimum working fluid mixture for a given operating condition.

Generation of synthetic solar datasets for risk analysis

In this paper, we present a method for the synthetic generation of long-term time series of coupled 1-min global horizontal solar irradiance (GHI) and direct normal solar irradiance (DNI). This method requires an input of 10–15 annual time series of hourly DNI and GHI values that can be retrieved from satellite-based irradiance databases, and produces 100 years of 1-min solar radiation values that can be used for risk analysis or as input for solar plants performance simulation in a wide range of scenarios.The method consists of the conjunction of three steps. The first one, based on a stochastic procedure, is used to generate 100 years of monthly DNI and GHI values. The second step consists of the subsequent generation of daily irradiation values. To that end we have used a bootstrapping technique. The synthetic daily sequences have the same serial correlation structure as the observed data. The last step consists of the generation of 100 years of 1-min solar irradiance data out of the daily values based on the non-dimensionalization of the daily profiles by the clear sky envelope approach. The method has been applied for the location of Seville showing satisfactory results in terms of cumulative distribution functions (CDFs) of the synthetic data. We obtain an average monthly KSI (Kolmogorov-Smirnov test integral) index of 0.11 kWh/m2 for GHI and 0.26 kWh/m2 for DNI. The minimum KSI value is 0.07 kWh/m2 for GHI and 0.15 kWh/m2 for DNI obtained in January. The maximum KSI value is 0.19 kWh/m2 for GHI and 0.34 kWh/m2 for DNI obtained in June and August respectively.

Thermodynamic assessment of a hybrid particle-based concentrated solar power plant using fluidized bed heat exchanger

In this study, we modify a practical hybrid system developed by National Renewable Energy Laboratory (NREL) and analyze it thermodynamically through energy and exergy methods for analysis, performance assessment and evaluation purposes. In this system, the solid particles are used as the direct heat transfer medium in solar receiver, thermal energy storage and fluidized bed heat exchanger. Furthermore, the hydrodynamic aspects of the fluidized bed (FB) are conducted to reveal the pressure drop and the fluidizing-gas compression power as two important factors in the FB consideration. The present results show that a application of the particle FB heat exchanger in the Rankine power cycle and the pressurized fluidized bed combustion (PFBC) in the air Brayton cycle enable the whole solar plant performance to achieve the energy and the exergy efficiencies of 50% and 53.8% respectively. In addition, the particle energy storage and the solar tower sub-systems potentially can achieve the high energy efficiencies up to 88% and 44.2% and the exergy efficiencies of 96% and 35.1%, respectively. Moreover, highest exergy destructions take place in the PFBC due to chemical reaction and heliostat field and particle receiver due to the high-temperature gradients.

Thermodynamic Optimization of Supercritical CO2 Brayton Power Cycles Coupled to Line-Focusing Solar Fields

An opportunity for increasing the parabolic solar power plant efficiency is substituting the actual subcritical Rankine power cycles with the innovative s-CO2 Brayton cycles. In this paper, three configurations are assessed: the recompression cycle (RC), the partial cooling with recompression cycle (PCRC), and the recompression with main compression intercooling cycle (RCMCI), with one reheating stage. The thermodynamic parameters are optimized with three algorithms: SUBPLEX, UOBYQA, and NEWUOA, and the results validated with thermoflow Software. The parabolic troughs and linear Fresnel solar collectors are studied with different heat transfer fluids (HTFs): Solar Salt, HITEC XL, Therminol-VP1, Syltherm 800, and Therminol 75. The dual-loop solar field (SF), combining thermal oil and molten salt (MS) in the same solar plant, is also analyzed. The plant power output and plant energy efficiency are translated into SF aperture area and cost at design point. From the point of view of the plant efficiency and SF cost, the PTC and LF solar collector with Solar Salt as HTF coupled to a s-CO2 Brayton RCMCI cycle is selected as the optimum design solution and compared with the actual PTC Rankine solar plant performance at design point. The total recuperator conductance (UA) plays an important role in optimizing the plant performance, limited by the minimum heat exchangers (HX) pinch point. The UA increment could compensate the HX pressure drop and the compressor inlet temperature (CIT) increment, both impacting very negatively in the s-CO2 plant performance.

Performance of solar photovoltaic installations: Effect of seasonal variations

A 20kWp Solar Photovoltaic (SPV) system was set up on the library roof-top in Indian Institute of Science, Bangalore, India. This roof-top photovoltaic (RTPV) system partly powers the Central Office of IISc. The main objective of setting up this SPV system was to study the performance of solar plants under different seasons and climatic conditions of Bangalore. The system has been producing an average daily yield of approximately 80kWh for the past two years which translates to an annual yield of 28.9MWh. The overall yield of the system up to 14th September 2015 is 70MWh. This work focuses on the evaluation of the performance of SPV systems using the popular grading systems, namely Capacity Utilization Factor (CUF) and Performance Ratio (PR). The CUF of the SPV system is 16.5%, which lies within the range of CUF of well-performing solar plants located in India. Average Performance Ratio (PR) of the SPV system is around 85%, which indicates that the performance of the SPV system is satisfactory. PR of the SPV system is correlated with the behaviour of SPV modules in different seasons, with module temperature (Tmod) as the key factor of comparison. In summer, the SPV modules attain maximum efficiency (ηmax) at Tmod of 45°C, but in winter, it is at 55°C. In summer, for Tmod>45°C, module efficiency (η) reduces by 0.08% per degree rise in temperature. In monsoon, for Tmod>35°C, η reduces by 0.04% per degree rise in temperature. In post-monsoon period, for Tmod>38°C, η reduces by 0.06% per degree rise temperature. However, in winters, the modules attain ηmax at Tmod of 55°C, without much drop in efficiency. This is mainly because of intermittent natural cooling that takes places at the surface of the modules, due to cool breeze and lower ambient temperatures.

Energy yield loss caused by dust deposition on photovoltaic panels

Large-scale solar plants are generally located in semi-arid and desert lands where abundant sunlight is available for solar energy conversion. These plants, however, suffer from two major environmental degradation factors: high ambient temperature and high concentration of atmospheric dust. Degradation of solar collectors’ performance caused by soiling results in a considerable loss of energy yield in all solar plants of the world. Dust and other particulate accumulation on solar collectors causes transmission loss. This is true with respect to transmission losses in photovoltaic (PV) and concentrated photovoltaic (CPV) systems, and for reflection losses in concentrated solar power (CSP) systems. We present here a brief review of the energy yield losses caused by dust deposition on solar collectors, with particular emphasis on flat-panel photovoltaic (PV) systems. The review includes some of the major studies reported on energy-yield losses on solar plants in operation in several regions of the world. In addition, laboratory-soiling studies are also included. We report on degradation in the performance of solar plants based on the type of solar collectors, geographical location, local climate, and exposure period of the collectors absent any manual cleaning. An analysis of the advantages of cleaning processes that include natural, manual, automatic, and passive methods is presented. Our objective is to provide solar plant designers with a database for predicting anticipated soiling losses in different parts of the world, and for assessing effective cleaning methods for restoring a system’s energy yield.

Optimal operation in solar trough plants: A case study

One of the most important challenges in the use of solar energy is to make it economical. This can be done by reducing investment and operating costs and by increasing solar plants performance. This paper presents a three-layer hierarchical control strategy to optimize the operation of solar trough plants. The first layer deals with the operational planning of the solar plant and it computes the electrical power to be produced and delivered. The second layer computes the optimal set-point for the solar plant, aiming at producing the electrical power provided by the first layer and optimizing the economic profit. The third layer addresses the problem of tracking the set-point calculated by the second layer. A model of the Plataforma Solar de Almería (PSA) solar trough plant is used to illustrate the proposed algorithm. Simulations show that important increments in electricity production are obtained by using the proposed control scheme.

An improved model to evaluate thermodynamic solar plants with cylindrical parabolic collectors and air turbine engines in open Joule–Brayton cycle

An improved model to analyze the performance of solar plants operating with cylindrical parabolic collectors and atmospheric air as heat transfer fluid in an open Joule–Brayton cycle is presented. In the new model, the effect of the incident angle modifier is included, to take into account the variation of the optical efficiency with the incidence angle of the irradiance, and the effect of the reheating of the fluid also has been studied.The analysis was made for two operating modes of the plants: with variable air flow rate and constant inlet temperature to the turbine and with constant flow rate and variable inlet temperature to the turbine, with and without reheating of the fluid in the solar field. When reheating is used, the efficiency of the plant is increased.The obtained results show a good performance of this type of solar plant, in spite of its simplicity; it is able to compete well with other more complex plants operating with different heat transfer fluids.

Effect of climatic variability on the performance of solar plants with interseasonal storage

A calculation method of the accumulated probability curves from the solar fraction provided by plants with interseasonal solar energy storage is proposed. The variability of the insolation sequences was achieved by randomly generating the monthly clearness indices according to normal distribution, and subsequently, the values of daily available energy by means of Markov’s matrix library [5]. The results obtained from simulations of the generated sequences of collected energy were subjected to experimental verification.

98/00454 A dynamic model for predicting solar plant performance and optimum control

98/00454 A dynamic model for predicting solar plant performance and optimum control

A dynamic model for predicting solar plant performance and optimum control

Planning over the intermediate and long terms for the operation of solar energy systems must be based on the use of measured data and performance models of system components. We present a dynamic model for predicting the thermal performance of a low-temperature solar power plant. We analyse the optimal control regime for energy management. Validation of the model was performed on a large solar plant located in the north of Tunisia. The results showed good agreement between measured and model values. During the cold season, the plant efficiency increases considerably when a greenhouse is connected to the power plant.