Operation Of Solar Power Plants Research Articles

ASPECTS OF THE FURTHER DEVELOPMENT OF PHOTOENERGY ACCORDING TO THE MATERIALS OF THE SCIENTIFIC AND PRACTICAL CONFERENCE «RENEWABLE ENERGY AND ENERGY EFFICIENCY IN THE 21st CENTURY» 2023

The main results of reports at the "Solar Energy" section were considered and summarized, in particular, one of the areas of solar energy – photoenergy. Specialists from Ukraine, Poland, the Czech Republic and the Republic of Uzbekistan took part in the work of the section in this area. A total of 22 reports were presented, which related to the issues of increasing the energy efficiency and reliability of the operation of solar power plants, the peculiarities of the operation of solar autonomous power plants and installations, new technologies for the production of photo modules, etc. For the most part, the reports focused on the creation and operation of energy-efficient and reliable solar power plants.

An interpretable horizontal federated deep learning approach to improve short-term solar irradiance forecasting

Solar irradiance forecasting is critical in the planning and operation of solar power plants for production scheduling, energy trading, and maintenance planning. Accurate solar irradiance forecasting is crucial for efficient and stable power systems. Various deep learning (DL) methods have been researched in solar irradiance forecasting because of their powerful capabilities of nonlinear mapping. However, traditional DL models face challenges with small sample sizes, irradiance fluctuations, and data privacy concerns. On this foundation, it is also critical to ensure the interpretability of artificial intelligence (AI) models, as interpretable and trustworthy predictive models can help operators make effective decisions. Focusing on the above-mentioned issues, a privacy-preserving, interpretable, and DL-based model is proposed to improve the forecast accuracy of solar irradiance while ensuring the data available but not visible to each participant and prediction comprehensibility. This model is constructed by horizontal federated framework (HFF), self-attention (SA) mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) network, named HFF-based SA-CNN-LSTM. A series of comprehensive experiments spanning various climate regions are designed to assess model performance under different federated conditions. The experiments show that this method significantly improves forecasting accuracy, especially in scenarios with incomplete data and highly fluctuating irradiance when federated with other datasets, and the model exhibits interpretability, adjusting attention for precise predictions at different time points. Moreover, the effects of different federated scales on the predictive performance of the target model are fully discussed for future practical applications. These findings hold significant promise for enhancing the accuracy of irradiance forecasting, contributing to energy efficiency, grid stability, and data privacy.

Перспективы Крайнего Северо-Востока России на производство "зеленого" водорода с использованием климатических источников возобновляемой энергии

For the first time, Russia's Far North-East prospects for hydrogen production by the method of water electrolysis, using climatic energy sources and ultra-fresh mountain-river water, are comprehensively assessed. Taking into account the identified regional features, it is calculated that, with the current technical level of development, the amount of hydrogen produced in the region will be controlled just by the amount of used ultra-fresh mountain-river water. Thus, using no more than 10 percent of the general runoff (18 billion m3 water), it is possible to annually obtain 2x109 tons of "green" hydrogen through the operation of wind and solar power plants. To accommodate the latter, it is ecologically feasible to use the areas in mountain tundras and forest-tundras on flattened and flat watersheds.

Organisation of the Structure and Functioning of Self-Sufficient Distributed Power Generation

During the operation of solar and wind power plants, it is necessary to solve issues related to the guaranteed capacity of these plants, as well as the frequency stabilisation in the power system where they operate, and maintain an operating mode of self-sufficiency conditions. One of the solutions to these problems is the use of energy storage systems. This article proposes a mathematical model for the study of frequency and power regulation processes in power systems with distributed generation, which includes renewable energy resources and energy storage systems. The novelty of the model lies in the possibility of determining energy cost indicators based on instantaneous energy power data. The model allows us to estimate the conditions under which distributed generation becomes self-sufficient. The results of the model calculations of two variants of power system operation, which includes wind generators with a capacity of 1500 MW, demonstrate the ability of the proposed model to accurately reproduce the dynamics of the frequency stabilisation process. The calculation results of the energy-economic indicators of a real power system combined with a powerful subsystem of wind generation and a battery-type energy storage system prove the competitiveness of self-sufficient renewable energy power plants.

Solar Electricity Energy: Utilization of Renewable Energy Sources to Realize a Sustainable Campus at UIN Raden Fatah Palembang

Universities play an essential role in achieving Indonesia's 2060 carbon-neutral target. Emissions in the campus are mainly sourced from its electrical energy, transportation, and water use, along with other wastes. Electrical energy from solar is renewable energy, free from pollutants and noise pollution. This paper examines the sustainable energy performance of solar energy at the Universitas Islam Negeri Raden Fatah Palembang (UINRFP) and its development efforts. From August 2022 to March 2023, a solar power plant (SPP) operation with a capacity of 7.74 kWp has generated 6,281.5 kWh of electricity (28.55 kWh/day). Using a radiation factor of 0.8 and an equivalent sun hour of 4.61 (3-5 hours/day), the rooftop capacity of all buildings on Jakabaring Campus is 2,000 kWp, the potential of SPP electricity of UINRFP will exceed 39.3% compared to the 2022 electricity demand. If the capacity is, half the electricity generated from renewable energy meets 70% of the demand. Gradually UINRFP continues to develop its energy sources towards sustainable energy. The development of other research-scale renewable energy with hybrid solar energy and wind power for the aesthetic lighting function on the rectorate building has the potential to generate 290 kWh/year. In addition, micro-hydro energy sources from campus canals (water volume ±9,400 m3) and biomass are potential renewable energy sources at UINRFP for a sustainable campus in the future and contributing to reducing the impact of climate change.

Performance analysis of MW-scale grid connected rooftop and ground-mounted solar power plants installed in Assam, India

Solar energy-based electricity generation has become an essential component of sustainable development and meeting the growing demand for energy. A study is conducted to compare the performance of rooftop and ground-mounted solar power plants installed in Assam, India. The study found that ground-mounted plants generated an average of 3.41 kWh per kWp of energy per day, while rooftop plants generated 2.89 kWh per kWp. The study also observed that rooftop solar power plants had non-operating days due to inverter faults, grid failures, and poor weather conditions, which were 1.1 and 0.95 days, respectively. The capacity factors were 14.2 % and 11.4 %, while the performance ratios were 77.8 % and 70.1 % for ground-mounted and rooftop plants, respectively. The average system efficiency of ground-mounted plants and rooftop plants is 12.59 ± 0.26 % and 11.88 ± 0.11 %, respectively. The study used the Weibull distribution function to describe the daily normalized energy generation frequency curve, which revealed a peak probability of 0.38 at a daily normalized energy generation of 2.9 kWh per kWp for rooftop plant and 0.34 at a daily normalized energy generation of 3.4 kWh per kWp for ground-mounted plant. The installation of a solar power plant has resulted in an average reduction of Rs. 43.7 lakhs per year in the University's electricity bill. This study demonstrates the successful operation of rooftop solar power plants, which not only reduces the electricity expenses of organizations but also has a positive impact on sustainability.

Maximizing Annual Energy Yield in a Grid-Connected PV Solar Power Plant: Analysis of Seasonal Tilt Angle and Solar Tracking Strategies

Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The study investigates the impact of fixed tilt, seasonal tilt, SAHST (single-axis horizontal solar tracking), and SAVST (single-axis vertical solar tracking) on energy yield, considering technical, economic, and environmental aspects. In the first scenario, a fixed tilt angle of 31 degrees was employed. The second scenario explored the use of seasonal tilt angles, with a summer tilt angle of 15 degrees and a winter tilt angle of 30 degrees. The third scenario analyzed SAHST. Finally, the fourth scenario focused on implementing SAVST. SAVST proved to be an exceptional solution, showcasing a remarkable increase in annual energy yield, and generating an additional 6680 MWh/year, 6336 MWh/year, and 5084 MWh/year compared to fixed, seasonal, and SAHST scenarios, respectively. As a result, surplus energy yielded an income of USD 554,440.00 per year compared to fixed tilt. However, the investment cost for the solar tracking system amounted to USD 1,451,932, accompanied by an annual operation and maintenance cost of 0.007 USD/W/year. The analysis revealed a promising payback period of 3 years, confirming the economic feasibility of this investment. The findings underscore the effectiveness of different strategies for optimizing solar power generation in the Kandahar region. Notably, the installation of SAVST emerged as an influential solution, significantly increasing power production. These research outcomes bear practical implications for solar tracking strategies for addressing the load challenges faced by Kandahar province and offer valuable insights for the operators and operation of solar power plants in similar regions.

Global Horizontal Irradiance in West Africa: Evaluation of the WRF-Solar Model in Convection-Permitting Mode with Ground Measurements

Abstract The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Methods and software for anomalies searching in the telemetry data of a solar power plant based on the normalized power analysis

Objectives. In connection with the increase in the number of solar power plants, the automation of monitoring their performance becomes an urgent task. The search for anomalies in the operation of solar power plants is one of the main components of monitoring. The purpose of the study is to develop new methods and software algorithms for finding anomalies in the operation of solar panels based on the results of a digital twin created and trained according to the telemetry data of a solar power plant.Methods. The developed technique is based on statistical studies of deviations of power values at the point of maximum efficient operation of the solar panel calculated by the digital twin. In addition, a normalized value of the power in the maximum efficient operation of the solar panel was introduced for more accurate clustering and anomaly search.Results. Using the developed method of static search for half a year of observations, 18 anomalies were detected in the operation of the solar panels of the power plant. All cases are analyzed for the causes of anomalies in the operation of solar panels.Conclusion. It has been established that when using normalized power values in the analysis of deviations at the point of maximum power PN, it is possible to detect abnormal operation of individual panels. The level of deviation of the normalized values at the point of maximum power was calculated, indicating the presence of an anomaly in the operation of solar panel.

Geographic information system‐based prediction of solar power plant production using deep neural networks

AbstractThe study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.

Experimental analysis of a novel confined bed system for thermal energy storage

Thermal energy storage (TES) is an essential subsystem for the uniform operation of concentrated solar power (CSP) plants. A sensible heat storage system based on a novel confined bed of small particle size granular material was experimentally evaluated. The bed of regular silica sand was mechanically confined between a bottom and a top perforated plate gas distributor, to prevent the motion of particles even for gas velocities above the minimum fluidization velocity of the solids, operating the bed under a fixed or packed bed regime. The discharge process of the confined bed was experimentally analyzed, preheating the granular material at 300–320 °C and supplying various volumetric flow rates of cold air through the bottom distributor. During the discharge process, the temperature of the bed was segregated, obtaining a high temperature zone at the top region and a low temperature zone at the bottom region of the bed. These regions are separated by a thermocline that evolves in the upwards direction as the discharge process progresses. The temperature distribution in the bed and the total pressure drop of the TES system were monitored during the tests. For all cases, the temperature of the bed at different heights evolves as in a fixed bed, confirming the proper confinement of the granular material. The thermocline velocity depends on the volumetric flow rate of cold air, obtaining a discharge time for the temperature located at the exit of the system of around 50, 40, and 30 min for air volumetric flow rates of 700, 900, and 1100 Nlpm, respectively, using 55 kg of regular silica sand as granular material. The experimental results of the evolution and distribution of temperature in the bed were compared with an analytical model of the process for a fixed/packed bed regime of operation, resulting in a good agreement between the experimental measurements and the numerical predictions.

НАКОПИЧУВАЧІ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ

The article provides an analytical review of reverse-acting electric energy storage devices that can be used to solve systemic problems of the United Energy System of Ukraine. The need for the use of electric energy storage is due to the existing shortage of maneuverable capacities of the United Energy System and the construction of solar and wind power plants, which, due to the stochastic mode of operation, are a significant destabilizing factor. Due to the lack of market pricing mechanisms, the possibility of adjusting the rates of the "green" tariff, and the decrease in the cost of solar and wind power plant technologies, a rapid increase in their number and capacity has been observed in recent years. At the beginning of 2021, the installed capacity of solar power plants was 6.87 GW, and wind power plants – was 1.31 GW, and by 2030, their growth is planned to be 10.5 GW and 5.0 GW, respectively. Without adequate regulatory capacity, this can lead to catastrophic consequences, as happened in South Australia. The conducted review and analysis showed that the dominant technology of electric energy storage in the world is hydro-storage power plants, the implementation costs of which are about 260 USD/kWh. Compressed air energy storage is the cheapest technology but needs to be located near the energy systems of natural caves. Accumulator systems of energy storage are developing at a fast pace, the costs of their implementation are 311-520 USD/kWh with a tendency to decrease by 2030 by about 25%. They are the most promising for work in energy systems. These storage devices can be used to regulate frequency and power, reduce electricity flows, reduce voltage fluctuations in electrical networks, consolidate daily schedules of electrical loads, prevent overloads of electrical networks and transformers, increase the reliability of electricity supply, increase the stability of the operation of solar and wind power plants, as well as for commercial purposes in various segments of the electricity market of Ukraine. Keywords: energy system, electricity storage, power, capacity.

Multidefect Detection Tool for Large-Scale PV Plants: Segmentation and Classification

Unmanned aerial vehicles (UAVs) with high-resolution optical and infrared ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> ) imaging have been introduced in recent years to perform inexpensive and fast inspections in operation and maintenance activities of solar power plants, reducing the labor needed, while lowering the on-site inspection time. Even though UAVs can acquire images extremely quickly, the analysis of those images is still a time-consuming procedure that should be performed by a trained professional. Therefore, a computer vision approach may be used to accelerate image analysis. In this work, a dataset of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> images was created from a 10-MW solar power plant and a comparative analysis between mask R- convolutional neural network (CNN) and U-Net was performed for two experiments. Concerning the defective module segmentation, the mask R-CNN algorithm achieved a mean average precision at intersection over union (IoU) = 0.50 of 0.96, using augmentation data. Regarding the segmentation and classification of failure type, the algorithm reached a value of 0.88 considering the same evaluation metric and data augmentation. When compared to the U-Net in terms of IoU, the mask R-CNN outperformed it with 0.87 and 0.83 for the first and second experiments, respectively.

Design, commissioning and operation of a mini hybrid parabolic trough solar thermal power plant for direct steam generation

Parabolic Trough Collectors (PTCs) are the most mature, reliable and widespread among Concentrated Solar Power (CSP) technologies for electricity generation. The conventional power plants relying on PTCs use synthetic oil as Heat Transfer Fluid (HTF) which has limited life span, has limited operation temperature, is expensive and harmful to the environment. In the present research work, a new perspective has been developed to directly generate steam in the collectors, eliminating the need for HTF in the solar field and avoiding auxiliary heat exchangers. A mini hybrid Direct Steam Generation PTC power plant having a boiler as a backup that can be operated with biogas or natural gas is installed at Ecole Nationale d'Ingénieurs de Tunis (ENIT) within the framework of REELCOOP project (Renewable ELectricity COOPeration). The description, commissioning and operation of the solar plant are presented in this paper. The plant is designed and tested under real-life operating conditions in the south Mediterranean region. The feasibility of the DSG process in horizontal parabolic trough collectors has been confirmed and an important know how has been acquired regarding the operation of DSG solar plant. The main instructions for the operation of the solar plant have been presented including start-up and shutdown procedures of the plant, as well as the safety measures to be considered.The development of this plant is aimed to address the challenge of coupling intermittent solar radiation and the presence of a two-phase fluid in the absorber tubes. An important know how has been acquired regarding the operation of DSG solar plants in solar only mode and in hybrid mode which allowed to balance strengths and weaknesses of solar and biogas/natural gas technologies. By combining both systems, we have tackled the problem of the controllability of the energy supply while exploiting as much as possible renewable sources.

Scope, Importance, and Tax Advantages of Continuous Revaluation: Application in a Solar Power Plant Operation

Amaç: Bu çalışmada, yeniden değerleme konusunda yapılan değişiklikler güncel haliyle değerlendirilerek, uygulamada dikkat edilmesi gereken hususların ortaya konulması amaçlanmıştır. &#x0D; Yöntem: Çalışmada nitel araştırma yöntemlerinden doküman incelemesi tekniği kullanılmıştır. &#x0D; Bulgular: Yeniden değerleme uygulanmadığı takdirde işletmelerin mali tablolarının gerçeği yansıtmadığı ve bu durumun da bilgi kullanıcılarının kararlarını olumsuz yönde etkilediği bulgusuna ulaşılmıştır. İşletmelerin yeniden değerleme yaparak iktisadi kıymetlerinin gerçeğe uygun hale getirilmesine yönelik incelemeler yürütülerek örnek uygulamalarla bu durumun işletmelere sağlayacağı olumlu katkılar tespit edilmiştir.&#x0D; Sonuç: Makalede günümüzde özellikli durumlar içeren yeniden değerleme ve enflasyon düzeltmesi uygulamaları karşılaştırılmıştır. Bilançolarda yer alan amortismana tabi iktisadi kıymetler ve taşınmazların defter değerleri ile piyasada geçerli cari fiyatlara göre oluşan değerleri arasındaki farklılıkların giderilmesine yönelik yapılan hesaplamalar ve muhasebe kayıtları ortaya konulmuştur. &#x0D; Özgünlük: Yeniden değerleme ile ilgili VUK’ da yer alan değişikliklerin güncel olması sebebiyle ilgili konuda literatürdeki çalışmaların kısıtlı olduğu gözlemlenmektedir. Ayrıca çalışmada gerçek işletme verileri kullanılmıştır. Bu bağlamda çalışmanın literatüre ve bilgi kullanıcılarına katkı sunması beklenmektedir.

Elektrolüminesans Görüntülerde Arızalı Fotovoltaik Panel Hücrelerin Evrişimli Sinir Ağı ile Otomatik Sınıflandırılması

Fotovoltaik (FV) panel hücrelerindeki arızaların tespiti ve sınıflandırılması güneş enerjisi santrallerinin verimli ve güvenilir bir şekilde işletilebilmesi için oldukça önemli bir konu haline gelmiştir. Bu çalışmada, FV panel hücrelerindeki arızaların hızlı ve doğru bir şekilde tespit edilmesi ve sınıflandırılması için etkin bir evrişimli sinir ağı (ESA) modeli önerilmiştir. Önerilen model, daha az parametre ve model boyutuna sahip SqueezeNet ile transfer öğrenme yaklaşımı kullanılarak geliştirilmiştir. Eğitim yakınsamasını iyileştirmek ve sınıflandırma başarımını arttırmak için modelin aktivasyon fonksiyonları değiştirilerek ateşleme modüllerinden atlama bağlantıları oluşturulmuştur. Deneylerde, elektrolüminesans (EL) görüntülerden elde edilen bir veri seti kullanılmıştır. Sınıf dağılımının dengesizliğini gidermek ve örnek sayısını arttırmak için veri artırma teknikleri uygulanmıştır. Önerilen yöntemin performansı AlexNet, ShuffleNet, GoogLeNet ve SqueezeNet gibi ön eğitimli ESA mimarileri ile karşılaştırılmıştır. Gerçekleştirilen deneysel çalışmalarda önerilen yöntemin doğruluk, kesinlik, duyarlılık, özgüllük ve F1-skor değerleri sırasıyla %91.29, %84.21, %89.72, %92.04 ve %86.88 olarak elde edilmiştir. Ayrıca, önerilen yöntem diğer yöntemlerin doğruluk ölçütündeki değerlerini %0.99 ile %6.29 arasında iyileştirmiştir. Elde edilen tüm sonuçlar analiz edildiğinde, önerilen yöntemin FV panel hücrelerindeki arızaların tespitinde etkili bir performansa sahip olduğu gözlemlenmiştir.

Determination of Angstorm Coefficients with curve fitting method by using Matlab Program

For the sustainable development of nations and to lessen the negative environmental effects of fossil fuels, more clean and renewable energy sources are now required. One of the most significant energy sources is solar energy. To utilize solar energy more efficiently in a particular area, it is crucial to be aware of the solar radiation levels. Furthermore, it's critical to accurately calculate solar energy for study into climate change, one of the biggest global challenges. Systems that utilize solar energy are frequently used nowadays to address the rising global need for energy. The high geographical and temporal resolution, global, diffuse, and direct sunlight data needed for the design and effective operation of solar power plants are now provided by satellite-based solar radiation predictions. In this work, satellite-based forecasting models were used to estimate diffuse solar radiation for the chosen region. In this study, the solar radiation irradiance values of the chosen region were estimated using the curve fitting approach. Angstorm coefficients were determined using the Matlab program for this investigation. Various statistical error analysis tests were used to evaluate how well the constructed model performed. The findings collected unequivocally demonstrate that the provided prediction models perform well.

Staff Training and Scientific Research Activities on the Basis of the Moscow Power Engineering Institute’s Renewable Energy Sources Educational and Experimental Site

The Institute of Hydropower and Renewable Energy Sources at the National Research University Moscow Power Engineering Institute is the only one in the Russian Federation that unites departments implementing educational programs in the areas of electric power engineering, electrical engineering, and power machinery design and construction, with placing focus on power and hydraulic engineering facilities. The need of training high-skilled personnel with broad knowledge in the design and operation of hydroelectric, wind and solar power plants has led to the development of a unique laboratory complex. The article gives a retrospective review of the process through which the Joint Laboratory of the Institute of Hydropower and Renewable Energy Sources was established.

Method for the Automated Inspection of the Surfaces of Photovoltaic Modules

One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use “object-detection” technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day.

A distinctive methodology to recover renewable penetration cost into electric grid

The current work portrays a unique methodology for the optimal penetration and operation of wind and solar power plants into the real-time Andhra Pradesh (AP) state electricity grid. The objectives of the presented work include identifying the suitable locations for the installation of wind and solar power plants, forecasting the wind and solar powers available using the proposed persistent extreme learning machine algorithm and finally obtaining the payback period of the installation with the optimal operation. It was observed that the suggested algorithm forecasts very precisely, which was validated with the help of hardware setup. The payback period was calculated with the incorporation of wind alone, solar alone and both wind-solar combination. With the obtained results, it can be derived that the incorporation of wind-solar combination into the AP real-time system yields to less payback period over the trio.