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Rajkumar, N.
- Application of Synchronised Phasor Measurement Technology in Renewable Energy Systems
Abstract Views :194 |
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Authors
Affiliations
1 Energy Efficiency and Renewable Energy Division, Cental Power Research Institute, Bangalore-560 080, IN
2 Diagnostics Cables and Capacitors Division, Central Power Research Institute, Bangalore 560 080, IN
3 Dept. of Electrical and Electronics Engg., Manipal institute of technology, Manipal,Karnataka- 576 104, IN
1 Energy Efficiency and Renewable Energy Division, Cental Power Research Institute, Bangalore-560 080, IN
2 Diagnostics Cables and Capacitors Division, Central Power Research Institute, Bangalore 560 080, IN
3 Dept. of Electrical and Electronics Engg., Manipal institute of technology, Manipal,Karnataka- 576 104, IN
Source
Power Research, Vol 9, No 3 (2013), Pagination: 399–406Abstract
This paper throws light on application of synchronised phasor measurement in renewable energy systems. Power generation from renewable energy system connected to the grid is highly dynamic, nonlinear which needs to be monitored continuously for efficient and reliable system. There are various methods of monitoring of power from renewable energy system to grid. Nowadays, Time synchronisedphasormeasuring method proves to be a revolutionary method for power system monitoring. Synchronised PMU has wide application such as wide area monitoring, Real time monitoring, post even analysis, visualization, state estimation etc. They are employed in micro grid and distributed generation plants mainly for solving islanding issues. This paper summarises the synchrophasor application and its future scope in renewable energy system.Keywords
Phasor Measurement Unit (PMU), Synchrophasor, Real-time Monitoring, State Estimation, Islanding
- Analysis of Solar Power Variability Due to Seasonal Variation and its Forecasting for Jodhpur Region Using Artificial Neural Network
Abstract Views :203 |
PDF Views:0
Authors
Affiliations
1 Indian Institute of Technology Jodhpur, Rajasthan, Old Residency Road, Ratanada, Jodhpur - 342 011, IN
2 Dayalbagh Educational Institute, Electrical Department Faculty of engineering, Dayalbagh Agra-282005, IN
3 ERED, Central Power Research Institute, Bangalore - 560 080, IN
1 Indian Institute of Technology Jodhpur, Rajasthan, Old Residency Road, Ratanada, Jodhpur - 342 011, IN
2 Dayalbagh Educational Institute, Electrical Department Faculty of engineering, Dayalbagh Agra-282005, IN
3 ERED, Central Power Research Institute, Bangalore - 560 080, IN
Source
Power Research, Vol 9, No 3 (2013), Pagination: 423-430Abstract
In 21st century solar power variability is an important issue due to grid integration. In these days grid integration is very popular because of heavy load. So solar power, wind power and conventional power are basic sources of grid integration. Solar power is playing a key role in grid integration. The main objective of this paper is to analyse solar power variability due to seasonal variation in Jodhpur. Jodhpur is known as sun-city for an average 320 sunny days in a year. Average solar insolation available in Jodhpur city is 5.7-6.0 kWh/m2 per day. This is second highest insolation in the world. In this paper, the Solar power variability analysis is carried out based on the data collected from a typical 43 kW amorphous silicon solar photovoltaic system installed in Jodhpur. Mansoon, winter and summer seasons are used for analysis of variation in Photovoltaic Generation due to change of solar insolation. Output of solar photovoltaic system depends on solar insolation and in this paper we have analysed the variation in solar power according to rainy, winter and summer seasons and used artificial neural network to predict the power output from PV system. The paper showed that proposed ANN model is more accurate and study of variability in solar power can help in plant operation, power scheduling and dispatchability.Keywords
No Keywords- Analysis of Solar Power Variability Due to Seasonal Variation and its Forecasting for Jodhpur Region Using Artificial Neural Network
Abstract Views :193 |
PDF Views:0
Authors
Affiliations
1 Indian Institute of Technology Jodhpur, Rajasthan, Old Residency Road, Ratanada, Jodhpur - 342 011, IN
2 Dayalbagh Educational Institute, Electrical Department Faculty of engineering, Dayalbagh Agra-282005, IN
3 ERED, Central Power Research Institute, Bangalore - 560 080, IN
1 Indian Institute of Technology Jodhpur, Rajasthan, Old Residency Road, Ratanada, Jodhpur - 342 011, IN
2 Dayalbagh Educational Institute, Electrical Department Faculty of engineering, Dayalbagh Agra-282005, IN
3 ERED, Central Power Research Institute, Bangalore - 560 080, IN
Source
Power Research, Vol 9, No 3 (2013), Pagination: 423-430Abstract
In 21st century solar power variability is an important issue due to grid integration. In these days grid integration is very popular because of heavy load. So solar power, wind power and conventional power are basic sources of grid integration. Solar power is playing a key role in grid integration. The main objective of this paper is to analyse solar power variability due to seasonal variation in Jodhpur. Jodhpur is known as sun-city for an average 320 sunny days in a year. Average solar insolation available in Jodhpur city is 5.7-6.0 kWh/m2 per day. This is second highest insolation in the world. In this paper, the Solar power variability analysis is carried out based on the data collected from a typical 43 kW amorphous silicon solar photovoltaic system installed in Jodhpur. Mansoon, winter and summer seasons are used for analysis of variation in Photovoltaic Generation due to change of solar insolation. Output of solar photovoltaic system depends on solar insolation and in this paper we have analysed the variation in solar power according to rainy, winter and summer seasons and used artificial neural network to predict the power output from PV system. The paper showed that proposed ANN model is more accurate and study of variability in solar power can help in plant operation, power scheduling and dispatchability.Keywords
No Keywords- A Methodology for Computation of Experimental Annual Station Heat Rate Benchmark
Abstract Views :281 |
PDF Views:0
Authors
Affiliations
1 Central Power Research Institute, Bangalore, IN
1 Central Power Research Institute, Bangalore, IN
Source
Power Research, Vol 5, No 2 (2009), Pagination: 33-41Abstract
This paper presents a methodology for assessment of annual heat rate of a coal fired thermal power unit based on a snap shot test to which various factors contributing to annual effects are added. This method is successfully used in a number of stations and represents the Unit Heat Rate (UHR) and Station Heat Rate (SHR) fairly well. This method is not a substitute for measurement of heat rate by direct measurement of coal flow and energy generated and is applicable only where direct measurement of coal flow into an individual boiler by gravimetric feeders or belt weighers is not available. This method is superior to other methods in view of its total coverage of all effects and no annual factor which affects heat rate is left out. Hence it is popularly accepted by most thermal stations. This method is superior to backward computation of UHR from SHR by apportioning.Keywords
Coal Fired Station, Unit Heat Rate, Station Heat Rate, Coal Quality Effects, Heat Rate Degradation, Cycling Losses, Stacking Losses, Make Up Losses Central Power. Station Heat Rate, Coal Quality Effects, Heat Rate Degradation, Cycling Losses, Stacking Losses, Make Up Losses Central Power.- Operational Optimization of Boilers in Coal-Fired Base Load Thermal Power Plants
Abstract Views :179 |
PDF Views:0
Authors
Affiliations
1 Energy Effi ciency and Renewable Energy Division, Central Power Research Institute, Bangalore-560080, IN
1 Energy Effi ciency and Renewable Energy Division, Central Power Research Institute, Bangalore-560080, IN
Source
Power Research, Vol 8, No 1 (2012), Pagination: 67–72Abstract
This paper describes the performance analysis of steam generators. Various energy conservation measures such as operating excess air level control and insulation condition and its impact on overall heat rate of a thermal power station are discussed. Study results show that the change in operating oxygen level in fl ue gas can give benefi t in unit heat rate to the tune of 10–40 kcal/kWh in 210 MW units and 10–50 kcal/kWh in 500 MW units. Reduction in the boiler skin temperature from 30°C to 10°C above the ambient dry bulb temperature will result in improvement in unit heat rate by 60 kcal/ kWh in 210 MW units and 80 kcal/kWh in 500 MW units.- Heat Rate Improvement in Utility Power Plants through Steam Turbine Performance Optimization
Abstract Views :196 |
PDF Views:0
Authors
Affiliations
1 Energy Conservation and Development Division, Central Power Research Institute, Bangalore - 560080, IN
1 Energy Conservation and Development Division, Central Power Research Institute, Bangalore - 560080, IN
Source
Power Research, Vol 8, No 1 (2012), Pagination: 73–76Abstract
This paper describes the performance enhancement of steam turbines. Various energy conservation measures, such as steam path audit, vacuum improvement in condenser, turbine retrofi ts, feed heater performance improvement, etc. are discussed. Study results show that the improvement in operating turbine effi ciency will lead to a quantum improvement in unit heat rate from 25 kcal/kWh to 225 kcal/ kWh.- Solar Radiation Forecasting for Moderate Climatic Zone
Abstract Views :246 |
PDF Views:0
Authors
Affiliations
1 Central Power Research Institute, Bangalore – 560 080, Karnataka, IN
1 Central Power Research Institute, Bangalore – 560 080, Karnataka, IN
Source
Power Research, Vol 15, No 1 (2019), Pagination: 52-57Abstract
The challenge with solar energy prediction is that the solar radiation is intermittent and uncontrollable. Energy forecasting can be used to mitigate some of the challenges that arise from the uncertainty in the resource. Weather data was sourced from India Meteorological Department for Bangalore and Chennai location. This paper provides statistical approach to predict the solar power in future. Analysis was done for different predictive models; Multiple Regression Model is used as we have multiple inputs. The results indicate the prediction of solar radiation has better accuracy during higher irradiation period rather than lower irradiation period.Keywords
Irradiation, Multiple Regression, Solar Forecasting, Solar Radiation.References
- Sanders S, Barrick C, Maier F, Rasheed K. Solar radiation prediction improvement using weather forecasts. 16th IEEE International Conference on Machine Learning and Applications (ICMLA); 2017. https://doi.org/10.1109/ICMLA.2017.0-112
- Orjuela A, Hernandez CJ, Rivero C. Very short term forecasting in global solar irradiance using linear and nonlinear models. IEEE Workshop on Power Electronics and Power Quality Applications (PEPQA); 2017. https://doi.org/10.1109/PEPQA.2017.7981691
- Hassan M, Ali M, Ali A, Kumar J. Forecasting dayahead solar radiation using machine learning approach. 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE); 2017. https://doi.org/10.1109/APWConCSE.2017.00050. PMid:28515669. PMCid:PMC5409806
- Abuella M, Chowdhury B. Solar power probabilistic forecasting by using multiple linear regression analysis. SoutheastCon; 2015. https://doi.org/10.1109/ SECON.2015.7132869
- Haupt S, Kosovic B, Jensen T, Cowie J, Jimenez P, Wiener G. Comparing and integrating solar forecasting techniques. IEEE 43rd Photovoltaic Specialists Conference (PVSC); 2016. https://doi.org/10.1109/PVSC.2016.7749751
- Snegirev D, Eroshenko S, Khalyasmaa A, Dubailova V, Stepanova A. Day ahead solar power plant forecasting accuracy improvement on the hourly basis. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus); 2019. https://doi.org/10.1109/EIConRus.2019.8657024
- Hussain S, Alili A. Day ahead hourly forecast of solar irradiance for Abu Dhabi, UAE. IEEE Smart Energy Grid Engineering (SEGE); 2016. https://doi.org/10.1109/SEGE.2016.7589502
- Serttas F, Hocaoglu F, Akarslan E. Short term solar power generation forecasting: A novel approach. 2018 International Conference on Photovoltaic Science and Technologies(PVCon); 2018. https://doi.org/10.1109/PVCon.2018.8523919
- Vijay V, Singh VP, Bhatt DM, Chaturvedi. Generalised neural network methodology for short term solar power forecasting”, 13th International Conference on Environment and Electrical Engineering (EEEIC); 2013.