Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result

FUZZY-DISTANCE FUNCTION APPROACH FOR MULTIPLE CRITERIA DECISION MAKING

In this paper, a method for decision making using fuzzy integral and distance function is presented. Case studies of multiple-response process with correlated responses are used to illustrate the effective application of the proposed approach. The efficacy of this method is compared with the existing methods of MCDM like TOPSIS and GRA. The proposed method is robust, requires less information and less complex as compared to many existing methods

A FRAMEWORK FOR PERFORMANCE EVALUATION AND MONITORING OF PUBLIC HEALTH PROGRAM USING COMPOSITE PERFORMANCE INDEX

A public health program (PHP) taken up by the government of a country refers to all organized measures to prevent disease and promote health among the population, by providing different planned cares/services to the people. Usually, the target population for different PHP are different. The basic requirement for success of a PHP is to ensure that all the planned cares/services are reached to each member of the target population. Therefore, the important performance measures for a PHP are the implementation status of all the planned cares/services under the PHP. However, management and monitoring of a PHP become quite difficult by interpreting separately the information contained in a large number of performance measures. Therefore, usually a metric, called composite performance index (CPI), is evaluated to understand the overall performance of a PHP. However, due a scaling operation involved in the CPI computation procedure, the CPI value does not reveal the true overall implementation status of a PHP and consequently, it is effective for management of a PHP. This paper presents a new approach for CPI computation, in which scaling/normalization of the performance variables is not required and therefore, it can be used for monitoring the true overall implementation status of a PHP in a region. A systematic approach for monitoring a PHP using the CPI values is proposed and applied for monitoring the maternal and child healthcare (MCH) program. The results are found effective towards continuous improvement of implementation status

Loading effect on friction behavior of ordered/disordered graphite in ambient and inert condition

19-25Load dependent friction behavior of structurally ordered and disordered graphite is measured in ambient and nitrogen gas atmosphere. Friction coefficient is significantly less in graphite in order as compared to disorder in ambient atmosphere. This behavior is attributed to structural defects in graphite lattice. However, under nitrogen gas, friction coefficient graphite is significantly high irrespective of structural order or disorder of graphite. This typical behavior is mainly attributed by chemical reactivity of graphite surface which is high in nitrogen gas and not much influenced by structural ordering/disordering. In both types of graphite, steep increase in friction coefficient is observed at high load. This is explained by reasonable increase in contact area and followed by the Johnson−Kendall−Roberts (JKR) model

Forecasting models for developing control scheme to improve furnace run length

In petrochemical industries, the gaseous feedstock like ethane and propane are cracked in furnaces to produce ethylene and propylene as main products and the inputs for the other plant in the downstream. A problem of low furnace run length (FRL) increases furnace decoking and reduces productivity along with the problem of reducing life of the coil. Coil pressure ratio (CPR) and tube metal temperature (TMT) are the two most important performance measures for the FRL to decide upon the need for furnace decoking. This article, therefore, makes an attempt to develop the prediction models for CPR and TMT based on the critical process parameters, which would lead to take the necessary control measures along with a prior indication for decoking. Regression-based time series and double exponential smoothing techniques are used to build up the models. The effective operating ranges of the critical process parameters are found using a simulation-based approach. The models are expected to be the guiding principles eventually to increase the average run length of furnace.furnace run length, decoking, coil pressure ratio, tube metal temperature, operating ranges, simulation,

Platelet indices in diabetics and influence of glycemic control – a hospital based study in North-East India

Introduction: Platelets from patients with type 1 and type 2 diabetes exhibit enhanced platelet aggregation activity early in the disease course that may precede the development of cardiovascular diseases. Both atherosclerosis and thrombosis appear to contribute significantly to the increased cardiovascular risk of diabetic patients. Platelet indices include mean platelet volume (MPV), platelet distribution width (PDW), and platelet large cell ratio (P-LCR). This study was undertaken with the aim to find out the differences in platelet indices between diabetics and non-diabetics and also between patients with controlled and uncontrolled diabetes mellitus in a tertiary health care centre in North East India. Methods: This is a cross sectional study conducted in Agartala Government Medical College and GB Pant Hospital (AGMC & GBP Hospital). 100 cases and 100 controls were selected from the Diabetes clinic of Medicine out patient department (OPD) and Medicine wards. Platelet indices and HbA1C levels of these cases and controls were measured. Platelet indices were measured by a 3-part differential hematology auto-analyzer and HbA1C by High Performance Liquid Chromatography (HPLC) method. Blood glucose levels were estimated by oxidase-peroxidase method. Results: Platelet indices were found to be significantly higher among diabetics compared to non-diabetics and also they were found higher among patients with poor glycemic control with HbA1C>7% in comparison to patients with good glycemic control with HbA1C<7% (P value <0.05). Conclusions: Platelet indices are significantly increased in diabetics and the extent of increment is more in diabetics with poor glycemic control

Early Detection of Cardiovascular Disease in Patients with Chronic Kidney Disease using Data Mining Techniques

 A constant obstacle for doctors is the high prevalence of cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Increasing efforts have been made to jointly treat patients with heart and kidney disease, as shown by an increasing number of basic research and clinical investigations concerning CVD in CKD. Typical risk factors for CVD are common in CKD, such as age, blood pressure (bp), hypertension (htn), and blood sugar (sg). Standard risk factors tend to be the major contributors to CVD in patients with mild to moderate CKD. However, in patients with advanced CKD, non-traditional CKD-specific risk factors (e.g. Potassium level in blood) are more prevalent than in the general population, contributing, in addition to traditional risk factors, to the high burden of CVD in CKD. However, in patients with CKD, CVD often remains underdiagnosed and undertreated. Nevertheless, CVD still remains under control and care in patients with CKD. Researchers in this paper aims to predict the probability of CVD from CKD by using various popular data mining techniques and definitively propose a decision tree and by using Random Forest analysis to test its specificity and sensitivity to achieve concrete results with sufficient precision