Location of voltage sag source by using artificial neural network

Power quality (PQ) is a major concern for number of electrical equipment such as sophisticated electronics equipment, high efficiency variable speed drive (VSD) and power electronic controller. The most common power quality event is the voltage sag. The objective is to estimate the location of voltage sag source using ANN. In this paper, the multi-monitor based method is used. Based on the simulation results, the voltage deviation (VD) index of voltage sag is calculated and assigned as a training data for ANN. The Radial Basis Function Network (RBFN) is used due to its superior performances (lower training time and errors). The three types of performance analysis considered are coefficient of determination (R2), root mean square error (RMSE) and sum of square error (SSE). The RBFN is developed by using MATLAB software. The proposed method is tested on the CIVANLAR distribution test system and the Permas Jaya distribution network. The voltage sags are simulated using Power World software which is a common simulation tool for power system analysis. The asymmetrical fault namely line to ground (LG) fault, double line to ground (LLG) fault and line to line (LL) fault are applied in the simulation. Based on the simulation results of voltage sag analysis, the highest VD is contributed by LLG for both test systems. Based on the proposed RBFN results, the best performance analysis are R2, RMSE and SSE of 0.9999, 5.24E-04 and 3.90E-05, respectively. Based on the results, the highest VD shows the location of voltage sag source in that system. The proposed RBFN accurately identifies the location of voltage sag source for both test systems

Porokeratosis ptychotropica: a rare case report with unusual presentation

Porokeratosis is a rare disorder of epidermal keratinization characterized clinically by annular plaque with thread like hyperkeratotic border with a central groove that expand centrifugally and this border corresponds to coronoid lamellae histologically which are the columns of parakeratosis that overlie an epidermal invagination with loss of granular layer and dyskeratosis of upper spinous keratinocytes. The disorder was erroneously named porokeratosis because the coronoid lamella was initially described as being present over a sweat pore, which is a fixed structure that cannot expand peripherally. Five primary clinical variants have been described: classic porokeratosis of mibelli, disseminated superficial actinic porokeratosis, linear porokeratosis, punctate porokeratosis and porokeratosis palmaris et plantaris disseminate. Porokeratosis ptychotropica one of the rare variants of porokeratosis described by lucker et al which has been added recently in the classification. It is characterized clinically by symmetrical verrucous papules and plaques resembling psoriasis plaque in the gluteal cleft, buttocks and rarely extends to genitalia and histologically by multiple coronoid lamella. We report a case of 43year old female, presented with 10years duration of pruritic raised skin lesion over the left gluteal region. Dermatological examination revealed single well defined erythematous scaly plaque with central atrophy, hyperpigmentation and peripheral thread like elevated border. Histopathological examination revealed multiple coronoid lamella which is the hallmark for porokeratosis ptychotropica, confirmed the diagnosis. The patient was treated with 5-fluorouracil cream. we report this case due to its rarity and the unusual presentation of single plaque of porokeratosis ptychotropica

Fundamentals Knowledge of Investor and Its Influence on Investment in Capital Market- A Study from Dhaka Stock Exchange

A comprehensive investment literacy questionnaire surveyed to the participants in the Dhaka Stock Exchange (DSE) to measure the basic financial literacy and its effects to the stock market participants. It has been found that the majority of the respondents shows basic financial literacy, but they don’t believe that they have sufficient basic knowledge of investing in DSE. So the conflict between knowledge and their confidence leads them to wrong prediction of investment and the dependency on rumors on their investment decision. Inadequate knowledge about capital market not only sad loss, but also turns to an unattractive place of investment. Basic financial literacy not necessary prerequisite for a good investment decision in the capital market but help to make a preliminary decision of investment as well as to avoid a major loss. Keywords: Financial information, Knowledge, Literacy, Investor, Dhaka Stock Exchang

Finite Element Analysis of Horizontal Axis Wind Turbine Blades Using NACA 4412 Series

Wind turbine technology is one of the rapid growth sectors of renewable energy all over the world. The ultimate objective of the project work is to increase the output power under specified atmospheric conditions. From the technical point of view, the output power depends on the shape of the blade. The blade plays a pivotal role, because it is the most important part of the energy absorption system. Finite element analysis was conducted by different materials used for blade fabrication namely glass fiber with epoxy resin, Aluminum and teak wood. The research work focuses on NACA4412. Also, the performance of a wind turbine blade is highly dependent on the structure Total deformation, Stress and Strain of the blade is critical to the wind turbine system service life. So, the wind turbine blades are analyzed taking these parameters into account

Calibration of ZMPT101B voltage sensor module using polynomial regression for accurate load monitoring

Smart Electricity is quickly developing as the results of advancements in sensor technology. The accuracy of a sensing device is the backbone of every measurement and the fundamental of every electrical quantity measurement is the voltage and current sensing. The sensor calibration in the context of this research means the marking or scaling of the voltage sensor so that it can present accurate sampled voltage from the ADC output using appropriate algorithm. The peakpeak input voltage (measured with a standard FLUKE 115 meter) to the sensor is correlated with the peak-peak ADC output of the sensor using 1 to 5th order polynomial regression, in order to determine the best fitting relationship between them. The arduino microcontroller is used to receive the ADC conversion and is also programmed to calculate the root mean square value of the supply voltage. The analysis of the polynomials shows that the third order polynomial gives the best relationship between the analog input and ADC output. The accuracy of the algorithm is tested in measuring the root mean square values of the supply voltage using instantaneous voltage calculation and peak-peak voltage methods. The error in the measurement is less than 1% in the peak-peak method and less than 2.5% in the instantaneous method for voltage measurements above 50V AC, which is very good for measurements in utility. Therefore, the proposed calibration method will facilitate more accurate voltage and power computing for researchers and designers especially in load monitoring where the applied voltage is 240V or 120V ranges

Recent approaches and applications of non-intrusive load monitoring

The Appliance Load Monitoring is vital in every energy consuming system be it commercial, residential or industrial in nature. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensors to every load of interest which makes the system to be costly, time consuming and complex. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. We presented a study in this paper providing a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM. The study also found that more efforts are needed from the researchers to apply NILM in appliance energy management, for example a Home Energy Management System (HEMS)

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.Comment: 18 pages, 8 figures, published in Electronics MDPI journa

Hand detection and segmentation using smart path tracking fingers as features and expert system classifier

Nowadays, hand gesture recognition (HGR) is getting popular due to several applications such as remote based control using a hand, and security for access control. One of the major problems of HGR is the accuracy lacking hand detection and segmentation. In this paper, a new algorithm of hand detection will be presented, which works by tracking fingers smartly based on the planned path. The tracking operation is accomplished by assuming a point at the top middle of the image containing the object then this point slides few pixels down to be a reference point then branching into two slopes: left and right. On these slopes, fingers will be scanned to extract flip-numbers, which are considered as features to be classified accordingly by utilizing the expert system. Experiments were conducted using 100 images for 10-individual containing hand inside a cluttered background by using Dataset of Leap Motion and Microsoft Kinect hand acquisitions. The recorded accuracy is depended on the complexity of the Flip-Number setting, which is achieved 96%, 84% and 81% in case 6, 7 and 8 Flip_Numbers respectively, in which this result reflects a high level of finite accuracy in comparing with existing techniques