Diagnosing failed distribution transformers using neural networks

An artificial neural networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing backpropagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transformer, the weather condition, if there are any damaged bushings, if there are any damaged casing or enclosure, if there is oil leakage, and if there are any faults in the windings. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained with the proposed approach applied and with the analysis of the attainable result

Automation of the Arabic sign language recognition

This paper introduces a system to recognize the Arabic sign language using an instrumented glove and a machine learning method. Interfaces in sign language systems can be categorized as direct-device or vision-based. The direct-device approach uses measurement devices that are in direct contact with the hand such as instrumented gloves, flexion sensors, styli and position-tracking devices. On the other hand, the vision-based approach captures the movement of the singer's hand using a camera that is sometimes aided by making the signer wear a glove that has painted areas indicating the positions of the fingers or knuckles. The proposed system basically consists of a PowerGlove that is connected through the serial port to a workstation running the support vector machine algorithm. Obtained results are promising even though a simple and cheap glove with limited sensors was utilized

GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations

Wind and wind power characteristics of the eastern and southern coastal and northern inland regions, South Africa

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.The Deanship of Scientific Research at King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.https://link.springer.com/journal/11356hj2023Mechanical and Aeronautical Engineerin

Wind Energy Forecasting at Different Time Horizons with Individual and Global Models

This paper has been presented at: 14th IFIP International Conference on Artificial Intelligence Applications and InnovationsIn this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R

O-C Study of 545 Lunar Occultations from 13 Double Stars

International audienceIn this article, we have studied the reports of lunar occultations by this project observation's teams (named APTO) in comparison with other observations of the objects. Thirteen binary stars were selected for this study. All the previous observations of these stars were also collected. Finally, an analysis of O-C of all reports were performed

An Integrated Intervention in Pregnant African Americans Reduces Postpartum Risk: A Randomized Trial

Objective—To evaluate the efficacy of an integrated multiple risk intervention delivered mainly during pregnancy, in reducing such risks (smoking, environmental tobacco smoke exposure, depression and intimate partner violence) postpartum. Design—Data from this randomized controlled trial were collected prenatally and on average 10 weeks postpartum in six prenatal care sites in the District of Columbia. African Americans were screened, recruited and randomly assigned to the behavioral intervention or usual care. Clinic-based, individually tailored counseling was delivered to intervention women. The outcome measures were number of reisks reported postpartum and reduction of these risks between baseline and postpartum. Results—The intervention was effective in significantly reducing the number of risks reported in the postpartum period. In Bivariate analyses, the intervention group was more successful in resolving all risks (47% compared with 35%, p=0.007), number needed to treat=9, 95% confidence interval [CI] 5-31) and in resolving some risks (63% compared with 54%, p=0.009), number needed to treat=11, 95% CI 7-43) as compared with the usual care group. In logistical regression analyses, women in the intervention group were more likely to resolve all risks (OR=1.86, 95% CI: 1.25-2.75) and in resolving at least one risk (OR=1.6, 95% CI: 1.15-2.22). Conclusions—An integrated multiple risk factor intervention addressing psychosocial and behavioral risks delivered mainly during pregnancy can have beneficial effects in risk reduction postpartum