Showing papers by "Mohamed Abdel-Nasser published in 2018"

A Novel Smart Grid State Estimation Method Based on Neural Networks

Abstract: The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.

Computer aided diagnosis system for skin lesions detection using texture analysis methods

Abstract: Malignant melanoma is considered as one of the most dangerous type of skin cancers as it increases the mortality rate. Computer-aided diagnosis (CAD) systems can help to detect melanoma early. In this paper, we propose a new skin melanoma CAD system using texture analysis methods. The proposed CAD system consists of four steps: hair removal, filtering, feature extraction and classification. In the feature extraction step, we evaluate the performance of five widely used texture analysis methods: grey level co-occurrence matrix, Gabor filters, a histogram of oriented gradients, local binary pattern and local directional number. Our CAD system classifies between non-melanoma skin lesions (represented as common nevi or dysplastic nevi) and melanoma. The experimental results show that extracting HOG features after hair removal yields the best classification results. HOG gives an AUC of 0.9783 with melanoma/common nevi classification and an AUC of 0.9439 with melanoma/dysplastic nevi classification.

Economic dispatch using stochastic whale optimization algorithm

Abstract: Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

Power loss estimation in smart grids using a neural network model

Abstract: The techniques of minimizing losses in a smart grid system need a fast algorithm to estimate the conditions of the active distribution system. Excessive losses threat the reliability and security of the smart grid system. This paper presents a novel method for estimating power loss in a real-time of each line in the active distribution system. The proposed method, which is called, a neural network power loss estimation (NN-PLE) is a computational method for estimation the line losses using an artificial neural network. The proposed method provides a fast calculation with high accuracy comparing to other traditional methods that take a very long execution time. Simulation results are presented to demonstrate the performance of (NN-PLE) for a 33-bus distribution system with different data resolutions.

Impacts of GERD on the Accumulated Sediment in Lake Nubia Using Machine Learning and GIS Techniques

Abstract: This chapter aims to study and discuss the effect (hypothesis) of constructing the GERD on the deposited sediment amount in the AHDL. To achieve the objective of this chapter; a machine learning approach represented in a regression tree (RTs) model was used and calibrated to simulate the changes in bed levels and water velocities in the study area within AHDL by using the field measured data and GIS analysis for the year 2008 (reference case). Furthermore, a model verification process has been done to ensure the applicability of the applied model using the available field data in the year 2012. The results of the bed levels and velocities during calibration and verification of the model show low values of RMSE % (for calibration 2.90 and 2.57 for bed levels and velocities, respectively, and for calibration 4.66 and 4.98% for bed levels and velocities, respectively) and high R2 (for calibration 0.9975 and 0.9978 for bed levels and velocities, respectively, and for verification 0.9921 and 0.9959 for bed levels and velocities, respectively), indicating that the model was efficiently calibrated and verified. It shows good agreement between the simulated and measured data (by comparisons of simulated longitudinal and cross sections with the measured ones). Thus, this model is considered trustful and reliable to the prediction of sediment and erosion (bed changes) in the study area within AHDL after GERD construction. Accordingly, four of the possible scenarios are performed through the well-calibrated and verified model by reducing the flow quantity and its associated annual sediment rate by 5–10 and 60–65%, respectively. These scenarios are considered as prediction cases after GERD construction. The impact of GERD construction is then studied by comparing some sections along and across the studied lake portion before and after GERD construction (applied scenarios). This impact appeared clearly as a reduction in the amount of the accumulated sediment (decrease in bed levels) accompanied by an increase in erosion amount. Based on the applied scenarios, results showed that the amount of sediment was reduced by 25–27%, 52–55%, 76–81%, and 90–97% in the year 2030, 2040, 2050, and 2060, respectively, compared to the predicted amount of sediment in the year 2020 without GERD operation/construction. As a positive impact of the GERD construction, the lifetime of the upstream AHD reservoir will be prolonged due to the decrease in the amount of the accumulated sediment. This study provides decision-makers with a preliminary knowledge about the impact of GERD operation/construction on AHDL sediment pattern and consequently on Egypt and Sudan. Moreover, the current study opens new windows for future research to investigate the impacts of the different aspects of GERD of AHDL.

Application of mutation operators to grey wolf optimizer for solving emission-economic dispatch problem

Abstract: This paper presents applying the grey wolf optimization (GWO) to find an optimal solution for the combined economic and emission dispatch problem which aims to minimize the generation costs and keeping emission reduction. Six mutation operators are applied to the GWO to enhance its performance. The effect of a weight factor between generation cost and emission is also studied in this paper. A test system that consists of 10 units is simulated, the results show the effect of applying the mutation operators to the GWO.

Optimal voltage control considering PV-inverter and OLTC using grey wolf optimizer

Abstract: In this paper, an optimal voltage regulation control method considering the inverter of photovoltaic (PV-inverter) and the on-load tap-changer (OLTC) transformer in distribution systems using a grey wolf optimizer (GWO) is proposed. The PV-inverter is incorporated with OLTC for minimizing the voltage deviation, thereby effectively preventing voltage rise/drop caused by the intermittent generation of PV. The proposed method can completely solve the real-time voltage regulation problems with high PV penetration. The 11 kV 119-bus large-scale distribution system is used to verify the effectiveness of the proposed optimal voltage control method. A 24-hour simulation is performed considering load and PV variation. The simulation results reveal that the use of the cooperative control method of the PV inverter and OLTC is better than the use of PV-inverter alone.

Deep visual embedding for image classification

Abstract: This paper proposes a new visual embedding method for image classification. It goes further in the analogy with textual data and allows us to read visual sentences in a certain order as in the case of text. The proposed method considers the spatial relations between visual words. It uses a very popular text analysis method called ‘word2vec’. In this method, we learn visual dictionaries based on filters of convolution layers of the convolutional neural network (CNN), which is used to capture the visual context of images. We employee visual embedding to convert words to real vectors. We evaluate many designs of dictionary building methods. To assess the performance of the proposed method, we used CIFAR10 and MNIST datasets. The experimental results show that the proposed visual embedding method outperforms the performance of several image classification methods. Experiments also show that our method can improve image classification regardless the structure of the CNN.