Hierarchical distributed framework for optimal dynamic load management of electric vehicles with vehicle-to-grid technology

The tendency towards carbon dioxide reduction greatly stimulates the popularity of electric vehicles against conventional vehicles. However, electric vehicle chargers represent a huge electric burden, which affects the performance and stability of the grid. Various optimization methodologies have been proposed in literature to enhance the performance of the distribution grids. However, existing techniques handle the raised issues from individual perspectives and/or with limited scopes. Therefore, this paper aims to develop a distributed controller-based coordination scheme in both medium and low voltage networks to handle the electric vehicles’ charging impact on the power grid. The scope of this work covers improving the network voltage profile, reducing the total active and reactive power, reducing the load fluctuations and total charging cost, while taking into consideration the random arrivals/departures of electric vehicles and the vehicle owners’ preferred charging time zones with vehicle-to-grid technology. Simulations are carried out to prove the success of the proposed method in improving the performance of IEEE 31-bus 23 kV system with several 415 V residential feeders. Additionally, the proposed method is validated using Controller Hardware-in-the-Loop. The results show that the proposed method can significantly reduce the issues that appear in the electric power grid during charging with minor changes in the existing grid. The results prove the successful implementation of different types of charging, namely, ultra-fast, fast, moderate, normal and vehicle-to-grid charging with minimum charging cost to enhance the owner’s satisfaction level

Two-Phase Image Encryption Scheme Based on FFCT and Fractals

This paper blends the ideas from recent researches into a simple, yet efficient image encryption scheme for colored images. It is based on the finite field cosine transform (FFCT) and symmetric-key cryptography. The FFCT is used to scramble the image yielding an image with a uniform histogram. The FFCT has been chosen as it works with integers modulo p and hence avoids numerical inaccuracies inherent to other transforms. Fractals are used as a source of randomness to generate a one-time-pad keystream to be employed in enciphering step. The fractal images are scanned in zigzag manner to ensure decorrelation of adjacent pixels values in order to guarantee a strong key. The performance of the proposed algorithm is evaluated using standard statistical analysis techniques. Moreover, sensitivity analysis techniques such as resistance to differential attacks measures, mean square error, and one bit change in system key have been investigated. Furthermore, security of the proposed scheme against classical cryptographic attacks has been analyzed. The obtained results show great potential of the proposed scheme and competitiveness with other schemes in literature. Additionally, the algorithm lends itself to parallel processing adding to its computational efficiency

A heuristics-based solution to the continuous berth allocation and crane assignment problem

Effective utilization plans for various resources at a container terminal are essential to reducing the turnaround time of cargo vessels. Among the scarcest resources are the berth and its associated cranes. Thus, two important optimization problems arise, which are the berth allocation and quay crane assignment problems. The berth allocation problem deals with the generation of a berth plan, which determines where and when a ship has to berth alongside the quay. The quay crane assignment problem addresses the problem of determining how many and which quay crane(s) will serve each vessel. In this paper, an integrated heuristics-based solution methodology is proposed that tackles both problems simultaneously. The preliminary experimental results show that the proposed approach yields high quality solutions to such an NP-hard problem in a reasonable computational time suggesting its suitability for practical use

Deep learning-based spam image filtering

Spam is some unwanted material that may be put in the form of images. While many machine learning approaches are effective at detecting textual spam, this is not true for image spam. In this paper, a new framework for identifying image spams is proposed. The images are divided into two categories: undesirable material contained in the form of images which is referred to as a spam image, whereas anything else is referred to as a ham image. Our proposed methodology is based on applying different pre-trained deep learning models, including InceptionV3, Densely Connected Convolutional Networks 121(DenseNet121), Residual Networks (ResNet50), Visual Geometry Group (VGG16) and MobileNetV2, to filter out the unwanted spam images. Different standard test datasets such as Dredze Dataset, Image Spam Hunter (ISH) Dataset and Improved Dataset are utilized in this paper for performance testing. Furthermore, transfer learning and data augmentation are employed to address the issue of a shortage of labeled data. In our implementation, the fully connected (FC) layer in the aforementioned pre-trained models is replaced with a Support Vector Machine (SVM) classifier, resulting in an improved accuracy. The obtained results reveal that ResNet50 model yields the best performance achieving 99.87% accuracy, 99.88% area under the curve (AUC), 99.98% sensitivity, 99.79% precision, 98.99% F1 score and a computational testing time of in the order of one to two seconds for the ISH dataset

Optimal capacitor placement and sizing in radial electric powe

The use of capacitors in power systems has many well-known benefits that include improvement of the system power factor, improvement of the system voltage profile, increasing the maximum flow through cables and transformers and reduction of losses due to the compensation of the reactive component of power flow. By decreasing the flow through cables, the systems’ loads can be increased without adding any new cables or overloading the existing cables. These benefits depend greatly on how capacitors are placed in the system. In this paper, the problem of how to optimally determine the locations to install capacitors and the sizes of capacitors to be installed in the buses of radial distribution systems is addressed. The proposed methodology uses loss sensitivity factors to identify the buses requiring compensation and then a discrete particle swarm optimization algorithm (PSO) is used to determine the sizes of the capacitors to be installed. The proposed algorithm deals directly with discrete nature of the design variables. The results obtained are superior to those reported in Prakash and Sydulu (2007)

A Gaussian random walk salp swarm algorithm for optimal dynamic charging of electric vehicles

Salp swarm algorithm (SSA) is one of the recently developed meta-heuristic optimization algorithms. Since SSA outperforms other swarm-based algorithms, it has recently been employed in various applications, including feature selection, neural network training and renewable energy systems. In this paper, an improved salp swarm algorithm based on a Gaussian random walk is proposed, which enhances the algorithm’s performance particularly for multidimensional constrained global optimization problems. The integration of a Gaussian random walk into the algorithm balances between its exploration and exploitation capabilities. Furthermore, the proposed algorithm introduces a new re-dispersion strategy in the case of stagnation at local optimum points, which considerably enhances exploration. The performance of the proposed algorithm is evaluated using a set of twenty-three benchmark test functions and is compared to the performance of prevalent metaheuristic algorithms. Statistical analysis is performed using Wilcoxon signed-rank test, and the results reveal considerable improvement over the competing algorithms. Then, 21 real-world optimization problems are used to further evaluate the efficacy of the proposed algorithm. The winners of the CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS, sCMAgES, EnMODE, and COLSHADE algorithms, are used as four comparable algorithms in the real-world optimization problems. The convergence curves and simulations provide very competitive performance compared to the comparative algorithms. The proposed algorithm is used to address one of the most challenging real-world constrained problems in power system applications, namely, determining the optimal charging schedule for electric vehicles at charging stations. The results reveal that the proposed algorithm outperforms other existing algorithms in terms of increasing the charging revenues and achieving maximum power grid stability

Effect of Sampling Rates Variation during Cylindricity Error Evaluation

Cylindrical parts are critical elements in many engineering equipment. To perform correctly, their dimensions and cylindricity should be within specified tolerances. Cylindrical parts can be checked using several dedicated and general purpose measuring systems. Coordinate Measuring Machines (CMMs) can been used to probe and scan cylindrical surfaces to evaluate dimensions and form errors. For accurate evaluation of cylindricity error using a CMM, several parameters should be taken into consideration such as measurement strategy, sampling rate, and cylindricity evaluation technique. The large measurement points required need an efficient evaluation algorithm, and due to their advantages, the Minimum Zone Cylindricity evaluation technique (MZC) and Particle Swarm Optimization technique (PSO) are used in this work. The proposed algorithm was developed using Matlab software and applied for the evaluation of cylindricity error of test cylinders. This paper investigates the effect of the change in sampling rate on the value of estimated cylindricity error when using two different strategies, namely, circles and helical. A comparison between both strategies is presented. The effect of the number of scanned circles and helix turns on the estimated cylindricity error is also investigated.nbsp The results showed that sampling rate has more significant effect on the value of estimated cylindricity error than the number of measurement circles or helix turns even for the same number of data points