Fault Diagnosis of Smart Grid Distribution System by Using Smart Sensors and Symlet Wavelet Function

In today’s era of smart grid system scenario, the fault diagnosis is of utmost important task. Present distribution networks change drastically due to expansion and inclusion of large number of distributed generation units into power system at distribution level. To face the challenges of modernized girds, conventional fault diagnosis methodologies require drastic change by making use of advanced infrastructure and technologies. This will be helpful to achieve automation in fault diagnosis tasks, improved power quality, reliability, resilience and self healing property of the power system. This paper proposes the use of smart sensors and advanced communication technology that will be available in future smart grids to carry out automated fault diagnosis tasks using signal processing techniques. Methods of using Standard deviation features of fault transient signal and a fault location factors are proposed. Performance of various scaling levels, features and components of fault transient current signals extracted using the latest non conventional Symlet mother wavelet function are evaluated and compared. The attempt is made to select optimal features and components of fault transient currents to improve the performance of present limited types of available fault locators. The tests are taken on standard model of smart grid distribution system but can be applied for fault diagnosis of any other power equipment. Results show adequate accuracy to extend the use of proposed method for real time applications.     

On the equivalence of multivariate polynomial matrices

The equivalence of system is an important concept in multidimensional (\(n\)D) system, which is closely related to equivalence of multivariate polynomial matrices. This paper mainly investigates the equivalence of some \(n\)D polynomial matrices, several new results and conditions on the reduction by equivalence of a given \(n\)D polynomial matrix to its Smith form are obtained.     

Image modeling based on a 2-D stochastic subspace system identification algorithm

Fitting a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. In image restoration, for instance, the goal is to recover an estimate of the true image, preferably in the form of a parametric model, given an image that has been degraded by a combination of blur and additive white Gaussian noise. In texture analysis, on the other hand, a model of a particular texture image can serve as a tool for simulating texture patterns. Finally, in image enhancement one computes a model of the true image and the residuals between the image and the modeled image can be interpreted as the result of applying a de-noising filter. There are numerous other applications within the field of image processing that require a causal dynamic model. Such is the case in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average and state-space models (i.e., Kalman filter) are the most commonly used. In this paper we introduce a 2-D stochastic state-space system identification algorithm for fitting a quarter plane causal dynamic Roesser model to an image. The algorithm constructs a causal, recursive, and separable-in-denominator 2-D Kalman filter model. The algorithm is tested with three real images and the quality of the estimated images are assessed.     

Active queue management algorithm based on data-driven predictive control

Model predictive control (MPC) is a popular strategy for active queue management (AQM) that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on explicit fluid model of TCP behavior with input time delay. In this paper, we propose a novel AQM algorithm based on data-driven predictive control, called Data-AQM. For Internet system with large delay, complex change and bad disturbance, data-driven predictive controller can be obtained directly based on the input–output data alone and does not require any explicit model of the system. According to the input–output data, the future queue length in data buffer, which is the basis of optimizing drop probability, is predicted. Furthermore, considering system constraints, the control requirement is converted to the optimal control objective, then the drop probability is obtained by solving the optimal problem online. Finally, the performances of Data-AQM are evaluated through a series of simulations.     

Putting Queens in Carry Chains,N<Superscript><Emphasis Type="Italic">o</Emphasis>̱</Superscript>27

The N-Queens Puzzle is a fascinating combinatorial problem. Up to now, the number of distinct valid placements of N non-attacking queens on a generalized N × N chessboard cannot be computed by a formula. The computation of these numbers is instead based on an exhaustive search whose complexity grows dramatically with the problem size N. Solutions counts are currently known for all N up to 26. The parallelization of the search for solutions is embarrassingly simple. It is achieved by pre-placing the queens within a certain board region. These pre-placements partition the search space. The chosen extent of the pre-placement allows for a wide range of the partitioning granularity. This ease of partitioning makes the N-Queens Puzzle a great show-off case for tremendously parallel computation approaches and a flexible benchmark for parallel compute resources. This article presents the Q27 Project, an open-source effort targeting the computation of the solution count of the 27-Queens Puzzle. It is the first undertaking pushing the frontier of the N-Queens Puzzle that exploits the complete symmetry group D ₄ of the square. This reduces the overall computational complexity already to an eighth in comparison to a naive exploration of the whole search space. This article details the coronal pre-placement that enables the partitioning of the overall search under this approach. With respect to the physical implementation of the computation, it describes the hardware structure that explores the resulting subproblems efficiently by exploiting bit-level operations and their mapping to FPGA devices as well as the infrastructure that organizes the contributing devices in a distributed computation. The performance of several FPGA platforms is evaluated using the Q27 computation as a benchmark, and some intriguing observations obtained from the available partial solutions are presented. Finally, an estimate on the remaining run time and on the expected magnitude of the final result is dared.     

Survey of ICIC techniques in LTE networks under various mobile environment parameters

LTE networks’ main challenge is to efficiently use the available spectrum, and to provide satisfying quality of service for mobile users. However, using the same bandwidth among adjacent cells leads to occurrence of Inter-cell Interference especially at the cell-edge. Basic interference mitigation approaches consider bandwidth partitioning techniques between adjacent cells, such as frequency reuse of factor m schemes, to minimize cell-edge interference. Although SINR values are improved, such techniques lead to significant reduction in the maximum achievable data rate. Several improvements have been proposed to enhance the performance of frequency reuse schemes, where restrictions are made on resource blocks usage, power allocation, or both. Nevertheless, bandwidth partitioning methods still affect the maximum achievable throughput. In this proposal, we intend to perform a comprehensive survey on Inter-Cell Interference Coordination (ICIC) techniques, and we study their performance while putting into consideration various design parameters. This study is implemented throughout intensive system level simulations under several parameters such as different network loads, radio conditions, and user distributions. Simulation results show the advantages and the limitations of each technique compared to frequency reuse-1 model. Thus, we are able to identify the most suitable ICIC technique for each network scenario.     

Dynamic attribute based vehicle authentication

Modern vehicles are proficient in establishing a spontaneous connection over a wireless radio channel, synchronizing actions and information. Security infrastructure is most important in such a sensitive scope of vehicle communication for coordinating actions and avoiding accidents on the road. One of the first security issues that need to be established is authentication via IEEE 1609.2 security infrastructure. According to our preliminary work, vehicle owners are bound to preprocess a certificate from the certificate authority. The certificate carries vehicle static attributes (e.g., licence number, brand and color) certified together with the vehicle public key in a monolithic manner. Nevertheless, a malicious vehicle might clone the static attributes to impersonate a specific vehicle. Therefore, in this paper we consider a resource expensive attack scenario involving multiple malicious vehicles with identical visual static attributes. Apparently, dynamic attributes (e.g., location and direction) can uniquely define a vehicle and can be utilized to resolve the true identity of the vehicle. However, unlike static attributes, dynamic attributes cannot be signed by a trusted authority beforehand. We propose an approach to verify the coupling between non-certified dynamic attributes and certified static attributes on an auxiliary communication channel, for example, a modulated laser beam. Furthermore, we illustrate that the proposed approach can be used to facilitate the usage of existing authentication protocols such as NAXOS, in the new scope of ad-hoc vehicle networks. We use BAN logic to verify the security claims of the protocol against the passive and active interception.     

Optimal simultaneous wireless information and energy transfer in OFDMA decode-and-forward relay networks

In this paper, we consider simultaneous wireless information and energy transfer in an orthogonal-frequency-division-multiple-access decode-and-forward relay network, in which an energy-constrained relay node harvests energy from a source node and uses the harvested energy to forward information to multiple destination nodes. Our objective is to maximize the end-to-end sum rate by resource allocation, subject to transmit power constraint at the source and energy-harvesting (EH) constraint at the relay. A non-convex and mixed-integer programming (MIP) problem is formulated to optimize time-switching (TS) ratios of EH and information decoding at the relay, TS ratio of information transmission from relay to destinations, subcarrier allocation as well as power allocation (PA) over all subcarriers at source and relay. We propose to decouple this problem into a convex problem and an MIP problem in fractional form. To solve the MIP problem, we transform it into an equivalent optimization problem in subtractive form which has a tractable solution. As a result, we propose a novel scheme to achieve jointly optimal TS ratios, subcarrier allocation and PA. Simulation results verify the optimality of our proposed resource allocation scheme.     

A survey and analysis of multipath routing protocols in wireless multimedia sensor networks

The main objective of this research is to conduct a performance analysis of various multipath routing protocols in wireless multimedia sensor networks for the efficient transmission of the image, audio and video data. To provide efficient routing for the large sized multimedia content, various multipath routing protocols such as energy-aware routing, QoS based routing and geographical routing methods are analyzed. In this analysis, the efficient routing techniques including geographical routing techniques such as GPSR, DGR, PW-DGR presented for wireless multimedia sensor networks are studied and the performance of each technique is evaluated to determine the efficient multipath routing technique. Comparisons are made for evaluated protocols and it is proved that the PW-DGR provides better routing performance for the multimedia data. The findings of the research also show that the PW-DGR method efficiently overcomes the routing problems such as energy bottleneck problem, energy-hole, reduced network lifetime and high delay in packet transmission.     

Weighted Hyperbolic DV-Hop Positioning Node Localization Algorithm in WSNs

The localization of nodes plays a fundamental role in Wireless Sensor and Actors Networks (WSAN) identifying geographically where an event occurred, which facilitates timely response to this action. This article presents a performance evaluation of multi-hop localization range-free algorithms used in WSAN, such as Distance Vector Hop (DV-Hop), Improved DV-Hop (IDV-Hop), and the Weighted DV-Hop (WDV-Hop). In addition, we propose a new localization algorithm, merging WDV-Hop, with the weighted hyperbolic localization algorithm (WH), which includes weights to the correlation matrix of the estimated distances between the node of interest (NOI) and the reference nodes (RN) in order to improve accuracy and precision. As performance metrics, the accuracy, precision, and computational complexity are evaluated. The algorithms are evaluated in three scenarios where all nodes are randomly distributed in a given area, varying the number of RNs, the density of nodes in the network, and radio coverage of the nodes. The results show that in networks with 100 nodes, WDV-Hop outperforms the DV-Hop and IDV-Hop even if the number of RNs is reduced to 10. Moreover, our proposal shows an improvement in terms of accuracy and precision at the cost of increased computational complexity, specifically in the algorithm execution time, but without affecting the hardware cost or power consumption.