A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications

Abstract

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

Optimizing Energy Consumption for Cloud Internet of Things

“…Evaluate the energy consumption of application requests based on the effect of crossover types on energy consumption. The consumed energy is determined by using deterministic mutation for various types of crossover (one-point, two-points) considering the number of generations equal to 8 for the different number of tasks (16,20,24,28,32). Based on the results in Figure 5, two-points crossover performed much better compared to one-point.…”

“…However, when it comes to Cloud IoT, the problem is even more challenging as new dimensions are introduced (i.e., Energy efficiency, resource allocation, etc.) [11][12][13][14][15][16][19][20][21][22][23][24][25][26]. To achieve the goal of energy-saving, which is the most important factor, the proposed approach (shown in Figure 3) attempts to handle the problem by optimizing the selection and placement of behavior of task execution time using a genetic algorithm.…”

“…Optimization technique is an approach to find the best solution for complex problems by minimizing or maximizing one or functions that are more objective stand on one or more decision variables that give a value of the objective function [17]. Combinatorial Optimization, Non-linear Programming, and linear Programming are designed to solve a wide range of optimization problems [18][19][20]. Optimization techniques are classified as exact methods and approximation methods (heuristics and meta-heuristics methods).…”

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

“…The DWT is also unable to provide the significant image features when the images have more local structures and geometric shapes in different orientations. Some image feature extraction techniques based on the wavelets [19] [20] have been developed for optimal image representation with more directional sensitivity, but they have some drawbacks. To overcome the drawbacks, in presented work, authors have considered the ATT [21] which represents the image in a very optimum way, where the tetrolets are derived based on the Haar wavelets.…”

“…The color autocorrelogram α k (l) is defined as the probability of calculating a pixel p of similar color at specific distance k form an other given pixel p of the l th color. It is computed as follows components by using equation (20) as follows:…”