Recent Advances in Electrical & Electronic Engineering - Volume 17, Issue 3, 2024

The last several years have seen a significant increase in interest in big data across a range of scientific and engineering fields. Despite having several benefits and applications, big data still has some difficulties that must be overcome for a higher level of service, such as big data analytics, big data management, and big data privacy and security. Big data services and apps stand to greatly benefit from blockchain decentralisation and security features. In this article, we present an overview of blockchain for big data with an emphasis on current methods, possibilities, and upcoming trends. We begin by providing a succinct explanation of big data, blockchain, and the purpose of their integration. After that, we look at different types of blockchain assistance for big data, such as blockchain for security in big data collection, data privacy protection, storage, and collection. Next, we examine the latest work on the utilization of blockchain applications for big data across different industries, including smart grid apps and applications, smart city applications, and smart healthcare applications. A few illustrative blockchain-big data initiatives are given and discussed for a good understanding. Finally, difficulties and potential directions are examined to advance research in an exciting field.

In the recent years, the development in communication system has grown rapidly, especially for the growth of broadband wireless technologies. This technology has some advantages, for instance, flexibility for the transmission of data with very high data rate communications. Antenna is one of the crucial components of broadband wireless systems because it has ability to emit and receive radio waves. There are various types of antennas and microstrip is one of the most popular designs nowadays. This antenna has some advantages and disadvantages. Low gain is known as one of the disadvantages of microstrip antenna. However, there are numerous methods which can be used to enhance gain. Some researches have been done by numerous researchers throughout the world in tackling this disadvantage. This paper presents a critical review of different methods employed to alleviate this problem. For ease of understanding, this paper is classified into five approaches: array configuration approaches, air substrate approaches, metamaterial approaches, yagiuda approaches, and other approaches.

Background: In this study, a new model of linear induction motor, along with the impact of end-effect on motor performance, is proposed. Moreover, a new strategy of control approach based on the polynomial RST controller is suggested and investigated. Objective: The proposed approach can provide a robust control strategy and overcome the limitations imposed by the proportional-integral (PI) regulator in the FOC technique. RST controller has a two-degree of freedom structure and consists of three polynomials, namely R, S and T, which are determined by the pole placement method and resolving of a Diophantine equation. Despite this, the implementation of this method is usually difficult and becomes more complicated with the complexity of the controlled plants. Methods: This study proposes the genetic algorithm (GA) optimization strategy for tuning RST controller parameters (adjust the controller coefficients) in order to achieve an adequate response time by minimizing the different objective functions, such as steady-state error, settling time, and rise time. Results: The accuracy and control performance of the proposed technique are checked and validated using Matlab /Simulink environment software tool. Simulation results reported that the proposed method (RST-GA) could provide robust solutions with perfect reference tracking and efficient disturbance rejection. Conclusion: These significant results make the proposed approach a promising technique for the design of a high-performance controller, which is highly suitable for industrial and electrical applications.

Background: Compared with traditional power generation systems, wind turbines have more units and work in a more harsh environment, and thus have a relatively high failure rate. Among blade faults, the faults of high-strength bolts are often difficult to detect and need to be analyzed with high-precision sensors and other equipment. However, there is still little research on blade faults. Methods: The improved complete ensemble empirical mode decomposition (ICEEMD) model is used to extract the fault features from the time series data, and then combined with the support vector machine optimized by sparrow search algorithm (SSA-SVM) to diagnose the bolt faults of different degrees, so as to achieve the purpose of early warning. Results: The results show that the ICEEMD model used in this paper can extract the bolt fault signals well, and the SSA-SVM model has a shorter optimization time and more accurate classification compared with models such as PSO-SVM. Conclusion: The hybrid model proposed in this paper is important for bolt fault diagnosis of operation monitoring class.

Background: With the rapid development of industry, the expansion capacity and frequency of large industrial users continue to increase. However, the traditional static prediction model is difficult to accurately predict the daily electricity consumption of industrial expansion, which is not conducive to the safe and stable operation of the power grid. Objective: In response to the above problems, this paper proposes a transfer learning-based method for the daily electricity consumption forecasting of large industrial users after business expansion. Methods: Firstly, a dynamic training framework for the prediction model of transfer learning is established, so that the prediction model can dynamically adapt to the capacity change brought about by the expansion of multi-user business. Then, a neural network for predicting daily electricity consumption of industrial users based on multi-resolution time series attention is established, which can deeply mine the characteristics of electricity sequence. Finally, a deep learning model parameter migration and adjustment method considering business expansion is proposed, which can realize efficient migration of prediction models. Results: The effectiveness of the proposed method is demonstrated by comparing it with state-ofthe- art electricity forecasting based on two-year historical data of a specific region. Conclusion: The proposed method is compared with state-of-the-art power forecasting techniques through the validation of local historical data. The obtained results demonstrate the effectiveness of the proposed method.

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multienergy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues.

Background: Wireless sensor networks have the characteristics of strong scalability, easy maintenance, and self-organization, but the energy of nodes is limited and it is difficult to replace the energy supply module. The survival time of the network has always been the key to restricting the development of wireless sensor networks. Objective: Aiming at the problems of short network lifetime and low coverage, a multi-objective optimization routing algorithm has been proposed, focusing on how to balance the communication energy consumption of each node in the network and improve the coverage area of the remaining nodes. Methods: Firstly, the node region was divided into several fan ring subregions. Then, the particle swarm optimization algorithm was used to find the fan angles and radii of each fan ring subregion. Next, Bayesian learning was used to select the appropriate cluster head. Results: The simulation results showed the convergence speed of the proposed algorithm to be improved, solving the problems of cluster head election and node routing planning, improving the utilization of node energy, and verifying the effectiveness. Conclusion: The particle swarm optimization algorithm and Bayesian learning have been introduced to cluster network nodes, and a multi-objective fitness function compatible with the energy consumption and coverage of network nodes has been designed. By optimizing the selection method of convergence nodes, the network communication cost of each node can be effectively balanced, and the speed of network coverage area reduction can be effectively reduced in the later period of node communication.

Background: The idea of being able to communicate with an electronic device in a similar way as human beings is now the new big thing in the world of Artificial Intelligence. The fusion of AI and Cloud computing has given rise to a new technology that can understand and learn conversations in the natural language used by humans. In this Era, where automation is taking over the world, the invention of smart chat-bots has made it possible to imitate humans in various applications to reduce human effort and thereby perform at maximum efficiency. Objective: The objective is to replace a human-constituted assignment with an error-free technology. By using the intent modular concept of dialog flow, the role of the hotel receptionist is eliminated. The purpose of using an API of Google Cloud Platform namely Dialog flow in this project is to conveniently perform NLP (Natural Language Processing) i.e. training a robot to perform according to our instructions and understand the natural language spoken by humans and the hardware attached to the device enables the listening and speaking of the smart bot. Methods: Utilization of Dialog flow Enterprise Edition to make “Hotel Agent” with the use of intents comprising of a general hotel glossary. Results: Dialog flow as a natural language processing recognizer running on the processor Raspberry pi with Python as its constituent language. Finally, it is connected to Google Assistant to make it publicly available in the execution phase. Conclusion: The successful testing of the Artificial Intelligence-based device has ensured that manpower could be conveniently replaced by Machine Intelligence by using knowledge-based databases.