Recent Advances in Electrical & Electronic Engineering - Volume 18, Issue 8, 2025

This article outlines the working principle of the nozzle baffle type electro-hydraulic servo valve and analyzes the progress of research on its characteristics more fully.

Firstly, it summarizes the research progress on the characteristics of the cavitation phenomenon, which is a common failure of nozzle baffle type electro-hydraulic servo valves; secondly, it comprehensively discusses the structural characteristics of nozzle baffle type electro-hydraulic servo valves: moving-iron torque motors and moving-coil torque motors, as well as the progress of the research on the characteristics of the power-stage valve spools and direct-injection valve spools.

It suggests to the readers other optimization methods that can be made for the cavitation phenomenon in the future, and brings innovative ideas for the readers in terms of moving-iron vs. moving-coil torque motors and other aspects that can be improved for the power-stage spools vs. direct-injection spools. Again, for the typical technical problems of nozzle baffle type electro-hydraulic servo valves, we have searched and organized the related technical patents at home and abroad and clearly outlined the current improvements for nozzle baffle type electro-hydraulic servo valves.

Through the discussion of related articles and patents, it is found that although many methods have been proposed at home and abroad to address the shortcomings of the nozzle baffle type electro-hydraulic servo valve, it still has a high failure rate and has not been comprehensively solved, there is still a lot of room for optimization.

Finally, the new solutions proposed for the performance deficiencies of nozzle baffle type electro-hydraulic servo valves are summarized, and the future development trend of nozzle baffle type electro-hydraulic servo valves is predicted and outlooked.

The application of Unmanned Aerial Vehicle (UAV) is a major turning point in the history of power line inspection and is of increasing interest to a growing number of researchers.

This study aimed to clarify the research status, hotspots, and evolutionary trends of UAV power line inspection to help researchers understand the dynamic evolution of research topics and provide guidance for future research directions.

737 high-quality papers were collected from the Web of Science Core Collection (WoSCC) database from 2001 to 2023, and descriptive statistical analysis, cooperation network analysis, keyword co-occurrence analysis, keyword clustering analysis, and keyword citation burst analysis were conducted using VOSviewer and CiteSpace.

The popularity of research on UAV power line inspection is increasing, with an average annual growth rate of 26.33% in publications between 2001 and 2023. China (444 publications, 60.24%) and USA (77 publications, 10.45%) are the most prominent countries. However, the level of cooperation between different countries, academic institutions, and scholars is low. The research topics are wide-ranging and interdisciplinary, mainly focusing on 4 areas: fault detection and diagnosis, path planning, and the application of deep learning in intelligent inspection. The research hotspot and trend is the integration of artificial intelligence, deep learning, and modern information technology to achieve UAV autonomous intelligent inspection. Some of the challenges faced in the development of the field are also summarized, and possible solutions are proposed.

This study provides a comprehensive and systematic review, and the results provide a quick overview of the research status, hotspots, and evolutionary trends.

This paper reports the design and characterization of three Interdigital electrode (IDE) water-level sensors at resonant frequencies. The geometries of the proposed IDE sensors are comb type, circular type, and Archimedean spiral type. These IDE sensors have been fabricated by the printed circuit board technology. The sensor’s performance has been evaluated on both tap and distilled water.

The multiple resonant frequencies are investigated for the frequency span of 40 Hz to 110 MHz using a 4294A impedance analyzer. The peak of the projected admittance graph appeared at the first resonant frequency. This first resonant frequency is chosen here for the characterization of the proposed sensors.

The study asses that the variations in resonant frequency are caused by both the sensor's geometry and the water under test. The resonant frequency subtly states that sensors can be presented as lumped element equivalent series RLC circuits. In this work, an attempt has been made to show that the change in capacitance plays a pivotal role in estimating the resonant frequency. It is found that the sensor’s sensitivity decreases with water elevation and always be negative.

The circular and Archimedean spiral sensors have comparable sensitivity performance, while the comb IDE sensor is found to be the most sensitive. The IDE sensor features the highest sensitivity at 1 cm of water elevation. The circular and spiral IDE sensor more closely follows the reference resonant frequency   when compared with the comb IDE sensor.

Accessing data services on the cloud raises serious concerns about security and privacy. To verify a client's stated identity, the existing authentication approach uses just one authentication factor. However, traditional techniques of one-factor authentication are inefficient since they demand an excessive amount of user information and expose their private data to eavesdropping.

Cloud-based data storage is susceptible to both external and internal threats. Information security relies heavily on authentication, which is a method for verifying user identities before granting them access to protected data. When it comes to protecting data in a cloud computing environment from the most cutting-edge methods of attack, traditional password authentication simply isn't enough. For the purpose of protecting user identities in the cloud, this study presents interactive multi-layer authentication systems. Various access control policies are used when validating users in the cloud. Registration, permissions, and authentication factors for users are all parts of the cloud app's security process.

An intrusion detection system is built into the security mechanism to help identify people who could be malevolent. Various techniques are available for user identity verification, including intrusion detection, access control, and multi-factor authentication. The last step is to encrypt the data so that no one else can read it.

To guarantee the effectiveness and accuracy of the suggested frameworks and methodology, they are experimentally evaluated. There were few false positives and a high detection rate.

Ultra-High Voltage Direct Current (UHVDC) system has a wide range of applications in the energy field, and its transmission process will cause a large amount of carbon loss. To reduce carbon loss under the premise of ensuring safety, it is necessary to construct a carbon loss model of the UHVDC transmission system.

In this study, we propose a multi-objective optimization model to achieve the goal of low-carbon emissions, which considers carbon transaction costs and equipment safety and proposes initialization improvement and congestion improvement.

Compared with the mainstream optimization algorithms, the results show that the improved model achieves at least a 29.97% effect in reducing carbon loss, reduces the carbon loss by 1222.79 t, meets the requirements of the low carbon loss, transaction cost and high safety, and realizes the triple goals of UHVDC transmission system.

The model we proposed can effectively reduce carbon loss and carbon trading costs on the premise of ensuring safety.

Double carbon background and the new energy power system policy, a large number of distributed photovoltaic access distribution networks have a great influence, not only on the low voltage distribution network user side power quality, but also on photovoltaic units, which are involved in the tide back phenomenon, for the stable operation of the medium voltage distribution network and scheduling. Therefore, this paper puts forward a scheduling of the voltage distribution network and low voltage distribution network optimization method, to solve the problems of large-scale distributed photovoltaic access.

From the perspective of improving the economic benefit of the distribution network and ensuring its safe operation, the medium and low voltage optimization model with the goal of reducing the operating cost is established.

The optimization problem of medium and low voltage distribution network is decomposed into the main optimization problems of medium voltage distribution network and the sub-problems of optimizing the low-voltage distribution network. An optimal solution is obtained by coordinating the boundary coupling variables between the two problems and solving them iteratively. Finally, the effectiveness of the coordination algorithm is verified by simulating the medium and low voltage distribution network.

The proposed method reduces the operating cost and ensures the stable and safe operation of the power grid, thus improving the economic benefits.

When the superior power grid fails and loses power, due to the traditional single power supply radial grid structure of low-voltage substation areas, all loads in the low-voltage substation area will lose power, causing a poor electricity consumption experience for low-voltage users. To enhance the resilience of low-voltage distribution areas against faults and power outages from higher-tier power grids, a collaborative planning and comprehensive evaluation method for grid-forming energy storage systems (GFM-ESS) and flexible interconnection in distribution networks, considering island operation, is proposed.

This method integrates GFM-ESS and flexible interconnections, taking islanding operations into account. Firstly, the structure of GFM-ESS and voltage source converters is analyzed. The control modes of GFM-ESS in parallel or off-grid scenarios are also scrutinized. Secondly, minimizing the annual comprehensive cost and the annual power outage load are the objective functions. A bilevel programming model for GFM-ESS and flexible interconnection of low-voltage distribution networks, considering island operation, has been established. Moreover, a comprehensive evaluation method based on the analytic hierarchy process for proposed low-voltage distribution network structural schemes is introduced. An evaluation index system is established to assess the rationality of these proposed network structural planning schemes. Finally, the superiority of the proposed collaborative planning method is verified through a comparative analysis of three planning methods.

The proposed collaborative planning method can effectively ensure the continuous power supply of low-voltage distribution networks under the fault scenario of the upper power network.

Moreover, the rationality of the results obtained from the proposed collaborative planning method is verified through a comprehensive evaluation method of grid structure schemes based on the analytic hierarchy process. The proposed comprehensive evaluation method can determine which two areas are optimal for grid planning.

The rapid development of Industry 4.0 has opened up new power systems, which face a more complex network topology and a broader range of security threats. This has led to an urgent demand for a coordinated, lightweight, and secure communication management method.

This paper presents a general power communication framework with a joint security protection mechanism that is suitable for coordinated management and transmission safety. The framework manages the protocol’s thread management of the device by converting it to the framework’s process management. It formulates specifications for protocols and equipment that access the framework to achieve support for multiple protocols and equipment. This reduces the difficulty of system operation and maintenance, as well as system complexity, and improves management efficiency. Additionally, the joint security protective mechanism for plugin authentication and protocol encryption is introduced to enable secure data transmission and guarantee the framework's security management. Finally, two experiments have been conducted to discuss and confirm the effectiveness and safety of the framework.

The results of the two experiments and the following analysis show that our framework is superior to the chosen well-known industrial commercial communication platforms, and the time delay cost by our mechanism doesn’t significantly affect the real-time data transmission in the power industry, proving its feasibility in both IoT and power applications.

Our framework demonstrates exceptional performance in terms of time lag and packet loss during the experiment, surpassing other IoT communication frameworks. Furthermore, it maintains its high standards even under rigorous security analysis.

With the increasing integration of renewable energies into smart microgrids, the parameter space of the microgrid system model has increased dramatically, leading to critical computational challenges in achieving the dispatch strategy.

Additionally, in remote areas, connecting renewable energy resources to the national grid is unavailable due to the high costs. Consequently, we have introduced microgrids as a viable alternative solution. Microgrids are small-scale distributed power grids that rely on renewable energy sources and generators to balance the load demand. The aim of this paper is to improve the reliability of microgrids by integrating machine learning techniques into their control. This technology ensures that all loads in the microgrid are met through energy trading.

This algorithm utilizes reinforcement learning as the control mechanism for the trading process. We have established a set of trading rules that facilitate energy trades among three microgrids. We designate one of these three microgrids as the primary microgrid, while the other two function as trading microgrids. Firstly, a deep reinforcement learning algorithm (FH-TD3) is developed as a solution. Subsequently, the performances obtained in the three regions are compared with those derived from traditional algorithms. Finally, the effectiveness of the proposed energy management strategy was validated through simulations.

FH-TD3 has shown superior performance to DDPG in three different microgrid experiments, with the FH-TD3 algorithm consistently exhibiting shorter iteration times compared to the DDPG algorithm.

The research shows that the algorithm realizes the balance of the power without the loss of the power grid, and realizes the profit in the transaction process.

Tree-contact single-phase-to-ground fault (TSF) is a kind of common fault in the distribution grid. Since the initial electrical signal of TSF is weak, it is difficult to detect and remove it in time. Finally, it may evolve into more severe faults such as arc fault.

Therefore, establishing a numerical model capable of depicting the trend of TSF transition resistance can contribute to clarifying the development of TSF and provide a reference for early fault detection. The moisture content of branches is one of the important factors affecting TSF development, yet the mechanism of moisture content affecting the fault development is still ambiguous.

In order to explore the fault development process of branches with various moisture content, a TSF experimental platform of a 10kV distribution network line is built to evolve a characteristic curve of transition resistance influenced by moisture content and the temperature change curve during the development of TSF. Based on the experimental data, a numerical model of TSF transition resistance considering moisture content of trees is established. According to the trend of experimental data, a segmented model with the first peak of leakage current as the dividing point is established.

The segmented model takes the change of tree resistivity with temperature as the core, reduces the dependence on the traditional empirical parameters, and improves the fitting degree with different tree branch parameters. The improved model has a higher matching degree with the experimental measurement data.

The comparison between simulation results and experimental data shows that the improved model can efficiently predict the development process of TSF transition resistance under various tree parameters.

This research presents an 8-port 3.6 GHz multiple-input multiple-output (MIMO) cell phone bezel antenna for 5G mobile communication.

Each antenna unit consists of a Chinese character “卫”. In order to reduce the mutual coupling between MIMO units, a defective ground structure (DGS) decoupling structure is employed in this research. By increasing DGS in between the antenna, the mutual coupling between adjacent antenna is greatly reduced. The transmission coefficient and surface current distribution account for the decoupling structure's efficacy.

Simulated and measured results show that the antenna can operate at Bluetooth/Wi-Fi (2.4-2.483.5 GHz), N77 (3.3-4.2 GHz), N78 (3.3-3.8 GHz), and N79 (4.4-5.0 GHz) frequencies.

In addition, the proposed MIMO antenna has excellent diversity performance, good isolation between antenna units (>15 dB), and envelope correlation coefficient (ECC) lower than 0.01, due to which the proposed MIMO antenna can be an excellent choice for 5G cellular communications.

The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation.

We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses.

This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation.

A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations.

The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.

In electrical systems, power quality is crucial for delivering stable and high-quality power to consumers. Analyzers of power quality (PQAs) are essential for identifying and resolving power quality issues. In order to enhance the capabilities and efficiency of a PQA, a Field Programmable Gate Array (FPGA) may be used. The FPGA acts as the central component for real-time processing and analysis. It provides a versatile and programmable solution.

This study aims to develop affordable solutions for improving power quality in electrical systems, with potential applications in various industrial and commercial settings.

There are several key components and stages involved in the design of the Power Quality Analyzer. In addition to ensuring stable operation, the power supply board also measures power and voltage deviations, harmonic analysis, flickering, voltage sags, voltage swells, waveform distortions, and live load variations across the phases. In the analyzer, the FPGA board facilitates the real-time processing of collected data. Keysight VEE Pro software is used to analyze the data on a PC. In this way, power quality issues can be comprehensively evaluated.

The implemented Power Quality Analyzer employs the FPGA as its core processing unit and proves to be very useful. It provides accurate measurements and insights into the performance of electrical systems by detecting and analyzing various power quality issues. The proposed design provides better results when compared with the Fluke model 1736 analyzer. In addition, its cost-effective nature makes it an attractive solution for practical applications.

In conclusion, a Field Programmable Gate Array is a promising and effective approach for designing a Power Quality Analyzer. This system provides accurate measurements and real-time analysis of various power quality issues. A comparison with existing analyzers, specifically the Fluke model 1736, reveals the improved performance of the proposed PQA.

With the widespread integration of distributed photovoltaics into the power grid, challenges such as voltage fluctuations and increased deviations have arisen, alongside heightened demands for power quality and economic dispatch.

This study tackles the complexities of integrating distributed photovoltaic systems into the power grid, encompassing network flow variations, node voltage fluctuations, and risks of exceeding limits while also considering scheduling costs. A comprehensive control strategy is proposed, integrating the coordinated output of reactive power adjustable devices, energy storage systems (ESS), and transformers. Subsequently, a multi-objective optimization mathematical model is formulated with the aims of minimizing network loss, operation cost, and voltage deviation. An enhanced Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is introduced, leveraging a ring neighborhood topology and specialized crowding distance measurement to establish a stable network in both decision and target spaces, devoid of any niche parameters.

This approach eliminates subjective influences while ensuring rationality. Tests on the IEEE 30-node network example demonstrate that the algorithm can generate a large number of Pareto optimal solutions, highlighting its advantages in the application of reactive power optimization.

The method proposed in this paper can fully utilize the dispatchable equipment in the power grid, effectively reducing network losses and voltage deviations while maintaining low operating costs.

This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path.

The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACO- Dijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB.

At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED.

In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).