Recent Advances in Electrical & Electronic Engineering - Current Issue

This provides an effective means to solve the problem of different ownership entities and regulation objectives between distribution networks and DER. Existing researches mainly focus on the active power operating envelopes while ignoring the reactive power. This paper studies the calculating method for the active-reactive power operating envelope of distribution networks. First, the rectangle model of active-reactive power operating envelopes is constructed to calculate the operating envelopes that provide more flexibility for DER control, while satisfying the voltage constraints robustly and meeting the actual demands in the operation of DER control. Second, the area that the active-reactive operating envelope covers is enlarged by finding a new vertex on the right side of the p-q plane to generate a pentagon operating envelope which is called the expanded operating envelope.

This leads to a higher active power output limit for DER control.

Finally, the effectiveness and safety of the proposed calculating method for distribution network operating envelopes is verified in distribution networks with different sizes

With the rapid popularization of electric vehicles, their charging load influences the stable operation of the power grid. An accurate prediction of EV charging station load is crucial for optimal resource allocation in power systems. The objective of this study is to address the issue of insufficient accuracy in existing prediction methods, this paper proposes a hybrid prediction model based on Bidirectional Long Short-Term Memory and Adaptive Boosting, aiming to improve the accuracy and stability of medium and long-term EV charging station load forecasting.

The study employs a three-step approach: (1) The pearson correlation analysis was utilized to evaluate multi-dimensional influencing factors and reduce dataset dimensionality; (2) implementation of a BiLSTM neural network for temporal feature extraction and preliminary prediction; and (3) application of the Adaboost algorithm to construct a weighted combination of strong classifiers. The model’s effectiveness was validated through comprehensive simulation tests using real-world charging station data.

The proposed Pearson feature selection-based BiLSTM-Adaboost model outperforms traditional benchmark models (LSTM and SVM), effectively reduces data redundancy through feature selection, achieves better performance in key indicators (MSE, RMSE, and MAPE), and demonstrates strong generalization ability and robustness while maintaining high accuracy.

Experimental results demonstrate that the proposed method effectively extracts key features of charging loads, achieving superior prediction accuracy and generalization ability compared to traditional methods. This provides a reliable decision-making tool for power grid operation, effectively supporting the resilience planning needs of urban power grids under continuously increasing EV penetration rates. But further research is needed to address robustness under extreme weather conditions.

This study provides an effective load forecasting methodology for power systems to address the challenges of large-scale electric vehicle integration. Future research will explore more robust feature engineering methods and deep learning architectures, such as combining other more advanced time series prediction models and improving optimization algorithms to enhance model adaptability and generalization capability for complex data patterns.

The performance of the hydraulic quadruped robots has not yet reached the level of quadrupeds. The whole-body stable motion control method of the hydraulic quadruped robot is the key to determining the motion ability. The paper summarizes the current development status of hydraulic quadruped robots and whole-body control methods, and the advantages of existing and future development trends of the main control methods, with a focus on related research papers.

The various typical hydraulic quadruped robots and their characteristics that have been published are summarized in this study. Additionally, the whole-body stable control methods of hydraulic quadruped robots are summarized, and the characteristics of various control methods are analyzed, especially the widely used model predictive control method and whole-body control method. Moreover, a few research results on hybrid control methods are introduced.

By summarizing the research results of hydraulic quadruped robots, it is evident that different control methods have different characteristics. The single control method is suitable for simple control tasks of hydraulic quadruped robots on flat road surfaces.

Due to the nonlinearity and time-varying parameters of hydraulic drive, hydraulic drive errors are inevitably present. There are still shortcomings in the development of hydraulic quadruped robots, such as energy utilization efficiency, lightweight design of structures, joint servo drive, and intelligent control.

In order to achieve stable control and industrial application of hydraulic quadruped robots, the control methods of whole body are summarized and analyzed. And it is pointed out that the future research work of hydraulic quadruped robots mainly focuses on the lightweight design of the system and the study of intelligent control algorithms with stronger adaptability.

Partial discharge in oil-paper insulated bushings represents a significant fault type in converter transformers during operation. Statistical analysis reveals that approximately 20% of insulation-related failures in converter transformers originate from partial discharges in bushings. When not detected promptly, these partial discharges can lead to insulation breakdown within 3–6 months. Each failure incident typically causes an 8–12 hour power interruption, resulting in substantial economic losses ranging from ¥500,000 to ¥800,000. This research addresses the critical challenge of precise partial discharge localization in bushings to enable effective casing maintenance.

The study initially developed an electromagnetic wave propagation simulation model for partial casing discharge to analyze the dynamic electromagnetic wave propagation process comprehensively. Subsequently, the Time Difference of Arrival (TDOA) localization algorithm underwent optimization through integration with the neural network, simulated annealing algorithm, and Bayesian algorithm.

Simulation results demonstrate that electromagnetic wave signals propagate into outer space as spherical waves through the oil gap between the flange end screen and the upper casing section. The maximum electric field intensity direction exhibits substantial variations between the casing surface and the far end. The enhanced algorithms demonstrate improved localization accuracy. The neural network-based TDOA achieves a reduced Mean Absolute Percentage Error (MAPE) of 5%, with over 80% of errors contained within 0.5 units of the actual position in each coordinate direction. The Bayesian-based improvement demonstrates a MAPE of 8%, with 70% of errors within 0.8 units. The simulated annealing-based enhancement achieves a MAPE of 6%, with 85% of errors within 0.6 units.

Based on the characteristics of the electromagnetic wave signal propagation process in the internal and external space of the oil-paper insulation sleeve, this article further improves the of the TDOA positioning algorithm based on the neural network.

The enhanced TDOA localization algorithm, incorporating neural network, simulated annealing, and Bayesian algorithms, successfully improves the accuracy of partial discharge localization in bushings.

Currently, modular multilevel converter (MMC)-based HVDC technology is utilized in the power grid. As known, the transmission line, integrated with MMC-based station, adopts travelling wave (TW) algorithm to identify partial discharge, which is highly dependent on the calculation of the initial TW velocity that correlates with the precise acquisition of the TW spectrum.

Firstly, in this work, the frequency-dependent feature of underground cables was analysed. Secondly, the correction algorithm for TW attenuation was obtained. Thirdly, a detailed partial discharge location algorithm was derived.

Using PSCAD/EMTDC, a ±400kV MMC-based power grid simulation model has been constructed, followed by performing a typical case study to verify the robustness of the proposed algorithm. To overcome these shortcomings, a novel partial discharge location principle for transmission line integrated with MMC-based station has been illustrated. However, it should be noted that the proposed method has only achieved frequency domain corrections, rather than the time-frequency domain, which still requires further research. Furthermore, the calculation has contained too many iterations, causing significant computational pressure.

The comprehensive frequency correction algorithm has exhibited the ability to recover the initial frequency spectrum information from highly attenuated TW signal, and the proposed fault location principle has been found suitable for transmission line integrated with MMC-based station.

Safety and efficiency have become critical issues in the quickly changing world of high-speed bus transit. The surge in high-speed bus-related traffic events is attributed to several factors, such as reckless driving, speeding, improper overtaking, vehicle health issues, sleep deprivation, alcohol consumption, and driver distractions. This paper proposes a state-of-the-art Internet of Things (IoT) system particularly intended for tracking and enhancing the security of high-speed buses on roads as a solution to these problems.

Innovative technologies like image processing, clever algorithms for computer and embedded vision (i.e., MobileNet, Canny Edge Detection, FaceMesh Model (Mediapipe), and Raspberry Pi 4 model B 8 GB are all included in the suggested solution. The system is made up of modules for online data visualization interfaces, driver monitoring systems, vehicle health and speed monitoring, and image processing for safety.

Real-time interaction, hardware implementation, model training, and web app integration are among the project's benchmarks.

Deliverables include creating a reliable IoT device, installing sensors for vital metrics, setting up a centralized interface for monitoring in real-time, and creating a clever algorithm that will produce alerts promptly. The project entails extensive testing and validation to guarantee dependability, accuracy, and compliance with safety and privacy requirements by providing valuable information to law enforcement authorities, improving the road safety and effectiveness of high-speed bus operations on highways.

The sensorless control technology for permanent magnet synchronous motors typically employs a sliding mode observer to obtain rotor position and speed information based on the back electromotive force. This study aims to improve the inherent chattering and poor observation performance of the traditional sliding mode observer (SMO) in the rotor position estimation of the surface-mounted permanent magnet synchronous motor.

The super twisting algorithm (STA) is introduced to improve the traditional SMO, and the super twisting sliding mode observer (STA-SMO) is constructed to solve the chattering problem of the traditional SMO. According to different speeds, the sliding mode variable gain coefficient is designed, and a continuous function L(x) is introduced as a switching function to make the switching of the sliding mode surface smoother. Considering the problem of stator current distortion caused by dead zone, the harmonic suppression strategy of adaptive notch filter (ANF) based on the least mean square (LMS) algorithm is studied and combined with the STA-SMO method to construct a position sensorless control system considering current harmonic compensation. Comparative verification under different speed conditions is carried out to verify the control performance of the method studied in this study under a wide speed range.

Firstly, the speed information is introduced as a variable into the gain coefficient of the traditional STA-SMO, and the parameters are adjusted with speed, which solves the parameter matching problem in different speed domains of STA-SMO and effectively improves the stability of the observer. On this basis, the current harmonic compensation strategy based on LMS-ANF is introduced. According to the characteristics of the adaptive filter, the harmonic current of a specific wave can be extracted, and the acquisition current is compensated to suppress the influence of current harmonics on the estimation results of the observer, which further improves the accuracy of the observer.

The proposed STA-SMO with LMS-ANF harmonic compensation demonstrates superior performance over traditional SMO, effectively reducing chattering and improving stability across wide speed ranges. Experimental results confirm its robustness under dynamic loads and adaptability to speed transitions, with chattering reduced by 1.1%. The LMS-ANF strategy mitigates current harmonics, enhancing low-speed accuracy. While the method balances simplicity and reliability, future work could address near-zero-speed performance and computational efficiency for broader industrial applications.

The STA-SMO + LMS-ANF proposed in this study can effectively improve the anti-interference ability of the observer, adapt to the application of a wide speed range, and have strong robustness and higher observer accuracy.

Introducing pilot protection in active distribution networks containing PV can improve the reliability and selectivity of protection. However, the basic communication facilities of the existing distribution network make it difficult to meet the requirements of data synchronization, and the PV T-connection to the network leads to sudden changes in the impedance angle.

Therefore, pilot protection of a negative sequence and additional network considering PV is proposed. The scheme is based on the feature that the PV model only outputs positive sequence components after a fault. For asymmetrical faults, the negative sequence impedance detected at both ends of the protection is utilized to construct a comparative negative sequence impedance protection criterion. For symmetrical faults, the voltage characteristics of the faulty additional network are utilized to construct a protection criterion.

The protection method requires less data information, low dependence on communication, and can quickly identify asymmetric faults occurring in the area. The operation results have high reliability and simple calculation; additional criteria can effectively avoid the impact of load current changes on the protection, can effectively withstand different transition resistance access conditions, and different penetration rates of photovoltaic power access. This study has two limitations: (1) The model considers only PV; (2) The proposed protection scheme applies only to local circuits.

Finally, an actual distribution network model with PV is constructed in PSCAD to verify the effectiveness and reliability of the protection method.

The protection method is selective and reliable and is not affected by high penetration rate and PV fault characteristics.