In modern smart grids, accurate and synchronized time signals are essential for effective monitoring, protection, and control. Various time synchronization methods exist, each tailored to specific application needs. Widely adopted solutions, such as GPS, however, are vulnerable to challenges such as signal loss and cyber-attacks, underscoring the need for reliable backup or supplementary solutions. This paper examines the timing requirements across different power grid applications and provides a comprehensive review of available time synchronization mechanisms. Through a comparative analysis of timing methods based on accuracy, flexibility, reliability, and security, this study offers insights to guide the selection of optimal solutions for seamless grid integration.

Conveyor belt-type checkweigher is a widely used industrial system, whose periodic disturbances generated by the mechanical vibration parts (such as the operating motors, the gears, and the rollers) deteriorate the measurement accuracy of the weighing signal. The conventional way of suppressing these disturbances is using low-pass filters, which cannot meet the requirement if the conveyor belt is operating at a high speed (usually higher than 1.0 m/s). In this paper, the digital multiple IIR notch filter-based on a novel design method is proposed to remove these periodical noises. And the conventional low-pass filters and the wavelet signal denoising method are compared with the method proposed in this paper, the results show that the new method can achieve greater performance. Finally, an experimental platform of checkweigher is built and the mean errors and the standard deviations of the error about the tests in-field can meet the requirements of category XIII(1) in OIML R 51 with the conveyor belt running speed up to 2.0 m/s.

To address the issue of reduced weighing accuracy in conveyor belt checkweighers, resulting from severe contamination of load cell signals caused by mechanical vibrations, measurement noise, load displacement, and other interferences, this paper proposes a novel dynamic weighing method based on IWOA-SWFTD-SSI (stationary wavelet forced threshold denoising and system identification optimized by the improved whale optimization algorithm). First, stationary wavelet transform (SWT) is applied to decompose the load cell signal. Based on the frequency characteristics of both ideal and interference signals, a forced threshold function is designed to process the detail coefficients obtained from the decomposition. The denoised signal is then reconstructed through inverse SWT using the processed detail coefficients and original approximation coefficients, effectively suppressing various interference noises. Secondly, based on the denoised signal, the recursive least squares algorithm is adopted to identify the parameterized model derived from prior knowledge of the checkweigher, and the model parameters are used to calculate the mass of the load. Finally, IWOA is introduced to adaptively optimizemultiple parameters of the SWFTD-SSI, further enhancing weighing accuracy. To validate the effectiveness of the proposed algorithm, extensive loading tests are conducted using loads of different masses under various operating conditions. Experimental results demonstrate that the proposed algorithm outperforms all comparison algorithms in weighing accuracy, and consistently meets the accuracy requirements of class XIII(1) checkweighers as specified in OIML R 51 across a wide range of operating conditions.

A weighing error compensation method using IGWO-LMBPNN, which combines an improved grey wolf optimization algorithm (IGWO) with the Levenberg-Marquardt backpropagation neural network (LMBPNN), is proposed to enhance truck scale accuracy. It utilizes output signals from multiple load cells as inputs to the neural network for establishing the error compensation model. The IGWO algorithm is employed to initialize the weights and biases of the model, while the LMBPNN algorithm further optimizes these parameters. To evaluate the effectiveness of the IGWO-LMBPNN method, it was compared with existing compensation methods, including BPNN, LMBPNN, GWO-LMBPNN (Grey Wolf Optimization with LMBPNN), PSO-LMBPNN (Particle Swarm Optimization with LMBPNN), and Fine Gaussian SVM. The experimental results demonstrate a significant improvement in error reduction for truck scale weighing. The mean absolute error of IGWO-LMBPNN is 8.5 kg, which was reduced by 38.8%, 29.2%, 19%, 21.3%, and 33.1% compared to BPNN, LMBPNN, GWO-LMBPNN, PSO-LMBPNN, and Fine Gaussian SVM, respectively. These findings not only verify the efficacy of the IGWO-LMBPNN in enhancing weighing accuracy but also highlight its potential for other practical applications.

To accelerate progress toward the realization of advanced energy systems, this review explores the potential of pulsar technology to create a more stable, economical, and environmentally friendly energy infrastructure. Pulsars, with their precise and reliable timing characteristics, have emerged as a promising tool for enhancing energy systems. This review begins by examining the development history of pulsar technology, shedding light on its evolution and the milestones achieved. It then provides a comprehensive summary of the current state of research, highlighting recent advancements and breakthroughs in this field. It also explores transformative pulsar applications in energy systems, including improved grid stability, advanced energy synchronization, and efficient energy storage management. However, implementing pulsar-related technologies presents significant technical, economic, and operational challenges. This review examines these hurdles and proposes strategies to overcome them, emphasizing the need for innovation, interdisciplinary collaboration, and supportive policies to fully integrate pulsar technologies into sustainable energy systems.