Resilience analysis and improvement strategy of microgrid system considering new energy connection.

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method.

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information.

High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time-frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment.

Design of robotic arm for the porcelain bushing in substation.

With the development and the application popularization of artificial intelligence robot technology and 5G technology, a robotic arm is designed and developed for rinsing porcelain bushing in high voltage substation in this paper. Firstly, the components and implementation of robotic arm are presented, subsequently, a circular cleaning structure with a 120-degree split is proposed to rinse the porcelain bushing. Secondly, a two-stage simple and effective method to realize automatic orientation is proposed utilizing photoelectric switches. Moreover, a prototype of robotic arm with control system is developed based on the regime switching function, and the result of edge computing is transmitted by 5G technology. Finally, feasibility and effectiveness of the robotic arm are verified in the Nanjing power grid. The case study manifests that the robotic arm developed by the proposed method in the paper can achieve efficient rinsing and all the corresponding information can be transmitted preciously. The proposed method lays a foundation for wide application of cleaning robot in high voltage substation.

Investigation on the fault monitoring of high-voltage circuit breaker using improved deep learning.

Mechanical faults are the main causes of abnormal opening, refusal operation, or malfunction of high-voltage circuit breakers. Accurately assessing the operational condition of high-voltage circuit breakers and delivering fault evaluations is essential for the power grid's safety and reliability. This article develops a circuit breaker fault monitoring device, which diagnoses the mechanical faults of the circuit breaker by monitoring the vibration information data. At the same time, the article adopts an improved deep learning method to train vibration information of high-voltage circuit breakers, and based on this, a systematic research method is employed to identify circuit breaker faults. Firstly, vibration information data of high-voltage circuit breakers is obtained through monitoring devices, this vibration data is then trained using deep learning methods to extract features corresponding to various fault types. Secondly, using the extracted features, circuit breaker faults are classified and recognized with a systematic analysis of the progression traits across various fault categories. Finally, the circuit breaker's fault type is ascertained by comparing the test set's characteristics with those of the training set, using the vibration data. The experimental results show that for the same type of circuit breaker, the accuracy of this method is over 95%, providing a more efficient, intuitive, and practical method for online diagnosis and fault warning of high-voltage circuit breakers.

Maize Shrek1 encodes a WD40 protein that regulates pre-rRNA processing in ribosome biogenesis.

Ribosome biogenesis is a fundamental and highly orchestrated process that involves hundreds of ribosome biogenesis factors. Despite advances that have been made in yeast, the molecular mechanism of ribosome biogenesis remains largely unknown in plants. We uncovered a WD40 protein, Shrunken and Embryo Defective Kernel 1 (SHREK1), and showed that it plays a crucial role in ribosome biogenesis and kernel development in maize (Zea mays). The shrek1 mutant shows an aborted embryo and underdeveloped endosperm and embryo-lethal in maize. SHREK1 localizes mainly to the nucleolus and accumulates to high levels in the seed. Depleting SHREK1 perturbs pre-rRNA processing and causes imbalanced profiles of mature rRNA and ribosome. The expression pattern of ribosomal-related genes is significantly altered in shrek1. Like its yeast (Saccharomyces cerevisiae) ortholog Periodic tryptophan protein 1 (PWP1), SHREK1 physically interacts with ribosomal protein ZmRPL7a, a transient component of the PWP1-subcomplex involved in pre-rRNA processing in yeast. Additionally, SHREK1 may assist in the A3 cleavage of the pre-rRNA in maize by interacting with the nucleolar protein ZmPOP4, a maize homolog of the yeast RNase mitochondrial RNA-processing complex subunit. Overall, our work demonstrates a vital role of SHREK1 in pre-60S ribosome maturation, and reveals that impaired ribosome function accounts for the embryo lethality in shrek1.