Substation Equipment Research Articles

Comparison Between Maintenance of Distribution Substations Using the Mobile Substation Unit (UGB) Method and Maintenance of Blackout Distribution Substations regarding Energy Not Served (ENS) which is not distributed to PLN UP3 Serpong

Electricity has become an important part of human life along with technological developments in this modern era. Economic progress, quality of life and the business world are very dependent on the electricity supply supplied by PT. PLN (Persero) UP3 Serpong. Electric power distribution is the final stage in the electric power delivery process. This is the process of distributing electric power from the electricity transmission system to electricity consumers. One of the main components in the electric power distribution system is the distribution substation. A distribution substation is an electrical installation device which contains disconnecting, connecting, safety and distribution transformer equipment to distribute electrical power according to consumer needs. Maintenance at distribution substations is carried out to support services to electricity consumers so that the substation equipment used is in reliable condition. and guaranteed continuity. Regular maintenance on distribution substations is important so that distribution substations do not experience damage and disruption while operating.

Vulnerability-based seismic resilience and post-earthquake recovery assessment for substation systems

The function of substation systems is significantly influenced by earthquakes. To assess the seismic resistance of substation systems and improve post-earthquake recovery efficiency, this study constructs a multi-module framework for seismic vulnerability assessment of substations. Based on the equipment functional status analysis module, a functional network model was established considering the structural function and electrical load of substation systems. The system functional state matrix was obtained through Monte Carlo simulation, and the seismic vulnerability was assessed from the aspects of electrical transmission reliability and the importance of power users. Subsequently, a substation functional index was established based on seismic vulnerability parameters. Post-earthquake functional recovery strategy analysis frameworks were constructed to quantitatively evaluate the recovery process. A stepped functional time-varying function was proposed through iterative analysis, thereby converting the probability parameters of functional status into seismic resilience deterministic indices. Through seismic resilience analysis of a typical 220 kV step-down substation, seismic vulnerability curves for both electrical transmission and power users were obtained. Furthermore, the seismic sensitive intervals of the substation and key equipment for post-earthquake recovery were clarified. Notably, the seismic resilience level and optimal recovery strategy of the typical 220 kV step-down substation were determined.

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

Review of Existing Tools for Software Implementation of Digital Twins in the Power Industry

Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization of the operation of power systems and their components. Digital twins are used to address different issues, including the management of equipment reliability and efficiency, integration of renewable energy sources, and increased flexibility and adaptability of power grids. Digital twins can be developed with the use of specialized software solutions for designing, prototyping, developing, deploying, and supporting. The existing diversity of software requires systematization for a well-informed choice of digital twin’s development tool. It is necessary to take into account the technical characteristics of power systems and their elements (equipment of power plants, substations and power grids of power systems, mini- and microgrids). The reviews are dedicated to tools for creating digital twins in the power industry. The usage of Digital Twin Definition Language for the description data of electromagnetic, thermal, and hydrodynamic models of a power transformer is presented.

Lightweight Substation Equipment Defect Detection Algorithm for Small Targets.

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model's feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model's ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.

Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.

Infrared Image Enhancement Method of Substation Equipment Based on Self-Attention Cycle Generative Adversarial Network (SA-CycleGAN)

During the acquisition of infrared images in substations, low-quality images with poor contrast, blurred details, and missing texture information frequently appear, which adversely affects subsequent advanced visual tasks. To address this issue, this paper proposes an infrared image enhancement algorithm for substation equipment based on a self-attention cycle generative adversarial network (SA-CycleGAN). The proposed algorithm incorporates a self-attention mechanism into the CycleGAN model’s transcoding network to improve the mapping ability of infrared image information, enhance image contrast, and reducing the number of model parameters. The addition of an efficient local attention mechanism (EAL) and a feature pyramid structure within the encoding network enhances the generator’s ability to extract features and texture information from small targets in infrared substation equipment images, effectively improving image details. In the discriminator part, the model’s performance is further enhanced by constructing a two-channel feature network. To accelerate the model’s convergence, the loss function of the original CycleGAN is optimized. Compared to several mainstream image enhancement algorithms, the proposed algorithm improves the quality of low-quality infrared images by an average of 10.91% in color degree, 18.89% in saturation, and 29.82% in feature similarity indices. Additionally, the number of parameters in the proposed algorithm is reduced by 37.89% compared to the original model. Finally, the effectiveness of the proposed method in improving recognition accuracy is validated by the Centernet target recognition algorithm.

Machine learning chain models for multi-response prediction of electrical equipment in substation subjected to earthquakes

Engineering structures often exhibit multiple potential vulnerable positions during strong earthquakes, such as porcelain insulators and connection flanges of electrical equipment in substations. This paper proposes two types of machine learning (ML) chain models, where multiple individual models are linked end to end, to predict multiple peak seismic responses of substation equipment at the same time using intensity measures (IMs). One is a simple chain where the next input is the previous output, while in another one, the next input is combined by IMs and the previous output. The training mechanisms of ML chain models are presented by means of bio-inspired multi-objective optimization techniques for the hyperparameters of multiple individual ML models. A case study on a 1100 kV transformer bushing is then conducted. Artificial neural network-gradient boosting regression and artificial neural network-kernel ridge regression chain models are respectively established for predicting the peak stresses at two most vulnerable positions. Both the evaluation indicators and shaking table tests show enough prediction accuracy of the two ML chain models. Such chain models are qualified for identifying the initial damage to equipment, which can be employed for assisting post-earthquake rapid judgement and relief.

State Evaluation of Electrical Equipment in Substations Based on Data Mining

This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability.

Review on Security Range Perception Methods and Path-Planning Techniques for Substation Mobile Robots

The use of mobile robots in substations improves maintenance efficiency and ensures the personal safety of staff working at substations, which is a trend in the development of technologies. Strong electric and solid magnetic fields around high-voltage equipment in substations may lead to the breakdown and failure of inspection devices. Therefore, safe operation range measurement and coordinated planning are key factors in ensuring the safe operation of substations. This paper first summarizes the current developments that are occurring in the field of fixed and mobile safe operating range sensing methods for substations, such as ultra-wideband technology, the two-way time flight method, and deep learning image processing algorithms. Secondly, this paper introduces path-planning algorithms based on safety range sensing and analyzes the adaptability of global search methods based on a priori information, local planning algorithms, and sensor information in substation scenarios. Finally, in view of the limitations of the existing range awareness and path-planning methods, we investigate the problems that occur in the dynamic changes in equipment safety zones and the frequent switching of operation scenarios in substations. Furthermore, we explore a new type of barrier and its automatic arrangement system to improve the performance of distance control and path planning in substation scenarios.

Analisis Arrester Sebagai Sistem Proteksi Pada Transformator di Gardu Induk Keramasan

Lightning overvoltage is a periodic overvoltage caused by external causes. An arrester is equipment that protects installations from overvoltage disturbances due to lightning strikes or electrical surges. The transformer functions to distribute electrical power by increasing or decreasing the voltage at the substation. This research focuses on substation equipment, namely the type 3EP 2150-2PL3 arrester connected to a 100 MVA IBT Transformer. Good protection is obtained if the arrester is placed as close to the transformer as possible. Therefore, this distance is determined so that protection can take place properly. The distance between the arrester and the transformer used at the Keramasan 150 KV substation is 5 m. while from the calculation results it is 22.5 m. based on SPLN 7: 1978 that for a 150 kV system the distance between the lightning catcher and the transformer does not exceed 80 m. This means that the distance between the arrester and the 100 MVA IBT Power Transformer is still within the limits set by PT PLN. The continuity of the spark that occurs in the arrester is still within safe limits and in accordance with mathematical analysis.

Development and application of multi-parameter real-time acquisition system for intelligent secondary equipment

Abstract A multi-parameter real-time collection and analysis system, including intelligent secondary equipment, data collection gateway, and health management backend, has been constructed to solve the problem of current incomplete monitoring data and inability to providential warning of hidden faults of intelligent secondary equipment in substations. The key feature parameters of the device’s functional modules were extracted, and a data collection gateway was developed to achieve IoT access to feature data. A fault identification and warning model for the secondary equipment functional module considering temperature is provided, which realizes the assessment of equipment health status and ensures the stable and reliable operation of the power grid.

Research on reliable coverage planning of substation based on equipment service level

Abstract 5G network can meet the multi-type service access needs of substations and promote the intelligent upgrade and transformation of power grids. The layout of buildings and electrical equipment in substations, as well as electromagnetic interference, will have a greater impact on 5G signal coverage. In this paper, a three-dimensional simulation model of the substation is first established, and basic parameters of base station planning are determined. Then, a safety function considering equipment service level and an electromagnetic interference calculation method are defined. Finally, a coverage planning algorithm based on electrical equipment service level and received power quality is proposed to improve the reliability of service access and ensure the safe and stable operation of the system. Simulation results show that the algorithm is effective and reliable.

Unified monitoring and online maintenance technology of substation secondary equipment

Abstract To meet the needs of the promotion of “equipment ownership” and the implementation of a centralized monitoring mode in substations, a technical scheme of unified monitoring, online operation, and maintenance for secondary equipment in substations is proposed, considering the monitoring, operation, and maintenance requirements of secondary equipment such as protection devices and automation devices. A secondary equipment monitoring real-time gateway is arranged in Security Zone I. The comprehensive operation status data of various secondary equipment is collected, analyzed, processed, diagnosed, and applied through various apps, to realize heterogeneous access, on-demand deployment, and flexible expansion. The master station is designed with functions such as human-machine interaction and remote analysis for secondary device monitoring and operation. GSP, MQTT, and other service-oriented and message mechanism communication protocols are adopted between the substation and the master station to meet the diversified and easily expandable information exchange requirements. Through the factory test, it is verified that the scheme can realize the panoramic perception of the operation state of the secondary system and effectively improve the ability of abnormality diagnosis and processing.

Indicator and sign recognition in real electricity scenario

Abstract A multitude of instruments and equipment are contained within the electricity scenario, and their operation leads to fluctuations in a wide range of parameters. These changes are typically represented through indicators and signs. This study introduces an efficient and robust method for recognizing these indicators and signs, utilizing a variety of image processing techniques, including the computation of image mean and variance, as well as template matching. Given the plethora of indicators and signs present on substation equipment, it is crucial to discern the illumination status of indicators at different locations and relay this information to the system based on the indicator’s significance. Considering the indoor setting, which is relatively uncomplicated, this study employs the variance and mean of the indicator area image for judgment. As for identifiers, which often vary in shape and color, this study uses template matching for identification, subsequently reporting the operational status of the equipment marked by this area. Experimental results demonstrate that compared to deep learning methods, traditional image processing algorithms yield superior outcomes while significantly reducing computational costs and time.

ISE-YOLO: A Real-Time Infrared Detection Model for Substation Equipment

ISE-YOLO: A Real-Time Infrared Detection Model for Substation Equipment

Seismic testing and fragility analysis considering material strength uncertainty of 1100 kV GFRP composite power transformer bushing

Nowadays glass-fibre reinforced polymer (GFRP) composite insulators are widely employed in electrical equipment in substations. However, there is a scarcity of reports on seismic qualification of transformer bushings that adopt GFRP insulators. Thereby, this case implements shaking table tests on a 1100 kV GFRP composite power transformer bushing. The study analyses the acceleration responses and dynamic amplification effects, as well as the strain responses. The combined stresses at the vulnerable positions are computed to authenticate the seismic performance. Using a simulation model, the seismic fragility of the transformer bushing considering the material strength uncertainty is assessed and discussed. The results indicate that the GFRP composite bushing meets the requirements of the relevant code and its seismic performance is qualified under an artificial ground motion with PGAs of 0.6 g and 0.8 g. For a given factor of safety of 1.67, the bushing can sustain 0.4 g earthquakes with a failure probability near 0 and 0.8 g earthquakes with a failure probability<50 %. Controlling coefficient of variation of the GFRP material strengths is beneficial to decreasing seismic fragility of the transformer bushing within a specific PGA range. Weibull and Normal distributions for fitting the strength distributions of the GFRP materials have different impacts on the seismic fragility of the bushing, which should be given more attention in seismic design.

To the problem of the shielding systems efficiency in constructions of extra-high voltage electrical apparatus

In the article, the authors proposed a method for estimating the electric load parameters to calculate the distribution of the electric field strength of the solid insulating structure of gas-filled high-voltage devices with axial symmetry. This structure consists of a support and insulating busbar filled with SF6 gas as an internal insulating medium. This technique allows to evaluate the parameters of the electrical load (voltage and field strength) depen-ding on the design features of the device and the shielding system used to equalize the electric field. The proposed methodology allows to evaluate the effectiveness of the influence of design parameters of the protection system for gas-filled high-voltage equipment (instrument transformers, arresters, surge arresters, etc.) when designing and improving individual design solutions. The calculation results are in good agreement with the data of experimental studies and statistical information obtained as a result of monitoring the operation of insulating structures, taking into account actual operating conditions and the influence of external factors. The implementation of this technique allows us to take into account the influence of external factors and operational characteristics inherent in instrument transformers and surge suppressors. In the proposed methodology, as an example, we consider a supporting insulating coating that is in operation under the most unfavorable conditions, such as external pollution, moisture and their combination, overvoltage of various origins, etc. The theoretical conclusions are confirmed by the results of calculations using the example of the support-insulating cover of the surge suppressor of the OПН-500 series. A more accurate determination of the effectiveness of the proposed methodology for predicting the field strength distribution parameters under the influence of a shielding system can be achieved by conducting an additional series of calculations and experimental tests of specific insulating structures. Thus, it was concluded that the obtained results can be used to evaluate the external insulation characteristics of both surge suppressors and gas-filled instrument transformers, as well as similar high-voltage equipment of switchgears and transformer substations in conditions of ultra- and transformer substations ultra-high voltages

Digital Twin System of Substation Equipment

This paper adopts the following technical solutions to achieve: a digital twin system for substation, including: database, database configuration module, data warehousing module, background data processing module and front-end browsing module; The front-end browsing module is used to display the data sent by the background data processing module in the corresponding form according to the user's selection; The form includes building the three-dimensional model of the substation based on BIM technology, and displaying the corresponding real-time data and status in the three-dimensional components of the three-dimensional model. Compared with the existing technology, it organically integrates the three-dimensional model and panoramic monitoring data of the substation, realizes the three-dimensional panoramic display of substation assets and the acquisition of substation monitoring data, improves the spatial experience of daily operation and maintenance comprehensive monitoring, and provides a solid data platform for the subsequent in-depth mining and intelligent analysis of multidimensional data of the substation.

A Novel Adversarial Deep Learning Method for Substation Defect Image Generation.

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects.