Substation Inspection Research Articles

An Improved HBA Method for Path Planning of Substation Inspection Robots

Aiming at the problems of honey badger algorithm (HBA) in the path planning of substation inspection robot, such as long path length, time-consuming search and high obstacle risk rate, an improved HBA algorithm called IHBA is proposed. Firstly, the honey badger population is initialized by reverse learning mechanism to increase its diversity. Secondly, the adaptive weight factor is used to improve the density factor of the HBA, which effectively balances the exploration capacity in different stages and improves the optimization accuracy of the population. Finally, the elite mutation strategy is used to strengthen the honey badgers’ location, which can guide the population to produce offspring in the food source area with better adaptability. To verify the improved effect of the algorithm and its path planning performance, the path planning experiments are designed, which indicate that compared with the BOA, IACO, GFA and classic HBA, the proposed IHBA in this paper has shorter path, higher search efficiency and lower obstacle risk rate, which can not only help the substation inspection robot plan the optimal path globally, but also increase the smoothness of the planned path.

An improved SLAM algorithm for substation inspection robot based on the fusion of IMU and visual information

In the past, manual inspection was often used for equipment inspection in indoor environments such as substation rooms and chemical plant rooms. This way often accompanies high labor intensity, low inspection efficiency, and low safety, which is difficult to meet the increasingly stringent requirements of indoor equipment operation and maintenance management. For dealing with these issues, a VIORB-SLAM2 algorithm based on the integration of IMU and visual information, was proposed by this paper. Firstly, the IMU data and image data were integrated to restore scale information of cameras, and then an error function was established to enhance the algorithm’s robustness. Secondly, in order to improve the accuracy of the algorithm, the random sampling consensus method was used to eliminate the wrong matching points in feature point matching, and the normalized cross-correlation matching was employed to constrain key frame matching conditions. Finally, through the iterative closest point method to stitch the point clouds, a dense map for navigation was constructed. The experimental results show that the algorithm designed by this paper has solved the shortcomings of applying the ORB-SLAM2 algorithm to indoor inspection robots while achieving high positioning accuracy, which can be combined with other algorithms in the field of artificial intelligence for object detection and semantic map construction in the future.

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.

DNN Adaptive Partitioning Strategy for Heterogeneous Online Inspection Systems of Substations

With the explosive development of power edge equipment and the continuous improvement in power inspection performance, the requirements of substations and terminal equipment, such as drones with limited resources, cannot meet the strict delay and energy consumption requirements. This paper proposes an adaptive partitioning strategy for heterogeneous substation inspection systems. First, a layer delay prediction model and layer energy consumption prediction model are established on each heterogeneous node, and nonlinear characteristics related to delay and energy consumption are trained. On this basis, a deep neural network (DNN) hybrid partitioning strategy is proposed. The DNN task is divided into synchronous cooperative reasoning between terminal devices and multi-heterogeneous edge nodes. The experimental results show that the average absolute percentage error (MAPE) of the delay model was reduced by 31.49% on average. On drones and mobile edge nodes, the energy consumption model MAPE reduced the average by 21.92%, and the DNN end-to-end latency was reduced by 31.48%. The total cost of the system was reduced and the efficiency of UAV inspection was improved.

Text classification for distribution substation inspection based on BERT-TextRCNN model

With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed energy resources, fine-grained perception of intelligent sensing devices, and the inherent stochasticity of source-load dynamics, a massive amount of raw data is being recorded and accumulated in the data center. Valuable information is often dispersed across different paragraphs of the raw data, making it challenging to extract effectively. Distribution substation inspection plays a crucial role in ensuring the safe operation of the power system. Traditional methods for inspection report text classification typically rely on manual judgment and accumulated experience, resulting in low efficiency and a significant misjudgment rate. Therefore, this paper proposes a text classification method for inspection reports based on the pre-trained BERT-TextRCNN model. By utilizing the dense connection between the BERT embedding layer and the neural network, the proposed method improves the accuracy of matching long texts. This article collected 2,831 maintenance data for the first quarter of 2023 from the distribution room, including approximately 58 environmental testing data, 738 environmental box testing data, approximately 672 distribution room testing data, and approximately 1,363 box type substation testing data. A text corpus was constructed for experiments. Experimental results demonstrate that the proposed model automatically classifies a large volume of manually recorded inspection report data based on time, location, and faults, achieving a classification accuracy of 94.7%, precision of 92%, recall of 92%, and F1 score of 90.3%.

Application research of UAV infrared diagnosis technology in intelligent inspection of substations

This study proposes an improved YOLOv4 algorithm based on mixed domain attention mechanism to design an intelligent substation inspection system. The proposed method combines improvement strategies such as lightweight, depthwise separable convolution, and mixed attention mechanism. The experimental results showed that the identification accuracy of the proposed model was only reduced by 0.2% for test samples at different positions, promoting the accuracy of intelligent inspection to reach 97.5%. The mIoU, mAP, detection speed, and recognition accuracy of the model constructed by the research were 78.34%, 95.12%, 62.05 frames per second, and 95.12%, respectively. Therefore, the proposed model could comprehensively enhance the information expression and recognition accuracy of the system, while promoting intelligent inspection to achieve high accuracy.

A Method for Identifying Power Equipment in Foggy Substation Based on Improved YOLOv4

Abstract In foggy weather, inspection robots detect low visibility and unclear targets in substation power equipment, and the commonly used target detection algorithms have too many parameters, making it difficult to achieve real-time requirements when deployed on inspection robots. To this end, a lightweight substation power equipment identification method combining Cycle Dehaze defogging algorithm is proposed. Pre process the collected fog map of the substation to improve the clarity and visibility of the target power equipment; Introducing MobileNetv3 and deep separable convolution in YOLOv4 network to achieve model lightweight; To increase the precision of power equipment identification, incorporate the SE attention mechanism into the target detection network. Based on the testing results, it is possible to reach a recognition mAP of 92.5% by combining Cycle Dehaze and improved YOLOv4, as the improved YOLOv4 has a detection speed that is 15.59 frames/second faster than the YOLOv4 method. The model parameter quantity has been reduced by 80.21%, which is more conducive to deployment on substation inspection robots.

Research and Simulation of Partial Discharge Phenomenon Sensing Technology for Substation Inspection Robots

Abstract This paper thoroughly investigates the phenomenon of partial discharge in substations and proposes a set of simulation experimental apparatus to replicate various types of partial discharge defects in switchgear. By modifying the switchgear, incorporating models for different types of partial discharge, and equipping various standard partial discharge sensors, accurate simulations of phenomena such as point discharge, suspended discharge, gap discharge, and particle discharge are achieved. Through this system, the effectiveness of different detection methods, such as ultrasonic, high-frequency, and transient voltage methods, is validated for various defect models. Furthermore, utilizing an indoor inspection robot guide rail, the paper simulates the environment of partial discharge in substations, enabling the validation of the robot’s partial discharge detection performance and typical patterns under different conditions. This work is dedicated to providing situational awareness technology for partial discharge phenomenon detection for substation inspection robots, so that the inspection robot can detect various abnormal partial discharge phenomena in transformers or other substation equipment through micro sensors to promote the stable operation of the substation.

A Dual-Coupled Double-LCC System With the Capability of Misalignment Tolerance Improvement for Wireless Charging Substation Inspection Robots

A Dual-Coupled Double-<i>LCC</i> System With the Capability of Misalignment Tolerance Improvement for Wireless Charging Substation Inspection Robots

Segmentation and Tracking Based on Equalized Memory Matching Network and Its Application in Electric Substation Inspection

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

AbstractIn contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

Pointer instrument positioning and indication recognition algorithm based on YOLOV5s

The substation inspection robot has low accuracy in positioning and indication reading of the pointer instrument. An algorithm design for indicator recognition is proposed. The algorithm mainly includes dial area extraction, scale line extraction and dial center determination, zero scale line determination, and indicator recognition. An experimental system for pointer instrument positioning and indication recognition is constructed to verify the proposed algorithm. The results indicate that the positioning accuracy of the two instruments is 90.5% and 100% respectively. Ten positioned instruments are selected, and the reading recognition error is 0.126. It shows that the proposed pointer instrument positioning and indication recognition algorithm can improve the efficiency of substation instrument recognition.

Combined navigation system for substation inspection robot based on DGPS/DR multi-sensor fusion

To realize the routine inspection work of the capacitor tower inspection operation robot on the capacitor tower in the substation, and to guarantee the accuracy and safety of the robot’s movement in the substation, a multi-sensor combination navigation system and the corresponding Kalman fusion filter are designed. Combined with the overall structure of the substation and the working environment of the inspection robot, the navigation and positioning requirements of the robot are analyzed and compared, and the multi-sensor combination positioning technology using a combination of DGPS, attitude sensors and velocity sensors is determined. The Kalman fusion filter based on the constant velocity model is used to fuse the positioning information of multiple sensors, and more accurate and reliable navigation information is obtained compared with the single-sensor positioning system. According to the working environment of the substation, an outdoor planar robot mobile navigation experiment is carried out to collect the positioning information of multiple sensors and carry out data fusion processing to verify the accuracy and robustness of multi-sensor combination navigation and positioning.

Path planning of substation inspection robot based on high-precision positioning and navigation technology

Abstract Outdoor substation is an important part of power system. Substation inspection robot based on intelligent autonomous inspection system has become the research focus of substation unmanned inspection. In order to improve the positioning accuracy and speed of the system, a high-precision positioning algorithm of transformer detection robot is proposed in this paper. Tikhonov regularization is used to correct the pathological problem of the localization algorithm model. The observation amount of the receiver is increased by using four signals of a single base station with double frequency and double antenna, and the position is solved by using single difference carrier phase observation and the integer ambiguity is fixed. The input–output mapping of the neural network is designed according to the information acquisition and two-wheel angular velocity control of the detection robot. Using the hyperbolic tangent function as the activation function of MLP neural network, the MLP neural network with 32 neurons in each of the three hidden layers is determined. By optimizing reinforcement learning reward function, adding scoring rules, and reward parameters, this paper carries out the following simulation exploration work. The high-precision positioning algorithm of transformer inspection robot is compared with the existing algorithm, and the superiority of the algorithm is verified. The basic motion ability of the robot installed with the system was tested.

Advancements in Substation Inspection Robots: A Review of Research and Development

The emergence of power station inspection robots signifies a significant leap forward in intelligent automation within the domain of substation maintenance. These robots are equipped with the capability to autonomously traverse through substations, conducting meticulous inspections of equipment and facilities. By replacing manual inspection methods, they bring forth a multitude of benefits including heightened efficiency, cost reduction, and enhanced personnel safety. As the realms of artificial intelligence and robotics continue to advance, the development of substation inspection robots has become a focal point in the power industry. This paper offers a comprehensive overview of the current landscape of inspection robots, offering insights from both domestic and international contexts. Furthermore, it delves deeply into the critical technologies that underpin substation inspection robots, showcasing the cutting-edge advancements driving their unparalleled performance. In addition to providing a comprehensive overview of existing technologies, this work offers a forward-looking perspective on the potential applications of these robots. It provides a glimpse into the future of substation maintenance practices, envisioning a landscape where these robots play an indispensable role in optimizing operational efficiency, ensuring regulatory compliance, and enhancing overall system reliability. Moreover, the paper outlines key areas for future technical development, paving the way for continued innovation and advancement in this dynamic and rapidly evolving field.

Path Planning for Substation Inspection Robots Based on a Fusion Algorithm Incorporation JPS and DWA

Path Planning for Substation Inspection Robots Based on a Fusion Algorithm Incorporation JPS and DWA

A New Assistance Navigation Method for Substation Inspection Robots to Safely Cross Grass Areas.

With the development of intelligent substations, inspection robots are widely used to ensure the safe and stable operation of substations. Due to the prevalence of grass around the substation in the external environment, the inspection robot will be affected by grass when performing the inspection task, which can easily lead to the interruption of the inspection task. At present, inspection robots based on LiDAR sensors regard grass as hard obstacles such as stones, resulting in interruption of inspection tasks and decreased inspection efficiency. Moreover, there are inaccurate multiple object-detection boxes in grass recognition. To address these issues, this paper proposes a new assistance navigation method for substation inspection robots to cross grass areas safely. First, an assistant navigation algorithm is designed to enable the substation inspection robot to recognize grass and to cross the grass obstacles on the route of movement to continue the inspection work. Second, a three-layer convolutional structure of the Faster-RCNN network in the assistant navigation algorithm is improved instead of the original full connection structure for optimizing the object-detection boxes. Finally, compared with several Faster-RCNN networks with different convolutional kernel dimensions, the experimental results show that at the convolutional kernel dimension of 1024, the proposed method in this paper improves the mAP by 4.13% and the mAP is 91.25% at IoU threshold 0.5 in the range of IoU thresholds from 0.5 to 0.9 with respect to the basic network. In addition, the assistant navigation algorithm designed in this paper fuses the ultrasonic radar signals with the object recognition results and then performs the safety judgment to make the inspection robot safely cross the grass area, which improves the inspection efficiency.

A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5

The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance.

Scale value guided Lite-FCOS for pointer meter reading recognition

As intelligent power grid construction advances, substation inspection becomes crucial, particularly in identifying meter readings. Existing meter reading methods are mainly based on the relationship between pointer and scale. However, these methods commonly suffer from the issue of over-relying on prior reading information, limiting readings to known meters. Hereby in this paper, we propose a method reaching more accurate and robust performance for meter reading by utilizing the unnoticed scale value. We determine the meter pointer direction with the aid of Hough transform and the pointer distribution. To detect meters from the scene as well as obtain scale values and pointers from the meter dial, we build an object detection network, named Lite-FCOS, whose backbone adopts a fast global context network that is lightweight and is of powerful feature extraction capabilities. For training these, meter dial detection dataset and dial reading information dataset are constructed. Lite-FCOS achieves 94.4 mAP50 and 96.7 mAP50 on the above two datasets with only 4.2 M parameters and 56.2 FPS. The entire pointer meter reading recognition process only spends 52 ms on an RTX 3080Ti with a successful meter reading rate (SMR rate) of 89.6%, which indicates that the proposed method achieves promising accuracy and speed.

A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN.

Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.