Search Results (200)

Maintenance techniques are fundamental in the context of the safe operation of continuous process installations, especially in electrical energy-transmission and/or -distribution substations. The operating conditions of power transformers are fundamental for the safe functioning of the electrical power system. Predictive maintenance consists of periodically monitoring the asset in use, in order to anticipate critical situations. This article proposes a methodology based on data science, machine learning and the Internet of Things (IoT), to track operational conditions over time and evaluate transformer aging. This characteristic is achieved with the development of a synchronization method for different databases and the construction of a model for estimating ambient temperatures using k-Nearest Neighbors. In this way, a history assessment is carried out with more consistency, given the environmental conditions faced by the equipment. The work evaluated data from three power transformers in different geographic locations, demonstrating the initial applicability of the method in identifying equipment aging. Transformer TR1 showed aging of $3.24\times {10}^{-3}\%$, followed by TR2 with $8.565\times {10}^{-3}\%$ and TR3 showing $294.17\times {10}^{-6}\%$ in the evaluated period of time. Full article

Substation equipment defect detection is a critical aspect of ensuring the reliability and stability of modern power grids. However, existing deep-learning-based detection methods often face significant challenges in real-world deployment, primarily due to low detection accuracy and inconsistent anomaly definitions across different substation environments. To address these limitations, this paper proposes the Language-Guided Enhanced Anomaly Power Equipment Detection Network (LEAD-Net), a novel framework that leverages text-guided learning during training to significantly improve defect detection performance. Unlike traditional methods, LEAD-Net integrates textual descriptions of defects, such as historical maintenance records or inspection reports, as auxiliary guidance during training. A key innovation is the Language-Guided Anomaly Feature Enhancement Module (LAFEM), which refines channel attention using these text features. Crucially, LEAD-Net operates solely on image data during inference, ensuring practical applicability. Experiments on a real-world substation dataset, comprising 8307 image–text pairs and encompassing a diverse range of defect categories encountered in operational substation environments, demonstrate that LEAD-Net significantly outperforms state-of-the-art object detection methods (Faster R-CNN, YOLOv9, DETR, and Deformable DETR), achieving a mean Average Precision (mAP) of 79.51%. Ablation studies confirm the contributions of both LAFEM and the training-time text guidance. The results highlight the effectiveness and novelty of using training-time defect descriptions to enhance visual anomaly detection without requiring text input at inference. Full article

In order to improve the reliability evaluation accuracy of a new generation of substation relay protection equipment under small-sample failure rate data, a Generative Adversarial Network (GAN) model based on the Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) to optimize the Long Short-Term Memory (LSTM) network is proposed. Because of the adaptability of LSTM for processing time series, LSTM is embedded into the GAN, and the LSTM optimized by ASFSSA is used as the generator of GAN. The trained model is used to expand the original data samples, and the least squares method is used to estimate the distribution model parameters, to obtain the reliability function of the relay protection equipment, and to predict the operating life of the equipment. The results show that compared with other methods, the correlation coefficient of the expanded data samples is closer to the original data, and the life estimation of the equipment is more accurate. The model can be used as a reference for reliability assessment and acceptance testing of the new generation of substation relay protection equipment. Full article

In order to accurately evaluate the operating status of secondary equipment in smart substations, this paper establishes a secondary equipment status evaluation index system and proposes a secondary equipment status evaluation method based on multi-model fusion ensemble learning according to the differences of multiple machine learning algorithms as learners. The method consists of a two-layer structure. First, the original data is divided, and the divided data is used to perform k-fold verification on several base learners in the first layer. Then, the fully connected cascade (FCC) neural network in the second layer is used to fuse multiple base learners, and the Levenberg–Marquardt (LM) algorithm is used to train the FCC neural network so that the model converges quickly and stably. Simulation experimental analysis shows that the accuracy of secondary equipment status assessment of the proposed method is 98.71%, which can effectively evaluate the operating status of secondary equipment and provide guidance for the maintenance of smart substation systems and secondary equipment. Full article

With the continuous improvement of China’s power grid, safety issues in substation operation and maintenance have become increasingly prominent. However, the existing electrical proximity early-warning devices are inadequate for the complex environments of substations, highlighting the urgent need to develop new electrical proximity early-warning technologies. Based on the safety needs of substation operators, this paper proposes an electrical proximity early-warning method that integrates ‘electric field + distance’. It combines MEMS electric field test technology with ultrasonic ranging technology and designs a double-criterion electrical proximity early-warning device. Based on the COMSOL 6.0 finite-element electric field simulation and the construction safety specification for substation equipment, a multistage electric-field early-warning threshold has been reasonably formulated. A field test conducted at a 220 kV substation demonstrates that this device can issue alerts for various electrical proximity threat levels of the circuit breaker within 0.1 s, which is faster and more accurate than existing commercial electrical proximity early-warning devices. The double-criterion early-warning system minimizes the risk of missed alarms during multi-distance measurements. Additionally, its flexible warning threshold accommodates the increasingly complex operational requirements of substations. Full article

State evaluation of relay protection equipment constitutes a crucial component in ensuring the stable, secure, and symmetric operation of power systems. Current methodologies predominantly encompass fuzzy-rule-based control systems and data-driven machine learning approaches. The former relies on manual experience for designing fuzzy rules and membership functions and exhibits limitations in high-dimensional data integration and analysis. The latter predominantly formulates state evaluation as a classification task, which demonstrates its ineffectiveness in identifying equipment at boundary states and faces challenges in model parameter selection. To address these limitations, this paper proposes a quantitative state evaluation method for relay protection equipment based on a two-stage artificial protozoa optimizer (two-stage APO) optimized improved Conformer (two-stage APO-IConf) model. First, we modify the Conformer architecture by replacing pre-layer normalization (Pre-LN) in residual networks with post-batch normalization (post-BN) and introducing dynamic weighting coefficients to adaptively regulate the connection strengths between the first and second feed-forward network layers, thereby enhancing the capability of the model to fit relay protection state evaluation data. Subsequently, an improved APO algorithm with two-stage optimization is developed, integrating good point set initialization and elitism preservation strategies to achieve dynamic equilibrium between global exploration and local exploitation in the Conformer hyperparameter space. Experimental validation using operational data from a substation demonstrates that the proposed model achieves a RMSE of 0.5064 and a MAE of 0.2893, representing error reductions of 33.6% and 35.0% compared to the baseline Conformer, and 9.1% and 15.2% error reductions over the improved Conformer, respectively. This methodology can provide a quantitative state evaluation and guidance for developing maintenance strategies for substations. Full article

The detection of equipment defects in substations is crucial for maintaining the normal operation of power systems. This paper proposes an object detection algorithm for substation equipment defect detection based on improvements to the YOLOv8 model. First, the backbone of YOLOv8 is replaced with EfficientViT, which not only reduces computational redundancy but also enhances the model’s feature extraction capabilities, thereby improving overall performance. Second, a Squeeze-and-Excitation (SE) attention mechanism module is incorporated at the terminal stage of the backbone network to reinforce channel-wise feature representation in input feature maps. Finally, the Bottleneck component within YOLOv8’s C2f module is substituted with FasterBlock, which significantly accelerates inference speed while maintaining model accuracy. Experimental results on the substation equipment defect dataset demonstrate that the improved algorithm achieves a mean average precision (mAP) of 92.8%, representing a 1.8% enhancement over the baseline model. The substantial improvement in average precision confirms the feasibility and effectiveness of the proposed modifications to the YOLOv8 architecture. Full article

Power equipment anomaly detection is essential for ensuring the stable operation of power systems. Existing models have high false and missed detection rates in complex weather and multi-scale equipment scenarios. This paper proposes a YOLO-SRSA-based anomaly detection algorithm. For data enhancement, geometric and color transformations and rain-fog simulations are applied to preprocess the dataset, improving the model’s robustness in outdoor complex weather. In the network structure improvements, first, the ACmix module is introduced to reconstruct the SPPCSPC network, effectively suppressing background noise and irrelevant feature interference to enhance feature extraction capability; second, the BiFormer module is integrated into the efficient aggregation network to strengthen focus on critical features and improve the flexible recognition of multi-scale feature images; finally, the original loss function is replaced with the MPDIoU function, optimizing detection accuracy through a comprehensive bounding box evaluation strategy. The experimental results show significant improvements over the baseline model: mAP@0.5 increases from 89.2% to 93.5%, precision rises from 95.9% to 97.1%, and recall improves from 95% to 97%. Additionally, the enhanced model demonstrates superior anti-interference performance under complex weather conditions compared to other models. Full article

Typical metal equipment in substations is exposed to high-temperature, high-humidity, and high-salt environments for a long time, and surface corrosion is a serious threat to operational safety. Traditional manual inspection is limited by the complexity of the environment and subjective assessment errors, and there is an urgent need for a method that can quickly and accurately locate the corrosion area and assess the degree of corrosion. In this paper, based on YOLOv8, the feature extraction ability is improved by introducing the attention mechanism; a mixed-mixed-sample data augmentation algorithm is designed to increase the diversity of data; and a cosine annealing learning rate adjustment is adopted to improve the training efficiency. The corrosion process of metal materials is accelerated by a neutral salt spray test in order to collect corrosion samples at different stages and establish a dataset, and a model of a corrosion-state recognition algorithm for typical equipment in substations based on an improved YOLOv8 model is established. Finally, based on ablation experiments and comparison experiments, performance analyses of multiple algorithmic models are conducted for horizontal and vertical comparisons in order to verify the effectiveness of the improved method and the superiority of the models in this paper. The experiments verify that the improved model is comprehensively leading in multi-dimensional indicators: the mAP reaches 96.3% and the F1 score reaches 93.6%, which is significantly better than mainstream models such as Faster R-CNN, and provides a reliable technical solution for the intelligent inspection of substation equipment. Full article

To fully utilize the reactive power resources of distributed photovoltaic (PV) systems, this study proposes a coordinated var-voltage control strategy for the main distribution network, incorporating the reactive power regulation capability of distributed PV. Firstly, the Automatic Voltage Control (AVC) tertiary and secondary voltage control methods and optimization models in the main and distribution networks area are analyzed, and the physical equivalence of the reactive power compensation equipment involved is carried out. In this study, a coordinated local var-voltage control method is proposed, which integrates AVC primary voltage control and divides the control scheme into feeder and station areas, respectively. Through the analysis of actual operation cases in a regional power grid, the results demonstrate a reduction in network loss by 171.14 kW through voltage adjustment, validating the effectiveness of the proposed strategy. This method fully leverages the reactive power regulation capability of distributed renewable energy sources, reduces the operational frequency of reactive power equipment in substations, and synergizes with the AVC system to achieve optimal power grid operation. Full article

Building-energy consumption constitutes a pivotal component of global energy systems, with the heating and cooling loads during the operational phase being particularly significant. Substation building, as nodes in the transmission and transformation network, deserve attention for their building-operating loads. This study investigates heating and cooling loads during substation operation in severe cold climates. By integrating energy consumption simulations with one-factor-at-a-time and orthogonal multivariate analyses, optimization strategies under key influencing factors are systematically explored. The impact analysis identifies the following order of influence magnitude on substation total loads: indoor equipment heat generation, ventilation rate, roof U-value, exterior wall U-value, and window U-value. The heating- and cooling-load characteristics exhibit distinct patterns depending on indoor equipment heat generation. The total building load can be reduced by 61.23 per cent under multifactor optimal de-sign conditions, highlighting the critical role of systemic design coordination. This study provides a case study reference for energy efficient design of heating and cooling loads in substations, especially where significant changes in equipment heat occur, and highlights the importance of controlling indoor heat sources to achieve optimal energy efficiency. Full article

Gas Insulated Switchgear (GIS) is a type of critical substation equipment in the power system, and its safe and stable operation is of great significance for ensuring the reliability of power system operation. To accurately identify partial discharge in GIS, this paper proposes an acoustic identification method based on improved mel frequency cepstral coefficients (MFCC) and dung beetle algorithm optimized random forest (DBO-RF) based on the ultrasonic detection method. Firstly, three types of typical GIS partial discharge defects, namely free metal particles, suspended potential, and surface discharge, were designed and constructed. Secondly, wavelet denoising was used to weaken the influence of noise on ultrasonic signals, and conventional, first-order, and second-order differential MFCC feature parameters were extracted, followed by principal component analysis for dimensionality reduction optimization. Finally, the feature parameters after dimensionality reduction optimization were input into the DBO-RF model for fault identification. The results show that this method can accurately identify partial discharge of typical GIS defects, with a recognition accuracy reaching 92.2%. The research results can provide a basis for GIS insulation fault detection and diagnosis. Full article

Existing seismic evaluations of electrical equipment in substations mainly focus on post-type equipment, with few studies addressing the suspended equipment that exhibits significant geometric nonlinearity. Most of the vulnerability analyses on substation equipment consider only ground motion uncertainty, not processing other uncertainties including material properties. Thus, this paper investigates the seismic responses of an ±800 kV suspended filter capacitor using a simulation model. A new approach for vulnerability assessment based on an adaptive boosting (AdaBoost) regressor is proposed considering the uncertainties of multiple material parameters of the suspension insulators. It is applied to the filter capacitor and validated by conventional incremental dynamic analysis (IDA). In addition, the influence of the pre-tension force of the bottommost suspension insulators is also discussed. The results indicate that increasing the pre-tension force can avoid pressure generation in the insulators and reduce the maximum displacements of the filter capacitor. The failure probability will significantly increase when the pre-tension force increases from 20 kN, although the growth rate continues to fall. The established AdaBoost regressors substantially lower the calculational cost while maintaining an accurate vulnerability prediction, compared to IDA. The proposed method is endorsed due to its high accuracy and low calculation cost, although its feasibility is validated by only one suspended filter capacitor in this paper. Full article

In substation working environments, analog dial gauges are widely used for equipment monitoring. Accurate reading of dial values is crucial for real-time understanding of equipment operational status and enhancing the intelligence of substation equipment operation and maintenance. However, existing dial reading recognition algorithms face significant errors in complex scenarios and struggle to adapt to dials with different measurement ranges. To address these issues, this paper proposes an automatic reading method for analog dial gauges consisting of two stages: dial segmentation and reading recognition. In the dial segmentation stage, an improved DeepLabv3+ network is used to achieve precise segmentation of the dial scale and pointer, and the network is made lightweight to meet real-time requirements. In the reading recognition stage, the distorted image is first corrected, and PGNet is used to obtain scale information for scale matching. Finally, an angle-based method is employed to achieve automatic reading recognition of the analog dial gauge. The experimental results show that the improved Deeplabv3+ network has 4.25 M parameters, with an average detection time of 19 ms per image, an average Pixel Accuracy of 92.7%, and an average Intersection over Union (IoU) of 79.7%. The reading recognition algorithm achieves a reading accuracy of 92.3% across dial images in various scenarios, effectively improving reading recognition accuracy and providing strong support for the development of intelligent operation and maintenance in substations. Full article

Ultraviolet (UV) imaging technology has been used in corona discharge detection by characterizing the discharge degree through the detected number of photons in the UV imager or facular area of the UV image. This paper is to expand the UV imaging method in the electrical equipment’s fault detection and improve the accuracy of its detection. First, an image segmentation algorithm based on Gaussian function color mapping is proposed for the segmentation of colored facular areas in the UV image, and its effectiveness is proved. Second, the testing results for a high-voltage insulator’s discharge are used to fit the distance attenuation function of the facular area. The relationship between the voltage and the attenuation function coefficient is obtained by analyzing the experimental results. Finally, by analyzing actual inspection data of insulators in a substation, it is found that different defect types of insulators have different characteristics of changes in the facular area during the discharge. Based on the characteristics, accurate direct detection by using UV imaging can be realized for the defect types of discharging insulators. Full article