Electrical Substation Equipment Research Articles

Optimized Wavelet Transform for the Development of an Algorithm Designed for the Analysis of Digital Substation Electrical Equipment Parameters

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored.

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.

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.

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.

Multi-Target Recognition of Substation Infrared Image Based on Improved Faster RCNN

Abstract This study presents a multi-target recognition approach for substation infrared images based on enhanced Faster RCNN, which addresses the challenge of identifying several high-voltage electrical equipment in substation infrared images simultaneously. This method uses VGG16 to extract image features of a variety of electrical equipment in infrared images. Generate regional suggestions through the regional suggestion network and adjust the regional suggestions through border regression. ROI Pooling maps regional suggestions of different scales to fixed-size output vectors and sends them to Softmax for classification, and corrects the incorrectly identified parts categories according to the inclusion relationship of the regional suggestions. In the experiment, 27586 infrared photos are chosen to create an infrared data set in the voc207 format. Then, statistical analysis is performed on the recognition outcomes of 5517 infrared images in the test set. Based on the testing data, the upgraded rapid RCNN has a 92.8% recognition accuracy, which is 9.7% higher than that before, and has higher engineering practical value.

Seismic Assessment of Electrical Equipment in Power Substations: A Case Study for Circuit Breakers

Electric power is essential in post-earthquake periods for the continuous functionality of disaster management and emergency services. In addition, interruption of electric power can cause significant economic losses due to downtime of critical facilities. Therefore, it is very important to maintain seismic safety of electric power systems and components. There are existing seismic regulations and standards regarding electric power systems, especially in the United States of America (USA) and Europe. A similar regulation has been prepared recently in Türkiye, which is a country in a seismically active region. This study focuses on the current state of practice regarding the seismic assessment of electrical equipment in power stations and implementation of the regulations on seismic qualification of these systems. Among many electrical equipment, circuit breakers have been selected for case study. The seismic assessment of the selected high voltage equipment has been performed according to the new regulation under the seismic hazard specifically defined for Türkiye. The case study experiment presents the new methodology in evaluating and classifying the seismic response of high voltage electrical equipment and provides insight to the expected behaviour of circuit breakers under earthquake induced action.

Infrared image target detection for substation electrical equipment based on improved faster region-based convolutional neural network algorithm.

Substation electrical equipment generates a massive number of infrared images during operation. However, the overall quality of the infrared images is low and it lacks image detail information. When using traditional target detection algorithms for detection, feature extraction poses great difficulties. Therefore, to address this problem, this paper proposes a target detection algorithm based on the improved faster region-based convolutional neural network (Faster R-CNN). It achieves the correct identification of different types of electrical equipment in infrared images. First, the algorithm improves the backbone network of Faster R-CNN for feature extraction. An InResNet structure is proposed to replace the residual block structure of the original ResNet-34 network, which enhances the richness of feature extraction. Second, the rectified linear unit activation function in the original feature extraction network is replaced by the exponential linear unit activation function, and group normalization is used instead of batch normalization as the network normalization method. Then, the dense connection structure is introduced into the ResNet-34 network, and the whole network is called residual dense connection network. Finally, the improved Faster R-CNN is compared to the original Faster R-CNN, a single-shot multibox detector, and you only look once v3 plus spatial pyramid pooling. The experimental results show that the improved algorithm has the highest mean average precision and average recall for most of the substation electrical equipment in infrared images. Moreover, from the confidence level of the detected electrical equipment and the accuracy of the prediction box, the improved Faster R-CNN has the best performance.

Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

Abstract Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search; secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed; then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism; finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

Research on visualization monitoring technology of vulnerable high-voltage electrical equipment in substation based on BP artificial neural network

Abstract This study develops a visualization monitoring system for substation equipment operational status, utilizing mobile monitoring technologies. The system architecture integrates a core functional module aligned with comprehensive system requirements, enhanced by a BP neural network to optimize the server’s data mining capabilities. The research focuses on the analysis of typical faults in crucial substation electrical equipment, applying a Fourier algorithm for preprocessing the fault data. Employing the diagnostic principles of the BP neural network, the study designs a thermal fault diagnosis process for the substation apparatus. Experimental scenarios were established to evaluate the BP neural network’s performance by comparing three linear regression sample values. The practical application of the BP neural network model was assessed through integration with substation field data. Cross-validation of the field data indicates that the fault location algorithm accurately identifies 11 types of faults from 85 alarm signals in the secondary condition monitoring of substations, achieving a reliability of 98% or higher, which underscores its high applicability and operational feasibility.

Target detection of substation electrical equipment from infrared images using an improved faster regions with convolutional neural network features algorithm

The failure of substation equipment can cause incalculable losses to the economy and power consumption of the whole country. The use of infrared images is a powerful tool to obtain equipment temperature, which can then be used directly to diagnose substation equipment without stopping the operation of the equipment. In this paper, the authors focus on the correct identification of different types of electrical equipment from the infrared images. An improved faster regions with convolutional neural network features (faster R-CNN) algorithm is proposed, which shows very high detection accuracy for substation equipment. Firstly, the backbone of the faster R-CNN is optimised. A new network, the ResNet-30 network, is designed to reduce the redundancy of the ResNet-34 network and increases the proportion of residual blocks in the network in the previous stages. Next, the deep receptive field is combined with the shallow receptive field of the network and a double-shortcut structure with a large convolutional kernel is proposed. This enhances the ability of network feature extraction. A cross-channel shortcut is proposed at the channel transition of the network based on the channel number relationship between the dual-shortcut structures. Finally, the proposed method is compared with faster R-CNNs whose backbones are ResNet-50 plus a feature pyramid network (ResNet-50+FPN), you only look once v3 plus spatial pyramid pooling (YOLOv3+SPP) and a single-shot multibox detector (SSD). The results show that the improved model not only has a smaller number of parameters and low requirements for graphics processing unit (GPU) equipment, but also has the highest mean average precision (mAP) for mostly substation equipment in the test-set. This lays a foundation for fault diagnosis of substation equipment in the future.

A rapid seismic fragility and risk analysis of electrical substation equipment considering modeling uncertainties

This study aims to evaluate the influences of modeling-related uncertainties on the seismic response and fragility function of the four vulnerable components of electrical substation equipment, taking into account the dynamic interaction between apparatuses via bus slider-rigid bus conductors. The 3D finite element model for interconnected disconnect switch- circuit breaker-current transformer-surge arrester system is developed in OpenSees software. A sensitivity analysis is conducted to evaluate and rank the importance of individual random variables on the seismic responses. To incorporate modeling uncertainties into the failure fragility function, probabilistic structural interconnected simulations are generated using the Latin hypercube sampling technique. The fragility curves for each apparatus in the connected system are derived using incremental dynamic analysis (IDA). An efficient procedure based on the endurance time (ET) method is presented for estimating response distributions and fragility parameters of the interconnected apparatuses while considering modeling uncertainty. The proposed approach provides acceptable results compared to those from the IDA while tremendously reducing computer run-time. This investigation also puts forth a rapid detailed seismic risk analysis framework using scenario sampling analysis. To this end, the ET response distributions are exploited as equipment response models within reliability-based risk analysis. Results indicate that the damping ratio and porcelain elastic modulus have a substantial effect on the seismic responses of equipment. In addition, modeling uncertainties affect the median and dispersion values of the failure fragilities by up to −5.9% and +22% on average for all equipment cases, respectively. According to the loss curves, it is observed that the circuit breaker contributes the greatest to the system's overall loss, whereas the surge arrester contributes the least.

Floor response spectra and seismic design method of electrical equipment installed on floor in indoor substation

Electrical equipment installed on floors is a typical type of nonstructural component in indoor substations, which could be subjected to earthquake loading during seismic events. To investigate the seismic design method of floor electrical equipment in indoor substations, simulation models of four whole-indoor substations comprising main control buildings and their floor electrical equipment were established. Seismic response spectrum analyses of main-attachment structures were performed with a high number of ground motions as inputs. Using mathematical statistics and data fitting methods, this research suggested the calculation formula of amplification factor of floor acceleration response spectrum. Specifically, when the periodic ratio of electrical equipment to main control building is in the range of 0.9–1.1, the system would produce a strong resonance-like effect with a peak spectral amplification factor. The floor response spectrum for seismic design was influenced by four factors: equipment importance category, floor position, damping ratio of electrical equipment, and torsion effect of main control building. Following that, a seismic design method especially applicable for electrical equipment installed on floor in indoor substation, which combines amplification factor and calculation method of seismic action indicated in the relevant specification, was presented. Lastly, a typical 220 kV whole-indoor substation was taken as an example to validate the accuracy of the design method highlighted in this article.

Seismic performance improvement using bolt-on isolators on interconnected slender electrical equipment

The slender electrical equipment in substations is fragile to damage during earthquakes owing to the seismically sensitive structure, low material strength, and coupling effects from interconnected equipment. To improve the seismic performance of the slender electrical equipment in the interconnected circuit system, the design and characterization of an assembled isolation system were recommended. The solution was based on a novelty combination application of bolt-on isolators and a support block by reducing the bending stiffness of slender equipment to achieve vibration isolation function. The isolation function test was performed on isolators from characteristics of stiffness variation and damping. Finally, the effectiveness of the proposed isolation system was proved in seismic performance improvement of interconnected slender electrical equipment using a comparison shaking table test, which was performed on a full-scale interconnected circuit, consisting of two ±800kV composite pillar insulators connected by a tubular busbar. Therefore, thus isolation system can provide effective seismic protection for interconnected slender equipment in proper design.

CURRENT TRENDS IN DESIGN OF ELECTRIC SUBSTATIONS

The issue of using modern equipment in the construction and modernization of electrical substations is discussed in the article. &#x0D; The current state of electrical substation equipment is analyzed. It is shown that more than half of the equipment has exhausted its technical resource. The percentage of refusals due to aging has increased significantly. &#x0D; Modern trends in design and new concepts of construction and installation of electrical substations are described. The complete-block technology of construction of electrical substations is considered. &#x0D; The experience of substation reconstruction in Europe is presented. ODD, MTS and GIS technologies are compared. &#x0D; The main suppliers of equipment for electrical substations in Ukraine are characterized. &#x0D; Variants of the use of electric and gas equipment are described.&#x0D; Concepts of closed modular construction and "hybrid modules" are given.&#x0D; Gradual changes to the traditional for the unified energy system of Ukraine scheme "two working bus systems with a bypass bus system" are presented, depending on the choice of switching devices.&#x0D; New schematic solutions for substations on desolderings are provided (the RP scheme of the TP supply voltage on desolderings with a jumper on separate equipment; a typical RP scheme of the TP supply voltage on desolderings on integrated power equipment, the RP scheme of the TP supply voltage on desolderings with a jumper on enlarged cells on an integral power equipment). The advantages and disadvantages of the presented schemes are analyzed. On the basis of a complex analysis, conclusions are made about the advantages and disadvantages of the presented schemes and the criteria for choosing one or another scheme and the corresponding equipment are presented.

Detailed seismic risk analysis of electrical substation equipment using a reliability based approach

Detailed seismic risk analysis of electrical substation equipment using a reliability based approach

Back Flashover Probability of the Overhead Power Line Insulation Taking into Account the Soil Ionization and Frequency Response Features

Back flashovers caused by a lightning strike on an overhead power line tower or overhead ground wire give rise to voltage surges dangerous for the insulation of power substation electrical equipment. A pulse back flashover turns into a short-circuit arc leading to the power line tripping. Calculations of voltage surges in a single–circuit 110 kV power line are carried out using the methods of the grounding devices theory, and the back flashover probability is estimated based on the curve of dangerous currents. The insulation back flashover probability for a power line tower with a ground wire in areas with high-resistance soil is determined by the grounding conductor surge impedance. It is shown that the analysis carried out with taking into account the soil ionization and frequency response features yields an essentially lower back flashover probability. The second ground wire placed under the power line wires reduces the inductance of the ground wires, thereby intensifying the spread of current through the grounding conductors of neighboring towers. This leads to a decreased tower voltage and an increased voltage induced on the wires, thus resulting in a decreased voltage across the insulation voltage and, hence, lower back flashover probability. The factors governing a positive effect from using grounding counterpoises of an overhead power line in areas with high-resistance soil are shown, and the conditions under which this effect is achieved are determined.

Research on the Design of UAV Relay Charging High-rise Platform for Electrical Equipment Live Detection in Substation

The multi-rotor UAV inspection technology has received extensive attention in the live detection of electrical equipment in substations and has begun to be practically applied. However, due to the short endurance and the characteristics of intermittent operations, the UAV needs to frequently travel between the operational airspace and the cabin, which seriously affects the operational efficiency of the inspection. This paper first introduces the traditional substation electrical equipment live detection technology and then summarizes the application of UAV inspection in the substation electrical equipment live detection. Finally, in view of the shortcomings of the existing technology, this paper proposes a design scheme of a relay-charging high-rise platform for live detection and inspection of electrical equipment in substations. The relay charging high-rise platform can be divided into six parts according to the functional structure: high-altitude apron, UAV landing guidance system, UAV automatic binding system, high insulation strength power supply system, and platform monitoring system. In this paper, the overall structure of the relay charging high-level platform and the technical solutions of each component are described in detail. The technical solution can effectively improve the efficiency and safety of multi-rotor UAV inspection operations for live detection of electrical equipment in substations.

Infrared Image Target Detection of Substation Electrical Equipment Using an Improved Faster R-CNN

Infrared camera can be used to monitor the condition of substation electrical equipment. Fast and accurate target detection algorithm is the key for infrared intelligent on-line routing inspection. However, the performance of traditional detection algorithm is poor due to the complex background of substation. To solve this problem, this paper proposes a target detection model based on the improved faster region-based convolutional neural networks (Faster R-CNN), which can be used for automatic detection of five kinds of electrical equipment in substations. The feature extraction network of this model is improved based on VGG16 by abandoning some high-level convolutions to accelerate the training and testing speed. Meanwhile, the 1:3 and 3:1 aspect ratio of anchor are added to improve the detection accuracy of elongated equipment. Experiments are performed on an infrared image dataset of substation to detect five types of equipment. The robustness tests of our model are carried out, too. The results show that our model performs well in detection accuracy and speed, achieving a mean detected accuracy of 95.32% and running at 11 frame/second. In addition, our model is robust to noise and lightness, which is suitable to other substations. Comparing to other models, our model has the highest mAP@0.5 of 92.78%.

Thermal Fault Diagnosis of Electrical Equipment in Substations Using Lightweight Convolutional Neural Network

Real-time equipment condition monitoring is crucial to ensure the regular operation of the electric system. However, the deployed heavy convolutional neural networks (CNNs) are defective for edge computation and offline diagnosis. The purpose of this article is to present a system for detecting overheating faults of substation equipment using infrared photos and U-Net deep learning techniques. First, a stepwise encoder employs a lightweight CNN (LCNN) based on inverted residuals with depthwise separable convolution. The fault location is then decoded in the decoder using stepwise upsampling and nearest-neighbor interpolation. We additionally incorporate low-level detail feature information from the encoder to include additional fault data. Finally, testing findings on our dataset demonstrated the proposed method’s superior reliability and efficiency under a variety of evaluation metrics. Ultimately, we reached a lighter structure and leading estimation with a small training dataset, so our method is well-suited for deployment on mobile devices.