Real-time Transmission Line Research Articles

ETLSH-YOLO: An Edge–Real-Time Transmission Line Safety Hazard Detection Method

Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model’s complexity and improve the model’s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model’s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network’s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment.

PSO driven designing of robust and computation efficient 1D-CNN architecture for transmission line fault detection

In spite of the amazing success of deep learning in various complex classification tasks, its real-time applications have been greatly hindered due to the enormous computational burden and extensive processing time. In this context, the present investigation is a path-breaking idea and journey toward designing robust and computationally efficient One-dimensional Convolutional Neural Networks (1D-CNN) for the classification of transmission line faults, which demands accurate decisions within 20 ms. Following the multi-channel signal processing approach, sampled three-phase line currents of each half-cycle duration are concatenated to form a one-dimensional array and fed to the 1D-CNN model for exploring unique patterns of line currents associated with various faults. A Particle Swarm Optimization (PSO) driven multi-objective optimization technique is exclusively used to select an optimum number of kernels and kernel sizes for designing a less computation-intensive but robust CNN architecture, making it suitable for fast fault detection and classification without compromising the performance. The efficacy of this holistic design approach has been demonstrated in the simulated environment of an IEEE 5-bus system with significant penetration of distributed generation. With excellent fault pattern extraction through the convolution process, the optimally designed 1D-CNN has proven its strength for accurate transmission line fault classification by combating bidirectional power flow due to renewable energy penetration. The emulation time of the optimally designed 1D-CNN architecture for low-cost, low-power FPGA demonstrates its fastness and suitability for time-critical real-time transmission line fault classification. In general the proposition in the paper explains the candidate Deep Learning (DL) algorithm for multi-channel signal processing focusing on low-computing edge devices.

UAV system with trinocular vision for external obstacle detection of transmission lines.

External obstacle detection is a significant task in transmission line inspection and is related to the safe operation of the power transmission grid. In recent years, unmanned aerial vehicles (UAVs) equipped with different devices have been widely used for transmission line inspection. However, because of the complex environment of transmission lines and weak power line textures in the obtained images, most existing methods and systems cannot meet the requirements for real-time and high-accuracy external obstacle detection of transmission lines. In this paper, a novel, to the best of our knowledge, UAV system integrated trinocular vision technology with remote sensing is developed to achieve better external obstacle detection of transmission lines in real time, which is composed of a DJ-Innovations (DJI) UAV equipped with a global positioning system (GPS), angle sensors, trinocular vision including three visible cameras with the same parameters, and a small processor with a pre-implanted software algorithm. In this paper, a new method for external obstacle detection of transmission lines is proposed to satisfy the requirements for real-time and high-accuracy practical inspection applications. First, the original trinocular images need to be rectified. Then, the rectified trinocular images are adopted to achieve three-dimensional reconstruction of power lines. Finally, based on trinocular vision, bag of feature, and GPS, the clearance distance measurement, obstacle classification, and obstacle location are realized. Experimental tests on 220 kV transmission lines reveal that our proposed system can be applied in practical inspection environments and has good performance.

Identification of line status changes using phasor measurements through deep learning networks

THE PURPOSE. To consider the problem of detecting changes in a power grid topology that occurs as a result of the power line outage / turning on. Develop the algorithm for detecting changes in the status of transmission lines in real time by using voltage and current phasors captured by phasor measurement units (PMUs) are placed on buses. Carry out experimental research on IEEE 14-bus test system. METHODS. This paper proposes a method from the field of artificial intelligence such as machine learning in particular "Deep Learning" to solve the problem. Deep Learning arises as a computational learning technique in which high level abstractions are hierarchically modelled from raw data. One of the means to effectively extract the inherent hidden features in data are Convolutional Neural Networks (CNNs). RESULTS. The article describes the topic relevance, offers to apply the method for detecting status of lines using a CNN classifier. The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. The effectiveness of the joint use of PMUs and CNN in solving this problem has been proven. CONCLUSION. A solution for the line status change detection in the transient states using a CNN classifier is proposed. A high accuracy of the line status detection was obtained despite the influence of noise on measurement data. A change in the network topology is detected at the very beginning of the transient state almost instantly. It will allow the operator several times during the first seconds to identify the line state in order to make sure that the decisions made are correct.

Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things

Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient.

The anti‐icing and deicing robot system for electricity transmission line based on external excitation resonant

Aiming at the problem of off‐line deicing in overhead transmission lines, such as poor continuity of power supply and possible damage of lines caused by over‐thick deicing, a new online anti‐icing and deicing method based on external excitation resonance is proposed. The theoretical basis of the external excitation resonance to anti‐icing and deicing is established. Then the modal characteristics of the external excitation resonance are determined through the modal analysis of the ice‐coated transmission lines, and the reasonable frequency range of the external excitation resonance is obtained. According to the theoretical analysis and calculation results, the anti‐icing and deicing robot system is designed by the external excitation resonance. Intelligent video technology is used to detect the icing condition of transmission lines in real time, and the vibration frequency range of transmission lines is analyzed and calculated. The vibration frequency is adjusted through the real‐time tracking of the resonance feedback mechanism to ensure the continuity of the resonance working state, so as to effectively realize the efficient anti‐icing and deicing of the transmission lines with the thickness of 1–3 mm at the initial icing stage. Finally, the parameters of the key components of the system are calculated and determined, and the related influencing factors are analyzed. A new deicing theory based on anti‐icing theory is proposed, and specific engineering measures are given. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An Improved AlexNet for Power Edge Transmission Line Anomaly Detection

Since most outdoor transmission line equipment suffers from harsh disasters, they are prone to wire breakage, tower collapse and insulator flashover. When anomaly occurs, too much time is required for the State Grid Corporation to fix it manually. To reduce the inspection burden, many methods have been presented in the past to diagnose and locate anomaly. In this paper, we propose an improved AlexNet model for anomaly detection. In the aspect of feature extraction, the proposed model extracts the characteristics of transmission line equipment through a deep convolutional neural network (DCNN). In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the support vector machine (SVM), an SVM classification method incorporating deep learning is proposed. Finally, the improved AlexNet model and SVM classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images, which has great potential for future real-time transmission line monitoring platform design.

Overhead Transmission Line Parameter Reconstruction for UAV Inspection Based on Tunneling Magnetoresistive Sensors and Inverse Models

Overhead transmission lines have played an important role in power transmission and distribution, and real-time monitoring of the line status is essential to maintain the stability and reliability of the power system. As the unmanned aerial vehicle (UAV) has gradually become the novel and effective tool for inspection of transmission lines, the acquisition of dynamic line parameters is also important to keep the UAV's track and safety. In this paper, a novel parameter reconstruction method for overhead transmission lines was proposed. A framework of approximated transmission line inverse problem was constructed, which transformed the problem into a nonlinear optimization problem. Based on the proposed comprehensive algorithm that combined metaheuristic algorithm and interior point method, the position and current parameters of the lines were reconstructed from the magnetic field data. Theoretical simulation indicated that the algorithm avoided the results being stuck in the local optima effectively. In terms of the experimental implementation, a dual-axial tunneling magnetoresistive magnetic field measurement device was prepared. The measurement and calculation results proved the robustness and accuracy of the comprehensive reconstruction algorithm together with the magnetic field measurement device, leading to a more promising approach for real-time transmission line monitoring and UAV trajectory controlling.

Transmission Lines Availability Online Assessment During Power System Restoration Using the Information From Protection Systems

Power system restoration after outages caused by natural disasters is difficult owing to the damage caused to transmission lines. To formulate an effective power system restoration scheme and avoid damage to power equipment, it is necessary to obtain the availability of transmission lines in real time. Due to the complexity and variability of natural disasters, the availability assessment of transmission lines that depends on statistical data cannot give effective availability, which will lead to the failure of recovery. Two correlation models between transmission line availability and information of relay action are established and then applied to different relays in this paper. Combined with the real time record from the sequence of event (SOE), the online availability assessment method for transmission lines is proposed, which can obtain the damage probability of transmission lines in real time. The proposed method is verified by testing on the IEEE 39-bus model, and the simulation results show that it can assess the availability of transmission lines in real time without needing to consider the type of disaster, thus, helping to avoid the use of damaged transmission lines in the recovery process.

Efficient algorithms for real‐time monitoring of transmission line parameters and their performance with practical synchrophasors

Accurate transmission line parameters are important for many applications that ensure reliable operation of a power system. The traditional theoretical calculations and offline measurements are widely used approaches obtaining line parameters, but they do not allow tracking of the parameters that change with the environmental factors and load conditions. Synchrophasor-based real-time transmission line parameter monitoring algorithms can track the changing parameters. In this study, two novel line parameter estimation algorithms: a lump parameter model and a distributed parameter model are proposed. The performance of the new algorithms are evaluated under various operating conditions using a real-time digital simulator, and compared with six existing algorithms in terms of both accuracy and computational efficiency. The algorithms were also tested and compared using synchrophasor data obtained from a hardware experimental setup. Furthermore, application of the algorithms to an actual 230 kV transmission line is demonstrated. Finally, the sensitivity of the estimated parameters to bias errors in the measurements is analysed.

A novel transmission line protection using DOST and SVM

This paper proposes a smart fault detection, classification and location (SFDCL) methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST). The methodology is based on synchronized current measurements from remote telemetry units (RTUs) installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM) has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.

Smart Fault Location for Smart Grid Operation Using RTUs and Computational Intelligence Techniques

The smart grid aims to improve the quality and reliability of power at the generation, transmission, and distribution levels. The transmission lines can be considered the arteries of the power system, as they carry power over long distances and are exposed to difficult terrain. Transmission line protection philosophy is undergoing a change of paradigm with the advent of digital relays and high-speed broadband communication with the global positioning system (GPS). This paper explores the possibility of transmission line protection for a multigenerator system using wavelet multiresolution analysis (MRA) and computational intelligence techniques in conjunction with GPS. The inputs for the wavelet transform are the synchronized currents measured from remote telemetry units (RTUs) using GPS technology on different buses. The smart location technique uses a wavelet MRA technique to extract the features of the transient current signals based on the harmonics generated at the instant of occurrence of faults due to the abrupt changes of currents in a three-phase transmission line. These extracted features, with such computational intelligence techniques as an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), lead the grid toward smarter strategies for locating the fault distance. A comparative study establishes the superiority of ANFIS over ANNs for more accurate and reliable smart fault location. Furthermore, the efficacy of the proposed method is validated through a Monte Carlo simulation to incorporate the stochastic (random) nature of fault occurrence in a real-time transmission line. The most significant contribution of this paper is that the proposed smart location technique is immune to the effects of the fault inception angle (FIA), fault impedance, fault distance, and power angle. The results contained in this paper validate the use of the proposed algorithm for the real-time smart grid operation of transmission lines.

A New Practical Fault Location Algorithm for Two-Terminal Transmission Lines

A new practical fault location algorithm using two-terminal electrical quantities is presented in this article, which takes into account the distributed parameter line model. The analytical expression of algorithm derives from Three-Phase decoupling. First, an analytical synchronization of the unsynchronized measurements is performed with use of the determined synchronization operator and the non-synchronizing angle is calculated with the two-terminal pre-fault electrical quantities. Then, the real-time transmission line parameters are calculated using two-terminal non-synchronized electrical quantities and the non-synchronizing angle. The algorithm overcomes the drawbacks of the traditional fault location algorithms, which does not exist the pseudo-root problem. Besides, it has the advantages of simple, practical, litter computation, no need to search and iterative and robustness. The algorithm has not influenced by fault types, the transition resistance and other factors. At last the developed fault location algorithm is tested using signals of ATP-EMTP versatile simulations of faults on a transmission line.

Two-Terminal Numerical Algorithm for Single-Phase Arcing Fault Detection and Fault Location Estimation Based on the Spectral Information

This paper presents a new numerical algorithm for the fault location estimation and arcing fault detection when a single-phase arcing ground fault occurs on a transmission line. The proposed algorithm derived in the spectrum domain is based on the synchronized voltage and current samples measured from the PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. In this paper, the algorithm uses DFT(Discrete Fourier Transform) for estimation. The algorithm uses a short data window for real-time transmission line protection. Also, from the calculated arc voltage amplitude, a decision can be made whether the fault is permanent or transient. The proposed algorithm is tested through computer simulation to show its effectiveness.

Mechanical State Estimation for Overhead Transmission Lines With Level Spans

Electric power transmission lines face increased threats from malicious attacks and natural disasters. This underscores the need to develop new techniques to ensure safe and reliable transmission of electric power. This paper deals with the development of an online monitoring technique based on mechanical state estimation to determine the sag levels of overhead transmission lines in real time and hence determine if these lines are in normal physical condition or have been damaged or downed. A computational algorithm based on least squares state estimation is applied to the physical transmission line equations to determine the conductor sag levels from measurements of tension, temperature, and other transmission line conductor parameters. The estimated conductor sag levels are used to generate warning signals of vertical clearance violations in the energy management system. These warning signals are displayed to the operator to make appropriate decisions to maintain the line within the prescribed clearance limits and prevent potential cascading failures.

An Improved Technique for Fault Location Estimation Considering Shunt Capacitance on Transmission Line

This paper presents a new two-terminal numerical algorithm for fault location estimation using the synchronized phasor in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the PMUs (Phasor Measurement Units) installed at both ends of the transmission lines. In this paper, the algorithm is given without shunt capacitance and with shunt capacitance using II -model and estimated using DFT (Discrete Fourier Transform) and LES (Least Error Squares Method). The algorithm uses a very short data window and classification for real-time transmission line protection. To verify the validity of the proposed algorithm, the Electro-Magnetic Transient Program (EMTP) and MATLAB are used.

A real-time transmission line model for a digital TNA

This paper presents a validation of a real-time digital simulation of a transmission line model with frequency dependent parameters, The real-time transmission line model is based on Marti's time domain formulation which is adopted in the EMTP and EMTDC programs. Improvements to speed up the calculation are made in order to realize electromagnetic transient simulation in real-time by parallel processing using two digital signal processors (DSPs) for each line. 41 /spl mu/s and 71 /spl mu/s time steps are readily achieved in real time with the new line model for three phase and double circuit (6 conductors) transmission lines respectively, The new line model is now installed in a real-time digital simulator (RTDS). Field test results are presented in this paper for validating the new line model.