State Of Transmission Line Research Articles

Analysis of Pollution in High Voltage Insulators via Laser-Induced Breakdown Spectroscopy.

Surface pollution deposition in a high voltage surface can reduce the surface flashover voltage, which is considered to be a serious accident in the transmission of electric power for the high conductivity of pollution in wet weather, such as rain or fog. Accordingly, a rapid and accurate online pollution detection method is of great importance for monitoring the safe status of transmission lines. Usually, to detect the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD), the pollution should be collected when power cut off and bring back to lab, time-consuming, low accuracy and unable to meet the online detection. Laser-induced breakdown spectroscopy (LIBS) shows the highest potential for achieving online pollution detection, but its application in high voltage electrical engineering has only just begun to be examined. In this study, a LIBS method for quantitatively detecting the compositions of pollutions on the insulators was investigated, and the spectral characteristics of a natural pollution sample were examined. The energy spectra and LIBS analysis results were compared. LIBS was shown to detect pollution elements that were not detected by conventional energy spectroscopy and had an improved capacity to determine pollution composition. Furthermore, the effects of parameters, such as laser energy intensity and delay time, were investigated for artificial pollutions. Increasing the laser energy intensity and selecting a suitable delay time could enhance the precision and relative spectral intensities of the elements. Additionally, reducing the particle size and increasing the density achieved the same results.

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

Design of data transmission system of human-autonomous devices for UAV inspection of transmission line status

The use of unmanned helicopters for line inspection is an important development direction for transmission line inspections. The unmanned helicopter inspection system can partially replace the line inspection workers and the manned helicopter inspection system, timely discover the faults and hidden dangers of the line, and transmit the situation on the ground to the ground control center to make correct judgments and eliminate line faults in time. The composition of the data link of the unmanned aerial vehicle (UAV) is designed as a data-code-coded code link implementation scheme based on single-chip microcomputer, which can realize the uplink remote control information, task information and management information. For multi-rotor UAVs, the load is limited, and long-distance operations are often required. In combination with people’s requirements for image quality, this paper designs a set of high-definition digital image data transmission system with small volume and low transmission delay, capturing images in real time with a high-definition camera mounted on a drone or single-player device, first convert the image to a video signal, and then transmitting the video signal to the modulation end of the microwave transmitter through compression processing, and loading the signal onto the carrier through the microwave transmitter. After being microwaved, the directional radiation is radiated to the microwave receiver of the tension tower or the drone, and the video image signal is demodulated by the microwave receiver. Sending this signal to a hard disk recorder or monitor enables unattended monitoring of the power line all day.

Electrical Power System Resilience Assessment: A Comprehensive Approach

Extreme weather events such as earthquake and hurricane have disastrous consequences on power systems. Due to the inherent nature of these events, as high-impact low-probability (HILP) events, selection of an appropriate method that can provide the effects of weather conditions on the power system behavior still remains a significant challenge. Resilience is a new concept that focuses on mitigating the destructive effects of such disastrous events on power systems. This article provides a fundamental framework for quantifying and modeling of power systems resilience, with focus on high wind incidence. The algorithm composes of four steps. In the first step, the prerequisites of the analysis are described. Disaster modeling is carried out in step second and the transmission line status and system topology obtaining are outlined in the third step. In the last step, based on optimal power flow tracing, a novel resilience index is introduced based on axiomatic design concepts. The core of the main algorithm involves a novel matrix-based approach to count automatically all possible routes from generators to loads, considering load importance, before and after a disruption. The effectiveness of the proposed approach is illustrated by the IEEE 14-bus system. The proposed methodology is original and a comprehensive approach.

MPLP-Based Fast Power System Reliability Evaluation Using Transmission Line Status Dictionary

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model.

Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines

Unmanned aerial vehicles (UAVs) are an emerging and promising alternative for monitoring of transmission lines in terms of flexibility, complexity, working speed, and cost. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the UAV placement with respect to the transmission grid. This work faces this challenge by developing a transmission line autonomous tracking system, which allows the placement of a commercial drone over a transmission grid using a monocular camera. This feature provides accurate positioning for the vehicle even where the Global navigation satellite system (GNSS) signal is denied, enabling to report the status of transmission lines, at any time. The system isolates transmission grid conductors in each acquired RGB-image using an image-processing algorithm based on Hough transform, morphological operations, and Gabor filters. With this information, the system computes the location of the UAV using a geometric approach that relates transmission lines building parameter and optical geometry. However, it has the problem of gradual error accumulation when the drone moves. In this regards, the estimated position of the drone is computed by the maximum likelihood estimation (MLE) by the position information estimated by visual-system, the inertial measurement unit (IMU) and GNSS. The proposed positioning system showed an efficiency of 91.44% in field experimentation in the extraction of transmission conductor, with a root mean square the error of 0.18 m in the UAV localization.

Current Tolerance Capability Calculation Model of Transmission Lines and its Application in Overload Protection

Traditional overload protection depends on pre-set values and cannot reflect the current tolerance capability of the line. If the current of a line exceeds the setting value of its overload protection, then it will be subsequently tripped off and cause power flow transferring, which may trigger the cascade tripping of power grid. So it is of significant importance for the overload protection to accurately evaluate the real-time safety status of transmission line and coordinate with power system control to eliminate the overload. In this article, starting from the mechanism analysis of conductor heat transfer, a thermal network model is established by focusing on radial and circumferential heat transfer paths. The temperature rise response of the network node is obtained through nonlinear iteration. The calculation model, which is verified by a test platform, is better than standard models. Then, the current tolerance capability calculation model of the whole line is constructed on the basis of the thermal network and applied to improve the overload protection. The proposed multi-parameter overload protection can effectively prevent unnecessary tripping to avoid the expansion of accidents. Finally, the validity of the proposed protection is verified by the New England 10-machine 39-bus test system.

A two-stage stochastic programming approach for reliability constrained power system expansion planning

Probabilistic realizations of outages and their effects on the operational costs are highly overlooked aspects in power system expansion planning. Since the effect of randomness in contingencies can be more prominent especially when transmission switching is considered, in this paper we introduce contingency-dependent transmission switching concept to ensure N-1 criterion. To include randomness of outages and the outputs (i.e. flow on the lines/generation amounts) during the outages, we represent each contingency by a single scenario. Status of transmission lines, generation amounts and power flow decisions are defined as recourse actions of our two-stage stochastic model, therefore, expected operational cost during the contingencies are taken into account in a more accurate manner. A solution methodology with a filtering technique is also proposed to overcome the computational burden. The model and the solution methodology are tested on the IEEE Reliability Test System and IEEE 118-bus power system and the results show that the solution method finds the solutions for these power systems in significantly shorter solution times. The solution method is also tested on a new data set for the 380-kV Turkish transmission network. Suggestions for possible extensions of the problem and the modifications of the solution approach to handle these extensions are also discussed.

An Evaluation of Overhead Transmission Line Status Based on Set Pair Analysis

An Evaluation of Overhead Transmission Line Status Based on Set Pair Analysis

Dynamic Detection of Transmission Line Outages Using Hidden Markov Models

We study the problem of detecting transmission line outages in power grids. We model the time series of power network measurements as a hidden Markov process, and formulate the line outage detection problem as an inference problem. Due to the physical nature of the line failure dynamics, the transition probabilities of the hidden Markov Model are sparse. Taking advantage of this fact, we further propose an approximate inference algorithm using particle filtering, which takes in the times series of power network measurements and produces a probabilistic estimation of the status of the transmission line status. We then assess the performance of the proposed algorithm with case studies. We show that it outperforms the conventional static line outage detection algorithms and is robust to both measurement noise and model parameter errors.

Assessment of Transmission Line Condition Based on Neural Networks and Fuzzy Logic Decision

Accurate assessment of the operational status of transmission line,and the line status timely warning, can protect the stable operation of the transmission line. Not high accuracy assessment for the current state of the transmission line,assessment model of transmission line state based on neural networks and fuzzy logic decisions are put forward,built on online monitoring system, Considering the conductor temperature, angle, tension and other parameters.Using BP neural network convergence line fault membership values, and the output state is line fault probability. Then ,the uncertainty of the state probability output line integrated assessmen through Fuzzy Reasoning.

Transmission Line Status Monitoring System Based on Ad Hoc Network

Based on the basic principle of wireless sensor networks, the communication platform of a new ZigBee wireless ad hoc network is applied to the on-line monitoring system of transmission lines. In this article, the hardware and software platform architecture of the system is introduced, the scheme and algorithm for cluster network of monitoring nodes based on ZigBee technology are described; moreover a reliable, energy-saving and efficient data transmission method is designed. Application results show that ZigBee technology has the features such as self-organization, dynamic topology, large network capacity and data-centeredness, and possess a distinct advantage in on-line monitoring of electrical equipments.

Structural health monitoring oriented stability and dynamic analysis of a long-span transmission tower-line system

Structural stability is an vital aspect for structural health monitoring of transmission lines. This paper presents a novel method to evaluate the structural status of transmission line based on dynamic and stability analysis. A newly built long-span transmission tower-line system that crosses the Huaihe River in China with a span of 1083 m was studied as an example. Finite element model of the transmission tower-line system was constructed. Nonlinear buckling analysis for both the tower and tower-line system were performed to determine the critical wind loads. Dynamic properties of both the tower and tower-line system under different levels of wind loads were analyzed. Numerical results show the law that modal frequency of low order modes decreases when the wind velocity increases before the structural instability happens for both cases. Therefore, for the structural health monitoring of transmission lines, frequency decrease of low order mode is an useful indicator to predict the happening of structural instability. This conclusion could be taken as the reference in the design of structural health monitoring system for transmission lines.

Convergence characteristics of two Monte Carlo models for reliability evaluation of interconnected power systems

The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.