Corona ions from high-voltage power lines : nature of emission and dispersion

Positive and negative small ions, aerosol ion and number concentration and dc electric fields were monitored at an overhead high-voltage power line site. We show that the emission of corona ions was not spatially uniform along the lines and occurred from discrete components such as a particular set of spacers. Maximum ion concentrations and atmospheric dc electric fields were observed at a point 20 m downwind of the lines. It was estimated that less than 7% of the total number of aerosol particles was charged. The electrical parameters decreased steadily with further downwind distance but remained significantly higher than background.

Investigation of corona sources in high voltage power lines

This paper describes the results of experiments made in the vicinity of EHV overhead lines to investigate sources of clouds of charged particles using simultaneously-recording arrays of electric field meters to measure direct electric fields produced under ion clouds. E-field measurements, made at one metre above ground level, are correlated with wind speed and direction, and with measurements from ionisation counters and audible corona effects to identify possible positions of sources of corona on adjacent power lines. Measurements made in dry conditions on EHV lines in flat remote locations with no adjacent buildings or large vegetation indicate the presence of discrete ion sources associated with high stress points on some types of line hardware such as connectors and conductor spacers. Faulty line components such as insulators and line fittings are also found to be a possible source of ion clouds.

Air ion concentrations under overhead power lines

Corona discharge is responsible for the flux of small ions from overhead power lines, and is capable of modifying the ambient electrical environment, such as the air ion concentrations at ground level. Once produced, small ions quickly attach to aerosol particles in the air, producing ‘large ions’, approximately 1 nm to 1 µm in diameter. However, very few studies have measured air ion concentrations directly near high voltage transmission lines. The present study involved the simultaneously measurement of small ion concentration and net large ion concentration using air ion counters and an aerosol electrometer at four power line sites. Both positive and negative small ion concentration (<1.6nm), net large ion concentration (2nm-5μm) and particle number concentration (10nm-2μm) were measured using air ion counters and an aerosol electrometer at four power line sites. Measurements at sites 1 and 2 were conducted at both upwind and downwind sides. The results showed that total ion concentrations on the downwind side were 3-5 times higher than on the upwind side, while particle number concentrations did not show a significant difference. This result also shows that a large number of ions were emitted from the power lines at sites 1 and 2. Furthermore, both positive and negative ions were observed at different power line sites. Dominant positive ions were observed at site 1, with a concentration of 4.4 x 103 ions cm-3, which was 10 times higher than on the upwind side. Contrary to site 1, sites 2 to 4 showed negative ion emissions, with concentrations of -1.2 x 103, -460 and -410 ions cm-3, respectively. These values were higher than the background urban negative ion concentration of 400 cm-3. At site 1 and site 2, the net ion concentration and net particle charge concentration on downwind side of the lines showed same polarities. Further investigations were also conducted into the correlation between net ion concentration and net charge particle concentration 20 m downwind of the power lines at site 2. The two parameters showed a correlation coefficient of 0.72, indicating that a substantial number of ions could attach to particles and affect the particle charge status within a short distance from the source.

Comparison of charged nanoparticle concentrations near busy roads and overhead high-voltage power lines

Overhead high-voltage power lines are known sources of corona ions. These ions rapidly attach to aerosols to form charged particles in the environment. Although the effect of ions and charged particles on human health is largely unknown, much attention has focused on the increasing exposure as a result of the expanding power network in urban residential areas. However, it is not widely known that a large number of charged particles in urban environments originate from motor vehicle emissions. In this study, for the first time, we compare the concentrations of charged nanoparticles near busy roads and overhead power lines. We show that large concentrations of both positive and negative charged nanoparticles are present near busy roadways and that these concentrations commonly exceed those under high-voltage power lines. We estimate that the concentration of charged nanoparticles found near two freeways carrying around 120 vehicles per minute exceeded the corresponding maximum concentrations under two corona-emitting overhead power lines by as much as a factor of 5. The difference was most pronounced when a significant fraction of traffic consisted of heavy-duty diesel vehicles which typically have high particle and charge emission rates.

Residence near power lines and mortality from neurodegenerative diseases: longitudinal study of the Swiss population

The relation between residential magnetic field exposure from power lines and mortality from neurodegenerative conditions was analyzed among 4.7 million persons of the Swiss National Cohort (linking mortality and census data), covering the period 2000-2005. Cox proportional hazard models were used to analyze the relation of living in the proximity of 220-380 kV power lines and the risk of death from neurodegenerative diseases, with adjustment for a range of potential confounders. Overall, the adjusted hazard ratio for Alzheimer's disease in persons living within 50 m of a 220-380 kV power line was 1.24 (95% confidence interval (CI): 0.80, 1.92) compared with persons who lived at a distance of 600 m or more. There was a dose-response relation with respect to years of residence in the immediate vicinity of power lines and Alzheimer's disease: Persons living at least 5 years within 50 m had an adjusted hazard ratio of 1.51 (95% CI: 0.91, 2.51), increasing to 1.78 (95% CI: 1.07, 2.96) with at least 10 years and to 2.00 (95% CI: 1.21, 3.33) with at least 15 years. The pattern was similar for senile dementia. There was little evidence for an increased risk of amyotrophic lateral sclerosis, Parkinson's disease, or multiple sclerosis.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Fast power line detection and localization using steerable filter for active UAV guidance

In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.

Visual guidance for fixed-wing unmanned aerial vehicles using feature tracking : application to power line inspection

This thesis presents novel vision based control solutions that enable fixed-wing Unmanned Aerial Vehicles to perform tasks of inspection over infrastructure including power lines, pipe lines and roads. This is achieved through the development of techniques that combine visual servoing with alternate manoeuvres that assist the UAV in both following and observing the feature from a downward facing camera. Control designs are developed through techniques of Image Based Visual Servoing to utilise sideslip through Skid-to-Turn and Forward-Slip manoeuvres. This allows the UAV to simultaneously track and collect data over the length of infrastructure, including straight segments and the transition where these meet.

Dispersion of corona ions downwind from a high-voltage power line

Corona discharge is responsible for the small ions found near overhead power lines, and these are capable of modifying the ambient electrical environment such as the dc electric field at ground level (Fews, Wilding et al. 2002). Once produced, small ions quickly attach to aerosol particles in the air, producing ‘large ions’ which are roughly 1 nm to 1 µm in diameter. However, very few studies have reported measurements of ions produced by power lines and its impact on particle charge concentrations. In this present study, the measurements were conducted as a function of normal downwind distance from a 275kV power line for investigating the effect of corona ions on air ions, aerosol particle charge concentration and dc e-filed.

Coupling of High Voltage AC Power Line Fields to Metallic Pipelines

In the recent years, there has been a trend to run metallic pipelines carrying petroleum products and high voltage AC power lines parallel to each other in a relatively narrow strip of land. Due to this sharing of the right-of-way, verhead AC power line electric field may induce voltages on the metallic pipelines running in close vicinity leading to serious adverse effects. In this paper, the induced voltages on metallic pipelines running in close vicinity of high voltage power transmission lines have been computed. Before computing the induced voltages, an optimum configuration of the phase conductors based on the lowest conductor surface gradient and field under transmission line has been arrived at. This paper reports the conductor surface field gradients calculated for the various configurations. Also the electric fields under transmission line, for single circuit and double circuit (various phase arrangements) have been analyzed. Based on the above results, an optimum configuration giving the lowest field under the power line as well as the lowest conductor surface gradient has been arrived at and for this configuration, induced voltage on the pipeline has been computed using the Charge Simulation Method (CSM). For comparison, induced voltages on the pipeline has been computed for the various other phase configurations also.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.