架空线路热评级预测的自适应智能传感器网络
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International Journal of Emerging Electric Power Systems
Volume 9, Issue 4 2008 Article 2
An Adaptive Smart Sensor Network for Overhead Lines Thermal Rating Prediction
Vaccaro and Villacci: An Adaptive Smart Sensor Network
1.0 Introduction Modern power systems are often asked to work under critical operating conditions because of the increasing demand of electrical power and the difficulty of introducing facility enhancements due to economical and environmental issues. A direct consequence is that, in many electrical networks, load demand is getting close to the nameplate system supplying capability. The power system capability of facing the load is set by network constraints, by limits on generated powers, line currents, nodal voltage amplitudes, and by stability margins (Villacci et al.). In the lack of new facilities, a re-exam of the limits the capability depends on, with the consideration of the actual capacity of the system components, could be useful to enhance system capability. Overhead lines are components whose exam seems to be fruitful in this light as outlined by the Study Committee 23-CIGRE’. Historically the need for providing a reliable service has led the asset owner to adopt precautionary thermal ratings in overhead line capability assessment. These ratings are typically computed assuming conservative weather conditions (i.e. zero wind speed and high ambient temperature). This worst-case approach decreases the risk of possible malfunctioning with a consequent underutilization of the plant infrastructures (McElvain and Mulnix, 2000). For instance, if during line operation the weather conditions are better that the nominal ones, as long as the line carries the nameplate load current the conductor temperature stays largely below the upper allowable limit. In this situation the line current could be increased without any notable risk. Consequently a better assessing of the overhead line loadability could be beneficial especially during emergency circumstances where the extra capability would help minimising the load shedding. This issue could be addressed by a dynamic loading methodology that assesses the real load capability curves reporting the magnitude and the time duration of the electrical loads that the overhead line can effectively support (Tan et al. 1995, Bontempi et al. 2004). This requires the development of an adaptive thermal model that predicts, for each hypothetical load level, the evolution of the conductor hot spot temperature and the associated maximum allowed duration, on the basis of the real conductor thermal state and environmental conditions. In this connection several mathematical models, based both on simplified heat transfer equations and detailed thermal modelling, have been proposed in several papers (Morgan 1991, IEEE Std. 783-1993). Such models compute the conductor temperature evolution by solving the balance equations ruling the heat exchanges between the conductor and the environment.
Alfredo Vaccaro∗ Domenico Villacci†
∗ †
University of Sannio, vaccaro@unisannio.it University of Sannio, villacci@unisannio.it
Copyright c 2008 The Berkeley Electronic Press. All rights reserved.
An Adaptive Smart Sensor Network for Overhead Lines Thermal Rating Prediction
Alfredo Vaccaro and Domenico Villacci
Abstract
The need for dynamic loading of overhead lines requires reliable assessment tools that should be able to predict both the evolution of the hot-spot temperature and the associated maximum allowed duration, at any load level and on the basis of the actual conductor thermal state and environmental conditions. In order to address this problem, the paper proposes the employment of a smart sensor network distributed along the line route. Each network’s node, starting from on-line measurements, assesses, by an indirect method of parameter identification, the value of the main variables which regulate the heat exchange between the conductor and its surrounding. Then, starting from these data, each node calculates the load capability curve by solving iteratively a built in dynamic thermal model and transmits the results to a central server by a cooperative based communication paradigm. To assess the performances of the proposed solution, experimental studies obtained on a laboratory overhead line are presented and discussed. KEYWORDS: overhead line monitoring, sensors network, thermal rating prediction
Published by The Berkeley Electronic Press, 2008
1
International Journal of Emerging Electric Power Systems, Vol. 9 [2008], Iss. 4, Aand detailed thermal models require some specific input data (i.e. the variables describing the thermal characteristics of the line and of its surrounding), which can be affected by many uncertainties (Piccolo et al. 2004). Large parameters uncertainties stem from several sources, depending on radiated heat loss and solar heat absorbed versus the operating voltage, conductor surface proprieties, level of atmospheric pollution, wind direction, aging and so on. In particular, it has been demonstrated by Wan et al. that conductor heat loss radiated in the surrounding atmosphere is a function of the surface condition via a coefficient of emissivity ε varying in a large interval. In addition, the solar heat absorbed by a conductor is a function of the surface condition and the pollutant levels which are described by the absorption coefficient α . Also this coefficient exhibits a wide range of variability. Further sources of uncertainty affecting the thermal model input parameters derive from the weather condition values utilized in the load capability assessment procedures. Although weather information can be acquired in real time from local weather bureaus or from other local weather stations, they can exhibit significant spatial variations. Therefore observations at one location do not identify average conditions along the line (Reding 1994) and thus the thermal rating of each span will be different even at the same point in time (due to variations of weather conditions relative to the conductor along the line). In this context, the field experiences (Hall et al. 1988 and Bernini et al. 2007) highlight that ambient temperature and solar radiation assessment do not give any particular problem while more difficulties are induced by the spatial deviation and the unpredictable time evolution of the wind velocity and direction. Therefore, in order to achieve acceptable accuracy and robustness in dynamic loadability assessment of overhead lines, it is necessary to take into account effectively these sources of uncertainty. To address this problem the paper proposes the employment of a smart sensor network distributed along the line route. Each network’s node starting from the online measurement of the conductor temperature and other easily measurable quantities (i.e. load current and ambient temperature) assesses, by an indirect method of parameter identification, the value of the parameters describing the conductor thermal behaviour (i.e. coefficient of emissivity, solar absorptivity) and the main variables which regulate the heat exchange between the line and its surrounding (i.e. solar heat gain, wind speed and wind direction). Starting from these data, each node (i) calculates dynamically the load capability curve by solving iteratively a built in dynamic thermal model and (ii) transmits the results to its neighbours by a short range communication system. These information are then propagated along the sensor network by employing a cooperative based communication paradigm. This allows the sensors network to
Volume 9, Issue 4 2008 Article 2
An Adaptive Smart Sensor Network for Overhead Lines Thermal Rating Prediction
Vaccaro and Villacci: An Adaptive Smart Sensor Network
1.0 Introduction Modern power systems are often asked to work under critical operating conditions because of the increasing demand of electrical power and the difficulty of introducing facility enhancements due to economical and environmental issues. A direct consequence is that, in many electrical networks, load demand is getting close to the nameplate system supplying capability. The power system capability of facing the load is set by network constraints, by limits on generated powers, line currents, nodal voltage amplitudes, and by stability margins (Villacci et al.). In the lack of new facilities, a re-exam of the limits the capability depends on, with the consideration of the actual capacity of the system components, could be useful to enhance system capability. Overhead lines are components whose exam seems to be fruitful in this light as outlined by the Study Committee 23-CIGRE’. Historically the need for providing a reliable service has led the asset owner to adopt precautionary thermal ratings in overhead line capability assessment. These ratings are typically computed assuming conservative weather conditions (i.e. zero wind speed and high ambient temperature). This worst-case approach decreases the risk of possible malfunctioning with a consequent underutilization of the plant infrastructures (McElvain and Mulnix, 2000). For instance, if during line operation the weather conditions are better that the nominal ones, as long as the line carries the nameplate load current the conductor temperature stays largely below the upper allowable limit. In this situation the line current could be increased without any notable risk. Consequently a better assessing of the overhead line loadability could be beneficial especially during emergency circumstances where the extra capability would help minimising the load shedding. This issue could be addressed by a dynamic loading methodology that assesses the real load capability curves reporting the magnitude and the time duration of the electrical loads that the overhead line can effectively support (Tan et al. 1995, Bontempi et al. 2004). This requires the development of an adaptive thermal model that predicts, for each hypothetical load level, the evolution of the conductor hot spot temperature and the associated maximum allowed duration, on the basis of the real conductor thermal state and environmental conditions. In this connection several mathematical models, based both on simplified heat transfer equations and detailed thermal modelling, have been proposed in several papers (Morgan 1991, IEEE Std. 783-1993). Such models compute the conductor temperature evolution by solving the balance equations ruling the heat exchanges between the conductor and the environment.
Alfredo Vaccaro∗ Domenico Villacci†
∗ †
University of Sannio, vaccaro@unisannio.it University of Sannio, villacci@unisannio.it
Copyright c 2008 The Berkeley Electronic Press. All rights reserved.
An Adaptive Smart Sensor Network for Overhead Lines Thermal Rating Prediction
Alfredo Vaccaro and Domenico Villacci
Abstract
The need for dynamic loading of overhead lines requires reliable assessment tools that should be able to predict both the evolution of the hot-spot temperature and the associated maximum allowed duration, at any load level and on the basis of the actual conductor thermal state and environmental conditions. In order to address this problem, the paper proposes the employment of a smart sensor network distributed along the line route. Each network’s node, starting from on-line measurements, assesses, by an indirect method of parameter identification, the value of the main variables which regulate the heat exchange between the conductor and its surrounding. Then, starting from these data, each node calculates the load capability curve by solving iteratively a built in dynamic thermal model and transmits the results to a central server by a cooperative based communication paradigm. To assess the performances of the proposed solution, experimental studies obtained on a laboratory overhead line are presented and discussed. KEYWORDS: overhead line monitoring, sensors network, thermal rating prediction
Published by The Berkeley Electronic Press, 2008
1
International Journal of Emerging Electric Power Systems, Vol. 9 [2008], Iss. 4, Aand detailed thermal models require some specific input data (i.e. the variables describing the thermal characteristics of the line and of its surrounding), which can be affected by many uncertainties (Piccolo et al. 2004). Large parameters uncertainties stem from several sources, depending on radiated heat loss and solar heat absorbed versus the operating voltage, conductor surface proprieties, level of atmospheric pollution, wind direction, aging and so on. In particular, it has been demonstrated by Wan et al. that conductor heat loss radiated in the surrounding atmosphere is a function of the surface condition via a coefficient of emissivity ε varying in a large interval. In addition, the solar heat absorbed by a conductor is a function of the surface condition and the pollutant levels which are described by the absorption coefficient α . Also this coefficient exhibits a wide range of variability. Further sources of uncertainty affecting the thermal model input parameters derive from the weather condition values utilized in the load capability assessment procedures. Although weather information can be acquired in real time from local weather bureaus or from other local weather stations, they can exhibit significant spatial variations. Therefore observations at one location do not identify average conditions along the line (Reding 1994) and thus the thermal rating of each span will be different even at the same point in time (due to variations of weather conditions relative to the conductor along the line). In this context, the field experiences (Hall et al. 1988 and Bernini et al. 2007) highlight that ambient temperature and solar radiation assessment do not give any particular problem while more difficulties are induced by the spatial deviation and the unpredictable time evolution of the wind velocity and direction. Therefore, in order to achieve acceptable accuracy and robustness in dynamic loadability assessment of overhead lines, it is necessary to take into account effectively these sources of uncertainty. To address this problem the paper proposes the employment of a smart sensor network distributed along the line route. Each network’s node starting from the online measurement of the conductor temperature and other easily measurable quantities (i.e. load current and ambient temperature) assesses, by an indirect method of parameter identification, the value of the parameters describing the conductor thermal behaviour (i.e. coefficient of emissivity, solar absorptivity) and the main variables which regulate the heat exchange between the line and its surrounding (i.e. solar heat gain, wind speed and wind direction). Starting from these data, each node (i) calculates dynamically the load capability curve by solving iteratively a built in dynamic thermal model and (ii) transmits the results to its neighbours by a short range communication system. These information are then propagated along the sensor network by employing a cooperative based communication paradigm. This allows the sensors network to