Patent Application
20140278162
This invention relates generally to networks having a plurality of nodes, and in particular to the detection of a power failure over of power line networks via low voltage grid mapping.
For power line networks over which power line communication is enabled, one means for detecting current power outages in a power grid is by inferring them from the failure of power line communications. This approach, however, is prone to false positives; a system can experience a communication failure for reasons other than a power outage, such as communication impairments caused by power consuming devices. For example, power-consuming devices could be metered residential devices, metered commercial devices, or non-metered devices such as streetlights. It is the nature of such impairments that they come and go as devices are powered on or off, or otherwise vary their behavior as demands on the devices change. In order for outage detection to be made useful, the percentage of false positives must be low enough to make the information reliable.
In a power grid capable of electrical power delivery and power line communications, a distribution transformer and at least one smart meter is connected to the power grid. One embodiment of the smart meter contains a smart meter module, a signal measurement module, and a communications module. One embodiment of the distribution transformer has a power meter module, a signal strength measurement module, a power line communications module, and an analysis module. In one embodiment, the distribution transformer is configured to map the smart meters on the power grid to a virtual grid based upon measurements of signal metrics received from the various smart meters on the power grid. The virtual grid can then be used to determine if a failure has occurred on the physical grid, based upon information from the analysis module, and the failure is located using mapping information obtained from the virtual grid. In one embodiment, a cross phase delta value is computed to adjust the signal strength metrics measured between nodes having differing phases of electrical distribution.
A better understanding of the present invention can be obtained from the following detailed description in conjunction with the following drawings, in which:
In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of various embodiments of the invention. It will be apparent, however, to one skilled in the art that the embodiments of the invention can be practiced without some of these specific details. Moreover, in some instances, well-known structures and devices are shown in block diagram form to avoid obscuring the underlying principles of the invention.
In one embodiment, the nodes 100, 101-103, 111-113, 121-125 and 131-134 form a distributed control network. In this embodiment, each of the nodes 100, 101-103, 111-113, 121-125, and 131-134 are programmed to perform a specific task. For example, individual nodes are configured as proximity sensors, switches, motion detectors, relays, motor drives, and/or other types of instruments (e.g., utility meters). The individual nodes of this embodiment are programmed to work together as a whole to perform a complex control application such as running a manufacturing line or automating a building. It should be noted, however, that the underlying principles of the invention are not limited to any particular type of node or any particular network configuration or application.
Notwithstanding other factors that can impact signal strength measurements, a presumed line-length to signal strength relationship is used to represent network elements on a virtual grid, as demonstrated in
Additionally, special values are used to indicate additional information on the virtual grid. For example, a special value (e.g., 16) is used to indicate a “no path” relationship between network elements, while an additional special value (e.g., 17) is used to indicate that the relationship between elements is “unknown.” A “no path” value arises if the signal strength between elements is too low for one element to communicate with the other element, while an “unknown” value indicates that no attempt to communicate has been made between two mapped network elements.
In one embodiment, a virtual network has points along grid lines such as P1 212 and P2 214 of
Returning to
The measured and calculated signal strength values are combined with statistical and historical analysis to generate a graphical representation of a physical network, which is then used to accurately detect network failures, and locate points of failure. When such method is applied to a power distribution network configured for network communication over the power lines, power failures are detected faster and more accurately than using signal strength measurements or communication failures alone.
In one embodiment, the analysis also includes an historical analysis, which takes into account the margin between the historically most reliable network elements and a potential point of line failure. For example, signal margin is ordinarily cyclical throughout a twenty-four hour day. However, if the margin is seen as continuously degrading throughout the day, this can be an indication of a cable degradation that can lead to an electrical outage if not addressed. In one instance, continuous margin degradation at a specific building or residence can indicate the impending failure of an electrical feeder cable from the distribution network. A margin measurement from each point on the distribution network can help to isolate where the feeder problem is before there an electrical outage occurs.
In one embodiment, the result of the statistical analysis in 506 is graphically represented on the virtual grid to facilitate the detection of a power failure event on the physical grid. Once a power line failure is detected, the precise location of the failure may not be immediately evident. In one embodiment, in 510, multiple factors are used to determine the location of a power line failure on the grid once a line failure has been detected. Factors considered include, the failure history of various elements on the grid. Impairments that occur due to powered devices on the power grid can sometimes be repetitive in nature. If a powered device has a history of causing communication failures with certain elements on the power line communications network, then that factor is considered when evaluating whether a communication failure is a false positive for a power line failure. Additionally, the pattern of certain failures is considered. For example, if a particular network element is known to suffer communication failures daily, for example, between 5:00 pm and 8:00 pm, then a communication failure during that time is considered to be a likely false positive. Communication failures outside that time range, however, are treated as indicative of an actual power outage. Additionally, in one embodiment, communication failures to network elements that are known to have repeated communication failures without power line failures are considered more likely to be false positives relative to network elements with no history of communication failure.
A more straightforward scenario for analysis in 510 is the case of multiple concurrent failures. If multiple failures occur that all suggest a specific point of failure, then statistically the odds increase that the communication failures are due to power line outages. For example, if all elements on the virtual grid that are located on the far side of a specific virtual point on the network, and all elements are reporting communication failures, then statistically it is significantly more likely than not that the line failure is at, near, or related to the specific virtual point. On the other hand, if some of the network elements are still in communication with the rest of the network, then the statistical likelihood is significantly reduced.
In one embodiment, energy consumption metrics are used to determine or predict a power outage. For example, if a statistically significant number of nodes lose power, then there will be a noticeable impact on the total energy use measured on the network. As shown at 512, the master node 100 can detect a loss of communication on the network, and correlate the loss of communication with a corresponding drop in energy usage. The correlation enhances the probability that the loss of communication is caused by an actual power outage. One aspect of the analysis would be to examine the energy usage trends over time to establish a comparison baseline. It is to be understood that the list of metrics discussed above is a nonexclusive list, and other metrics are possible, some of which are discussed below.
As measurements are made, meters will be found, such as meters 702 and 704 that are measured, based on signal strength, or some other metric linearly related to distance, to be essentially at the same place physically. This scenario can occur when meters are in a meter room 701a, 701b where a several meters are collocated, such as in a commercial office building. This can provide a means of grid visualization optimization, as measurement are taken relative to one meter in the collection of collocated meters. The precise value of the signal metric that indicates collocation can vary. In one embodiment, a typical minimum measurement of distance via, for example, signal strength, is a value of one, because of losses occurring in the coupling circuits and the distance, however short, between meters collocated meters, even if the highest possible signal strength used in the signal strength scale is zero. In one embodiment, a default minimum value (and this maximum signal strength) is used to determine collocation, and that value is adjusted as needed. The physical grid arrangement of the distribution transformer 700 and the meters housed in meter room 701a and 701b is illustrated in
In one embodiment, the virtual grid does not wait for all data to be collected before computing a graph, and a best estimate of the graph is computed and then refined over time. Accordingly, connections are split and re-joined, as the virtual grid is refined based on new data. Virtual grid topologies can change radically as switches are thrown in the network and other modifications are made in the wiring of the physical power distribution grid represented by the virtual grid. Thus, the grid calculation and data gathering is a constant process that updates and evolves over time. This evolving nature makes the virtual grid useful for detecting, locating, and rapidly resolving power outages on the physical grid.
For example, if smart meter node 810c is attached to a residential building to measure power consumption, a communication link is maintained between smart meter 810c and the distribution transformer master node 800. If a communications failure were to occur at the residence due to the introduction of noise from a power-consuming device in the residence, the impact on the communications grid is generally greatest at the meter node 810c attached to the residence. The typical impact is that the meter 810c will no longer be able to receive messages due to the line noise; however, the meter's ability to transmit messages may not be impacted. For example, if the master node 100 attempts to communicate with meter node 810c and there is impairment due to noise generation on the load side (e.g. inside the power system of the residential building), then node 810c may be unable to receive communications from the master node 100 while the master node 100 can still receive transmissions from the smart meter 810c. In one embodiment of the smart meter node 810, each meter tracks whether it was in communication with an agent, such as a distribution transformer, or some other master node 100. If the smart meter 810 is unable to communicate to the master node 100 for a period of time, but the power delivery grid is functioning normally, the meter will send an unsolicited message to the agent indicating that it was still powered. The agent will then make this information available on the virtual grid to indicate that even though the node is out of communication, a power failure has not occurred.
In one embodiment of the smart meter node 810, contains a battery as a backup power supply, and a “last gasp” message is sent when a power failure is detected. While this message cannot be depended on to be received due to the potentially unreliable nature of the power grid during a power distribution failure, receiving such a message gives a high degree of probability that a power failure has occurred. Smart meters can also be equipped with noise detection modules to detect the amount of noise on the communication line. A communications failure to a smart meter node 810 that is currently experiencing a large amount of line noise can indicate that the communication failure is for reasons other than a power failure. In one embodiment, factors external to the power distribution grid, such as an impending storm, or other weather patterns, or geological events, are analyzed, such that certain external factors can indicate that a system power grid failure is more likely to occur than otherwise.
When measuring signal strength on power distribution grids utilizing three phase alternating current distribution, measurements between two smart meters, or a smart meter and a distribution transformer that are on different phases can be less than the equivalent pair of meters on the same phase. The difference cause by a phase differential is the Cross-Phase Delta (CPD), and can vary from utility to utility, from transformer to transformer, and from electrical feeder to electrical feeder. To account for these differences, an embodiment of the grid mapping system iterates through all possible CPD values (0 to 15, which corresponds to the total range of the signal strength measurements) for all of the meters on a single feeder until a valid result is obtained.
Validation of the CPD is dependent upon the ability to record unimpaired readings, either from a distribution transformer or between smart meters having the same phase. The raw and unimpaired readings are used to validate the adjustment used for meters on different phases. Without unimpaired readings, any CPD value will appear to be valid, and a default configured CPD will be used.
Additionally, since the distribution transformer communicates on all 3 phases, it acts as a coupler between the phases, which reduces the effect of the CPD for nearby meters. For the purposes of grid mapping, this effect is assumed to be linear, decaying from 12 dB to 0 dB over a distance of 15 units. While each feeder can have a different CPD, it is assumed that the CPD for each node on a single feed is equal.
Table 1 below is a table of the actual line distances between each node as indicated in
The measured signal strength values of the grid are determined by data from the signal measuring modules in each node (e.g., signal measuring module 1008 of smart meter 810 in
As shown in
Presuming a CPD value of 0, an analysis module in the distribution transformer 1102 will compute the set of distance values shown in Table 2.
In Table 2, a CPD value of 0 is used, and a distance of 15 is used for the lowest measurable signal between nodes. For example, the signal strength measurement in Table 1 between the analysis module in the distribution transformer (DT) 1102 and node D 1114 is zero. Accordingly, a distance of 15 is estimated. Likewise, the signal measured between node E 1108 and node F 1116 is 5, so a distance of 10 is estimated. Comparing these estimates with the actual distances of
The lack of a CPD between the DT 1102 and each node allows a partial grid constructed based on the estimated distances between the DT 1102 and each node. For example, it can be determined that the values of table 2 using an estimated CPD value of zero are incorrect because of the internal inconsistency of the measurements. For example, if the distance between the DT 1102 and node A 1104 is 7.0, and the distance between the DT 1102 and node B is 9, the distance between node A 1104 and node B 1112 should be 8, as opposed to the 10 that is estimated using a CPD of 0. In one embodiment, a proper CPD value can then be determined by iterating through possible CPD values until a consistent grid is constructed. In one embodiment, a CPD value can be estimated by analyzing the delta between in phase and cross phase measurements.
Table 3 below shows a set of distance values estimated using a CPD of 2.
Assuming a CPD value of 2 would result in the signal strength measurements shown in Table 2. The estimated distances between the distribution transformer DT 1102 and each node are unchanged. However, a cross phase delta of two is added for each cross phase signal strength measurement before distances are estimated. Accordingly, the estimated distance between node A 1104 at phase 0, and node B 1112 at phase 1 is reduced to 8.0, which is consistent with the distances estimated based between the DT 1102 and each node, or the various nodes which are in the same phase (e.g., node A 1104 and node D 1114).
Various components described above are used as a means for performing the operations or functions described. Each component described herein is constructed from hardware components, or hardware components supplemented with software instructions. Additionally, some components are constructed using embedded controllers and hardwired circuitry. Besides what is described herein, various modifications can be made to the disclosed embodiments and implementations of the invention without departing from their scope, therefore, the illustrations and examples herein should be construed in an illustrative, and not a restrictive sense.
