Power Grid Monitoring

Abstract

This document discloses a solution for a method for monitoring a power grid. According to one aspect, the method includes detecting (200) a fluctuation on the power grid, obtaining (202) measurements of one or more electrical parameters of the power grid at a given number of grid measurement points while the fluctuation is in effect, determining (204) a relationship between the measured parameters at the given number of grid measurement points based at least in part on the one or more electrical parameters and an attribute of the fluctuation, and determining (206) a relative voltage sensitivity of the measurement points to the detected fluctuation based on the relationship.

Description

[Technical Field]

[0001] The present invention relates to measurement-based power grid monitoring, for example, to determining the relative voltage susceptibility of a power grid. [Background technology]

[0002] Since the frequency of alternating current (AC) electricity for large power grids was standardized worldwide in the mid-20th century, electricity consumers have been able to enjoy consistent and reliable electrical service while ensuring the safe and reproducible use of electrical equipment. Providing such reliable service can include monitoring the characteristics of the power grid and taking action in response to anomalies detected within the grid.

[0003] For example, system strength, or the ability of a system to withstand voltage changes at any given location, is a parameter that has been monitored within the power grid. Inverter-based generation resources do not effectively participate in voltage regulation and system strength to support the operation of the power grid, and therefore, as future levels of these sources increase, the system strength of the power grid will decrease and need to be monitored to ensure a minimum level of inertia and system strength to maintain resilient and reliable grid operation.

[0004] Therefore, reliable measurements of relative voltage susceptibility or system strength are important in power grids. Summary of the Invention

[0005] The invention is defined by the independent claims. Embodiments are defined in the dependent claims.

[0006] According to one aspect, a method for monitoring a power grid is provided that includes detecting a variation on the power grid, obtaining measurements of one or more electrical parameters in the power grid at a given number of grid measurement points while the variation is in effect, determining a relationship between the measured parameters at the given number of grid measurement points based at least in part on attributes of the one or more electrical parameters and their variations, and determining a relative voltage sensitivity of the measurement points to the detected variation based on the relationship.

[0007] In one embodiment, the fluctuations are at least one of intentionally induced voltage modulation or fluctuations on the power grid caused by, for example, switching, load changes, or transformer tap changes, i.e., forced, environmental, or transient oscillations in the power grid.

[0008] In one embodiment, the relationship is a correlation.

[0009] In one embodiment, measuring the one or more electrical parameters includes measuring the amplitude, derivative, transient, and shape of the voltage waveform.

[0010] In one embodiment, the method further includes analyzing to establish a relationship between two or more different measurement points of the power grid, for example, by using machine learning, by using a first set of measurement data obtained at a first measurement point and a second set of measurement data obtained at a second measurement point as training data.

[0011] According to another aspect, a system for monitoring a power grid is provided that includes means for detecting a variation on the power grid, obtaining measurements of one or more electrical parameters in the power grid at a given number of grid measurement points while the variation is in effect, determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on attributes of the one or more electrical parameters and their variations, and determining a relative voltage sensitivity of the measurement points to the detected variation based on the relationship.

[0012] According to another aspect, a computer-readable computer program product is provided that comprises computer program instructions that, when executed by a computer, cause the computer to perform a computer process including detecting a variation on a power grid; obtaining measurements of one or more electrical parameters in the power grid at a given number of grid measurement points while the variation is in effect; determining a relationship between the measured parameters at the given number of grid measurement points based at least in part on attributes of the one or more electrical parameters and the voltage-modulated signal; and determining a relative voltage sensitivity of the measurement points to the detected variation based on the relationship. [Brief explanation of the drawings]

[0013] The present invention will now be described in detail with reference to preferred embodiments with reference to the accompanying drawings. [Figure 1] 1 illustrates an example of a power grid to which embodiments of the present invention may be applied. [Figure 2] FIG. 1 is a flow diagram illustrating one embodiment. [Figure 3] FIG. 2 is a signal control diagram illustrating an embodiment. [Figure 4] 1 shows a simple example of a portion of an electrical grid. [Figure 5] FIG. 1 is a flow diagram illustrating one embodiment. [Figure 6]1 shows a simplified structure of a power grid. [Figure 7] 1 shows an example of the operation of the measurement system of an embodiment. [Figure 8] 1 is a block diagram of an apparatus according to one embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0014] The following embodiments are illustrative. Although this specification may refer to "an," "one," or "some" embodiments in several places throughout the text, this does not necessarily mean that each reference is to the same embodiment or that a particular feature applies only to a single embodiment. Single features of different embodiments may be combined to provide other embodiments.

[0015] The delivery of electricity from sources such as power plants to consumers such as homes, offices, and industries is typically via a distribution network or power grid. Figure 1 shows an exemplary power grid 100, comprising a transmission grid 102 and a distribution grid 104, in which embodiments of the present invention may be implemented.

[0016] The power transmission grid 102 is connected to power generating equipment, which may be a power plant such as a nuclear power plant, a hydroelectric power plant, a wind power plant, or a gas-fired power plant, and transmits large amounts of electrical energy at very high voltages (typically on the order of hundreds of kilovolts (kV)) from the equipment to the power distribution grid 104 via power lines, such as overhead power lines 110.

[0017] The transmission grid 102 is connected to the distribution grid 104 via a transformer 112, which converts the supplied power to a lower voltage, typically around 66 kV, for distribution on the distribution grid 104.

[0018] The distribution grid 104 is connected to local grids through substations 114, 116, and 118, which contain additional transformers for conversion to lower voltages, and these local grids provide power to power-consuming devices connected to the power grid. Local grids may include a grid of domestic consumers, such as a metropolitan distribution grid 115, which supplies power to household appliances in individual residences 132 and 134, each drawing a relatively small amount of power, on the order of a few kilowatts. Individual residences may also use photovoltaic devices or other power generating devices to provide a relatively small amount of power for consumption by the appliances in the home or for contribution to the grid. Local grids may also include industrial facilities, such as factories 130, which operate larger appliances that draw larger amounts of power, on the order of a few kilowatts to a megawatt. Local grids may also include a grid of smaller generating devices, such as wind farms, that provide power to the power grid. Local grids may also include energy storage devices 136 for storing power locally. Such storage devices 136 may be used to bridge the gap between power supply and demand.

[0019] Although FIG. 1 shows only one transmission grid 102 and one distribution grid 104 for simplicity, in reality a typical transmission grid 102 may supply power to multiple distribution grids 104, and one transmission grid 102 may be interconnected to one or more other transmission grids 102.

[0020] Electric power flows within a power grid as alternating current (AC) at a system frequency, sometimes referred to as the grid frequency (typically in the range of 50 Hz or 60 Hz, varying by country). Power grids operate at synchronized frequencies so that the frequency is substantially the same at each point on the grid. A power grid may include one or more direct current (DC) interconnects (not shown) that provide DC connections between the power grid and other power grids. Typically, the DC interconnects connect to the power grid's high-voltage transmission grid 102. The DC interconnects provide DC links between the various power grids, thereby defining regions where the power grid operates at a given synchronized grid frequency that is not affected by changes in the grid frequency of the other power grids. For example, the UK transmission grid is connected to the European Continental Synchronous Grid via a DC interconnection.

[0021] The power grid 100 also includes a measurement system in the form of a plurality of measurement devices 120-129 configured to measure the power grid at given measurement points on the grid. The measurement devices 120-129 may be configured to measure one or more electrical parameters of the power grid at given measurement points. At least some of the measurement devices 120-129 may be measurement points on the power distribution grid 104, such as measurement devices 122-129, while some of the measurement devices 120, 121 may be at measurement points on the power transmission grid. The measurement device 120 is directly coupled to a high-voltage bus, and the measurement device 121 is coupled to a low-voltage level bus on the power transmission grid 102. A separate transformer 113 may be provided to convert the higher voltage level to a lower voltage level. As shown in FIG. 1, the measurement devices may be coupled to various measurement points at various voltage levels on the power grid. For example, measuring device 120 coupled to power transmission grid 102 may be configured to make measurements at a very high transmission grid voltage level, such as 132 kV. Measuring device 122 may be coupled to the power distribution grid to make measurements at a lower voltage level, such as 11 or 33 kV. Measuring devices 124-129 may be coupled to the power distribution grid at even lower voltage levels, such as 220 V, 400 V, and / or 11 kV. The voltage level at measuring device 121 may also be at even lower voltage levels, such as 220 V, 400 V, or 11 kV. Note that the actual voltage levels are merely exemplary and different power grids may employ different voltage levels.

[0022] 1 shows only a small number of measurement devices for simplicity, it is understood that in practice a greater number of such measurement devices may be coupled to the power grid at various voltage levels and / or at various measurement points, such as different substations or sub-networks of the power grid. It should also be understood that some embodiments may employ measurement devices for only a subset of the voltage levels of the power grid or electrical distribution network 104.

[0023] Typically, the electrical parameter(s) of interest may include at least one of voltage waveform, voltage quality, transient voltage, current (instantaneous or continuous supply), grid frequency, phasor display, phase angle, reactive power, synchronous oscillation voltage and / or current magnitude, and voltage and / or current phase. A timestamp may be provided in association with each measurement. In some embodiments, the measurement device is configured to process the measurement data to produce higher-level measurement data. For example, the measured voltage and current may be used to calculate a fault level at the location of the measurement device. The fault level at a location may be defined as the maximum current that would flow in the event of a short-circuit fault at that location. In some literature, the fault level is known as short-circuit capacity or grid strength. The fault level may be measured from the effect of voltage fluctuations on the power grid, for example, by using the Thévenin equivalent circuit concept. Upon detecting a voltage fluctuation on the power grid, the source impedance at the measurement location may be calculated using the following equation:

[0024]

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[0029] Several embodiments of the stimuli are described below. To perform the measurements, each of the measuring devices 120-129 may include a voltage detector configured to sample the measured voltage and an analog-to-digital converter configured to convert the sampled voltage into a digital voltage signal. Each of the measuring devices 120-129 may also include a current detector configured to sample the current, and the analog-to-digital converter may be configured to convert the sampled current into a digital current signal. The digital voltage and current signals may then be transferred to the processing system 150 for processing or may be processed locally at the respective measuring device. Each of the measuring devices 120-129 may include one or both of a voltage detector and a current detector. When the sampling interval is sufficient, the grid frequency may be calculated from the measured voltage and / or current.

[0030] In some embodiments, at least some of the measuring devices 120-129 comprise processing means, for example in the form of a processor, and the processor of the measuring device 120-129 may be configured to determine an electrical parameter related to the measured voltage and / or current. This may be advantageous in that it may reduce the amount of information that the measuring devices 120-129 need to communicate to the processing system, which may also reduce the burden on the processing system 150.

[0031] In one embodiment, the measurement system comprises means for inducing a voltage modulated signal in the power grid, which may be generated, for example, by a suitable signal modulator.

[0032] In one embodiment, the measurement system comprises means for detecting fluctuations in the power grid. Such fluctuations are random events in the grid and may be caused, for example, by switching, load changes, or transformer tap changes, i.e., forced, environmental, or transient oscillations in the power grid. By studying the occurrence of these fluctuations on the grid, it is possible to determine attributes of the grid, including voltage susceptibility and harmonics. Using voltage-modulated signals injected into the grid, in combination or separately, it is also possible to measure and / or estimate those same attributes, plus short circuit levels (fault levels), using measurement devices to study the effect of the generated signals on voltages at various points across the power grid.

[0033] FIG. 1 illustrates multiple devices 140-146 coupled to a power grid. The devices 140-146 may include signal modulators that, when connected to the power grid, cause a change in the voltage of the power grid. In one embodiment, the signal modulators may cause a sinusoidal signal to be induced in the voltage of the power grid. While sinusoidal signals all have the same general shape, they do not all have the same characteristics. There are three characteristics that distinguish one sinusoidal wave from another: amplitude, frequency, and phase.

[0034] The measurement devices may be configured to report measurement data to processing system 150. Processing system 150 may be configured to analyze the measurement data and, in some embodiments, perform some control of the power grid based on the analysis. Detailed embodiments are described below. The processing system may include processing circuitry in the form of one or more computers. The processing system may include a local network server, a remote server, a cloud-based server, or any other means for performing analysis of the measurement data. The processing system may form a virtual network for performing the analysis. In general, virtual networking may include the process of combining hardware and software network resources and network functions into a single software-based management entity, a virtual network. Network virtualization may involve platform virtualization, which is often combined with resource virtualization. Network virtualization may be classified as external virtual networking, which combines many networks or portions of networks into a server computer or host computer. A virtual network may allow for flexible distribution of operations among various processing units for performing analysis.

[0035] FIG. 2 is a flow diagram illustrating an embodiment for monitoring a power grid and determining the relative voltage susceptibility or system strength of the power grid.

[0036] In step 200, fluctuations in the power grid are detected. This may be done, for example, by a measurement device. In another embodiment, a voltage modulation signal is directly induced. This may be done, for example, by devices 140-146, which may comprise signal modulators. The voltage modulation signal may be a controlled or autonomously generated known signal onto the electrical grid that can be read by a measurement device, and from these measurements, the same system parameters, such as voltage maintenance capability and short circuit level, can be calculated.

[0037] In step 202, measurements of one or more electrical parameters in the power grid are obtained at a given number of grid measurement points while the variation is in effect. This may be done, for example, by measurement devices 120-129. The measurement devices may measure the effect of the voltage modulation signal at the given grid measurement points.

[0038] In one embodiment, measuring the one or more electrical parameters includes measuring the derivative of the amplitude, transients, and shape of the voltage waveform.

[0039] In step 204, a relationship between the measured parameters at a given number of grid measurement points is determined based at least in part on one or more electrical parameters. If a voltage-modulated signal is used, attributes of the voltage-modulated signal are taken into account. This may be done, for example, by processing system 150.

[0040] Based on the relationship, the relative voltage susceptibility of the measurement points to the detected variations is determined in step 206. This may be done, for example, by processing system 150. In one embodiment, system strength parameters or voltage sustainment capability, harmonics, and fault levels may also be determined.

[0041] In one embodiment, one of the objectives is to determine how the derivative of the amplitude, transients, and shape of the voltage waveform (which may include aspects such as voltage RMS, harmonics, and other distortions of power quality) correlate between different measurement points. It is also possible to measure the current at the measurement points to find the short circuit fault level.

[0042] Statistical analysis of measurements can be used to solve various problems related to power systems. In this context, correlation coefficients or other analytical techniques can be used to represent the relationship between measurement points, allowing the relationship between different variables to be evaluated from a statistical point of view.

[0043] When the relationship between two node voltages is strong, the node voltage of one measurement point has a greater influence on the node voltage of the second measurement point, and the deviation between the differentiated measurement points is more obvious. This analysis results in an analysis of the propagation of voltage susceptibility in the distribution grid, which can reveal valuable information about the grid.

[0044] FIG. 3 illustrates a signal control diagram of one embodiment. Devices 140-146, which may comprise signal modulators, induce a voltage modulation signal 300 within the power grid. The modulation signal and / or other variation signal used for analysis may have an effective period, indicated by box 300 in FIG. 3. While the modulation and / or other variation is effective, measurement data may be acquired (302) by measurement device(s) under the effective region of the modulation signal. Step 302 may include measuring the derivative of the amplitude, transients, and shape of the voltage waveform at a given measurement point, e.g., the voltage and current at a given measurement point on the power grid. Upon making the measurements, the measurement device(s) may report the measurement data to processing system 150 in step 304. Such measurement reports may be provided, for example, in the form of a COMTRADE or other similar data file format.

[0045] From a further perspective, the measurements performed in step 302 may be referred to as active measurements if an intentionally generated voltage-modulated signal is used to perform the measurement(s). As shown in FIG. 3, multiple measurements can be performed under the influence of a voltage-modulated signal. Similarly, multiple voltage-modulated signals may be generated at different locations on the power grid. The triggering of the voltage-modulated signals may be synchronized so that the voltage-modulated signals or other reference signals occur substantially simultaneously at different locations. Synchronization may be achieved by using a common time reference, such as a global positioning system clock. The synchronized voltage-modulated or other reference signals implicitly trigger synchronized measurements at different measurement points (or a subset thereof) in step 302. This allows for a snapshot of the low-voltage electrical conditions of the entire power grid or a large area. Therefore, synchronized measurements may allow for more accurate correlation calculations.

[0046] Figure 4 shows a simplified example of a portion of an electrical grid. Figure 4 shows an example of determining correlations between measurement points, in this case, and determining relative voltage susceptibility or system strength parameters. It may be noted that the calculations can be performed in several ways.

[0047] This example shows upstream grid 1, transformer 400, and buses 2 through 15. Assume an event, such as a voltage modulation signal, occurs on bus 6. The voltage change on bus 6 is propagated to the other buses according to the impedances between the buses and based on the superposition theorem, as shown below:

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[0051] The elements of the voltage sensitivity matrix indicate the ratio of impedances between different measurement points and the equivalent circuit of the upstream grid. For example, if the system is stronger, Z th becomes smaller, and the sensitivity of measurement points 7 to 15 to measurement point 6 is very small.

[0052] In one embodiment, the susceptibility matrix can be analyzed after each event in the grid and artificial intelligence based techniques can be used to determine the relative voltage susceptibility or system strength of different buses.

[0053] Referring to FIG. 5, the processing system 150 may first acquire static parameters of the power grid (block 500). Such static parameters may include a power grid topology 502, defined with respect to the interconnections of elements of the power grid. FIG. 1 illustrates one topology of a power grid. Thus, the topology 502 may represent the structure of the power grid, or a subset thereof, depending on the intended target area of ​​the correlation model. The static parameters may include impedances (504) at various locations of the power grid. The impedance values ​​describe the electrical interrelationships between various portions of the power grid and, therefore, may be utilized to evaluate the relationships between measurement points in a correlation model or other analytical model. The static parameters may include the measurement locations at given measurement points 506, i.e., the measurement points to which the measurement devices 120-129 are coupled. The static parameters may further include the grid status and power generation profile of the power supply system. The grid status may describe, for example, the condition of power lines, transformers, load(s), and / or power generation equipment(s). Additional static parameters may be provided, such as the locations of the signal generators 140-146, weather data, temperature data, solar radiation data, price schedules, market mechanisms and responses to those mechanisms, models of power grid usage / consumption and generation, including traffic flow within the power grid, etc. These static parameters may form one set of training data for forming the correlation model. These static parameters may include or be included in information about the electrical characteristics of the power grid.

[0054] At block 508, the processing system collects another set of training data for forming the correlation or relationship model. This set of training data may include measurement data measured from the power grid. The measurement data may include, for example, the measurement data received by the processing system in step 304, as described above. As described above, the measurement data may include one or more of voltage, current, grid frequency, or any one of the above-described higher-level measurement data.

[0055] The processing system may monitor when a sufficient amount of training data has been collected (510). The parameter of block 510 may be the number of different measurement devices that have reported measurement data. If a sufficient number of measurement devices have reported measurement data, it may be determined that a sufficient amount of measurement data is available. If a sufficient amount of measurement data is not available, additional measurement data may be collected.

[0056] Once a sufficient amount of training data has been collected, the process may proceed to block 512, where a correlation model of the correlation between given measurement points is constructed by the processing system. Block 512 may include running a machine learning algorithm using the above set of training data as input for machine learning. The machine learning algorithm may employ a neural network, such as a deep neural network or a recurrent neural network, to form the correlation model. Generally, the machine learning algorithm can search for patterns in the measurement data using basic knowledge of the static parameters obtained in block 500. By analyzing the measurement data and the static parameters, the above correlation model can be constructed within a voltage level and even across multiple voltage levels.

[0057] Once the correlation model has been constructed (block 510 completed), the correlation model can be used to determine the relative voltage susceptibility or system strength parameters of the power grid based on the correlation.

[0058] In one embodiment, the model may also map the electrical parameters measured at a given measurement point to corresponding electrical parameters at another measurement point for which measurement data is not currently available or is outdated. The correlation model may also make it possible to predict or forecast future behavior of the electrical parameters at measurement points or locations for which measurement data is not currently available or is outdated by using measurement data obtained from other measurement points or locations on the power grid. This makes it possible, for example, to predict future evolution of fault levels. Thus, one embodiment uses block 510 to calculate a correlation model representing the electrical parameters being measured to be used as the basis for the correlation model.

[0059] As described in this example, a correlation model (which could be an alternative analysis technique) allows a global picture of the power grid to be maintained whenever measurement data from at least one location is received. The correlation model can map single measurement data received from a single measurement location to the global picture of the power grid. As a result, it is not necessary to provide measurement devices at every location where an electrical parameter is needed. Furthermore, it is not necessary to frequently receive up-to-date measurement data from every measurement location. When the correlation model is accurate, measurement data from a single measurement location or a subset of measurement locations is sufficient for the processing system to evaluate the electrical parameter over the coverage area of ​​the correlation model. As described above, the coverage area can span multiple measurement locations and across multiple devices at different locations on the power grid.

[0060] FIG. 6 illustrates a simplified structure of a power grid to which measurement devices may be coupled. As described above, one measurement device 122 may be coupled to a higher voltage level, such as a transmission grid feed point 600. Multiple primary buses 1-N (602) may be coupled to a transmission grid feed point, with a digital fault recorder (DFR) or similar measurement device coupled to each primary bus or a subset of primary buses 1-N. A DFR is an example of the measurement devices 120-129 described above. In one embodiment, the DFRs are configured to perform measurements based on voltage-modulated signals and / or other variations detected within the power grid. The DFRs may all be located in the same unit of the power grid, such as a substation, or each DFR may be connected to a different primary bus and thus to a different point within the power grid. In other embodiments, the DFRs may be located in multiple units, such as different substations, thereby providing a broader overview of fault levels in the power grid or a sub-network of the power grid. As shown in Figure 6, a signal modulator 602 or similar device used to generate intentional disturbances may be coupled to the primary bus or a subset thereof, and a measurement device 128, 129 may be coupled to each signal generator 602 or subset of signal generators 602. As shown in Figure 6, measurement data may be provided at various voltage levels, three in this embodiment. Measurement device 122 performs measurements at the highest voltage level, DFR performs measurements at a lower voltage level, and measurement device 129 performs measurements at the lowest voltage level.

[0061] FIG. 7 shows an example of the operation of the measurement system of one embodiment.

[0062] The system includes measuring 700 voltage modulation at a given measurement point on an electrical grid. Timestamp measurements are taken. The measurements may be accomplished using measurement devices 120-129. The measured voltage profile at the given measurement point may be analyzed 702. For example, voltage deviations may be tracked.

[0063] The relationship or correlation between voltage deviations on different buses may be determined (704). This is a complex task that requires data analysis to identify patterns of voltage deviations from different measurement points and the origin. By finding the strongest relationship or correlation among the voltage changes on the grid under investigation, it is possible to identify measurement points that are more sensitive to voltage deviations. If there is a large cross-correlation between grid measurement points, they are likely to have a large mutual voltage sensitivity and, therefore, a low system strength between the measurement points. Therefore, if the cross-correlation is small, they are likely to have a small mutual voltage sensitivity and, therefore, a high system strength between the measurement points.

[0064] An indication of the found results regarding relative voltage susceptibility or system strength (for example) may be provided (708). For example, the results may be presented by the processing system to relevant electric grid system personnel. The acquired data may be utilized in various ways. When the strongest correlation is found with voltage changes in the grid under investigation, it may be possible to identify measurement points that are most sensitive to voltage deviations. For example, suggestions 710 for locations or measurement points for placing signal generators and current measurements may be provided to obtain efficient data regarding the grid's relative voltage susceptibility or system strength in the event of a short-circuit fault. Additionally, real-time relative voltage susceptibility or system strength measurements 712 may be obtained.

[0065] 8 illustrates one embodiment of an apparatus configured to perform at least some of the functions described above. The apparatus may comprise an electronic device including at least one processor or processing circuitry 12 and at least one memory 20. The apparatus may comprise a single computer or a computer system such as the cloud computing system described above. The apparatus may further comprise communications circuitry 26 connected to the processing circuitry. The communications circuitry 26 may comprise hardware and software suitable to support one or more computer network protocols, such as Internet Protocol (IP), Ethernet protocols, etc.

[0066] The memory 20 may store a computer program (software) 22 comprising computer program code that defines the functionality of the processing circuitry 12. The computer program code, when read and executed by the processing circuitry 12, may cause the processing circuitry to perform the process of Figure 2, blocks 300-308 of Figure 3, or any one of the computer-implemented process embodiments. The memory may further store a database 24 that stores correlation models and voltage sensitivity matrices, acquired measurement data, and static parameters of the power grid.

[0067] The processing circuit 12 may include a measurement data acquisition circuit 16 configured to acquire measurement data from measurement devices coupled to the power grid and store the measurement data in a database. Upon acquiring a sufficient amount of measurement data, the measurement data acquisition circuit 16 may control the initialization circuit 15 to initialize a procedure for generating or calibrating the correlation model and voltage sensitivity matrix. The initialization may include retrieving static parameters and measurement data of the power grid and inputting the information into the machine learning circuit 14. The static parameters may include internal parameters of the power grid, such as the power grid impedance, power supply / consumption profile, and topology. The static parameters may also include external parameters of the power grid, such as weather profiles, price schedules, and solar radiation patterns. The machine learning circuit 14 may then execute block 710 to create or update the correlation model. Once the correlation model is complete, the machine learning circuit 14 stores the correlation model and voltage sensitivity matrix in the database. This circuit may also notify the mapping circuit 17 of the availability of the (updated) correlation model. Once the new electrical parameters are calculated, they may be output to a decision circuit 18 configured to determine parameters related to relative voltage susceptibility or system strength. The decision circuit 18 may determine whether recalibration of the correlation model is required. If calibration is required, the decision circuit may configure the initialization circuit 15 to initialize the calibration in a manner similar to that described above.

[0068] As used in this application, the term "circuitry" refers to all of the following: (a) hardware-only circuit implementations, such as implementations in analog and / or digital circuitry only; and (b) combinations of circuitry and software (and / or firmware), such as (where applicable) (i) a combination of processor(s) or (ii) a portion of a processor(s) / software, including digital signal processor(s), software, and memory(s) that work together to cause a device to perform various functions; and (c) circuitry, such as microprocessor(s) or a portion of a microprocessor(s), that requires software or firmware for operation even when the software or firmware is not physically present. This definition of "circuitry" applies to all uses of the term in this application. As a further example, as used in this application, the term "circuitry" also covers implementations of simply a processor(s) or a portion of a processor and its (or their) accompanying software and / or firmware. The term "circuit" also covers, for example, a baseband integrated circuit or an application processor integrated circuit for a mobile phone, or a similar integrated circuit in a server, cellular network device, or another network device, if applicable to the particular element.

[0069] In one embodiment, at least some of the processes described in connection with the above figures may be performed by an apparatus comprising corresponding means for performing at least some of the described processes. Some exemplary means for performing a process may include at least one of a detector, a processor (including dual-core and multi-core processors), a digital signal processor, a controller, a receiver, a transmitter, an encoder, a decoder, memory, RAM, ROM, software, firmware, a display, a user interface, a display circuit, a user interface circuit, a user interface software, a display software, a circuit, an antenna, an antenna circuit, and a circuit. In one embodiment, at least one processor, memory, and computer program code form a processing means or include one or more computer program code portions for performing one or more operations according to any one of the embodiments described in connection with the above figures.

[0070] According to yet another embodiment, an apparatus for carrying out the embodiments comprises circuitry including at least one processor and at least one memory containing computer program code, which, when activated, causes the apparatus to perform at least some of the functions according to any one of the embodiments described in relation to above.

[0071] The techniques and methods described herein may be implemented by various means. For example, these techniques may be implemented in hardware (one or more devices), firmware (one or more devices), software (one or more modules), or a combination thereof. In the case of a hardware implementation, the apparatus(es) of an embodiment may be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. In the case of firmware or software, implementation may be via a module consisting of at least one chipset (e.g., procedures, functions, etc.) performing the functions described herein. Software code may be stored in a memory unit and executed by a processor. The memory unit may be implemented within the processor or external to the processor. In the latter case, the memory unit may be communicatively coupled to the processor via various means, as known in the art. Furthermore, the components of the systems described herein may be rearranged and / or supplemented with additional components to facilitate accomplishing the various aspects described therein, etc., and are not limited to the configurations depicted in the given figures, as will be understood by those skilled in the art.

[0072] The described embodiments may be implemented in the form of a computer process defined by a computer program or a portion thereof. The method embodiments described in connection with the above figures may be performed by executing at least a portion of a computer program containing corresponding instructions. The computer program may be in source code form, object code form, or any intermediate form and may be stored on any type of carrier capable of carrying the program, which may be any entity or device. For example, the computer program may be stored on a computer program distribution medium readable by a computer or processor. The computer program medium may be, for example, but is not limited to, a recording medium, a computer memory, a read-only memory, an electrical carrier signal, a telecommunications signal, and a software distribution package. The computer program medium may be, for example, a non-transitory medium. Coding software to implement the illustrated and described embodiments is well within the scope of one skilled in the art. In one embodiment, a computer-readable medium includes the computer program.

[0073] Although the present invention has been described above with reference to examples according to the accompanying drawings, it is clear that the present invention is not limited thereto and can be modified in several ways within the scope of the appended claims. Therefore, all words and expressions should be interpreted broadly and are intended to illustrate, not limit, the embodiments. It is clear to those skilled in the art that as technology advances, the concept of the present invention can be implemented in various ways. Furthermore, it is clear to those skilled in the art that the described embodiments may, but need not, be combined with other embodiments in various ways.

Claims

1. 1. A method for monitoring a power grid, comprising: detecting fluctuations in the power grid; obtaining measurements of one or more electrical parameters within the power grid at a given number of grid measurement points while the variations are in effect; determining a relationship between the measured parameters at the given number of grid measurement points based at least in part on the one or more electrical parameters and the attribute of the variation; determining the relative voltage susceptibility of the measurement points to the detected variations based on the relationship; and A method comprising:

2. 10. The method of claim 1, wherein the fluctuations are at least one of intentionally induced voltage modulation or fluctuations on the power grid caused by forcing, environmental, or transient oscillations within the power grid.

3. The method of claim 1 or 2, wherein the relationship is a correlation.

4. 4. The method of claim 1, 2, or 3, wherein measuring one or more electrical parameters includes measuring the amplitude, derivative, transient, and shape of the voltage waveform.

5. 5. The method of claim 1, further comprising: using machine learning to form a relationship between two or more different measurement points of the power grid by using a first set of measurement data acquired at the first measurement point and a second set of measurement data acquired at the second measurement point as training data.

6. 6. The method of claim 5, wherein the first measurement point is at a first voltage level and the second measurement point is at a second voltage level different from the first voltage level.

7. The method of claim 5 , wherein both the first measurement point and the second measurement point are located at the same voltage level of the power grid.

8. A system (120-129, 150) for monitoring a power grid (100), comprising: detecting fluctuations in the power grid; obtaining measurements of one or more electrical parameters within the power grid at a given number of grid measurement points while the variations are in effect; determining a relationship between the measured parameters at the given number of grid measurement points based at least in part on the one or more electrical parameters and the attribute of the variation; determining the relative voltage susceptibility of the measurement points to the detected variations based on the relationship; and A system comprising: means for performing the steps of:

9. 9. The system of claim 8, wherein the fluctuations are at least one of intentionally induced voltage modulation or fluctuations on the power grid caused by forcing, environmental, or transient oscillations within the power grid.

10. The system of claim 8 or 9, wherein the relationship is a correlation.

11. 11. The system of claim 8, 9, or 10, wherein measuring one or more electrical parameters includes measuring amplitude, derivative, transient, and shape of a voltage waveform.

12. 10. The system of claim 8, comprising means for using machine learning to form a relationship between two or more different measurement points of the power grid by using a first set of measurement data acquired at the first measurement point and a second set of measurement data acquired at the second measurement point as training data.

13. A computer program product readable by a computer, which when executed by said computer: detecting fluctuations in the power grid; obtaining measurements of one or more electrical parameters within the power grid at a given number of grid measurement points while the variations are in effect; determining a relationship between the measured parameters at the given number of grid measurement points based at least in part on the one or more electrical parameters and the attribute of the voltage-modulated signal; determining the relative voltage susceptibility of the measurement points to the detected variations based on the relationship; and 1. A computer program product comprising computer program instructions for causing a computer process to be executed, the computer program instructions comprising:

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