Monitoring electrical power grid

Abstract

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

Description

Technical Field

[0001] The present invention relates to measurement-based monitoring of an electric power grid and, for example, to determining the relative voltage sensitivity of an electric power grid. Background Art

[0002] Since the standardization of the frequency of alternating current (AC) electricity in the world's large-scale electric grid in the mid-20th century, electricity consumers have been able to enjoy consistent and reliable power service, ensuring the safe and reproducible use of electrical appliances. Providing this reliable service can include monitoring the characteristics of the electric grid and taking action on anomalies detected in the grid.

[0003] For example, system strength, or the ability of a system to withstand voltage variations at any given location, is a parameter that is already monitored in electric power grids. Inverter-based generation resources cannot effectively participate in voltage control and system strength to support grid operation, so as the level of these sources increases, system strength in electric power grids will become lower in the future and will need to be monitored to ensure minimum levels of inertia and system strength to maintain resilient and reliable grid operation.

[0004] Therefore, in electric power grids, reliable measurement of relative voltage sensitivity or system strength is important. 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 an electric power grid is provided, the method comprising: detecting a fluctuation on the grid; obtaining measurements of one or more electrical parameters in the electric power grid at a given number of grid measurement points while the fluctuation is active; determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on characteristics of the fluctuation and the one or more electrical parameters; and determining relative voltage sensitivities that those measurement points have with respect to the detected fluctuation based on the relationship.

[0007] In one embodiment, the fluctuations are at least one of intentionally induced voltage modulations or fluctuations on the grid caused by, for example, switching operations, load changes or transformer tapping (ie forced oscillations, ambient oscillations or transient oscillations in the power grid).

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

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

[0010] In one embodiment, the method further comprises establishing a relationship between different two or more measurement points of the electric power grid by performing an analysis, for example using machine learning, by using a first set of measurement data obtained at the first measurement point and a second set of measurement data obtained at the second measurement point as training data.

[0011] According to another aspect, a system for monitoring an electric power grid is provided, the system comprising components for detecting a fluctuation on the grid; obtaining measurements of one or more electrical parameters in the electric power grid at a given number of grid measurement points while the fluctuation is in effect; determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on characteristics of the fluctuation and the one or more electrical parameters; and determining relative voltage sensitivities that those measurement points have with respect to the detected fluctuation based on the relationship.

[0012] According to another aspect, a computer program product is provided, which is readable by a computer and includes computer program instructions that, when executed by the computer, cause execution of a computer process, the computer process comprising: detecting a fluctuation on the power grid; obtaining measured values ​​of one or more electrical parameters in the power grid at a given number of grid measurement points when the fluctuation is valid; determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on characteristics of a voltage modulation signal and the one or more electrical parameters; and determining relative voltage sensitivities that those measurement points have with respect to the detected fluctuation based on the relationship. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will now be described in more detail by way of preferred embodiments with reference to the accompanying drawings, in which:

[0014] Figure 1 illustrates an example of an electric power grid to which embodiments of the present invention may be applied;

[0015] Figure 2 is a flow chart illustrating an exemplary embodiment;

[0016] Figure 3 is a signaling diagram of an example implementation scheme;

[0017] Figure 4 A simple example of a portion of an electrical grid is illustrated;

[0018] Figure 5 is a flow chart illustrating an exemplary embodiment;

[0019] Figure 6 illustrates a simplified structure of an electric power grid;

[0020] Figure 7illustrates an example of the operation of the measurement system of an embodiment; and

[0021] Figure 8 A block diagram of an apparatus according to an embodiment of the present invention is illustrated. DETAILED DESCRIPTION

[0022] The following embodiments are exemplary. Although the specification may refer to "one," "an," or "some" embodiments in several places in the text, this does not necessarily mean that each reference is to the same embodiment, or that a particular feature applies to only a single embodiment. Individual features of different embodiments may also be combined to provide other embodiments.

[0023] The supply of electricity from suppliers such as power stations to consumers such as homes, offices, industries etc. is typically via an electricity distribution network or electricity grid. Figure 1 An exemplary electric power grid 100 is shown, including a transmission grid 102 and a distribution grid 104 , in which embodiments of the present invention may be implemented.

[0024] The transmission grid 102 is connected to a generator, which may be a power plant, such as a nuclear power plant, a hydroelectric power plant, a wind turbine, or a gas-fired power plant, for example, from which the transmission grid transmits large amounts of electrical energy at very high voltages (typically hundreds of kilovolts, kV) via power lines, such as overhead power lines 110 .

[0025] The transmission grid 102 is linked to the distribution grid 104 via a transformer 112 , which converts the electrical power supply to a lower voltage, typically on the order of 66 kV, for distribution in the distribution grid 104 .

[0026] The distribution network 104 is connected to a local network via substations 114, 116, and 118, which include additional transformers for converting to lower voltages. This local network supplies power to electrical appliances connected to the power grid. The local network may include a network for household consumers, such as a city network 115, which supplies power to appliances within private residences 132 and 134. These appliances draw relatively small amounts of power, on the order of a few kW. Private residences may also use photovoltaic systems or other generators to provide relatively small amounts of power for consumption by appliances within the residence or to contribute power to the grid. The local network may also include industrial sites, such as factories 130, where larger appliances draw larger amounts of power, on the order of a few kW to several MW. The local network may also include a network of smaller generators, such as wind farms, that supply power to the power grid. The local network may also include energy storage devices 136 for local storage of electricity. Such storage devices 136 can be used to compensate for any discrepancies between supply and demand for electricity.

[0027] Although for the sake of brevity, Figure 1 Only one transmission grid 102 and one distribution grid 104 are illustrated in FIG. 1 , but in reality, a typical transmission grid 102 supplies power to multiple distribution grids 104 , and one transmission grid 102 may also be interconnected with one or more other transmission grids 102 .

[0028] Electricity flows in the electric power grid as alternating current (AC) that flows at a system frequency, which may also be referred to as the grid frequency (typically in the range of 50 Hz or 60 Hz, depending on the country). The electric power grid operates at a synchronous frequency so that the frequency is substantially the same at every point in the grid. The electric power grid may include one or more direct current (DC) interconnectors (not shown) that provide a DC connection between the electric power grid and other electric power grids. Typically, the DC interconnectors are connected to the high voltage transmission grid 102 of the electric power grid. The DC interconnectors provide a DC link between the individual electric power grids so that the electric power grid defines an area that operates at a given synchronous grid frequency that is not affected by changes in the grid frequencies of other electric power grids. For example, the UK transmission grid is connected to the European continental synchronous grid via a DC interconnector.

[0029] The electric power grid 100 further comprises a measuring system in the form of measuring devices 120 to 129 at given measuring points of the grid, which are arranged to measure the electric power grid. The measuring devices 120 to 129 may be configured to measure one or more electrical parameters of the electric power grid at a given measuring point. At least some of the measuring devices 120 to 129 may be measuring points at the distribution network 104, such as the measuring devices 122 to 129, but some of the measuring devices 120, 121 may be at measuring points at the transmission grid. The measuring device 120 is directly coupled to the high voltage bus, whereas the measuring device 121 is coupled to a lower voltage level bus of the transmission grid 102. A separate transformer 113 may be provided to transform the higher voltage level to a lower voltage level. As Figure 1 As shown, the measuring devices can be coupled to various measuring points at various voltage levels of the electric power grid. For example, the measuring device 120 coupled to the transmission grid 102 can be configured to perform measurements at a very high voltage level of the transmission grid (e.g., 132 kV). The measuring device 122 can be coupled to the distribution network to perform measurements at a lower voltage level (e.g., 11 kV or 33 kV). The measuring devices 124 to 129 can be coupled to the distribution network at one or more lower voltage levels (such as 220 V, 400 V and / or 11 kV). The voltage level at the measuring device 121 can also be a lower voltage level, such as 220 V, 400 V or 11 kV. The reader is reminded that the actual voltage levels are exemplary only, and different electric power grids may employ different voltage levels.

[0030] Although for simplicity, Figure 1 Only a few measuring devices are illustrated in the figures, but it should be understood that in practice, a greater number of such measuring devices may be coupled to the electric power grid at various voltage levels and / or at various measuring points (such as at different substations or subnetworks of the electric power grid). It should also be understood that some embodiments may employ measuring devices at only a subset of the voltage levels of the electric power grid or distribution network 104.

[0031] In general, the electrical parameters of interest may include at least one of the following: voltage waveform, voltage quality, voltage transients, current (instantaneous or continuous supply), grid frequency, phasors, phase angles, reactive power, synchronous oscillating voltage and / or current amplitudes, voltage and / or current phases. A timestamp may be provided in conjunction with each measurement. In some embodiments, the measuring device is configured to process the measurement data into higher-level measurement data. For example, the measured voltage and current can be used to calculate the fault level at the location of the measuring device. The fault level at a location can be defined as the maximum current that will flow in the event of a short circuit fault at that location. In some literature, the fault level is referred to as short-circuit capacity or grid strength. The fault level can be measured based on the effects of voltage fluctuations in the power grid, for example by using the concept of Thevenin equivalence. When voltage fluctuations in the power grid are detected, the source impedance at the measurement location can be calculated using the following equation:

[0032]

[0033] where Z FL is the source impedance, and are the voltage phasor measurement and the current phasor measurement, respectively, before the stimulus causing the voltage fluctuation, and and are the voltage and current phasor measurements after stimulation, respectively. The fault level S can then be calculated using the following equation FL :

[0034]

[0035] where Z FL is the source impedance calculated during the event, is the voltage measured at the observation time, which can be before or after the event, depending on which fault level is of most interest.

[0036] Below, some embodiments of stimulation are described.

[0037] To perform the measurements, each of the measuring devices 120 to 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 to 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 signal and the digital current signal may then be forwarded to the processing system 150 for processing, or processed locally at the respective measuring device. Each of the measuring devices 120 to 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 based on the measured voltage and / or current.

[0038] In some embodiments, at least some of the measuring devices 120 to 129 include processing means, for example in the form of a processor, and the processors of the measuring devices 120 to 129 may be arranged to determine electrical parameters related to the measured voltage and / or current. This may be advantageous because the amount of information that needs to be communicated from the measuring devices 120 to 129 to the processing system may be reduced, and the burden placed on the processing system 150 may also be reduced.

[0039] In one embodiment, the measurement system comprises means for generating a voltage modulated signal in the electric power grid.For example, the voltage modulated signal may be generated by a suitable signal modulator.

[0040] In one embodiment, the measurement system includes components for detecting fluctuations on the power grid. Such fluctuations are random events in the power grid and may be caused by, for example, switching operations, load changes, or transformer tapping (i.e., forced oscillations, ambient oscillations, or transient oscillations in the power grid). By examining the occurrence of these fluctuations on the power grid, it is possible to determine the characteristics of the power grid, including voltage sensitivity and harmonics. In combination with or alone using a voltage modulation signal injected onto the power grid, the measurement equipment can also be used to measure and / or estimate the same characteristics as well as the short circuit level (fault level) to see the impact of the generated signal on the voltage at various points across the power grid.

[0041] Figure 1 A plurality of devices 140 to 146 coupled to an electric power grid are illustrated. Devices 140 to 146 may include signal modulators that, when connected to the electric power grid, cause a change in the voltage of the electric power grid. In one embodiment, the signal modulators may generate a sinusoidal signal in the voltage of the electric power grid. Sinusoidal signals have the same general shape, but they do not have the same characteristics. Three characteristics distinguish one sinusoid from another: amplitude, frequency, and phase.

[0042] The measuring device may be configured to report the measurement data to the processing system 150. The 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 component for performing analysis of the measurement data. The processing system may form a virtual network for performing analysis. Generally speaking, virtual networking may involve the process of combining hardware and software network resources and network functionality into a single software-based management entity (virtual network). Network virtualization may involve platform virtualization, typically in combination with resource virtualization. Network virtualization may be classified as external virtual networking, which combines many networks or parts of networks into a server computer or host computer. A virtual network may provide flexible operation allocation between the various processing units used to perform analysis.

[0043] Figure 2 is a flow chart illustrating an embodiment of monitoring an electric power grid and determining the relative voltage sensitivity or system strength of the electric power grid.

[0044] In step 200, fluctuations in the power grid are detected. This can be performed, for example, by a measuring device. In another embodiment, a voltage modulation signal is directly generated. This can be performed, for example, by devices 140 to 146, which may include a signal modulator. The voltage modulation signal can be a known, controlled or autonomously generated signal on the grid that can be read by the measuring device, and based on those measurements, the same system parameters, such as voltage stiffness and short-circuit level, can be calculated.

[0045] In step 202, when fluctuations are active, measured values ​​of one or more electrical parameters in the power grid are obtained at a given number of grid measurement points. This may be performed, for example, by measurement devices 120 to 129. The measurement devices may measure the effect of the voltage modulation signal at a given grid measurement point.

[0046] In one embodiment, the measurements of one or more electrical parameters include measuring voltage waveform amplitude, derivative, transients, and shape.

[0047] In step 204, a relationship of the measured parameters at a given number of grid measurement points is determined based at least in part on the one or more electrical parameters. If a voltage modulation signal is used, characteristics of the voltage modulation signal are taken into account. This may be performed, for example, by processing system 150.

[0048] In step 206, the relative voltage sensitivity of those measurement points to the detected fluctuations is determined based on the relationship. This may be performed, for example, by the processing system 150. In one embodiment, system strength parameters or voltage stiffness, harmonics, and fault levels may also be determined.

[0049] In one embodiment, one of the goals is to determine how the voltage waveform amplitude, derivative, transients and shape (which will include aspects such as voltage RMS, harmonics and other distortions of power quality) are related between different measurement points. The current in the measurement points can also be measured to obtain short circuit fault levels.

[0050] In this case, various problems related to power systems can be solved by using statistical analysis of measurement results. Correlation coefficients or other analysis techniques that represent the relationship between measurement points can be used to statistically evaluate the relationship between different variables.

[0051] When the relationship between two node voltages is strong, the node voltage at one measurement point has a greater impact on the node voltage at the second measurement point, and the deviation between the differentiated measurement points is more obvious. This analysis can lead to a propagation analysis of voltage sensitivity in the distribution network, which can reveal valuable information about the power grid.

[0052] Figure 3 The signaling diagram of the embodiment is illustrated. The devices 140 to 146 which may include signal modulators generate a voltage modulation signal 300 in the power grid. The modulation signal and / or other fluctuation signals for analysis may have Figure 3 300 . When modulation and / or other fluctuations are active, measurement data may be acquired by the measurement device during the active region of the modulation signal 302 . Step 302 may include measuring the amplitude, derivative, transients, and shape of a voltage waveform at a given measurement point, such as the voltage and current at a given measurement point on the electric power grid. Upon performing the measurement, the measurement device may report the measurement data to the processing system 150 in step 304 . For example, such measurement reports may be provided in Comtrade or other similar data file formats.

[0053] From another perspective, if an intentionally generated voltage modulation signal is used to perform the measurement, the measurement performed in step 302 may be referred to as an active measurement. Figure 3As shown, multiple measurements can be performed under the influence of the voltage modulation signal. Multiple voltage modulation signals can also be generated at different locations on the power grid. The triggering of the voltage modulation signal can be synchronized so that the voltage modulation signal or other reference signal can occur substantially simultaneously at different locations. Synchronization can be achieved by using a common time reference (such as a global positioning system clock). In step 302, the synchronized voltage modulation or other reference signal implicitly causes synchronized measurements at different measurement points (or subsets thereof). This makes it possible to take a snapshot of the electrical state of the low voltage level of the entire or large-area power grid. Therefore, due to the synchronized measurement, the calculation of the correlation can be made more accurate.

[0054] Figure 4 A simple example of a portion of an electrical grid is illustrated. Figure 4 An example of determining the correlation between the measurement points and determining the relative voltage sensitivity or system strength parameter in this case is illustrated. It may be noted that the calculation can be done in several ways.

[0055] This example shows an upstream grid 1, a transformer 400, and buses 2 to 15. Assume that an event such as a voltage modulation signal occurs at bus 6. Depending on the impedance between the buses as shown below, the voltage change at bus 6 will be propagated to the other buses based on the superposition theorem:

[0056] ΔV6=(Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )*ΔI

[0057] ΔV5=(Z th +Z 12 +Z 23 +Z 34 +Z 45 )*ΔI

[0058] ΔV4=(Z th +Z 12 +Z 23 +Z 34 )*ΔI

[0059] ΔV3=(Z th +Z 12 +Z 23 )*ΔI

[0060] ΔV2=(Z th +Z 12 )*ΔI

[0061] ΔV7=ΔV8=ΔV9=ΔV 10 =ΔV 11=ΔV 12 =ΔV 13 =ΔV 13 =ΔV 14 =ΔV 15 =ΔV2

[0062] where ΔI is the current change in bus 6, ΔV i is the voltage change in bus i, Z ij is the impedance between bus i and bus j, and Z th is the equivalent impedance of the upstream grid. The sensitivity of these voltage changes to each other can be expressed in a matrix, called the voltage sensitivity matrix:

[0063]

[0064] Each element of the voltage sensitivity matrix (ΔVi / ΔVj) from each event can reveal some data based on the impedance between the lines. For example, in the above event in bus 6, the main elements of the voltage sensitivity matrix will be

[0065] ΔV5 / ΔV6=(Z th +Z 12 +Z 23 +Z 34 +Z 45 ) / (Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0066] ΔV4 / ΔV6=(Z th +Z 12 +Z 23 +Z 34 ) / (Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0067] ΔV3 / ΔV6=(Z th +Z 12 +Z 23 ) / (Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0068] ΔV2 / ΔV6=(Z th +Z 12) / (Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0069] ΔV7 / ΔV6=ΔV8 / ΔV6=ΔV9 / ΔV6=(Z th +Z 12 ) / (Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0070] ΔV 10 / ΔV6=ΔV 11 / ΔV6=ΔV 12 / ΔV6=ΔV 13 / ΔV6=(Z th +Z12) / (Z th +Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0071] ΔV 13 / ΔV6=ΔV 14 / ΔV6=ΔV 15 / ΔV6=(Z th +Z12) / (Z th h+Z 12 +Z 23 +Z 34 +Z 45 +Z 56 )

[0072] The elements of the voltage sensitivity matrix show the impedance ratio between different measurement points and the equivalent of the upstream grid. For example, if the system is stronger, then Z th will be smaller, and the sensitivity of measuring points 7 to 15 to measuring point 6 will be a very small value.

[0073] In one embodiment, it is possible to analyze the sensitivity matrix after each event in the grid and utilize artificial intelligence based methods in determining the relative voltage sensitivity or system strength of the different buses.

[0074] refer to Figure 5 , the processing system 150 may first obtain static parameters of the electric power grid (block 500). Such static parameters may include a topology 502 of the electric power grid defined according to the interconnection of elements of the electric power grid. Figure 1 A topology of an electric power grid is illustrated. Thus, the topology 502 may represent the structure of the electric power grid or a subset thereof, depending on the intended coverage of the correlation model. Static parameters may include impedances (504) at various locations on the electric power grid. Impedance values ​​describe the electrical interrelationships between various parts of the electric power grid and may therefore be used in correlation models or other analytical models to evaluate relationships between measurement points. Static parameters may include the measurement location at a given measurement point 506 (i.e., the measurement point to which the measurement devices 120 to 129 are coupled). Static parameters may also include the network state and power generation curve of the power supply system. For example, the network state may describe the state of the power lines, transformers, loads, and / or generators. Additional static parameters may be provided, such as the location of the signal generators 140 to 146, weather data, temperature data, solar radiation data, pricing tariffs, market mechanisms and responses to these mechanisms, usage / consumption and power generation models of the electric power grid, including, for example, power flows within the electric power grid. Static parameters may form a set of training data used to form the correlation model. Static parameters may include information about the electrical characteristics of the electric power grid or be included in the information about the electrical characteristics of the electric power grid.

[0075] In block 508, the processing system collects another set of training data for forming a correlation or relationship model. This set of training data may include measurement data measured from the electric power grid. The measurement data may include, for example, the measurement data received by the processing system in step 304. As described above, the measurement data may include any one or more of voltage, current, grid frequency, or higher-level measurement data described above.

[0076] The processing system may monitor 510 when a sufficient amount of training data has been collected. 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.

[0077] When a sufficient amount of training data has been collected, the process may proceed to block 512, where a correlation model of the correlations between given measurement points is constructed by the processing system. Block 512 may include executing a machine learning algorithm using the aforementioned 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 speaking, the machine learning algorithm may utilize the basic knowledge of the static parameters acquired in block 500 to search for patterns in the measurement data. By analyzing the measurement data and the static parameters, the aforementioned correlation model may be constructed within a voltage level and even across multiple voltage levels.

[0078] When the correlation model has been constructed (block 510 completed), the correlation model may be used to determine a relative voltage sensitivity or system strength parameter of the electric power grid based on the correlations.

[0079] In one embodiment, the model can also map an electrical parameter measured at a given measurement point to a corresponding electrical parameter at another measurement point where the measurement data is currently unavailable or outdated. The correlation model can also enable the use of measurement data acquired from another measurement point or location on the power grid to forecast or predict the future behavior of the electrical parameter at a measurement point or location where the measurement data is currently unavailable or outdated. For example, this can enable prediction of the future development of fault levels. Thus, the embodiment uses block 510 to calculate a correlation model representing the electrical parameters used as the basis for the correlation model during the measurement period.

[0080] As explained in this example, whenever measurement data from at least one location is received, a correlation model (which may also be an alternative analysis method) enables maintaining a holistic view of the electric power grid. The correlation model can map a single measurement data fragment received from a single measurement location to a holistic view of the electric power grid. Therefore, there is no need to provide measurement equipment at all locations where electrical parameters are required. In addition, it is not necessary to frequently receive the latest measurement data from all measurement locations. When the correlation model is accurate, measurement data from only a single measurement location or a subset of measurement locations is sufficient for the processing system to evaluate the electrical parameters over the coverage area of ​​the correlation model. As described above, the coverage area spans multiple measurement locations and spans multiple devices at different locations of the electric power grid.

[0081] Figure 6 A simplified structure of an electric power grid to which measuring devices may be coupled is illustrated. As described above, one measuring device 122 may be coupled to a higher voltage level, for example to a transmission grid supply point 600. A plurality of main buses 1 to N 602 may be coupled to the transmission grid supply point, and a digital fault recorder (DFR) or similar measuring device may be coupled to each main bus or a subset of the main buses 1 to N. The DFRs are examples of the measuring devices 120 to 129 described above. In one embodiment, the DFRs are configured to perform measurements based on a voltage modulation signal and / or another fluctuation detected in the electric power grid. The DFRs may all be arranged in the same unit of the electric power grid (e.g. a substation), but each DFR may be connected to a different main bus and thus to a different point in the electric power grid. In other embodiments, the DFRs are arranged in multiple units (e.g. different substations) thereby providing a broader overview of the fault level in the electric power grid or a subnetwork of the electric power grid. As Figure 6As shown, a signal modulator 602 or similar device for generating intentional disturbances may be coupled to the main bus or a subset thereof, and measurement devices 128, 129 may be coupled to each signal generator 602 or a subset of the signal generators 602. Figure 6 As shown, measurement data may be provided at various voltage levels, in this embodiment three: the measurement device 122 performs measurements at the highest voltage level, the DFR performs measurements at lower voltage levels, and the measurement device performs measurements at the lowest voltage level.

[0082] Figure 7 An example of the operation of the measurement system of the embodiment is illustrated.

[0083] The system includes measuring the voltage modulation at a given measuring point of the power grid 700. The measured value is obtained with a time stamp. The measurement can be performed using the measuring devices 120 to 129. The measured voltage distribution at the given measuring point can be analyzed 702. For example, voltage deviations can be tracked.

[0084] Relationships or correlations between voltage deviations on different buses can be determined 704. This presents a complex challenge, requiring data analysis to identify patterns in voltage deviations from different measurement points and origins. By finding the strongest relationships or correlations within the voltage variations of the studied grid, measurement points that are more sensitive to voltage deviations can be identified. If there is a large cross-correlation between grid measurement points, these grid measurement points are likely to also have a large mutual voltage sensitivity, and thus, the system strength between the measurement points is low. Similarly, if there is a small cross-correlation, these grid measurement points are likely to also have a small mutual voltage sensitivity, and thus, the system strength between the measurement points is high.

[0085] An indication of the findings regarding relative voltage sensitivity or system strength (for example) may be provided 708. For example, the results may be displayed by the processing system to relevant power grid system personnel. The data obtained can be utilized in various ways. When the strongest correlation within the voltage variation of the studied power grid is found, the measurement points most sensitive to voltage deviations may be identified. For example, recommendations 710 may be provided regarding locations or measurement points for placing signal generators and current measurements to obtain valid data regarding the relative voltage sensitivity or system strength of the power grid in the event of a short circuit fault. In addition, real-time relative voltage sensitivity or system strength measurements 712 may be obtained.

[0086] Figure 8An embodiment of an apparatus configured to perform at least some of the functions described above is illustrated. The apparatus may include an electronic device comprising at least one processor or processing circuit 12 and at least one memory 20. The apparatus may include a single computer or computer system, such as the cloud computing system described above. The apparatus may also include communication circuitry 26 connected to the processing circuitry. The communication circuitry 26 may include hardware and software suitable for supporting one or more computer network protocols, such as the Internet Protocol (IP), Ethernet protocol, etc.

[0087] The memory 20 may store a computer program (software) 22 including computer program code that defines the functionality of the processing circuit 12. The computer program code, when read and executed by the processing circuit 12, may cause the processing circuit to perform Figure 2 process, Figure 3 The memory may also store a database 24 storing the correlation model and the voltage sensitivity matrix, the acquired measurement data, and the static parameters of the electric power grid.

[0088] Processing circuitry 12 may include measurement data acquisition circuitry 16 configured to acquire measurement data from measurement devices coupled to the electric power grid and store the measurement data in a database. Upon acquiring a sufficient amount of measurement data, measurement data acquisition circuitry 16 may control initialization circuitry 15 to initialize a process for generating or calibrating a correlation model and a voltage sensitivity matrix. Initialization may include retrieving the measurement data and static parameters of the electric power grid and inputting this information into machine learning circuitry 14. Static parameters may include internal parameters of the electric power grid, such as impedance, power supply / consumption profiles, and topology within the electric power grid. These static parameters may also include parameters external to the electric power grid, such as weather conditions, pricing tariffs, and solar radiation patterns. Machine learning circuitry 14 may then execute block 710 and form or update the correlation model. Upon completing the correlation model, machine learning circuitry 14 stores the correlation model and the voltage sensitivity matrix in a database. The machine learning circuitry may also notify mapping circuitry 17 of the availability of the (updated) correlation model. Upon calculating new electrical parameters, these new electrical parameters may be output to decision circuitry 18, which is configured to determine relative voltage sensitivity or system strength-related parameters. The decision circuit 18 may also determine whether the correlation model needs to be recalibrated. If calibration is required, the decision circuit may configure the initialization circuit 15 to initialize the calibration in a similar manner as described above.

[0089] As used in this application, the term "circuitry" refers to all of the following: (a) hardware circuit implementations only, such as implementations in analog and / or digital circuits only; and (b) combinations of circuitry and software (and / or firmware), such as, as applicable: (i) a combination of a processor or (ii) portions of a processor / software, including a digital signal processor, software, and memory that work together to enable the device to perform various functions; and (c) circuitry that requires software or firmware to operate, such as a microprocessor or a portion of a microprocessor, even if the software or firmware is not physically present. This definition of "circuitry" applies to all uses of the term in this application. As another example, as used in this application, the term "circuitry" would also cover an implementation of only a processor (or multiple processors) or a portion of a processor and the accompanying software and / or firmware for the processor (or multiple processors). The term "circuitry" would also cover, for example and if applicable to the particular element, a baseband integrated circuit or an applications processor integrated circuit for a mobile phone, or a similar integrated circuit in a server, cellular network device, or another network device.

[0090] In one embodiment, at least some of the processes described in conjunction with the above figures may be performed by an apparatus including corresponding components for performing at least some of the processes described. Some example components for performing the process may include at least one of the following: 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, a memory, RAM, ROM, software, firmware, a display, a user interface, a display circuit, a user interface circuit, user interface software, display software, a circuit, an antenna, an antenna circuit, and a circuit system. In one embodiment, at least one processor, a memory, and a computer program code form a processing component or include one or more computer program code portions for performing one or more operations according to any of the embodiments described in conjunction with the above figures.

[0091] According to another embodiment, an apparatus for performing an embodiment includes circuitry comprising at least one processor and at least one memory comprising computer program code. When activated, the circuitry causes the apparatus to perform at least some of the functionality according to any of the embodiments described in conjunction with the embodiments above.

[0092] The techniques and methods described herein can be implemented in various ways. For example, these techniques can be implemented in hardware (one or more devices), firmware (one or more devices), software (one or more modules), or a combination thereof. For hardware implementations, the apparatus of the embodiments can 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. For firmware or software, the implementation can be performed by a module (e.g., a process, a function, etc.) of at least one chipset that performs the functions described herein. The software code can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, as is known in the art, the memory unit can be communicatively coupled to the processor via various means. Additionally, the components of the system described herein can be rearranged and / or supplemented by additional components to facilitate the implementation of various aspects thereof, and these components are not limited to the precise configurations set forth in the given figures, as will be understood by those skilled in the art.

[0093] The described embodiments can also be implemented in the form of a computer process defined by a computer program or a portion thereof. The embodiments of the methods described in conjunction with the above figures can be implemented by executing at least a portion of a computer program comprising corresponding instructions. The computer program can be in source code form, object code form, or some intermediate form, and it can be stored in a carrier, which can be any entity or device capable of carrying the program. For example, the computer program can be stored on a computer program distribution medium readable by a computer or processor. For example, a computer program medium can be, for example, but 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 can be, for example, a non-transient medium. Software decoding for executing the embodiments shown and described is fully within the scope of those of ordinary skill in the art. In one embodiment, a computer readable medium includes the computer program.

[0094] Although the present invention has been described above with reference to the examples according to the accompanying drawings, it is obvious that the present invention is not limited thereto, but can be modified in several ways within the scope of the appended claims. Therefore, all words and expressions should be interpreted broadly, and they are intended to illustrate rather than limit the embodiments. It is obvious to those skilled in the art that, as technology advances, the inventive concept can be implemented in various ways. In addition, it is clear to those skilled in the art that the described embodiments can, but are not required to, be combined with other embodiments in various ways.

Claims

1. A method for monitoring an electric power grid, the method comprising: detecting fluctuations on the electrical grid; obtaining measured values ​​of one or more electrical parameters in the electric power grid at a given number of grid measurement points while the fluctuations are in effect; determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on the characteristics of the fluctuations and the one or more electrical parameters; Based on the relationship, the relative voltage sensitivity of those measuring points with respect to the detected fluctuations is determined. 2 . The method of claim 1 , wherein the fluctuation is at least one of an intentionally induced voltage modulation or a fluctuation on the power grid caused by a forced oscillation, an ambient oscillation, or a transient oscillation in the grid. The method according to claim 1 , wherein the relationship is a correlation.

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

5. The method according to any one of the preceding claims 1 to 4, further comprising: A relationship between different two or more measurement points of the electric power grid is formed 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. 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 according to claim 5 , wherein the first measurement point and the second measurement point are both located at the same voltage level of the electric power grid.

8. A system (120-129, 150) for monitoring an electric power grid (100), the system comprising components for: detecting fluctuations on the electrical grid; obtaining measured values ​​of one or more electrical parameters in the electric power grid at a given number of grid measurement points while the fluctuations are in effect; determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on the characteristics of the fluctuations and the one or more electrical parameters; Based on the relationship, the relative voltage sensitivity of those measuring points with respect to the detected fluctuations is determined.

9. The system of claim 8, wherein the fluctuations are at least one of intentionally induced voltage modulations or fluctuations on the power grid caused by forced oscillations, ambient oscillations, or transient oscillations in the grid.

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

11. The system of claim 8, 9 or 10, wherein the measurements of one or more electrical parameters include measuring voltage waveform amplitude, derivative, transients and shape.

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

13. A computer program product, readable by a computer and comprising computer program instructions which, when executed by the computer, cause the execution of a computer process comprising: detecting fluctuations on the electrical grid; obtaining measured values ​​of one or more electrical parameters in the electric power grid at a given number of grid measurement points while the fluctuations are in effect; determining a relationship of the measured parameters at the given number of grid measurement points based at least in part on a characteristic of the voltage modulation signal and the one or more electrical parameters; Based on the relationship, the relative voltage sensitivity of those measuring points with respect to the detected fluctuations is determined.

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