DE202013009956U1 - Device and measuring system for the automatic characterization and monitoring of an electrical network or a power supply section of an electrical network or an electrical system - Google Patents

Abstract

Device and measuring system for the automatic characterization and monitoring of an electrical network or a power network section of an electrical network or an electrical system, characterized in that the device (15) has at least a) a first data memory (16) for programming and maintenance of a first matrix or semi- Matrix (4) from historical measured value pairs (1 '') for the acquisition of point clouds 3 and / or partial point clouds (3 ', 3' ', 3' '') from historical measured value pairs (2 '') and Storage of the respectively associated expectation range limits (8 '), b) a second data memory (20) for a second matrix or half-matrix (5) from current pairs of measured variables (1') for the acquisition of currently measured pairs of measured values (2 ') c) a program part (23) which checks whether currently measured values (22 ') of the current pairs of measured quantities (1') within expected ranges (14) and the defined expected range limits (8 ') of the historical ones Measured variable pairs (1 '') are located, d) a third data memory (24) for a third matrix or half matrix (6) for describing abnormal pairs of measured values (2 '' ') and e) an output device (25) that responds to undesirable error conditions with primary alarms and / or category-specific alarms and / or switching activities.

Description

The invention relates to a device for the automatic characterization and monitoring of an electrical network or a power network section of an electrical network or an electrical system by determining measured values of a plurality of measured variables, wherein comparison results of the measured values of a measured variable are assigned to the measured variables of another measured variable and the associated value pairs are displayed ,

To a certain extent, this has already been practiced, for example, if the resistance, also referred to as impedance, of a subnetwork is determined from the ratio of voltage and current intensity. For example, in the DE19948694B4 a method for generating at least one signal indicating a pendulum in an electric power grid described.

From the WO2005 / 088802A1 a device for the prevention of major disturbances in electrical supply networks by spectral analysis of the mains voltage is known. In this case, the spectrum determined by a voltage AD converter and a spectral analysis device is subjected to a classification according to frequency intervals. In a comparator, the values of the frequency interval classes are correlated with the frequency interval class reference patterns assigned to the specific disturbances, and an evaluation device outputs a signal, one or more relay messages or one or more messages via communication interfaces when a fault is detected. The frequency values and the number of frequency interval classes are adaptively adapted via a bifurcation analyzer to the local load and vibration dynamics of the network. The comparator is equipped with programmable limit values of the interval voltage for each frequency interval, whereby the limit values of the interval voltage automatically adapt to the load conditions of the network.

In the area of network analysis, the focus is on finding weak points in the supply of individual consumers, describing them and eliminating them with suitable measures. To recognize the causes, all data of the network parameters must be available for this purpose. In order to detect a malfunction due to events in the supply network, the Applicant has developed interference-resistant measuring systems which measure the network quality as well as the network and consumer behavior continuously and completely. The measured values are recorded in powerful databases. For example, is from the DE 10 2007 041 565 A1 a method and a measuring arrangement are known for transmitting time-stamped measurement data to a server if the measured values are outside a standard or outside an individually defined tolerance range.

It is also known from the EP2355412A1 a method and an apparatus for evaluating an electrical system of an electrical energy system, wherein the electrical system comprises a communication network for transmitting data. The method comprises automatically acquiring real-time data transmissions in the communication network and automatically assessing the electrical equipment based on the acquired real-time data transmissions. For example, a scanning unit samples a reference signal comprising reference time information and transmits it as a real-time transmission over the communication network, wherein the electrical system is automatically evaluated based on a comparison of the reference time information with the time information of the real time transmission.

Furthermore, from the WO 2011 / 032579A1 a method for monitoring an electrical power supply network is known in which at least two measuring points in the power supply network, a state of the electrical power supply network characterizing measured values are detected and each measured value is assigned a time stamp of his time stamping. Normalized thresholds are determined for each measuring point, and from the position of the current normalized measured values in relation to the normalized thresholds, a scalar value is calculated which is intended to indicate network stability. A disadvantage of such a method is the independent consideration of the measured variables of the individual measuring points, whereby disturbances which, without exceeding limit values of individual measured variables, manifest themselves in special relations of the measured variables of different measuring points, remain unrecognized.

Also is from the US 2004/0243328 a method is known in which current time series of individual measured variables are compared to similarity with the time series during previously known events. A disadvantage of this method is that the time series of all examined variables individually with relatively much computational complexity must be checked.

In the DE 41 11 831 A1 describes a method for triggering an electrical switch and an apparatus for carrying out the method, in which thresholds for the variables current I and current change dI / dt are used as a switching trigger criterion. The disadvantage is that while short circuits are detected quickly, other criteria for network instabilities remain completely unconsidered.

It is also known from the DE 10 2009 038 351 A1 a method for monitoring an energy-efficient operation of a building-technical system, in particular in the field of air conditioning, refrigeration, lighting and / or heating technology, and / or a production-technical system, in particular a compressed air generating system by means of the determination of measured values of several analog and / or discrete measured variables, wherein the comparison results of the measured values of a measured variable are assigned to the measured values of another measured variable and the assigned value pairs are displayed and the relationships between the measured values are evaluated. The disadvantage is that no categories of unwanted states can be formed.

Electricity grids are dynamic structures in which it is normal for them to constantly change their structure by switching on and off operating equipment, and that the electrical load flows change in the short term and are also reversed by decentralized power generation. The current and power flows in given sections of the power grid can thus vary within wide limits, without violating technical and normative given limits. Behind these acute changes in network operating states, systematic shifts in dynamics often remain hidden.

The conventional measurement technology in the power grids is aimed in particular at ensuring the proper operation of the power grid and thus the network protection and tariffing. Power quality measurements, also referred to as power quality measurements, also measure whether, for example, the voltage curves present at the load comply with the normative specifications.

A disadvantage of all known measuring systems and evaluations, however, is that the evaluation time is always in the past of a disturbing event, also the transmission of the measured data in real time and the evaluation done immediately and almost in real time. Not every disturbing event can then be averted in time without any significant consequences for an electrical system or a consumer.

Another disadvantage of the known measuring systems and evaluations is the separate consideration of the measured values of individual measured variables.

Object of the present invention is therefore to provide a device for early detection of incidents in the electrical network and / or electrical systems, so decisions in advance to avoid accidents and / or required switching operations or other countermeasures can be performed automatically. The invention is also intended to help to optimize planning of current operations and investment decisions.

This object is achieved according to the present invention by a device according to claim 1. The dependent claims define advantageous embodiments of the invention.

According to the invention, a comparison of mutual relationships for network characterization is carried out by the evaluation of a large number Z of measured variable pairs. In this case, historical measured value pairs are generated from large amounts of data of the electrical network or a data collection point, which are recorded in historical measured value pairs of a first matrix or half-matrix as point clouds. During a learning mode, at least one expected range with at least one expectation range limit is generated for each historical measured value pair in each historical measured value pair from the point clouds of the historical measured value pairs. The expectation range limits of the first matrix or half-matrix are then stored in a second matrix or half-matrix. During a monitoring mode, in the second matrix or half matrix, the currently measured pairs of measurements for each current pair of measurements are checked to see if they are within the expected range limits. Anomalous measured value pairs that are outside the respective expected range limit are displayed and can trigger alarms and / or switching activities.

The expectation range bounds may include closed curves that are represented as ellipses or look polygonal or hyperbolic. The expectation range limits can also be are limited by rectangular fingers, which maintain a minimum distance to all measured value pairs of the point cloud. Also, levels of criticality can be defined.

However, the point cloud according to the invention can also be a disjoint point cloud and can be formed from a plurality of unconnected partial point clouds whose expected ranges are within reference data on an X-axis (X _Ref , X _{Ref +} ) and a Y axis (Y _ref. , Y _{Ref +} ) are located.

Those currently measured pairs of measurements of the current pairs of measured variables which lie outside the expected ranges and / or outside the expected range limit are described in the measured variable pairs of a third matrix or half-matrix as abnormal measured value pairs and can trigger primary alarms. Upon triggering of one or more primary alarms by one or more abnormal pairs of measurements, a user-defined protection or switching action may be initiated to correct the problem before a negative event may occur, which characterizes the state of the primary monitoring phase.

According to the invention, the large number Z of measured quantity pairs comprises 10 ⁵ to 10 ⁶ data records, and during the monitoring mode, the levels of criticality of all pairs of measured variables whose limits are defined are checked, the current levels of criticality of all primary alarms as colored dots or the like in a semi-matrix arrangement as a correlation triangle ( 7 ) can be displayed in an output device.

The current current pair of measured value pairs with abnormal pairs of measurements are treated as patterns and similar patterns are learned as characteristics for new categories of system states and used to trigger category-specific alarms and protections, respectively, indicating the state of the categories -specific monitoring phase characterized.

The currently measured pairs of measured values, which are outside the expectation ranges, recorded during the primary monitoring phase are tested for their relative position to the expected range, which results in their criticality, and abnormal measured value pairs which lie outside the expected ranges can be graded according to their criticality Trigger alarms and trigger assigned switching actions. During the primary monitoring phase, patterns are formed from the location of the currently measured pairs of measurements with primary alarms in the first matrix or half-matrix. Such pattern of primary alarms can be assigned to error categories and after assigning the error categories to the primary alarm patterns, the system goes into a second monitoring phase, in which it learns when primary alarm patterns occur with category alarms. reacts to specific alarms and switching activities.

The historically measured values stored in a data collection point can, according to the invention, also be supplied to the second matrix or half-matrix. Historical comparison measurements can then be carried out between the measured-value pairs of the second matrix or half-matrix and those of the first matrix or half-matrix, the abnormal measured-value pairs of the historical comparison measurements having the expectation-range limits already formulated in the first matrix or half-matrix other historical pairs of measurements are compared to carry out a trend analysis or to characterize the change in consumer or producer behavior in the electrical network or for performance evaluation of an electrical system in continuous operation.

• • abgeleitete Messgrößen, wie Änderungsraten des Lastflusses und/oder
• • instationäre Messgrößen, für die über die Zeit nur eine Wahrscheinlichkeitsdichte angegeben werden kann und/oder
• • Messgrößen, die mittels Spektral- oder Autokorrelationsanalysen aus einer Basis-Messgröße zu berechnen sind und/oder
• • Messgrößen, die diskrete Werte, wie beispielsweise Schalterstellungen, repräsentieren,

zur Charakterisierung eines Netzabschnitts herangezogen werden. Dabei wird aus jeweils zwei unterschiedlichen Messgrößen jeweils ein Messgrößen-Paar erzeugt und die Messgrößen-Paare werden in Form eines Korrelation-Dreiecks dargestellt, wobei die aktuellen Messgrößen-Paare vorzugsweise die gleiche Zahl Z an Messgrößen-Paaren wie die historischen Messgrößen-Paare aufweisen sollten.As measured variables, all typically used to describe the power quality measured variables, such as current, voltage, power, harmonics of current and voltage, phase, interharmonic, flicker, etc., but also non-electrical factors such as day of the week, time of day, outside temperature, temperature of Transformers or precipitation and / or other parameters, such as

• • derived measures, such as rates of change of the load flow and / or
• • transient measurements for which only one probability density can be given over time and / or
• • Measurands to be calculated by means of spectral or autocorrelation analyzes from a basic measurand and / or
• • Measurands representing discrete values, such as switch positions,

used to characterize a network section. In each case, a pair of measured variables is generated from two different measured variables in each case and the measured variable pairs are in the form of a Correlation triangle shown, wherein the current pairs of measured variables should preferably have the same number Z on pairs of measured variables as the historical pairs of measured variables.

• • einen ersten Datenspeicher zur Programmierung und Pflege einer ersten Matrix oder Halb-Matrix aus historischen Messgrößen-Paaren zur Erfassung von Punktwolken oder von mehreren Teil-Punktwolken aus historischen Messwert-Paaren und Speicherung der jeweils zugehörigen Erwartungsbereichsgrenzen,
• • einen zweiten Datenspeicher für eine zweite Matrix oder Halb-Matrix aus aktuellen Messgrößen-Paaren zur Erfassung von aktuell gemessenen Messwert-Paaren,
• • ein Programmteil, der prüft, ob aktuell gemessene Messwerte der aktuellen Messgrößen-Paare innerhalb von Erwartungsbereichen und den definierten Erwartungsbereichsgrenzen der historischen Messgrößen-Paare liegen,
• • einen dritten Datenspeicher für eine dritte Matrix oder Halb-Matrix zur Beschreibung von anormalen Messwert-Paaren sowie
• • eine Ausgabeeinrichtung, die auf unerwünschte Fehler-Zustände mit Primär-Alarmen und/oder Kategorien-spezifischen Alarmen und/oder Schaltaktivitäten reagiert.

The device according to the invention has at least

• A first data memory for programming and maintaining a first matrix or half-matrix of historical measured value pairs for recording point clouds or of several partial point clouds from historical measured value pairs and storing the respective associated expected range limits,
• A second data memory for a second matrix or half-matrix of current measured-value pairs for the acquisition of currently measured measured-value pairs,
• A program part which checks whether currently measured values of the current measured-value pairs lie within expectation ranges and the defined expected-range limits of the historical measured-value pairs,
• A third data memory for a third matrix or half-matrix for describing abnormal measured value pairs as well as
• An output device that responds to undesired fault conditions with primary alarms and / or category-specific alarms and / or switching activities.

The device according to the invention optionally has a database server with a data bus and at least one data collection point for the acquisition and storage of historically measured values. A first data line connects the data collection point to the first data memory via a first connection and transmits the historical measured values from the data collection point via the first data line to the first matrix or half-matrix of the first data memory. The data bus has connections to the electrical network as well as equipment data sources, time data sources, calendar data sources, weather data sources and / or other data sources of systems or devices to be monitored or protected. In addition, the data bus is connected via a second data line and a second terminal to the second matrix or half-matrix of the second data memory. The second data line transmits currently measured values of the data sources to the second matrix or half-matrix of the second data memory and records them as currently measured value pairs for each current pair of measured quantities. The program part arranged between the first data memory, the second data memory and the third data memory is a comparator.

The data collection point of the database server is preferably connected to a measuring device known per se for power quality measurement. Alternatively, the device according to the invention can be designed as an additional module for the measuring device for power quality measurement or the device according to the invention is integrated directly into the measuring device for power quality measurement. In this way, the data collection point can permanently supply the first data memory with historical measured value pairs during ongoing measurement operation and thus continuously update the expected range limits in a learning mode.

According to the invention, therefore, not individual measured values of different measured variables are considered and compared with one another, but all possible pairs from the measured values of the different measured measured variables.

Instead of measured value pairs of the different pairs of measured variables, measured value n-tuples from measured variable n-tuples could also be considered according to the invention, but this is less practicable because the number of combinations in question increases extremely rapidly with the number of measured variables.

In the following the invention will be explained on the basis of exemplary embodiments and illustrated with reference to the attached drawings. They show schematically:

1 a correlation triangle according to the invention of a first matrix or half-matrix, shown schematically here in a possible first embodiment with expectation range limits defined in each pair of measured variables, based here on 21 measured quantities, resulting in 20 measured values per column S and row R and 210 different measured variables Couples result.

2 an inventive according to a pair of measured variables according to 1 associated point cloud, consisting of a plurality of measured value pairs as the basis of the calculation of an expectation range limit, shown here as an ellipse.

3 a representation of the upper and lower limits for a pair of measured variables after analysis of the reference data for a second embodiment of the invention.

4 an enlarged view of the reference range of the point cloud according to the invention in the second embodiment according to 3 ,

5 a schematic diagram of the basic structure of the invention and according to another embodiment.

All measured variables 1a responsible for the characterization and / or monitoring of an electrical network 36 , a power line section of an electrical network 36 or an electrical system are of interest, according to the measuring system according to the invention in a correlation triangle 7 Are defined. Such a correlation triangle according to the invention 7 is in 1 shown schematically in a possible first embodiment in the form of a first half-matrix 4 , In the embodiment according to 1 were twenty-one metrics 1a numerically as measured variable pairs in the most diverse dependencies 1 defined and detected in columns S1 to S20 and rows R1 to R20. Two different measured variables each form a pair of measured variables 1 , From historically measured values 18 ' , which were previously or in the past, among other things determined by means of power quality measurements, can now expect areas 14 with expectation range limits 8th' for each pair of measured variables to be observed 1 define. The expectation range limits 8th' are according to the first embodiment of 1 and 2 as ellipses 8th shown. However, it is within the scope of the invention that the expectation range limits 8th' may also look polygonal or hyperbolic, or may have one or more other closed curves.

To characterize a network section, the measured values of all measured variables are available 1a typically used to describe power quality: current, voltage, power, harmonics of current and voltage, phase angles, interharmonics, flicker, etc.

• • Werte und/oder Messwerte von nicht-elektrischen Größen, wenn diese Einfluss auf die Auslastung des untersuchten Netzabschnittes haben. Beispiele für solche nicht-elektrischen Einflussgrößen sind Wochentag, Tageszeit, Außentemperatur, Temperatur von Trafos oder Niederschlag.
• • Abgeleitete Messgrößen 1a Ein Beispiel wären Änderungsraten des Lastflusses, weil Leistungsänderungen die Entstehung von Flicker und von Interharmonischen bewirken können und weil die Antwort auf die Frage, ob und inwieweit dies der Fall ist, ein Charakteristikum eines elektrischen Netzes 36 bzw. Teilnetzes ist.
• • Messgrößen 1a, die an sich instationär sind und für die daher über die Zeit nur eine Wahrscheinlichkeitsdichte angegeben werden kann. So sind in normalen Netzen Über- und Unterspannungs-Transienten, Brownouts und Blackouts seltene Ereignisse. Trotzdem ist es wichtig zu wissen, ob und wie diese Ereignisse mit anderen charakteristischen Netzgrößen korrelieren.
• • Messgrößen 1a, die mittels Autokorrelationsanalysen aus einer Basis-Messgröße zu berechnen sind
• • Messgrößen 1a, die diskrete Werte wie beispielsweise Schalterstellungen repräsentieren.

Also to be considered are:

• • Values and / or measured values of non-electrical quantities, if these have an influence on the utilization of the examined network section. Examples of such non-electrical influencing variables are day of the week, time of day, outside temperature, temperature of transformers or precipitation.
• • Derived measurands 1a An example would be rates of change in load flow, because power changes can cause the formation of flicker and interharmonics and because the answer to the question of whether and to what extent this is the case is a characteristic of an electrical network 36 or subnet is.
• • Measured variables 1a which are transient in nature and for which therefore only a probability density can be given over time. Thus, in normal networks, over and under voltage transients, brownouts and blackouts are rare events. Nevertheless, it is important to know if and how these events correlate with other characteristic network sizes.
• • Measured variables 1a , which are to be calculated by means of autocorrelation analyzes from a basic measurand
• • Measured variables 1a representing discrete values such as switch positions.

For some pairs of measurements 1 It is recommended that one or both of the measured quantities 1a only to be used as an argument after filtering via a low-pass filter, a drag-pointer function or the like. For example, a transformer operating temperature of 70 ° per se can be completely uncritical if the transformer is designed for a maximum full load operating temperature of 90 °, while the same operating temperature of 70 ° is highly likely to be an indicator of error, if already at low utilization of the transformer is achieved. In the combination of the measured quantities 1a Operating temperature and load is more information about the state of the transformer than in the two individual variables 1a alone.

Basically, between pairs of measured variables 1 and metric pairs 2 to distinguish. Many reading pairs 2 a pair of measured variables 1 each form a point cloud 3 within the measured variable pair 1 , In 2 is a pair of measured variables 1 with such a point cloud 3 shown.

The point cloud according to the invention 3 in the measured quantity pair 1 consists of a large number of measured value pairs 2 within the defined expectation range limit 8th' that also in 2 as an ellipse 8th is shown. The expectation range limit 8th' but may also be rectangular or in another closed form, if it is known that one or more measured value pairs 2 while still in the margins, but still within an ellipse 8th or massive problems or other problems in the electrical network 36 or at an electrical system to be monitored. Such expectation ranges 14 must be outside the expected range limit 8th' be settled, an ellipse 8th is not more then possible. For the sake of simplicity, however, the emergence of an expected range limit according to the invention 8th' in this first embodiment based on an ellipse 8th explained.

As a description of the point clouds 3 for the different measured variable pairs 1 those mathematical methods are suitable, with which from the analysis of the point cloud 3 existing measured value pairs 2 a pair of measured variables 1 expectancy ranges 14 determine where new pairs of readings must be if the system behaves as it did during the reference time. These expectation areas 14 You can think of yourself as boundaries at different distances around the point cloud 3 the measured value pairs 2 in normal operation. Readings pairs 2 outside of this expectation range 14 are in monitoring mode as abnormal pairs of measured values 2 ''' and considered as potential indicators of critical or erroneous operating conditions.

• • die lineare Regression mit ihren Vertrauensbereichen oder
• • die Ellipse 8 um die Punktwolke 3 mit einer kurzen Achse 10 und einer langen Achse 9, die den minimalen mittleren Abstand zu den Messwert-Paaren 2 der Punktwolke 3 hat, wie in 2 dargestellt.
• • die Bestimmung rechteckförmiger „Finger” 37, welche einen Mindestabstand zu allen Messwert-Paaren 2 der Punktwolke 3 einhalten, wie in 3 und 4 dargestellt.

Examples of calculation methods that have such expectation ranges 14 can deliver

• • the linear regression with its confidence intervals or
• • the ellipse 8th around the point cloud 3 with a short axis 10 and a long axis 9 that the minimum mean distance to the measured value pairs 2 the point cloud 3 has, as in 2 shown.
• The determination of rectangular "fingers" 37 , which is a minimum distance to all measured value pairs 2 the point cloud 3 comply as in 3 and 4 shown.

• • haben keine Bedeutung,
• • sind trivial,
• • sind redundant oder
• • enthalten zu wenige Daten für eine signifikante Aussage,

können somit vernachlässigt werden.Many of the relationships that are captured

• • have no meaning
• • are trivial,
• • are redundant or
• • contain too little data for a significant statement,

can thus be neglected.

According to the embodiment according to the invention 2 is the point cloud 3 through an ellipse 8th described, whose long axis 9 the smallest mean distance to the measured value pairs 2 has that in 2 are shown punctiform. The long axis 9 and the short axis 10 the ellipse 8th intersect at the center of gravity of the point cloud 3 as ellipse center 11 , Four bold points 12 on the axes 9 . 10 give the mean distance of the measured value pairs 2 to each perpendicular to axes. The length of these half-axes is determined by the distances of the points 12 to the ellipse center 11 plus 2.5 times σ, the scattering of mean distances. The in 2 arbitrarily chosen factor 2.5 is a tolerance parameter. Other values of the tolerance parameter would result in larger or smaller elliptical axis lengths 9 . 10 and thus different sized ellipses 8th but not smaller than a cross 13 within the points 12 can be. Each field of a half-matrix 4 thus contains descriptive information about the point cloud 3 every measurand pair 1 ,

A second embodiment in which measured value pairs 2 a pair of measured variables 1 a point cloud 3 , consisting of several unconnected partial point clouds 3 ' . 3 '' . 3 ''' within a measurand pair 1 form is in 3 and 4 shown. In this embodiment, it is a first partial point cloud 3 ' , a second partial point cloud 3 '' and one, just a pair of measurements 2 comprehensive third partial point cloud 3 ''' , which are not connected, together as a disjoint point cloud 3 designated. The expectation ranges 14 the first, second and third partial point clouds 3 ' . 3 '' . 3 ''' are within reference data on an X-axis and a Y-axis.

• 1. X_Ab–: Untergrenze des Arbeitsbereichs eines Sensors für die jeweilige Messgröße 1a in der X-Achse,
• 2. X_Lim–: Untergrenze des zulässigen Bereichs der Messgröße 1a in der X-Achse,
• 3. XR_ef–: Untergrenze des in den Referenzdaten gefundenen Bereichs der Messgröße 1a in der X-Achse,
• 4. X_Ref+: Obergrenze des in den Referenzdaten gefundenen Bereichs der Messgröße 1a in der X-Achse,
• 5. X_Lim+: Obergrenze des zulässigen Bereichs der Messgröße 1a in der X-Achse,
• 6. X_Ab+: Obergrenze des Arbeitsbereichs des Sensors für die Messgröße 1a in der X-Achse,
• 7. Y_Ab–: Untergrenze des Arbeitsbereichs des Sensors für die Messgröße 1a in der Y-Achse,
• 8. Y_Lim–: Untergrenze des zulässigen Bereichs der Messgröße 1a in der Y-Achse,
• 9. Y_Ref–: Untergrenze des in den Referenzdaten gefundenen Bereichs der Messgröße 1a in der Y-Achse,
• 10. Y_Ref+: Obergrenze des in den Referenzdaten gefundenen Bereichs der Messgröße 1a in der Y-Achse,
• 11. Y_Lim+: Obergrenze des zulässigen Bereichs der Messgröße 1a in der Y-Achse,
• 12. Y_Ab+: Obergrenze des Arbeitsbereichs des Sensors für die Messgröße 1a in der Y-Achse.

For each pair of measured variables 1 from analogue measured values, after analysis of the reference data, there are the following in 3 shown limits

• 1. X _Ab- : Lower limit of the working range of a sensor for the respective measured variable 1a in the X-axis,
• 2. X _Lim- : Lower limit of the permissible range of the measured variable 1a in the X-axis,
• 3. XR _ef- : Lower limit of the range of the measured variable found in the reference data 1a in the X-axis,
• 4. X _{Ref +} : Upper limit of the range of the measured variable found in the reference data 1a in the X-axis,
• 5. X _{Lim +} : upper limit of the permissible range of the measured variable 1a in the X-axis,
• 6. X _{Ab +} : Upper limit of the working range of the sensor for the measured variable 1a in the X-axis,
• 7. Y _Ab- : Lower limit of the working range of the sensor for the measured variable 1a in the Y-axis,
• 8. Y _Lim- : Lower limit of the permissible range of the measured variable 1a in the Y-axis,
• 9. Y _Ref- : Lower limit of the range of the measured variable found in the reference data 1a in the Y-axis,
• 10. Y _{Ref +} : Upper limit of the range of the measured variable found in the reference data 1a in the Y-axis,
• 11. Y _{Lim +} : upper limit of the permissible range of the measured variable 1a in the Y-axis,
• 12. Y _{Ab +} : Upper limit of the working range of the sensor for the measured variable 1a in the Y-axis.

An example of a calculation method, the expectation ranges 14 for this embodiment is based on rectangular fingers 37 from _ref are at one end of the lower limits of X and Y _{Ref- REF} and the ceilings X _{Ref +} and Y ₊ is limited and at the other to different depths into the sub-point clouds 3 ' . 3 '' . 3 ''' protrude. The expectation range limits 8th' therefore have corners and edges. The rectangular fingers 37 are calculated to be a predeterminable minimum distance from all points of the first, second and third sub-point clouds 3 ' 3 '' . 3 ''' comply. The expectation area 14 then consists of the area covered by no finger 38 and can as in the in the 3 and 4 example also contain discontinuous surface elements. It has proved to be practicable from each of the four edges of the extreme value rectangle of the measured variable pair 1 ten to twenty rectangular fingers 37 to let go out. In the embodiment of the 3 and 4 it's twenty rectangular fingers 37 from every edge.

In 3 the cut edges of the value range lower limits X _Ab , X _Lim , X _Ref and Y _Ab , Y _Lim , Y _Ref and the cut edges of the range upper limits X _{Ab +} , X _{Lim +} , X _{Ref +} and Y _{Ab +} , Y _{Lim +} , Y _{Ref +} fields A1 to G7 on. For each of these fields, a level of criticality is defined, which in this embodiment is between zero and seven. Fields A1 to G7 are described as follows: field X-value range Y-value range description criticality A1 X <X _waste Y _{Ab +} <Y The values of both axes are outside the sensor ranges. 7 A2 X _Ab- <X ≤ X _Lim- Y _{Ab +} <Y The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the sensor range. 6.3 A3 X _Lim- <X ≤ X _Ref- Y _{Ab +} <Y The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the sensor range. 6.2 A4 X _Ref- <X ≤ X _{Ref +} Y _{Ab +} <Y The X value is within the X extrema of the expected range 14 , The Y value is outside the sensor range. 6.1 A5 X _{Ref +} <X ≤ X _{Lim +} Y _{Ab +} <Y The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the sensor range. 6.2 A6 X _{Lim +} <X ≤ X _{Ab +} Y _{Ab +} <Y The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the sensor range. 6.3 A7 X _{Ab +} <X Y _{Ab +} <Y The values of both axes are outside the sensor ranges. 7 B1 X <X _waste Y _{Lim +} <Y ≤ Y _{Ab +} The X value is outside the sensor range. The Y value is outside the specified limits, but within the measuring range of the sensor. 6.3 B2 X _Ab- <X ≤ X _Lim- Y _{Lim +} <Y ≤ Y _{Ab +} The value of both axes is outside the specified limits, but within the measuring range of the sensor. 5 B3 X _Lim- <X ≤ X _Ref- Y _{Lim +} <Y ≤ Y _{Ab +} The X value is outside the X extrema of the expected range 14 but within the given limits Lim ± The Y value is outside the specified limits, but within the measuring range of the sensor. 4.2 B4 X _Ref- <X ≤ X _{Ref +} Y _{Lim +} <Y ≤ Y _{Ab +} The Y value is outside the specified limits, but within the measuring range of the sensor. The X value is within the X extrema of the expected range 14 , 4.1 B5 X _{Ref +} <X ≤ X _{Lim +} Y _{Lim +} <Y ≤ Y _{Ab +} The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the specified limits, but within the measuring range of the sensor. 4.2 B6 X _{Lim +} <X ≤ X _{Ab +} Y _{Lim +} <Y ≤ Y _{Ab +} The value of both axes is outside the specified limits, but within the measuring range of the sensor. 5 B7 X _{Ab +} <X Y _{Lim +} <Y ≤ Y _{Ab +} The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the sensor range. 6.3 C1 X <X _waste Y _{Ref +} <Y ≤ Y _{Lim +} The X value is outside the sensor range. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 6.2 C2 X _Ab- <X ≤ X _Lim- Y _{Ref +} <Y ≤ Y _{Lim +} The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 4.2 C3 X _Lim- <X ≤ X _Ref- Y _{Ref +} <Y ≤ Y _{Lim +} The value of both axes is outside the "learned range", but within the given limits Lim ±. 3 C4 X _Ref- <X ≤ X _{Ref +} Y _{Ref +} <Y ≤ Y _{Lim +} The X value is within the X extremes of the expected range. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 2 C5 X _{Ref +} <X ≤ X _{Lim +} Y _{Ref +} <Y ≤ Y _{Lim +} The value of both axes is outside the "learned range", but within the given limits Lim ±. 3 C6 X _{Lim +} <X ≤ X _{Ab +} Y _{Ref +} <Y ≤ Y _{Lim +} The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 4.2 C7 X _{Ab +} <X Y _{Ref +} <Y ≤ Y _{Lim +} The X value is outside the sensor range. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 6.2 D1 X <X _waste Y _Ref- <Y ≤ Y _{Ref +} The X value is outside the sensor range. The Y value is within the Y extrema of the expected range 14 , 6.1 D2 X _Ab- <X ≤ X _Lim- Y _Ref- <Y ≤ Y _{Ref +} The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is within the Y extrema of the expected range 14 , 4.1 D3 X _Lim- <X ≤ X _Ref- Y _Ref- <Y ≤ Y _{Ref +} The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is within the Y extrema of the expected range 14 , 2 D4a X _Ref- <X ≤ X _{Ref +} Y _Ref- <Y ≤ Y _{Ref +} The values of both axes are within the X and Y extrema of the expected range 14 but also in the finger area 39 within those during the reference time no measured value pairs 2 were found. 1 d4b X _Ref- <X ≤ X _{Ref +} Y _Ref- <Y ≤ Y _{Ref +} Y and X values are within the expected range 14 So the measurement point is OK. 0 D5 X _{Ref +} <X ≤ X _{Lim +} Y _Ref- <Y ≤ Y _{Ref +} The X value is outside X-extremes of the expected range 14 but within the given limits Lim ±. The Y value is within the Y extrema of the expected range. 2 D6 X _{Lim +} <X ≤ X _{Ab +} Y _Ref- <Y ≤ Y _{Ref +} The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is within the Y extrema of the expected range 14 , 4.1 D7 X _{Ab +} <X Y _Ref- <Y ≤ Y _{Ref +} The X value is outside the sensor range. The Y value is within the Y extrema of the expected range 14 , 6.2 E1 X <X _off Y _Lim- <Y _≦ Y _Ref- The X value is outside the sensor range. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 6.2 E2 X _ab- <X ≤ X _Lim- Y _Lim- <Y _≦ Y _Ref- The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 4.2 E3 X _Lim- <X ≤ X _Ref- Y _Lim- <Y _≦ Y _Ref- The value of both axes is outside the X and Y extrema of the expected range 14 but within the given limits Lim ±. 3 E4 X _Ref- <X ≤ X _{Ref +} Y _Lim- <Y _≦ Y _Ref- The X value is within X extremes of the expected range 14 , The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 2 E5 X _{Ref +} <X ≤ X _{Lim +} Y _Lim- <Y _≦ Y _Ref- The value of both axes is outside the X and Y extrema of the expected range 14 but within the given limits Lim ±. 3 E6 X _{Lim +} <X ≤ X _{Ab +} Y _Lim- <Y _≦ Y _Ref- The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 4.2 E7 X _{Ab +} <X Y _Lim- <Y _≦ Y _Ref- The X value is outside the sensor range. The Y value is outside the Y extrema of the expected range 14 but within the given limits Lim ±. 6.2 F1 X <X _off Y _Ab- <Y _≦ Y _Lim- The X value is outside the sensor range. The Y value is outside the specified limits, but within the measuring range of the sensor. 7.3 F2 X _ab- <X ≤ X _Lim- Y _Ab- <Y _≦ Y _Lim- The value of both axes is outside the specified limits, but within the measuring range of the sensor. 5 F3 X _Lim- <X ≤ X _Ref- Y _Ab- <Y _≦ Y _Lim- The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the specified limits, but within the measuring range of the sensor. 4.2 F4 X _Ref- <X ≤ X _{Ref +} Y _Ab- <Y _≦ Y _Lim- The X value is within the X extrema of the expected range 14 , The Y value is outside the specified limits, but within the measuring range of the sensor. 4.1 F5 X _{Ref +} <X ≤ X _{Lim +} Y _Ab- <Y _≦ Y _Lim- The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the specified limits, but within the measuring range of the sensor. 4.2 F6 X _{Lim +} <X ≤ X _{Ab +} Y _Ab- <Y _≦ Y _Lim- The value of both axes is outside the specified limits, but within the measuring range of the sensor. 5 F7 X _{Ab +} <X Y _Ab- <Y _≦ Y _Lim- The X value is outside the sensor range. The Y value is outside the specified limits, but within the measuring range of the sensor. 6.3 G1 X <X _off Y <Y _Ab- The values of both axes are outside the sensor ranges. 7 G2 X _ab- <X ≤ X _Lim- Y <Y _Ab- The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the sensor range. 6.3 G3 X _Lim- <X ≤ X _Ref- Y <Y _Ab- The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the sensor range. 6.2 G4 X _Ref- <X ≤ X _{Ref +} Y <Y _Ab- The X value is within the X extrema of the expected range 14 , The Y value is outside the sensor range. 6.1 G5 X _{Ref +} <X ≤ X _{Lim +} Y <Y _Ab- The X value is outside the X extrema of the expected range 14 but within the given limits Lim ±. The Y value is outside the sensor range. 6.2 G6 X _{Lim +} <X ≤ X _Ab- Y <Y _Ab- The X value is outside the specified limits, but within the measuring range of the sensor. The Y value is outside the sensor range. 6.3 G7 X _{Ab +} <X Y <Y _Ab- The values of both axes are outside the sensor ranges. 7

A measured value pair 2 can be in one of the in 3 Stirred fields A1 to G7, to which the respective, above-mentioned criticality level is assigned.

• • 0: Y- und X-Wert liegen innerhalb des Erwartungsbereiches 14, sind also OK
• • 1: Die Werte beider Achsen liegen innerhalb der X- und Y-Extrema des Erwartungsbereiches 14 aber außerhalb des Erwartungsbereiches 14 selbst, z. B. in der Finger-Fläche 39 innerhalb derer während der Referenzzeit keine Messwert-Paare 2 gefunden wurden.
• • 2: Der Wert einer Achse liegt außerhalb der X- und Y-Extrema des Erwartungsbereichs 14, aber innerhalb der vorgegebenen Grenzen Lim±. Der Wert der anderen Achse liegt innerhalb der Grenzen des Erwartungsbereichs 14.
• • 3: Der Wert beider Achsen liegt außerhalb der X- und Y-Extrema des Erwartungsbereichs 14, aber innerhalb der vorgegebenen Grenzen Lim±.
• • 4: Der Wert einer Achse liegt außerhalb der vorgegebenen Grenzen, aber innerhalb des Messbereichs des Sensors.
• – 4,1: Der Wert der anderen Achse liegt innerhalb der Grenzen des Erwartungsbereichs 14.
• – 4,2: Der Wert der anderen Achse liegt außerhalb der Grenzen des Erwartungsbereichs 14, aber innerhalb der vorgegebenen Grenzen Lim±.
• • 5: Der Wert beider Achsen liegt außerhalb der vorgegebenen Grenzen, aber innerhalb des Messbereichs des Sensors.
• • 6: Der Wert einer Achse liegt außerhalb des Sensor-Messbereichs.
• – 6,1: Der Wert der anderen Achse liegt innerhalb der Grenzen des Erwartungsbereichs 14.
• – 6,2: Der Wert der anderen Achse liegt außerhalb der Grenzen des Erwartungsbereichs 14, aber innerhalb der vorgegebenen Grenzen Lim±.
• – 6,3: Der Wert der anderen Achse liegt außerhalb der vorgegebenen Grenzen, aber innerhalb des Messbereichs des Sensors.
• • 7: Die Werte beider Achsen liegen außerhalb der Sensor-Messbereiche.

The criteria for the criticality levels are:

• • 0: Y and X values are within the expected range 14 so are OK
• • 1: The values of both axes are within the X and Y extrema of the expected range 14 but outside the expected range 14 itself, z. In the finger area 39 within those during the reference time no measured value pairs 2 were found.
• • 2: The value of an axis is outside the X and Y extrema of the expected range 14 but within the given limits Lim ±. The value of the other axis is within the bounds of expectation 14 ,
• • 3: The value of both axes is outside the X and Y extrema of the expected range 14 but within the given limits Lim ±.
• • 4: The value of an axis is outside the specified limits, but within the measuring range of the sensor.
• - 4.1: The value of the other axis is within the bounds of expectation 14 ,
• - 4,2: The value of the other axis is outside the bounds of expectation 14 but within the given limits Lim ±.
• • 5: The value of both axes is outside the specified limits, but within the measuring range of the sensor.
• • 6: The value of an axis is outside the sensor range.
• - 6.1: the value of the other axis is within the bounds of the expected range 14 ,
• - 6.2: The value of the other axis is outside the expected range 14 but within the given limits Lim ±.
• - 6.3: The value of the other axis is outside the specified limits, but within the measuring range of the sensor.
• • 7: The values of both axes are outside the sensor ranges.

The criticality levels four to seven are also determined and evaluated in conventional measuring and monitoring systems.

For measured variable pairs 1 in which one of the sizes is not analog, such as. In the case of a switch position represented by the values "0" and "1", for example, the measuring range limits and limit values and thus the stated levels of criticality are not defined. Nevertheless, reference data and expectation ranges can be specified. The rectangular fingers 37 then lie around "0" and "1" However, a program can automatically detect the absence of limit values, measuring range limits and thus criticality levels and branch to a suitable analysis method.

Is user-defined to enter the limit values for some or all measured variables 1a omitted, the corresponding fields of the criticality levels four and five and thus the criticality levels four and five are not themselves defined.

• • einen ersten Datenspeicher 16 zur Programmierung und Pflege der ersten Halb-Matrix 4 zur Berechnung und Erfassung der Erwartungsbereichsgrenzen 8' in historischen Messgrößen-Paaren 1'' auf der Basis von Referenzdaten historischer Messwert-Paare 2'',
• • einen ersten Anschluss 17 für eine erste Datenleitung 18, die historische Messwerte von einer Datensammelstelle 19 eines Datenbankservers 31 über die erste Datenleitung 18 an den ersten Datenspeicher 16 überträgt,
• • einen zweiten Datenspeicher 20 für die zweite Halb-Matrix 5 zur Erfassung und Zwischenspeicherung von aktuell gemessenen Messwert-Paaren 2' innerhalb entsprechend definierter aktueller Messgrößen-Paare 1',
• • einen zweiten Anschluss 21 für eine zweite Datenleitung 22, die aktuelle Messwerte von Datenquellen an den zweiten Datenspeicher 20 überträgt,
• • ein Programmteil 23 für die Durchführung des Vergleichs, auch als Vergleicher bezeichnet, der alle aktuell gemessenen Messwert-Paare 2' der aktuellen Messgrößen-Paare 1' daraufhin überprüft, ob sie innerhalb der für die historischen Messgrößen-Paaren 1'' in der ersten Halb-Matrix 4 hinterlegten Erwartungsbereichsgrenzen 8' liegen,
• • einen dritten Datenspeicher 24 für eine dritte Halb-Matrix 6 zur Erfassung und Bewertung von anormalen Messwerten 2'''. Hier wird abgelegt, für welche Messgrößen-Paare 1 die aktuell gemessenen Messwert-Paare 2' außerhalb der für die historischen Messgrößen-Paare 1'' festgelegten Erwartungsbereichsgrenzen 8' liegen sowie
• • eine Ausgabeeinrichtung 25, die ein zuvor definiertes Signal abgibt und/oder versendet und die auch als Steuergerät arbeiten kann.

The basic structure of the invention and the device according to the invention 15 is in 5 shown. The device 15 has in this embodiment:

• • a first data store 16 for programming and maintaining the first half-matrix 4 for calculating and recording the expected range limits 8th' in historical quantities pairs 1'' on the basis of reference data of historical measured value pairs 2 '' .
• • a first connection 17 for a first data line 18 , the historical readings from a data collection point 19 a database server 31 over the first data line 18 to the first data store 16 transmits,
• • a second data store 20 for the second half matrix 5 for the acquisition and temporary storage of currently measured value pairs 2 ' within correspondingly defined current measured value pairs 1' .
• • a second connection 21 for a second data line 22 , the current readings from data sources to the second data store 20 transmits,
• • a program part 23 for the implementation of the comparison, also referred to as comparator, all currently measured pairs measured 2 ' the current measured variable pairs 1' then check to see if they're within the range for the historical metric pairs 1'' in the first half matrix 4 stored expectation range limits 8th' lie,
• • a third data store 24 for a third half-matrix 6 to record and evaluate abnormal readings 2 ''' , Here is filed, for which pairs of measured variables 1 the currently measured pairs of measurements 2 ' outside the for the historical metric pairs 1'' defined expectation range limits 8th' lie as well
• • an output device 25 which emits and / or transmits a previously defined signal and which can also operate as a control unit.

The second data line 22 is on a data bus 26 tethered. In addition, the data bus points 26 in this embodiment, connections to a time data source 27 , a calendar data source 28 , a weather data source 29 , a resource data source 30 and possibly to other data sources of installations or equipment to be monitored or protected.

The device according to the invention 15 to 5 has a third data line in this embodiment 35 on. This allows those in the data collection point 19 stored, historically measured values 18 ' the second half-matrix 5 be fed and that between the pairs of measured variables 1 the second half-matrix 5 and those of the first half-matrix 4 historical comparison measurements are performed, with the abnormal measured value pairs 2 '' the historical comparison measurements with those in the first half-matrix 4 already formulated expectation range limits 8th' that come from other historical metric pairs 2 '' be compared to perform a trend analysis or Characterization of the change in consumer behavior in the electrical network or for the performance evaluation of an electrical system in continuous operation.

Basically, the current measured value pairs 1' the same number of measured variable pairs 1 on like the historical metric pairs 1'' , There are differences if, for example, a data source is switched on or off during ongoing measuring operation. The comparison of current measured value pairs 1' with historical measured value pairs 1'' It is only possible to do so if there are already expectation limits 8th' from historical metric pairs 2 '' are defined. However, since all currently measured values 22 ' via the data busbar 26 both the second data store 20 with the second half-matrix 5 as well as the data collection point 19 can be fed via a later historical comparison measurement in learning mode 32 z. B. the missing expectation areas 14 and expectation range limits 8th' in historical metric pairs 1'' newly defined and to the current measured value pairs 1' be adjusted.

It is particularly advantageous if the data collection point 19 with a measuring device, for example with a measuring device known per se for power quality measurement 34 connected is. All in the meter for power quality measurement 34 currently measured values for an electrical network or a power supply section or a resource to be monitored 30 , z. As transformers in a substation, then both the data collection point 19 as well as the second data store 20 fed and in the device 15 be processed directly according to the invention.

It is within the scope of the invention that the data collection point 19 directly in the meter for power quality measurement 34 is arranged. Also, the device can 15 as additional module for a measuring device for power quality measurement 34 or another suitable measuring device. But according to the invention can also the entire device 15 into the meter for power quality measurement 34 be integrated. This is particularly advantageous if it is provided that the data collection point 19 the meter for power quality measurement 34 the first data store 16 permanently supplied with measured values in the course of measuring operation and thus the learning capability of the device 15 guaranteed. Measuring devices for power quality measurement 34 , as they are already used today for the power quality measurement at the entrance or at the entrances to a network section under investigation, provide a stream of data of various parameters 1a to the network state. Same data sources or calendar data sources 28 and time data sources 27 provide time information, weather data sources 29 provide weather information. Optionally, further data sources provide further data on the state of resources, such. B. the transformer internal temperature of the resource data source 30 ,

The at the data bus 26 incoming data go to the data collection point 19 , Their content becomes areas of expectation 14 determined and in the first half-matrix 4 Are defined. The over the second data line 22 and the second port 21 in the second data store 20 incoming data will be measured as currently measured pairs 2 ' captured and as current pairs of measurements 1' in the second half-matrix 5 processed. In the program part 23 then becomes a comparison of the current measured value pairs 1' with the historical measured value pairs 1'' performed and those measured pairs 2 in which the current measurement pairs 1' outside the expected ranges 14 lie, as abnormal measured value pairs 2 ''' in the third half matrix 6 displayed. In the case of the abnormal measured value pairs 2 ''' this can be live data or real-time data or even test data.

• • Zu Beginn des Lernmodus selektiert der Anwender diejenigen Messgrößen 1a, welche in die Analyse eingehen sollen.
• • Dann bestimmt der Anwender den Zeitraum, dessen Daten als Referenzdaten dienen sollen.
• • Optional bestimmt der Anwender noch, ob alle verfügbaren Messwerte oder ob beispielsweise nur zeitlich gerasterte Stichproben der Daten als Referenzdaten dienen sollen.
• • Optional können auch Daten aus einem Simulationsprogramm, welches das Verhalten des zu überprüfenden Netzes synthetisiert, als Referenzdaten dienen.
• • Dann werden die Zusammenhänge der vom Anwender vorher selektierten Messgrößen-Paare 1 bestimmt.

The measuring system according to the invention with the device according to the invention 15 works in at least two modes: a learning mode 32 and a monitoring mode 33 , The learning mode 32 works with reference data.

• • At the beginning of the learning mode, the user selects those measured variables 1a which should be included in the analysis.
• • Then the user determines the period of time whose data should serve as reference data.
• • Optionally, the user also determines whether all available measured values or, for example, only time-sampled samples of the data should serve as reference data.
• • Optionally, data from a simulation program that synthesizes the behavior of the network to be checked can also serve as reference data.
• • Then the relationships of the pairs of measured variables previously selected by the user become 1 certainly.

As a result of the latter operation, each position in the first half-matrix becomes 4 the description of a historical pair of measured variables 1'' z. B. a correlation analysis, an ellipse 8th , a surface covered by no finger 38 or the like and thus the definition of the expectation ranges 14 for data in monitoring mode 33 assigned. The calculations can be made from those in the data collection point 19 archived data of historically measured values 18 ' respectively.

It is important that the reference data comes from a sufficiently long and typical period, which covers all operating conditions relevant in normal operation. With the aid of a tolerance parameter, the user can define the width of the actual expected ranges of expectation 14 individually or together for all areas of expectation 14 pretend.

• • berechnet das System in regelmäßigen Abständen die aktuell gemessenen Messwert-Paare 2' und
• • prüft das System, ob die aktuell gemessenen Messwert-Paare 2' innerhalb der Erwartungsbereiche 14 der im Lernmodus 32 selektierten historischen Messgrößen-Paare 1'' liegen.
• • Liegt ein aktuell gemessenes Messwert-Paar 2' außerhalb des Erwartungsbereichs 14 des entsprechenden historischen Messgrößen-Paars 1'', wird nach dem Vergleich im Programmteil (23) durch anormale Messwert-Paare 2''' ein Alarm, auch als „Primär-Alarm” bezeichnet, ausgelöst und in der dritten Halb-Matrix 6 angezeigt.
• • Jeder Primär-Alarm enthält die Information, welches Messgrößen-Paar 1 mit welchem anormalen Messwert-Paar 2'' ihn ausgelöst hat. Diese Information kann dem Anwender auf Wunsch im Klartext angezeigt werden.

In monitoring mode 33

• • the system calculates the currently measured measured value pairs at regular intervals 2 ' and
• • the system checks whether the currently measured measured value pairs 2 ' within the expected ranges 14 the one in learning mode 32 selected historical variables pairs 1'' lie.
• • Is a currently measured pair of measured values 2 ' outside the expected range 14 of the corresponding historical parameter pair 1'' , after the comparison in the program part ( 23 ) by abnormal measured value pairs 2 ''' an alarm, also called "primary alarm", is triggered and in the third half-matrix 6 displayed.
• • Each primary alarm contains the information, which pair of measured variables 1 with which abnormal pair of measured values 2 '' has triggered him. This information can be displayed to the user on request in plain text.

The primary alarms, which contain all the information of the underlying measured variable pairs 1 can also be used for a controlled extension of the expected ranges 14 be used. If archived raw data of the sensors are available to the system according to the invention, the suitability of the expected ranges can be determined 14 for detecting known changes in the electrical network 36 or an electrical system.

The measuring system according to the invention comprises on the order of 10 ⁵ to 10 ⁶ of the in 3 represented records. People can not look at such a number of diagrams in due time, let alone adequately respond to changes in the diagrams. Instead, the measuring system according to the invention determines during a monitoring mode 33 regularly the criticality levels of all pairs of measured variables 1 , The current criticality levels may be colored dots in a semi-matrix arrangement in the output device 25 are displayed. Because of the large number of points, the third half-matrix appears 6 as a triangular image. Whenever one or more points with a color other than Criticality Level 0 is / are visible, the system is in a state that did not occur during the learning phase and can issue a corresponding primary alarm.

New system states in which only the criticality levels 1 to 3 occur can but do not necessarily represent error states. Whenever new system states occur, the measuring system according to the invention can display non-specific warning messages and a list of measured variable pairs 1 with criticality levels> 0. For experienced users, the type of measured variables involved indicates the type of system error status.

Different types of new system states may be used after a category learning phase of the system, which occurs during the beginning of the monitoring mode, but also for issuing specific warnings and / or control signals. It can be exploited that different types of new system states produce different patterns of points with criticality levels> 0. Because of the large number of points in the "triangle" of the third half-matrix 6 It makes sense to automatically distinguish new system states with the help of image processing algorithms and to categorize them with methods of automatic learning. After this category learning phase, a measurement system according to the invention can distinguish between different categories of the deviation of the system state from the learned target state (deviation categories). Skilled users can now assign specific warnings and / or control signals to all or a subset of these deviation categories automatically found by the measuring system according to the invention. These should be selected so that the actuations triggered thereby drive the state of the monitored system back into the reference range. Only now is the final state of the monitoring mode reached.

The advantage of the measuring system according to the invention over conventional measuring systems is that it does not respond only when one or more individual measured variables of the monitored system exceed physical or regulatory limits, but that it automatically complex Deviation categories detected, which only in the context of several variables 1a show that it can react selectively.

The improvement of the measuring system according to the invention of the device according to the invention compared to a conventional monitoring of N measured variables for limit value exceedings consists of a formal mathematical description of a drastic restriction of the permissible state space. A monitoring system that monitors N measurands independently for limit violations classifies all N-dimensional state vectors in the entire N-dimensional space between the limits of the individual measurands as permissible. The invention limits the allowable state space to the much smaller expected range by the N-dimensional state vectors actually measured during the reference time.

• • Anzahl und Auswahl der Messgrößen-Paare 1, welche in die Analyse eingehen sollen,
• • Die Auswahl der Referenzdaten,
• • Die Auswahl des Zeitrasters der Referenzdaten sowie der Stichproben im Überwachungsmodus 33
• • Die Wahl der Toleranz-Parameter für die Messgrößen-Paare 1
• • Ein automatisierbarer Austausch der Referenzdaten, weil zum Beispiel bekannt ist, dass das untersuchte Netzsegment sich an Werk- und Feiertagen sehr verschieden verhält.

The following degrees of freedom exist in the configuration of the measuring system according to the invention:

• • Number and selection of the measured variable pairs 1 which should be included in the analysis,
• • the selection of reference data,
• • The selection of the time frame of the reference data as well as the samples in monitoring mode 33
• • The choice of tolerance parameters for the measured quantity pairs 1
• • An automatable exchange of the reference data, because it is known, for example, that the examined network segment behaves very differently on workdays and public holidays.

Further advantages of the invention over the usual network analysis systems are that the measuring system according to the invention is "self-learning", ie it "learns" the limits of the "standard operation" of the network section automatically and automatically. In addition, the measuring system according to the invention functions in principle independently of limit values, standards and physical limitations. However, existing limitations can be within the bounds of expectation 14 be incorporated and act as alarm triggering thresholds.

Also, the inventive measuring system is "lazy user-capable". This means that the manufacturer can have a "over-cautious" configuration with an excessive number of pairs of measurements 1 and deliver too tight tolerance parameters. Then the user gets too many and too narrow expectation ranges 14 generated. In monitoring mode, this initially leads to an excessive number of alarms. During a preliminary phase of the monitoring operation, these alarms can then be used to extend the tolerance parameters to the expected ranges 14 gradually adapt automatically to the normal operation of the target network segment. Also, the new reading pairs can 2 in order to keep the expected range limits in such operation 8th' to recalculate at constant tolerance parameters to adapt the system to the existing measurement situations.

Another advantage is that configurations can be pre-tested. For this purpose, the measuring system according to the invention can be tested, for example, offline with archive data from times before and after a known change of the network segment to be examined. First of all, expectation ranges from reference data of the time before the change in the network segment are expected 14 and then tested with data from the time after the change in the network segment to see if the change with the selected configuration would have been detected as desired. The user can - provided appropriate software tools - the expected ranges 14 all measured variable pairs 1 query at any time, represent (comparable to 2 to 4 ) and for compatibility with technical and economic requirements.

A further advantage of the invention is that systematic shifts in the dynamics of network operating states can be detected in good time. Such changes arise, for example, from creeping degradation of equipment, from changes in the type and number of connected consumers and generators, etc .. These hidden trends in systematic shifts in dynamics are of particular interest to network operators to the planning of ongoing operations and investment decisions optimize.

Advantageous compared to conventional measuring methods, such. B. from the WO 2011/032579 A1 It is also known that disturbances that occur without limits of individual measures 1a to exceed, in particular relations of the measured quantities 1a different measuring points to each other manifest, be recognized and not go unnoticed.

Another advantage of the invention, especially over DE 10 2009 038 351 A1 , is that categories of unwanted states can be formed that are only affected by the See simultaneous consideration of a variety of deviations according to the invention. Consequently, a monitoring system according to the in DE 10 2009 038 351 A1 also do not react to category-specific alarms or switching activities.

The measuring system according to the invention receives despite the variety of used pairs of measured variables 1 the traceability of all alarms up to the raw data. This distinguishes the invention of network monitoring solutions with neural networks. The number of measured variable pairs 1 and the associated expectation ranges 14 increases with the square of the number of measured variables: Z _{measured variable pairs} = ≈0.5 · N _{measured variables}

If the amplitudes and phase angles of the first 50 harmonics are included as measured variables in a 3-phase network, a total of about 1000 measured variables, ie 500,000 pairs of measured variables, can be achieved 1 of which, however, a large part may be superfluous.

In summary, it can be stated that from the historical measured value pairs 2 '' and the currently measured measured value pairs 2 ' Characteristics can be extracted automatically, with which a characterization of the electrical network 36 or the examined network section is possible and with which after a learning mode 32 Automatically detect significant changes and trends in the characteristics of these network sections online or offline.

It is within the scope of the invention to determine several sets of expectation ranges, e.g. B belong to different operating conditions of the network and on the matrices 4 . 5 . 6 collectively mirror image.

With the invention is thus by the complex approach of the measured value pairs 2 a large number of measured variable pairs 1 achieved that the normal operating conditions of a given electrical network 36 or a power supply section can be automatically characterized and acute and creeping deviations from a previously learned characteristic can be detected and averted in advance of an event. If incidents already become apparent in the run-up to an event, maintenance work can be carried out or wearing parts can be replaced in good time before major damage to property can occur. Also can be z. B. at a network operator define conditions that trigger switching operations, such as the connection or disconnection of other network operators or the throttling of power plants or the like from a control center allow. In general, fault diagnosis in the run-up to a disruptive event makes it easier to plan the ongoing operation of an electrical installation, and investment decisions can be significantly improved by recognizing possible events at a much earlier point in the present invention.

LIST OF REFERENCE NUMBERS

1

Metrics pair

1a

measurand

1'

current pair of measured variables

1''

historical measure pair

2

Reading pair

2 '

currently measured value pair

2 ''

historical reading pair

2 '' '

abnormal reading pair

3

point cloud

3 '

first partial point cloud

3 ''

second sub-point cloud

3 '' '

third partial point cloud

4

first matrix or half-matrix

5

second matrix or half-matrix

6

third matrix or half-matrix

7

Correlation triangle

8th

ellipse

8th'

Expected range limit

9

long axis of the ellipse 8th

10

short axis of the ellipse 8th

11

Ellipse center

12

Points

13

cross

14

Expected range

15

contraption

16

first data store

17

first connection

18

first data line

18 '

historically measured values

19

data collection

20

second data store

21

second connection

22

second data line

22 '

currently measured values

23

program part

24

third data memory

25

output device

26

Data bus

27

Time data sources

28

Calendar data sources

29

Weather data sources

30

Resource data sources

31

Database server

32

learning mode

33

monitoring mode

34

Meter for power quality measurement

35

third data line

36

Electrical network

37

rectangular fingers

38

surface not covered by a finger

39

Finger area

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• DE 19948694 B4 [0002]
• WO 2005/088802 A1 [0003]
• DE 102007041565 A1 [0004]
• EP 2355412 A1 [0005]
• WO 2011/032579 A1 [0006, 0077]
• US 2004/0243328 [0007]
• DE 4111831 A1 [0008]
• DE 102009038351 A1 [0009, 0078, 0078]

Claims (10)

Device and measuring system for automatically characterizing and monitoring an electrical network or a power supply section of an electrical network or an electrical installation, characterized in that the device ( 15 ) at least a) a first data memory ( 16 ) for programming and maintaining a first matrix or half-matrix ( 4 ) from historical measured value pairs ( 1'' ) for the detection of point clouds 3 and / or partial point clouds ( 3 ' . 3 '' . 3 ''' ) from historical measured value pairs ( 2 '' ) and storage of the respectively associated expectation range limits ( 8th' ), b) a second data memory ( 20 ) for a second matrix or half-matrix ( 5 ) from current measured value pairs ( 1' ) for the acquisition of currently measured value pairs ( 2 ' ) c) a program part ( 23 ), which checks whether currently measured values ( 22 ' ) of the current measured variable pairs ( 1' ) within expected ranges ( 14 ) and the defined expectation range limits ( 8th' ) of historical measured value pairs ( 1'' ), d) a third data memory ( 24 ) for a third matrix or half-matrix ( 6 ) for the description of abnormal measured value pairs ( 2 ''' ) and e) an output device ( 25 ) which responds to undesired fault conditions with primary alarms and / or category-specific alarms and / or switching activities. Device according to claim 1, characterized in that the device ( 15 ) a database server ( 31 ) with a data bus ( 26 ) and at least one data collection point ( 19 ) for the acquisition and storage of historically measured values ( 18 ' ) and that a first data line ( 18 ) the data collection point ( 19 ) via a first connection ( 17 ) with the first data memory ( 16 ) and the historical measured values ( 18 ' ) from the data collection point ( 19 ) over the first data line ( 18 ) to the first matrix or half-matrix ( 4 ) of the first data memory ( 16 ) and that the data busbar ( 26 ) Connections to the electrical network ( 36 ) and to a) resource data sources ( 30 ) and / or b) time data sources ( 27 ) and / or c) calendar data sources ( 28 ) and / or d) weather data sources ( 29 ) and / or e) other data sources of equipment or devices to be monitored or protected, and that the data bus ( 26 ) via a second data line ( 22 ) and a second port ( 21 ) with the second matrix or half-matrix ( 5 ) of the second data memory ( 20 ) and that the second data line ( 22 ) currently measured values ( 22 ' ) of the data sources to the second matrix or half-matrix ( 5 ) of the second data memory ( 20 ) transmits and as currently measured measured value pairs ( 2 ' ) for each current pair of measured variables ( 1' ) and that between the first data memory ( 16 ), the second data store ( 20 ) and the third data memory ( 24 ) program part ( 23 ) is a comparator. Device according to claim 1 and / or 2, characterized in that the data collection point ( 19 ) of the database server ( 31 ) with a per se known measuring device for power quality measurement ( 34 ) or that the device ( 15 ) as an additional module for the measuring device for power quality measurement ( 34 ) or that the device ( 15 ) into the power quality meter ( 34 ) and that the data collection point ( 19 ) the first data memory ( 16 ) in ongoing measuring operation permanently with historical measured value pairs ( 2 '' ) and in a learning mode ( 32 ) the expectation range limits ( 8th' ) continuously updated. Device according to one or more of the preceding claims, characterized in that in the program part ( 23 ) the comparison of the mutual relationships by evaluating a large number Z of measured value pairs ( 1 ) is effected by: - that from large amounts of data of the electrical network or the data collection point ( 19 ) in the first matrix or half-matrix ( 4 ) historical measured value pairs ( 2 '' ) generated in historical pairs of measurements ( 1'' ) as point clouds ( 3 ) and - that the first data store ( 16 ) from the point clouds ( 3 ) of historical measured value pairs ( 2 '' ) during the learning mode ( 32 ) in each historical measure pair ( 1'' ) at least one expectation range ( 14 ) with at least one expectation range limit ( 8th' ) for each historical measure pair ( 1'' ) and - that the expectation range limits ( 8th' ) of the first matrix or half matrix ( 4 ) in a second matrix or half-matrix ( 5 ) and - that during the monitoring mode ( 33 ) the second data memory ( 20 ) in the second matrix or half-matrix ( 5 ) the currently measured measured value pairs ( 2 ' ) for each current pair of measured variables ( 1' ) is checked to see if they are within the expectation 8th' ) and that the output device ( 25 ) abnormal measured value pairs ( 2 '' ) outside the respective expectation range limit ( 8th' ), in the third matrix or half-matrix ( 6 ) of the output device ( 25 ), the output device ( 25 ) can also trigger alarms and / or switching activities. Device according to one or more of the preceding claims, characterized in that the expected range limits ( 8th' ) may have closed curves that are ellipses ( 8th ) or look polygonal or hyperbolic, or that the expectation range limits ( 8th' ) by rectangular fingers ( 37 ), which provide a minimum distance to all measured value pairs ( 2 ) the point cloud ( 3 ), are limited, and that levels of criticality are defined. Device according to one or more of the preceding claims, characterized in that the currently measured measured value pairs ( 2 ' ) of the current measured variable pairs ( 1' ) outside the expected ranges ( 14 ) and / or outside the expectation range limit ( 8th' ), in the third matrix or half-matrix ( 6 ) as abnormal measured value pairs ( 2 ''' ) and can trigger primary alarms and that when one or more primary alarms are triggered by one or more abnormal pairs of measurements ( 2 ''' ) a user-specified protection or switching operation can be initiated to correct the problem before a negative event can occur, which characterizes the state of the primary monitoring phase. Device according to one or more of the preceding claims, characterized in that the large number Z of pairs of measured variables ( 1 ) 10 ⁵ to 10 ⁶ data records, wherein the current measured value pairs ( 1' ) preferably the same number Z of measured quantity pairs ( 1 ) like the historical measures pairs ( 1'' ) and that during the monitoring mode ( 33 ) the levels of criticality of all current pairs of measurements ( 1' ), for their measured quantities ( 1a ) Are defined, the third matrix or half-matrix ( 6 ) of the output device ( 25 ) the current levels of criticality of all primary alarms as colored dots or the like in a correlation triangle ( 7 ). Device according to one or more of the preceding claims, characterized in that the current pair of measured variables ( 1' ) with abnormal measured value pairs ( 2 '' ) are treated as patterns and that similar patterns are learned as characteristics for new categories of system states and are used to trigger category-specific alarms or protection and switching states, which characterizes the state of the category-specific monitoring phase. Device according to one or more of the preceding claims, characterized in that the currently measured measured value pairs detected during the primary monitoring phase ( 2 ' ), which are outside the expectation ranges ( 14 ) to their relative position to the expected range ( 14 ), from which their criticality results and that abnormal measured value pairs ( 2 ''' ), which are outside the expectation ranges ( 14 ), according to their criticality triggered staggered primary alarms and can trigger associated switching operations and that during the primary monitoring phase from the location of the currently measured pairs of measured values ( 2 ' ) with primary alarms in the first matrix or half-matrix ( 4 ) and that such patterns of primary alarms are assigned to error categories and that, after assigning the error categories to the primary alarm patterns, the system enters a second monitoring phase in which it learns when primary learned alarms have occurred. Alarm pattern reacts with category-specific alarms and switching activities. Device according to one or more of the preceding claims, characterized in that in the data collection point ( 19 stored, historically measured values ( 18 ' ) of the second matrix or half-matrix ( 5 ) and that between the pairs of measured variables ( 1 ) of the second matrix or half-matrix ( 5 ) and those of the first matrix or half-matrix ( 4 ), the abnormal measured value pairs ( 2 ''' ) of the historical comparison measurements with those in the first matrix or half matrix ( 4 ) already on the basis of other historical measured value pairs ( 2 '' ) formulated expectation range limits ( 8th' ) are compared to carry out a trend analysis or to characterize the change in consumer behavior in the electrical network or to evaluate the performance of an electrical system in continuous operation.

Images