WO2017125605A1 - Procédé et dispositif de détection de consommateurs présents dans un réseau d'alimentation - Google Patents

Procédé et dispositif de détection de consommateurs présents dans un réseau d'alimentation Download PDF

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Publication number
WO2017125605A1
WO2017125605A1 PCT/EP2017/051317 EP2017051317W WO2017125605A1 WO 2017125605 A1 WO2017125605 A1 WO 2017125605A1 EP 2017051317 W EP2017051317 W EP 2017051317W WO 2017125605 A1 WO2017125605 A1 WO 2017125605A1
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WO
WIPO (PCT)
Prior art keywords
data
consumer
network
determined
detected
Prior art date
Application number
PCT/EP2017/051317
Other languages
German (de)
English (en)
Inventor
Axel DRÖGE
Erwin Hemming
Sebastian BRATO
Pia B. COERSMEIER
Bernhard ALBERS
Original Assignee
Gelsenwasser Ag
Task9 Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Gelsenwasser Ag, Task9 Gmbh filed Critical Gelsenwasser Ag
Publication of WO2017125605A1 publication Critical patent/WO2017125605A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D4/00—Tariff metering apparatus
    • G01D4/002—Remote reading of utility meters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D2204/00—Indexing scheme relating to details of tariff-metering apparatus
    • G01D2204/20—Monitoring; Controlling
    • G01D2204/24—Identification of individual loads, e.g. by analysing current/voltage waveforms

Definitions

  • the invention relates to a method for detecting existing in a supply network various technical consumers and their consumption behavior and an apparatus for performing this method.
  • the use of the method and the device should relate in particular to private households and municipal institutions and commercial enterprises.
  • Various methods and devices for the above purpose are known in the art.
  • the disadvantage of these prior art methods and devices is that the individual consumers either have built-in modules that allow the detection of the respective consumer in the network, or must be provided with such modules later.
  • Other publications in the prior art for example DE 10 2012 108 536 A1 describe the possibility of recognizing the behavior and the power consumption of various devices even without the use of such modules. For this purpose, it is necessary to disable all other devices to detect a device or a newly installed device. This type of learning is complicated and time consuming.
  • Such a method is known for example from DE 20 2008 009 128 U1.
  • the invention has for its object to provide a method of the type mentioned so that the consumer to be determined not must be technically manipulated and also in the network only minimal technical changes must
  • the invention achieves this object by a method in which, based on the respective total consumption value or subsets thereof in the network at equal or varying intervals by combining the data measured by sensors, which are based on consumer-specific properties in the total consumption value as well as plausibility data such as date determined by external sensors, Time, temperature and the like, are detected by means of a variety of the data on different types of different algorithms analyzing different individual consumers and / or the system state S z (t) is calculated, and by using artificial intelligence and statistical methods is an overall view of the various algorithm results, and then by vote automatically a decision is made whether a particular consumer or a consumer group has been detected or not, with the detected consumer or the consumer group zugzug stored data in the system are stored, evaluated and further processed.
  • the method can be used in any supply network such as gas, water or electricity grid.
  • any supply network such as gas, water or electricity grid.
  • the invention will be described using the example of a power grid.
  • the total power consumption is continuously monitored and analyzed by the addressed sensors at intervals. From the determined active power and also the reactive power, possibly from the harmonic or subharmonic frequencies of the current and the voltage, data are generated which are given up to the mentioned different algorithms.
  • Each of these algorithms uses the data offered in its own way, and the data collected by the external sensors (time measurement, location and date, year, temperature, etc.) are also included in the analysis. None of the algorithms used is perfect. The typical hit rate would be 70 to 90%. Only the combination of the results (if necessary after the voting process mentioned above) brings the final decision. Although an approximately comparable process is known from EP 2 779 527 A1. However, here the post-processing of the determined data of the invention completely different. In addition, the data obtained is transmitted via the Internet to the unit for evaluation, which is associated with high risks.
  • one or more devices or groups of devices are already detected after calculation of further features.
  • the data is passed to a unit of the microprocessor in which the system state S z (t) is calculated.
  • This can be the system state of a single consumer such as oven, washing machine, dishwasher, pump or a complete building, with system states such as "night's sleep", "plant approach”, etc. may be.
  • an estimate of the system state S z (t + tau) for the current actual time plus a time difference in the future is also performed, ie, which system state is likely to be reached next.
  • An example may be a dishwasher, in which the purging always follows the purging.
  • the final decision maker also remembers this forecast in the future and correlates it with the next condition measurement.
  • a refrigerator door is opened with high probability, after which it is subsequently cooled.
  • Another example is that, for example, in winter the lights are switched on earlier than in the summer. The series of these examples could be continued indefinitely.
  • a set of rules is generally valid for the calculation of system states for each device to be detected. Through functions of artificial intelligence, this set of rules develops independently.
  • Logic programming languages Lip, Prolog
  • numerical programming languages Python, R, Matlab
  • This rule or fact base describes the logical process, but not the size information.
  • the size information is available in a parameter list:
  • the analyzes there are basically two process differences in the invention. It is on the one hand an event analysis and on the other hand a condition analysis.
  • the event analysis takes into account events in the change of the active power values, reactive power values and the harmonics at a certain point in time.
  • the state analysis juxtaposes different states over time and assesses both the sequence of states and the combination of different electrical consumers in a permissible combination. Both methods involve different algorithms.
  • the event-based methods use algorithms that affect the edge detection of the active power, reactive power or harmonics in terms of shape, size, duration and temporal constancy.
  • Example here is the characteristic curve when switching on a fluorescent tube.
  • the state-based analyzes use algorithms such as the Maximum Likelihood Decoder and Viterbi algorithm, respectively. Here states are observed over time and the most reasonable transitions to the next states are calculated. Another algorithm is known as Strange Attractor.
  • a Strange Attractor serves to generate a pattern description from an n-dimensional space with only one one-dimensional measured value.
  • a function y f (t) -f (t-tau) is calculated, where tau should be 60 seconds, for example. This can be used to refer to figures in two-dimensional space that reflect the pattern of certain consumers.
  • SVM Serial Vector Machine
  • Kohonen Maps Reinforcement Learning
  • Nearest Neighbor Naive Bayes
  • LSTM Denoising Autoencoder
  • Deep Belief Networks Time Series Analysis , here in particular ARMA, ARIMA, GARCH.
  • edges differ in shape, size and height, duration and the temporal constancy.
  • the performance curves of different devices differ, for example, the power curve of a fluorescent tube in a curve with different slopes in the positive range.
  • the power curve of, for example, a compressor runs in several stages in the negative direction.
  • the harmonic 33 is taken into account with a fan:
  • alpha is the current negative or positive slope of the power curve.
  • Another method is the time series evaluation. Many consumers can be analyzed in a time series such as refrigerators, heaters, air conditioners, etc ..
  • the underlying algorithm includes:
  • MC-DCNN Multi-Channel Deep Convolution Neural Network
  • Another algorithm refers to the data already given above and coming from external sensors.
  • correlators are used that have similar waveforms stored as typical consumers. By correlating the actual signal with the stored signals, matches can be found. Of course, even simple things like averages, variances, etc. are used. Other tools include Kohonenmaps, which can play a role in the study of similarities of signals. The learning takes place both before the box is put into operation and during use. The following detection method is also conceivable: Many electrical consumers change or influence the high frequencies of current and voltage curves. These can be used as an indication by consumers.
  • harmonics also referred to as harmonic and interharmonic helps. These frequency components> 50 Hz enter
  • Rogowski coils a kind of coaxial cable solution, are used here and are placed around the conductor and can then provide the corresponding high-frequency transmission. The further the Rogowski coils are placed away from the consumers, the more the high frequency components are damped by the normal copper content of the conductors.
  • the normal Eisenkernumbauwandler be used for pure current measurement at 50 Hz and measure up to the 25th harmonic.
  • the Rogowski coils should only detect the harmonics from the 25th harmonic upwards. This guarantees optimal analog-to-digital converter resolution. Because the low-frequency waves, especially the 50 Hz fundamental wave is represented significantly more with a high amplitude than frequencies in the KHz or MHz range. Therefore, a high-pass filter will filter out the low-frequency components. Also conceivable is a multiple installation using bandpass filters. To save the then many Rogowski coils, a high-frequency switch can connect the coils to the respective bandpass. However, this method is currently still device technology and financially complex. The "algorithm method" offers the advantage that reliable results can be achieved despite measuring cycles in the seconds range.
  • Result (a * (sum (detector 1, detector 2, detector_n) / n + b * sum (probability 1, probability 2 ..., probability_m) / m)) / 2.
  • a and b are weighting factors.
  • the probability of detection of an electrical load is very probable with a result of 0.8, with a result of 0.5 still probable and with a result of less than 0.5 unlikely.
  • the device-specific data determined here are encrypted and stored locally in the system. No data will be sent out without the consent of the user.
  • the key to decode the data is either embedded in an executable file or it is fetched time and again by an external security server for decoding.
  • the user can provide the data at his own request to third parties.
  • a user can log in directly to an internal web server in their home network. So the data is processed only in the home network itself.
  • the data can also be viewed by the user, for example, by mobile phone from the outside, for example, by a dynamic DNS service, each assigning a pseudo-fixed IP address.
  • New software features such as new services or the detection of new electrical consumers or the charging of new improved algorithms is performed by software downloads into the system. Only if the user agrees, the system itself gets the software update, so that nothing is foreign-determined.
  • One of the further advantageous possibilities offered by the method according to the invention is that it can be determined by the method that, for example, a photovoltaic system supplies sufficient power to a bakery in the next one to two hours, so that clean energy is used for this purpose certain clientele, for example, bread rolls can be baked. With the help of an app, for example, these people are informed and can then place their order.
  • the bakery is able to provide the required resources (baking trays, dough quantity, number of ovens, etc.).
  • the device for carrying out the method is housed in a housing which is designed in its dimensions so that it can be installed in the fuse box or in the digital / intelligent meter of the measuring point operator or as a plug device.
  • the device has a microcomputer, which includes at least a microprocessor and a memory unit, as well as connectivity to the total consumption of the system detecting sensors and to external sensors that provide the total consumption affecting measurements. It has input interfaces for connecting to the Internet and a mobile network, as well as output interfaces for the microprocessor-determined and prepared data of the respective consumers.
  • the device provides further interfaces for memory expansion and for expanding the computing power as well as for controlling or communicating with third-party devices.
  • current transformers can be used or the data from a standardized digital counter.
  • three current sensors are either placed around the electrical conductors of the house connection or folded. This method therefore requires an extra installation of current sensors.
  • the entire current is conducted via induction conductors.
  • the impressed current in the current sensors is converted in the measuring unit via a shunt resistor in the milli-ohm range into a measuring voltage and sampled by analog-digital conversion.
  • the mains voltage is either transformed by transformers of about 230 V to about 5 V or reduced by a resistor network so far that the 3-phase voltages can also be sampled analog digital.
  • the device has a microcomputer that includes at least a microprocessor and a memory unit and connectivity to the Total consumption value or individual phases (P1, P2, P3) of the system detecting sensors and external sensors that provide the consumer behavior affecting measurements, with input interfaces for connection to the Internet and mobile networks and output interfaces for the microprocessor determined and prepared data the respective consumer.
  • a microcomputer that includes at least a microprocessor and a memory unit and connectivity to the Total consumption value or individual phases (P1, P2, P3) of the system detecting sensors and external sensors that provide the consumer behavior affecting measurements, with input interfaces for connection to the Internet and mobile networks and output interfaces for the microprocessor determined and prepared data the respective consumer.
  • the device according to the invention requires only a read head, which is placed on the customer interface of the digital / intelligent counter (smart meter) or alternatively a bus connection (RS 485) is used.
  • a read head which is placed on the customer interface of the digital / intelligent counter (smart meter) or alternatively a bus connection (RS 485) is used.
  • Fig. 1 Block diagram of the invention
  • the feed-in point for the data coming from the power grid such as the active power, harmonic current frequencies, etc., is provided with the reference symbol X.
  • the blocks A1 to An are supplied with the corresponding data, whereby different algorithms operate in blocks A1 to An.
  • the data are analyzed using these algorithms and already at this point a device is detected as well as other properties.
  • the data analyzed by the algorithms are now either passed directly to block B1, which contains the System state S z (t) calculated. This can be the system state of a single consumer or of an entire building.
  • the calculated data can also be transferred directly to the final decision D1 (see very right path from B1 to D1).
  • Block B1 also returns an estimate of the system state S z (t + tau) for the current actual time plus tau in the future, which system state is likely to be reached next.
  • a final decision D1 the decision is then made as to whether a system or a device has been recognized.
  • a block B2 necessary external influences are collected as additional information (daytime and nighttime, seasons, weather etc.), which are also made available to the final decision maker, so that the plausibility of the determined data can be checked.
  • additional information daytime and nighttime, seasons, weather etc.
  • significant inconsistencies are revealed and also made available to the decision maker D1.
  • a feedback of the decider D1 is supplied via a block F 1 for the system state analysis in block B1.
  • the feedback strength can be specified as 0 1.
  • Delay is a sample of the active power, z. B. at least one second.
  • the reference numeral 1 denotes the device itself, which is housed in a housing 2.
  • the housing 2 has a USB interface, which is connected to the tapping of the data of a digital / intelligent counter 3, such that on the customer interface 4, a schematically illustrated read head 5 is placed.
  • the data can also be transmitted via an RS485 interface via a bus system 6.
  • the determined and processed by means of the device in the housing 2 data can be retrieved by means of a designated by the reference numeral 7 smartphone, tablet, etc. of the device.
  • the device is plugged by means of a power supply in a socket of the building network. Thereafter, by pressing the button 8 and then entering a password in the device 7, the connection is made to the device in which a temporary Wlan access point is generated here. After configuration and pressing the button 8 again, the configuration is completed.
  • the reference numeral 9 designates the in-house router, which forms the actual WLAN access point. This communicates after the initial configuration with the wireless station 10 in the device.
  • the LED indicators provided with the reference numeral 17 stand for "Power”, “Measure” and “Send. Other ads are of course possible.
  • FIG. Another possibility of using the device 1 is shown in FIG.
  • a digital / intelligent counter smart meter
  • the smart meter 3 has a slot in which the housing 2 of the device is inserted for installation, whereby the connections shown in Figure 2 (USB, RS485, etc.) are made.
  • the customer interface 4 is not needed here. Otherwise, the interfaces are provided, as indicated in FIG.
  • FIG. 4 shows a further possible use of the device 1, which is here plugged onto the top hat rail 11 in a fuse box 12.
  • the reference numeral 13 schematically fuses are indicated.
  • I and U are the taps for power and voltage of the power supply called.
  • the corresponding current and voltage data receives the device 1 via current / voltage sensors, not shown.
  • the device 1 is equipped like that described in FIGS. 2 and 3.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

L'invention concerne un procédé de détection de divers consommateurs techniques présents dans un réseau d'alimentation et de leur comportement en termes de consommation, procédé selon lequel différents consommateurs individuels sont détectés à partir de la valeur de consommation totale respective ou de sous-quantités de celle-ci dans le réseau à des intervalles identiques ou variant dans le temps par combinaison des données mesurées par des capteurs, lesquelles dérivent de caractéristiques spécifiques au consommateur dans la valeur de consommation totale, ainsi que de données de plausibilité déterminées par des capteurs externes, telles que la date, l'heure, la température et similaire, au moyen d'une pluralité d'algorithmes analysant les données d'une manière respectivement différente et/ou l'état du système Sz(t) est calculé, et par utilisation d'une intelligence artificielle et d'une méthode statistique, une vue d'ensemble des différents résultats d'algorithme est réalisée et ensuite, par adaptation, une décision est prise automatiquement quant à savoir si un consommateur déterminé ou un groupe de consommateurs a été détecté ou non, les données associées au consommateur détecté ou au groupe de consommateurs étant mises en mémoire, évaluées et traitées ultérieurement dans le système.
PCT/EP2017/051317 2016-01-21 2017-01-23 Procédé et dispositif de détection de consommateurs présents dans un réseau d'alimentation WO2017125605A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016101021.9 2016-01-21
DE102016101021.9A DE102016101021A1 (de) 2016-01-21 2016-01-21 Verfahren und Vorrichtung zur Erkennung von in einem Versorgungsnetz vorhandenen Verbrauchern

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WO2017125605A1 true WO2017125605A1 (fr) 2017-07-27

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DE (1) DE102016101021A1 (fr)
WO (1) WO2017125605A1 (fr)

Cited By (1)

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CN112649644A (zh) * 2020-12-22 2021-04-13 常州常工电子科技股份有限公司 一种学生公寓用电安全负载学习方法

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DE102017118958A1 (de) * 2017-08-18 2019-02-21 Christian Schuch Energiemanagementsystem-Vorrichtung und ein Energiemanagementsystem-Verfahren
DE102019110447A1 (de) * 2019-04-23 2020-10-29 Innogy Se Verfahren und System zur Erstellung einer Zuordnung zwischen einem Verbrauchsmengenzähler und einer Steuereinrichtung

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DE102004055088A1 (de) * 2004-11-15 2006-05-18 Ennovatis Gmbh Verbrauchsermittlungs-, analyse- und -steuerungssystem; Verfahren zur Verbrauchsermittlung, -analyse und -steuerung
DE202008009128U1 (de) 2007-07-06 2009-01-02 Aizo Ag Verbrauchs- und Zustandsmesser
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EP2499462A2 (fr) * 2009-11-12 2012-09-19 Onzo Limited Identification d'événements
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DE102012108536A1 (de) 2012-09-12 2014-03-13 Deutsche Telekom Ag Verfahren und Vorrichtung zur Bestimmung des Stromverbrauchs
EP2779527A1 (fr) 2013-03-15 2014-09-17 Yetu AG Système et procédé permettant l'analyse de la consommation d'énergie de consommateurs électriques dans un réseau

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DE102004055088A1 (de) * 2004-11-15 2006-05-18 Ennovatis Gmbh Verbrauchsermittlungs-, analyse- und -steuerungssystem; Verfahren zur Verbrauchsermittlung, -analyse und -steuerung
DE202008009128U1 (de) 2007-07-06 2009-01-02 Aizo Ag Verbrauchs- und Zustandsmesser
US20120290230A1 (en) * 2009-07-01 2012-11-15 Carnegie Mellon University Methods and Apparatuses for Monitoring Energy Consumption and Related Operations
EP2499462A2 (fr) * 2009-11-12 2012-09-19 Onzo Limited Identification d'événements
GB2488164A (en) * 2011-02-18 2012-08-22 Globosense Ltd Identifying electrical appliances and their power consumption from energy data
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EP2779527A1 (fr) 2013-03-15 2014-09-17 Yetu AG Système et procédé permettant l'analyse de la consommation d'énergie de consommateurs électriques dans un réseau

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Publication number Priority date Publication date Assignee Title
CN112649644A (zh) * 2020-12-22 2021-04-13 常州常工电子科技股份有限公司 一种学生公寓用电安全负载学习方法
CN112649644B (zh) * 2020-12-22 2021-06-29 常州常工电子科技股份有限公司 一种学生公寓用电安全负载学习方法

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