WO2015059272A1 - Procédé et dispositif de contrôle amélioré non intrusif de charge d'appareil - Google Patents

Procédé et dispositif de contrôle amélioré non intrusif de charge d'appareil Download PDF

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WO2015059272A1
WO2015059272A1 PCT/EP2014/072843 EP2014072843W WO2015059272A1 WO 2015059272 A1 WO2015059272 A1 WO 2015059272A1 EP 2014072843 W EP2014072843 W EP 2014072843W WO 2015059272 A1 WO2015059272 A1 WO 2015059272A1
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events
event
clustering
cluster
clusters
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PCT/EP2014/072843
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Quenton JOSSEN
Frédéric KLOPFERT
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Universite Libre De Bruxelles
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • 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
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R21/00—Arrangements for measuring electric power or power factor
    • G01R21/133—Arrangements for measuring electric power or power factor by using digital technique
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/30—Smart metering, e.g. specially adapted for remote reading

Definitions

  • the invention pertains to the technical field of automatic appliance detection, in particular Non-Intrusive Appliance Load Monitoring (NIALM), which refers to the automated detection of the state of appliances, e.g. household appliances, industrial appliances, a company's appliances, etc., from a total energy consumption signal. More in particular, the present invention relates to obtaining information at the component level of electrical appliances in a system, e.g. a household or a company, from a measurement of the total electricity consumption of said system. This information at the component level may include but is not limited to: the presence, the state and/or the energy consumption.
  • the system may also be comprised of a single or a few appliances, whereby the methods, devices and/or systems of the present invention can be used for condition monitoring of one or more appliances. Background
  • WO 2012/160062 Al discloses a method for detecting transitions in a measured signal e.g. a discrete time signal and current waveform, which is induced by elements i.e. components of appliances, of a physical system i.e. a house.
  • the method involves generating a residual signal, i.e. transition likelihood signal, from a measured signal, i.e. a discrete time signal, where the residual signal is provided with high amplitude when transitions occur and with low amplitude in other cases.
  • Rules concluding that transitions occur when the residual signal is larger than a threshold stable value are provided, where the stable value is automatically defined from local values of the residual signal and defined as a function of local background noise.
  • the measured signal is filtered before generating residual signal.
  • the method enables defining a time index corresponding to end of transient state to maximize a distance between transient states and a straight line passing through the transient state, thus improving separation between transient and steady states, and hence detecting transitions in the measured signal, and monitoring automatic-setup non-intrusive appliance load for identifying appliances energy consumption in a reliable manner.
  • This document also discloses an automatic-setup non-intrusive appliance load monitoring method (50) for identifying appliances energy consumption and comprising the steps of:
  • the step of identifying components is hereby performed by selecting spectral features or features describing a current waveform for the steady states, with the possibility of the chosen features being grouped together in well-separated clusters for different components.
  • a first step of component identification consists in clustering and classifying the components according to their nature (motor, resistor, heater, television are examples of different natures).
  • a second step classifies the components within their nature cluster; this is the component classification itself.
  • Identifying a nature of a component is a classification problem with a predefined number of classes. Therefore, a K-means algorithm is preferably chosen.
  • a K- means method is a clustering technique that partitions n observations into K clusters, where K is a predefined value. It classifies the components according to their nature; there will be as much classes as defined component natures.
  • Three examples of different natures are resistors, motors and electronic devices. Not all features are relevant when looking only at a component nature.
  • THD total harmonic distortion
  • the second classification step is far different because the number of clusters to identify is generally large and unknown; no prior information is generally available about a number of appliances contained in a house, so no prior information is available concerning the identification of components within their nature.
  • a DBScan (Density Based Spatial Clustering for Applications with Noise) classification algorithm is preferably used. Looking at the steady states, the components will differ for instance by a magnitude of their consumption from others within a component nature cluster. As far as the transient state features are concerned, two nearly identical but different components could have different transient shapes.
  • the number of clusters does not have to be specified up front when using the DBSCAN algorithm, the number of clusters which is found by the DBSCAN algorithm has been observed not always to correspond with the actual number of electrical components in the system.
  • the above methodology in particular regarding the second step above, may work in a controlled set-up, it has been found that problems (that are sometimes important) remain in real-life application of the above clustering and component identification methods. More in particular, the fact that the number of clusters to identify is unknown for any household, company, industry or other environment, poses severe problems for present state of the art clustering techniques within the field of NIALM.
  • WO2009/103998 discloses a method of inference of appliance usage from a point measurement on a supply line, said supply line being common to multiple appliances and/or components of appliances comprising the steps of:
  • This document does not seem to disclose how to separate clusters of events in an automated method, independent of the system is being monitored.
  • This document discloses the possibility of using a look-up table in which ranges of clusters' properties are provided that correspond to particular appliances and appliance components. Obviously, such a look-up table technique will depend highly on the quality and continuous updating of the look-up table, and will allow identification of a limited number of electrical components only.
  • this document discloses a step- wise cluster identification on the basis of the distance of events, whereby events within a pre-set distance are assumed to belong to the same cluster, after which the events belonging to the cluster with the greatest number of events are removed from the data, and the next cluster is identified.
  • Prior art techniques for identifying electrical components within a system from a measured electrical power consumption signal have been noticed to require extra input with respect to the appliances comprised in the system, either by simply providing the method with characteristic signals of each single appliance up front, or by providing an extensive look-up table of characteristic signals of appliances.
  • Such techniques could be deemed impractical for systems in which the number or type of appliances changes over time, such as a common household or a company, and could be deemed unworkable as it requires constant updating of a look-up table with characteristic signals from all possible appliances.
  • Prior art techniques for identifying electrical components within a system from a measured electrical power consumption signal have been seen to be not completely reliable with respect to the grouping of events extracted from the measured signal into clusters. Furthermore, prior art techniques do not seem to allow fully computing or estimating the uncertainty or error which arises from the clustering of the events.
  • the present invention provides a NIALM method which allows improved component detection, in particular in the case no prior information on the components or appliances in the system is known. In such a case, the number of components (N) is a priori unknown and an automated determination of this number of components is required. Hereby, each correctly detected event can be associated to one of the N components.
  • the present invention hereto provides an improved clustering technique for events, which in cooperation with the other steps of the method, allows better identifying electrical components.
  • the ideal case for the evaluation of the state sequence is as follows: first, events correspond to on/off switching of components and there are no fictitious events and, second, for a given component, signatures of both on and off transitions belong to one single cluster. Further, no events related to other components share that cluster.
  • components are directly represented by clusters, their operations correspond to time intervals between on and off events and the evaluation of their state sequence simply consists to pair successive on and off events that are within identical clusters. The aggregate consumption is then modeled by combining the individual sequences.
  • one cluster corresponds to one electric component and inversely.
  • several clusters might correspond to a single component, e.g. if turn-on and turn-off features differ, and several components might correspond to a single cluster, e.g. a household or a company which own many similar electrical components. Therefore, prior art methods using clustering do not automatically lead to component detection.
  • the present invention provides a NIALM method which allows an improved component detection, in particular in case a system comprises one or more components having different turn-on and turn-off features and thus giving rise to separate clusters, or in case of a system comprising many similar electrical components which gives rise to events which are attributed to one cluster, or in any of the above four cases SI to S4.
  • the present invention hereto provides an improved component detection technique, which in cooperation with the other steps of the NIALM method, allows to better identify electrical components on the basis of clusters of events.
  • the present invention provides a non-intrusive appliance load monitoring (NIALM) method for monitoring (preferably electrical) components of (preferably electrical) appliances in a system.
  • NIALM non-intrusive appliance load monitoring
  • the NIALM method comprises the steps of: detecting events in an electrical signal (that is preferably a measured electrical signal), said electrical signal comprising power consumption information of said system, said events representing state transitions of the appliances in the system;
  • clustering events into a set of clusters on the basis of their event signatures; - identifying components on the basis of said set of clusters;
  • said clustering comprises an initial clustering of said events into an initial set of clusters on a basis of a first clustering criterion, and a subsequent reclustering of at least one of said initial clusters on the basis of a second clustering criterion different from the first clustering criterion.
  • said first clustering criterion is computed by taking into account event signatures from subtantially all events detected in said electrical signal
  • said second clustering criterion is computed by taking into account event signatures of essentially only events in said one initial cluster which is to be reel uste red.
  • said electrical signal is a measured electrical signal.
  • the NIALM method can be used to identify (preferably electrical) components in a system on a basis of a (preferably measured) electrical signal comprising power consumption information of said system.
  • the identification of the (preferably electrical) components allows a further identification of (preferably electrical) appliances, which is particularly useful in systems where the number and/or nature of appliances is not known a priori, or can change in time. Therefore, in a preferred embodiment, the method comprises a step of identifying appliances from the components on the basis of the identified (preferably electrical) components.
  • the NIALM method can also be used for condition monitoring of an appliance. Typically, but not in a limitative way, condition monitoring of an appliance can be performed to check if or when the appliance is functioning normally or abnormally, and/or to give an alarm if a malfunction is observed.
  • the appliance which is being monitored using the NIALM method of the present invention can be identified using a method as described in this document, or by another method.
  • the method comprises providing energy consumption of appliances or of components.
  • Energy consumption of appliances or of components can be given in a form of a report to a user of an appliance or of the system, or to the electricity provider to detect malfunctions or to optimize energy efficiency either for a single system or for a number of systems.
  • the present invention further provides for a recursive clustering method for clustering events obtained from a (preferably measured) electrical signal comprising power consumption information of a system, said events being at least partially characterized by an event signature, said recursive clustering method comprising : an initial step of clustering events of an initial event set into a set of clusters using a clustering criterion for deciding whether two events belong to the same cluster and/or whether an event belongs to an existing cluster, said clustering criterion being computed on a basis of event signatures from substantially all events from said initial event set; and
  • the present invention also provides for a method for clustering events detected in a (preferably measured) electrical signal comprising electrical power consumption information of a system, each of said events being at least partially characterized by an event signature comprising a set of event features, the method comprising the steps of:
  • step (e) optionally, if two or more sub-clusters are obtained after step (d)(iii), performing step (d) taking at least one of said sub-clusters from step (d)(iii) as a cluster to be sub-clustered.
  • step (d) is preferably performed recursively for each cluster of said set of clusters and/or for all sub-clusters obtained by performing step (d) until no further sub-clustering in two or more sub-clusters is obtained.
  • the events relate to on-off switching of components of appliances in the system .
  • Events can be classified by their signatures, which refer to a set of properties or features which characterize transitions in the measured signal. These properties or features can be computed from a current waveform extracted from the measured signal, and preferably from a delta waveform extracted from the measured signal. More preferably, features are computed on the basis of a frequency spectrum of the delta waveform, for example features could relate to harmonic magnitudes or phases.
  • Preferred features which are computed to characterize events are : active power (P), reactive power (Q), fundamental frequency (Hi), odd harmonics preferably of order 3 to 13 and preferably normalized to the amplitude of the fundamental frequency com ponent of the current, continuous component and/or second order harmonics which a re preferably normalized to the amplitude of the fundamental frequency com ponent of the current, total harmonic distortion (THD), ratio
  • a signature of an event comprises a time series of a quantity derivable from the electrica l signa l, preferably a quantity being the active power and/or the current.
  • said time series of sa id quantity comprises a set of va lues for sa id number ordered chronologica lly.
  • a decision criterion When clustering events, a decision criterion, or clustering criterion, needs to be applied to decide whether or not an event belongs to a cluster, or two events belong to the same cluster.
  • a criterion preferably com prises computing an inter-event distance to compare events, said inter-event distance being compared to one or more delimiting va lues, which are parameters of the clustering a lgorithm .
  • the clustering criterion is updated su bstantia lly only on the basis of the events which have been identified previously as being part of the pa rent cluster, i.e. events which could have been part of the initial event set, but were not identified as being part of the parent cluster, are not taken into account when updating the clustering criterion for reclustering or subclustering the parent cluster.
  • events are compared by computing an inter-event distance.
  • This distance could preferably, and in particular for comparing events characterized by steady-state features, be a Euclidean distance or a Mahalanobis distance, or any combination thereof.
  • This distance could preferably, and in particular for comparing events characterized by transient-state features, be a Minkowski distance, a Dynamic Time Warping (DTW) distance, a Longest Common Subsequence (LCSS) distance, or any combination thereof.
  • distances which are at least partly in a one-to-one correspondence with the above-mentioned distances or any combination thereof.
  • events are characterized by steady-state features or transient-state events.
  • the clustering criterion is computed on the basis of the principal components of events
  • events which are characterized by steady- state features are clustered as these events have been noticed to comprise principal components which can be identified unambiguously in a principal component analysis, e.g. by means of looking at those linear functions of the event features which maximize the variance, e.g. as further explained in I. T. Jolliffe, "Principal Component Analysis", Vol. 30, Springer Series in Statistics, Springer, 2 nd ed., 2002.
  • said clustering and/or reclustering and/or subclustering is performed using a density-based algorithm, such as a DBSCAN algorithm, an OPTICS algorithm and/or a DBCLASD algorithm, said algorithm based on a density defined by the amount of events which can be found in an e-neighborhood of a point of the cluster, said e-neighborhood of a point p defined as
  • N £ (p) ⁇ q e X ⁇ d(p, q) ⁇ e ⁇ , where X is the signature space, preferably a feature space, d() represents a distance function between points and/or events, and e is an inter-event distance delimiting the neighborhood, whereby two kinds of points are defined : core points which have a density ⁇ N e (p) ⁇ MinPts, MinPts being a number higher than 1, and preferably lower than 20, and border points which belong to an e-neighborhood of a core point without being a core point itself, whereby two points are defined as density-reachable if there exists a sequence of points such that each one belongs to the an e-neighborhood of its predecessor, the latter being a core point, whereby two points are defined as density- connected if they are density-reachable from a common point, whereby said clustering criterion comprises evaluating if an event is density-connected to a point and/or an event in a cluster.
  • said clustering criterion is computed and/or recomputed by computing and/or recomputing e and/or MinPts, preferably e.
  • said events are characterized by steady-state features and/or transient- state features.
  • prior art techniques seem to rely on external input, e.g. maximal inter-event distance or minimal number of events, in order to determine which event belongs to which cluster or which cluster can be deemed complete. Although such prior art techniques allow identification of relatively simple components at a reasonable level, these techniques seem to fail when applied to more intricate systems, such as households or companies, which comprise electrical appliances with rather complicated electrical components.
  • the present invention also provides a component detection method comprising the steps of: obtaining clusters of events obtained from a (preferably measured) electrical signal comprising power consumption information of a system, said events comprising on-events and off-events, whereby each of said events comprises a cluster ID which allows to identify the cluster to which the event belongs, preferably whereby said clusters of events are obtained using a method according to the present invention;
  • each paired event comprising paired cluster ID information representing the cluster ID of the on-event and the cluster ID of the off-event in said paired event;
  • said step of pairing on-events and off-events comprises extremizing a cost function which depends on variations in energy, in power, in current, in energy fit errors, in power fit errors, in current fit errors or in any combination thereof, preferably in a linear combination of at least two of said variations. More preferably, this cost function depends on variations in power and/or in power fit errors.
  • the present invention further concerns the use of any of the clustering methods and/or the component detection method in a non-intrusive appliance load monitoring method and/or for condition monitoring of an appliance.
  • the component detection method of the present invention is particularly suited for a system wherein electrical appliances are on/off components, or consist of such on/off components, and, their consumption can be modeled by constant power segments during steady states.
  • any on-event should be followed by an off- event with similar power step (with opposite sign).
  • "Explained power steps” are the ones for which complementary power steps can be identified following the idea that: if a component has been turned on at time t on , it should be turned off at a time t off > t on . Further, constant power draw is assumed and we search for the pair (t on , t off ) minimizing fit-errors.
  • the present invention also concerns a processing unit arranged for performing a NIALM method, any clustering method and/or a component dtetction method according to the present invention.
  • processing unit is arranged for performing a recursive clustering method or a method for clustering according to the present invention, and a component detection method according to the present invention.
  • the present invention further concerns a device, preferably a computer-mountable and/or meter-mountable device, comprising instructions for executing a NIALM method, any clustering method and/or a component detection method according to the present invention.
  • 'meter-mountable' device refers to a device which can be linked to an electrical meter, such as an electrical meter which can be found in a residence, a business or any system comprising an electrically powered device, and which measures the consumption, and/or optionally the generation, of electrical energy.
  • said processing unit is arranged for performing a recursive clustering method or a method for clustering according to the present invention, and a component detection method according to the present invention.
  • said device is a separate device, i.e. which can be releasably linked to a computer or a meter or a system comprising electrical appliances.
  • said device is integrated in a meter, such as a smart meter, or in a circuit breaker of said system.
  • the present invention further concerns a NIALM system comprising a client device and a server device, whereby said client device and server device are linkable, and optionally are linked, and whereby said client device and server device are configured to together execute a NIALM method according to the present invention, a recursive clustering method according to the present invention, a method according to the present invention, and/or a component detection method according to the present invention.
  • This system allows performing the separate steps of the methods according to the present invention on the client device or on the server device or on both. In particular certain steps of the methods of the present invention can be performed on the client device while the remaining steps of the methods can be performed on the server device, i.e.
  • the client device can be configured to perform a first subset of the steps of the methods of the present invention, while the server device can be configure to perform a second subset of the remaining steps of the methods of the present invention.
  • said client device is configured to obtain a measured electrical signal comprising power consumption information of a system.
  • the client device is configured to perform the following steps: detecting events in a (preferably measured) electrical signal comprising power consumption information of said system, said events representing state transitions of the appliances in the system;
  • the server device is configured to perform the following steps: obtaining said information representing said detected events from said client device;
  • clustering events into a set of clusters on the basis of their event signatures; - identifying components on a basis of said set of clusters;
  • the client device is configured to perform the following steps: detecting events in a (preferably measured) electrical signal comprising power consumption information of said system, said events representing state transitions of the appliances in the system;
  • the server device is configured to perform the following steps: obtaining said information representing said event signatures from said client device;
  • clustering events into a set of clusters on the basis of their signatures
  • the client device is configured to perform the following steps: detecting events in a measured electrical signal comprising power consumption information of said system, said events representing state transitions of the appliances in the system;
  • clustering events into a set of clusters on the basis of their signatures
  • the server device is configured to perform the following steps: obtaining said information representing said set of clusters from said client device;
  • said clustering comprises an initial clustering of said events into an initial set of clusters on a basis of a first clustering criterion, and a subsequent reclustering of at least one of said initial clusters on a basis of a second clustering criterion different from the first clustering criterion.
  • a NIALM system with a client device and a server device provides a number of advantages, including, but not limited to: the possibility to optimize and update the algorithms in an easy way, in particular for the algorithms of the server device, and in particular the clustering algorithm,
  • the present invention also concerns a database comprising information representing identified components in a system, preferably of a multitude of systems, said information obtained using a NIALM method according to a method disclosed in this document.
  • the database also comprises information representing identified appliances and/or information representing energy consumption of the appliances and/or components in said system or said multitude of systems.
  • Figure 1 shows three methods to evaluate similarities between time series: the Minkowski distance, the dynamic time warping (DTW) and the least common subsequence (LCSS).
  • Minkowski distance the Minkowski distance
  • DTW dynamic time warping
  • LCSS least common subsequence
  • Figure 2 shows how the amount of outliers mainly decreases with k, the amount of nearest neighbors in the k-dist criterion.
  • Figure 3 shows that when increasing k, the decrease of outlier points is mainly located in the origin of the P-Q plane but small clusters are then considered as outliers.
  • Figure 4 illustrates substructures which are better emphasized if principal components are evaluated for specific data subsets.
  • Figure 5 shows a modeled aggregate power based on paired events. The evaluation is independent from the classification results.
  • Figure 6 shows that, once pairs have been assigned to components, the considered power steps derive from the clusters.
  • Figure 7 shows sequences of on-off cycles which are generated for nine appliances such that their aggregate consumption is not cyclic over the three-hour period.
  • Figure 8 shows nine appliances which draw specific current waveforms.
  • Figure 9 shows that the microwave is not a two state-device. Moreover, it exhibits cycling operations depending on the average power asked by the user.
  • Figure 10 shows events and their features - events corresponding to: the kettle, the vacuum cleaner and the microwave oven.
  • Figure 11 shows events and their features, zooming in at the origin of the P-Q plane.
  • Figure 12 shows that some turn-on transients systematically lead to several detections.
  • Figure 13 shows power variations of the ventilator which are significant compared to the power it consumes. Outliers risk to be associated with clusters of such a variable and low power device.
  • Figure 14 shows that clusters which are obtained after a first clustering iteration do not correctly reveal the complete data structure.
  • Figure 15 shows that an iterative clustering procedure according to the present invention efficiently captures the data substructure.
  • Figure 16 shows a classification according to the highest membership value based on the Mahalanobis distance.
  • Figure 17 shows that there is almost no confusion between clusters of transient patterns. However, no clusters are discovered for the kettle, the ventilator and the vacuum cleaner.
  • Figure 18 shows that component ID are assigned to recurrent pairs of nonoutliers. As a result, equivalent component ID are assigned to on and off event clusters.
  • Figure 19 shows first, the modeled aggregate power is evaluated with all paired events; and second, electrical components are discovered from recurrent pairs of nonoutliers. Finally, state sequences are evaluated based on classified event pairs.
  • Figure 20 shows paired events are assigned to components according to their cluster identifiers.
  • Figure 21 shows state sequences evaluated from the event pairs assigned to pure component. Results from the state sequence evaluation are given in the top plots and the measurement in the bottom ones.
  • the drawings of the figures are neither drawn to scale nor proportioned. Generally, identical components are denoted by the same reference numerals in the figures.
  • NIALM techniques are either pattern recognition-based or optimization-based approaches, also referred to as event-based or non event-based.
  • samples correspond to state changes of appliances, referred to as 'events'.
  • event-based NIALM signatures are used to associate events to appliances. State sequences of appliances then result from classification algorithms.
  • supervised approaches changes of appliance states are matched one- by-one to known signatures. Where learning is unsupervised, the recurrence of signatures is exploited to recognize appliances.
  • the present invention concerns an unsupervised, event-based NIALM method according to claim 1.
  • the present invention also concerns methods which optimize the NIALM method, including a recursive clustering method for recursively clustering similar events according to claim 5, a method for clustering similar events taking into account principal components of the event's signature according to claim 9, a component detection method according to claim 13, as well as the use of any of these methods or their combination in a NIALM method or for condition monitoring of an appliance.
  • the present invention also concerns a processing unit arranged for executing any of these methods and a device comprising instructions for carrying out these methods, as well as a NIALM system comprising a client device and a server device as specified in claims 19 and 20 and a database according to claim 21.
  • a compartment refers to one or more than one compartment.
  • the value to which the modifier "about” refers is itself also specifically disclosed.
  • the DBSCAN algorithm refers to density-based spatial clustering of applications with noise.
  • a distance parameter i.e. an inter-event distance parameter
  • a zone is considered as dense if a sufficiently large number of points, i.e. events, is found within the given neighborhood.
  • DBSCAN hereby requires only two parameters and no range of values must be specified for the number of clusters to investigate. Instead, the number of clusters is an output of the algorithm.
  • M. Ester, H.-P. Kriegel, J. Sander and X. Xu "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", Knowledge Discovery and Data Mining 96: 226-231, 1996 for more detailed information about this algorithm.
  • the OPTICS algorithm refers to an algorithm for ordering points to identify the clustering structure.
  • the DBCLASD algorithm refers to a distribution-based clustering algorithm for mining in large spatial databases.
  • Such a criterion preferably comprises computing an inter-event distance to compare events. Therefore, in an embodiment, events are compared by computing an inter-event distance.
  • This distance could preferably, and in particular for comparing events characterized by steady-state features, be a Euclidean distance or a Mahalanobis distance, or any combination thereof.
  • This distance could preferably, and in particular for comparing events characterized by transient-state features, be a Minkowski distance, a Dynamic Time Warping (DTW) distance, a Longest Common Subsequence (LCSS) distance, or any combination thereof.
  • distances which are at least partly in a one-to-one correspondence with the above-mentioned distances or any combination thereof.
  • Euclidean distance As used herein, the term “Euclidean distance”, “Mahalanobis distance”, “Minkowski distance”, “Dynamic Time Warping (DTW) distance” and “Longest Common Subsequence (LCSS) distance” refer to specific types of distances between events, which are discussed in what follows.
  • X and Y refer to event signatures characterized by a set of N features X, and y, resp.
  • these features are normalized, e.g. with respect to the minimum and maximum values of the features from the events which are taken into account to define or compute the clustering criterion, according to wherein x, refers to the i'th feature in the signature, and x i;n refers to the normalized feature value.
  • Mahalanobis distance e.g. with respect to the minimum and maximum values of the features from the events which are taken into account to define or compute the clustering criterion, according to wherein x, refers to the i'th feature in the signature, and x i;n refers to the normalized feature value.
  • X c (n c x p) be a set of n c p-feature signatures grouped within a cluster.
  • the variance-covariance matrix of cluster X c is
  • x be a signature whose distance w.r.t. X c is to be evaluated and X c the vector of mean feature values of X c .
  • the Mahalanobis distance is defined as follows:
  • the Mahalanobis distance better deals with the dispersion of clusters along different directions than e.g. the Euclidean. Therefore, the Mahalanobis distance is better suited to identify the cluster to which outlier events belong.
  • Outliers are isolated points in the feature space. They do not systematically exhibit extreme feature values. Instead, the distance to their nearest neighbors is high compared to the one of non outlier points. Consequently, the distance of a point to its k nearest neighbors is an image of its likelihood to be an outlier. We refer to such a distance as k-dist. Outliers are then points with the highest k-dist values.
  • a threshold on k-dist values could be defined to decide whether or not points are outliers.
  • a maximum nonoutlier (MNO) value of the k-dist distribution could be used : it is defined as the highest observed value below the upper quartile increased by 1.5 times the interquartile range.
  • MNO maximum nonoutlier
  • k can preferably take any value between 1 and 20, preferably between 4 and 15, more preferably 10.
  • Dynamic time warping (DTW) distance and least common subsequence (LCSS) distance The principle of the dynamic time warping (DTW) and the least common subsequence (LCSS) are illustrated in figure 1, which shows three methods to evaluate distances, in particular distances between time series: the Minkowski distance, the dynamic time warping (DTW) and the least common subsequence (LCSS).
  • DTW allows to deal with sequences with different lengths and different localization in the window.
  • LCSS further allows to skip samples considered as outliers in the pattern.
  • the globally optimal alignment obtained with DTW is a suitable way to measure the similarity.
  • the DTW is chosen over the LCSS method.
  • the DTW has the advantage that the entire sequences must be matched which avoids that spurious transient states be matched to subparts of true transients. In other words, DTW performs matching with time shift analysis robust to positive false detections.
  • the average distortion is used instead of the total distortion in order to normalize for different segment lengths.
  • a distance metric d(x,, y,) between two elements x, and y (e.g. Euclidean distance)
  • the total cost of a warping path p is i
  • the optimal warping path p* is the one minimizing the total cost among all possible paths.
  • the distance DTW(X, Y) is defined as the total cost associated to the optimal path p* :
  • the average DTW distance is DTW(X,Y)/L where L is the length of the optimal path p*.
  • Example 1 Feature extraction on the basis of principa l components
  • Electric signatures have been evaluated because they characterize the electric consumption of components, not because they allow to classify components.
  • Features might be correlated a nd less features could then be used to represent the data structure.
  • some features might be better than others to reveal similarities and differences between signatures. Considering non relevant features would add similarities between events.
  • PCA principal component analysis
  • PCA principal component analysis
  • the first step is to look for a linear function ⁇ ⁇ x of the elements of x having maximum variance, where Oi is a vector of p constants an, a 12 , lp , and ' denotes transpose, so that
  • the procedure is repeated so that at the k'th stage a linear function a x is found that has maximum variance subject to being uncorrelated with ⁇ ⁇ , ⁇ ' 2 ⁇ , ..., a' k -ix.
  • the k'th derived varia ble, a' k x is the k'th PC.
  • Detailed equations for the principal component analysis can be found in the work by Jolliffe.
  • PC principa l components
  • Outliers might impact the result of the principal component analysis. They are mainly related to errors from the underlying data generation, e.g . they can be related to errors insignal segmentation leading to outliers.
  • the errors can be due to noise or non-linearities, or due to transients which can be very long and hence could be seen as two or more different events. They are also rare events resulting from simultaneous on/off switching of components. They should be removed from the data set before searching for the principa l components.
  • Outliers a re isolated points in the feature space. They do not systematica lly exhibit extreme feature values. Instead, the distance to their nearest neighbors is high com pared to the one of non outlier points.
  • Each feature x i;n ranges between 0 and 1 and the k-dist of events is the Euclidean distance between their normalized feature vectors and the one of their kth nearest neighbor.
  • a threshold on k-dist values must be defined to decide whether or not points are outliers.
  • FIG. 2 illustrates the relative evolution of the number of outliers according to the value of k for five different data subsets. We see that increasing the number of neighbors considered in the k-dist decreases the number of outliers. Variations are mainly located in the origin of the P - Q plane which corresponds to false positive detections and small loads whose signature evaluation is impacted by fluctuations of the mixture. However, increasing k also leads to considering points of small clusters as outliers. These two aspects are illustrated in figure 3.
  • Non outlier PCA Detecting loads consuming low power compared to the aggregate consumption is the biggest challenge of NIALM. Consequently, it is desired that the false positive detections be correctly identified as outliers. This will facilitate the detection of points which cluster around the origin of the P - Q plane and are related to signatures of small loads. Taking this reasoning into account and the choice of DBSCAN parameters presented below, k is set to 10. 3. Non outlier PCA
  • PCA selects features in order to maximize the variance of the data, variables with higher amplitudes will be given higher weight and will be systematically considered in the first PCs. For instance, the active power ranges from some dozens of watts up to some kilowatts whereas normalized harmonic amplitudes range between 0 and 1. Data should then be reduced before the PCA be applied in order to give the same weight to the different features.
  • step c normalize with the transformation of step a
  • PCA has been applied to the normalized dataset whose normalized active and reactive power are shown in the upper plot of figure 4.
  • the two first PC are plotted in the leftmost below plot.
  • the PCA is also applied to the same data set limited to events with active power first higher and then lower than 500W (respectively in the middle and right-most plots).
  • the weights assigned to the features in the first PC are given in Table 1 and the percentage of total variance of the three first PCs in Table 2.
  • Table 1 Weights of the features in the PCI for the data in figure 4
  • the first PC is the combination of most features. But for both data subsets, only some features have relatively high weights: the PCI of the > 500W' subset involves the fundamental components whereas the one of the ⁇ 500W subset is mainly limited the harmonic content and the reactive power. This could have been expected.
  • PC2 is needed in the case of the entire data set to satisfy the 85% criterion.
  • the three first PCs must be used.
  • Example 2 Component discovery
  • the method to discover instances of electrical components from clusters and the event time sequence can be illustrated by the following example.
  • cluster identifiers of paired events yield links between clusters which reflect electrical components.
  • Pairing events by minimizing energy errors and error variations Considering two-state devices, if a component is turned off, it has previously been turned on. Following this, off-events should be paired to on-events to represent occurrences of unknown components. Constant power loads being assumed, the aggregate power is straightforwardly obtained for any sample k as the power of all pairs with k on ⁇ k ⁇ k off . If y k is the power measured at sample k and AP k is a vector whose nonzero entries correspond to the signed power step values of paired events, the modeled aggregate power at sample k, y k , can be written as y 0 stands to account for loads that would have been switched on before the first sample of the observation window.
  • the ideal set of event pairs is the one for which the modeled aggregate power is the closest to the measured one. At each time, the power of the unknown components considered as active should sum up to the measured aggregate power. However, segmentation errors or simultaneous switch events lead to impossibility to truly fit the measured power simply by pairing events. Their impact on subsequent decisions should be minimized. This can be achieved by penalizing the bestfit solution with the total variation of the fit-error. We formalize this in the next paragraphs.
  • y k is the median value of the power samples within steady state k.
  • energy fit-errors and variations of energy fit-errors can be defined analogously for different time periods. This corresponds to modeling the observed aggregate power as constant segments.
  • median values instead of mean values allows the method to be more robust to segmentation errors: if there is a non detected power step within a steady state (i.e. there are actually two steady states), the longer one determines the constant power segment.
  • Pairing on-events and off-events can be done by extremizing a cost function which depends on variations in energy, in power, in current, in energy fit errors, in power fit errors, in current fit errors or in any combination thereof, preferably in a linear combination of at least two of said variations, more preferably variations in power and in power fit errors.
  • the cost function l ⁇ k ⁇ is evaluated with summations limited to the local window.
  • Step b allows to hide deviations from ideal fit due to previous erroneous pairing since is systematically initialized to the first sample in the local window.
  • step c the minimum cost l ⁇ k ⁇ obtained with the different candidates in the window is compared to the costs of two other cases: • ⁇ pair with event k' ( ⁇ ⁇ ) : AP k is kept equal to zero, considering that event k is a fictitious transition
  • the next step is conditioned by the case that minimized the cost function :
  • the event is not paired. It probably results from a false positive detection or its partner has not been detected.
  • the local window size is enlarged and new candidates are investigated in the new time interval.
  • a component can be defined as a recurrent link between clusters required to appropriately model the aggregate power.
  • Example 3 Results of laboratory testing
  • Algorithms were developed considering both laboratory and field data. However, to evaluate performances at the different stages of the NIALM procedure, a reliable ground truth is required. The latter is only available with laboratory data. Algorithms have been developed with the aim of providing parameter settings which are not tuned to specific data. Consequently, a new data set was generated in the laboratory (ELDA) specifically for this performance evaluation.
  • This appliance has more than two states: it consists of a light bulb, a motor and a magnetron. Furthermore, it can be noticed that, according to the average power asked by the user, the magnetron is turned on and off during its operations. Consequently events were manually added to the ground truth to account for these state changes.
  • the rate of correct detections and the amount of missed detections can be evaluated for each individual load.
  • the figures are reported in Table 3. We observe a global rate of 91%.
  • the detected events are plotted in the ⁇ -AQ plan in figure 10 and figure 11.
  • Each couple color-symbol represents one appliance, as indicated by the ground truth.
  • Turn-on events with slow time decay lead to several detections. In the studied data set, this impacts the segmentation of turn-on transients corresponding to the mixer and the vacuum cleaner, as shown in figure 12. This leads to different steady state feature values for turn-on and turn-off events. This can be seen with the green point cluster in figure 10 for the vacuum cleaner and with the red point cluster in figure 11 for the mixer. The events corresponding to both these loads form substructures. Furthermore, fictitious second events limit the search for the knee in the power trace which prevent from properly capturing the transient dynamics.
  • Second events of microwave turn-on transients are also divided in two clusters.
  • Each column corresponds to one cluster: the first row gives the cluster identifier (ci D ) corresponding to figure 15 and events are distributed in rows according to the ground truth. Outliers are reported in the last column along with nondetected events.
  • Rows correspond to appliances as defined by the ground truth. The last row corresponds to events for which no identifier has been assigned in the ground truth. They are mostly false positive detections, but second events of microwave turn-on transients also lie in this category.
  • microwave oven events assigned to clusters 2 and 14 correspond to the light bulb belonging to this appliance.
  • Ta ble 6 All a ppliances have a pure or quasi pure group of event pairs that can be used to learn component signatures.
  • Table 7 Energy assigned to individual components (in Wh). The link between the components and the devices is manually defined. T (True) stands for energy consumed by the appliance and properly assigned to a component state sequence. F (False) stands for energy assigned to a state sequence whereas the corresponding appliance exhibits no consumption. 9 4 5 1 12 2 6 10 7 8 3 11 /
  • Table 8 Paired events are assigned to components according to their cluster identifiers.

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Abstract

La présente invention concerne un procédé NIALM utilisé pour contrôler les appareils électriques dans un système. Le procédé consiste à : détecter des événements dans un signal électrique mesuré comprenant des informations de consommation électrique dudit système, lesdits événements représentant des transitions d'état des appareils dans le système ; caractériser lesdits événements par une signature d'événements en tenant compte de différences entre des états en régime établi avant et après l'événement et/ou d'états temporaires se situant entre des états en régime établi avant et après l'événement ; grouper des événements dans un ensemble de groupes d'après leurs signatures ; identifier les composants d'après ledit ensemble de groupes ; identifier des appareils à partir des éléments identifiés à l'étape précédente ; et fournir éventuellement la consommation d'énergie des appareils. L'invention est caractérisée en ce que ledit groupement comprend un groupement initial desdits événements en un ensemble initial de groupes d'après un premier critère de groupement, et un regroupement consécutif d'au moins un desdits groupes initiaux d'après un second critère de groupement différent du premier critère de groupement.
PCT/EP2014/072843 2013-10-24 2014-10-24 Procédé et dispositif de contrôle amélioré non intrusif de charge d'appareil WO2015059272A1 (fr)

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