EP0826259A1 - Procede et systeme permettant de determiner les etats de fonctionnement d'un reseau d'alimentation en energie electrique - Google Patents

Procede et systeme permettant de determiner les etats de fonctionnement d'un reseau d'alimentation en energie electrique

Info

Publication number
EP0826259A1
EP0826259A1 EP96914197A EP96914197A EP0826259A1 EP 0826259 A1 EP0826259 A1 EP 0826259A1 EP 96914197 A EP96914197 A EP 96914197A EP 96914197 A EP96914197 A EP 96914197A EP 0826259 A1 EP0826259 A1 EP 0826259A1
Authority
EP
European Patent Office
Prior art keywords
neuron
network
neurons
synaptic
weight
Prior art date
Legal status (The legal status 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 status listed.)
Withdrawn
Application number
EP96914197A
Other languages
German (de)
English (en)
Inventor
Gerhard DÖDING
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
AEG Atlas Schutz und Leittechnik GmbH
Original Assignee
AEG Atlas Schutz und Leittechnik 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 AEG Atlas Schutz und Leittechnik GmbH filed Critical AEG Atlas Schutz und Leittechnik GmbH
Publication of EP0826259A1 publication Critical patent/EP0826259A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology

Definitions

  • the invention relates to an arrangement for determining the operating states of an electrical energy network with which electrical energy 10 is transported, distributed and made available to consumers, and to a method for exercising with the arrangement.
  • the operating states of an electrical power supply network are u. a. found in switching operations requirements to before executing the
  • the current operating state of the switchgear is formed in each case from the current position signal signals of the switchgear and a basic set of topological elements with which all possible operating states of switchgear assemblies can be specified.
  • the current operating state is saved by means of stored switching error protection interlocking rules
  • the release or blocking of the switching action request command is determined from the dominant states of the areas by comparison with switching error protection locking rules (DE 38 12 072 C3).
  • the calculation time for the electrical potential distribution of the networks depends on the network size and the degree of meshing of the respective network.
  • a procedural algorithmic method for calculating the Potential distribution used. Starting from formative network elements such as generators and feeders, viable routes through the network were searched along closed switches. The objects that can be reached from a formative network element along the walkable path then show the same thing
  • An arrangement for determining the operating states of an electrical energy network is known, with which energy is transported, distributed and made available to consumers 20, in which a neural network is used.
  • State variables of the energy network are entered into a state memory to which the neural network is connected, which carries out stability calculations for the energy network at high speed (JP 07-107 667 A. IN: Patent Abstract of Japan, CD-ROM).
  • Hopfield network recurrent neural network
  • the invention is based on the problem of developing an arrangement and a method for carrying out the arrangement for determining the operating states of an electrical energy network, with which the potential distribution can be quickly obtained even with large energy networks.
  • the problem is solved for the arrangement according to the invention in that a number of neural networks with the same structure, which depend on the number of defining electrical properties of the nodes of a network, is provided, which each have neurons for the network nodes, each corresponding to the Connections between the network nodes are synaptically connected to one another in such a way that, in the case of connections running over two-pole systems, the synaptic weights are set to zero or one in accordance with the respective position of the two-pole system from the outside in the network, so that the scalar products of the weight vector and input vector of the neurons each have a threshold value of one are compared, that depending on the result greater or equal or less than one the neurons each generate a one or a zero and that the weights of the outputs of the neurons corresponding to one another in the individual neural networks are superimposed as a function of the priority of the defining properties become.
  • each network node is replaced by a binary neuron.
  • the paths between the nodes are set up in accordance with the network topology as synaptic connections that are bidirectional.
  • the connections between the network nodes often run
  • the bipoles are simulated by synaptic weights, where an open two-pole corresponds to weight zero and a closed two-pole corresponds to weight one.
  • the energy network is therefore a recurrent neural network with a symmetrical weight matrix
  • Formative network nodes can be simulated by formative neurons that output the value one.
  • weight vectors of one are provided in the neural networks instead of characteristic neurons and weight vectors of zero instead of non-characteristic neurons.
  • the neural networks each have 35 additional neurons instead of edge neurons, which are coupled via a synaptic weight corresponding to the coupling of the edge neuron to the neuron adjacent to the edge neuron in the neural network, which is coupled via a synaptic weight corresponding to the coupling of the edge neuron a downstream one Neuron is coupled forward, with which the state of the edge neuron is output.
  • the measures described above each create a neural network with less recurrence.
  • the edge neuron is to be understood here as a neuron that has only a bidirectional connection to a neighboring neuron.
  • an additional neuron is attached to each of the neighboring ones within the network structure via a weight corresponding to the synaptic coupling of the transfer neuron
  • Non-synaptically coupled neurons of the neural network are coupled forward, the neighboring neurons being connected to one another by a bidirectional synaptic coupling in accordance with a weight calculated by an auxiliary neuron, which is determined by the synaptic weights of the transfer neuron, and the neighboring neurons being connected one of the synaptic
  • Transfer neuron is to be understood here as a neuron that has two bidirectional, synaptic connections to neighboring neurons. The recurrent network therefore no longer contains the transfer neurons or the transfer neurons. It is important to
  • the replacement of some of the transfemeurons means a substantial reduction in the time required to determine the network status.
  • the overall network is expanded by two additional neurons, the subnetworks upstream and downstream of the respective rest of the recurrent network being linear.
  • an additional neuron is preferably coupled forwards via a weight corresponding to the synaptic coupling of the transfer neuron to one of the neighboring neurons, which are bidirectionally coupled to one another by a synaptic weight calculated by an auxiliary neuron,
  • the auxiliary neuron which is calculated by the auxiliary neuron as a function of the weight of the synaptic neurons and the weight between the two neighboring neurons, the neighboring neurons each being coupled to a downstream neuron via synaptic weights which correspond to the synaptic weights of the transfer neuron, the indicates the state of the transfer neuron.
  • the bidirectional couplings of one or more transfer neurons can be replaced by the partial networks and auxiliary networks, which are linear, which results in a reduction in the time required for determining the state of the networks. If there are neurons in the respective neural network that are connected to neighboring neurons via more than two synaptic connections, then each synaptic connection to a neighboring neuron is replaced in the manner described in connection with 5 transfer neurons. Neurons that are connected to neighboring neurons by means of more than two connections are referred to as distribution neurons.
  • the result is a feedforward linear network that is structured in three subnetworks, namely an upstream network, an auxiliary weight network that calculates additional synaptic weights from known weights of the recurrent network, and from a downstream network on whose neurons the result of the energy network is available.
  • the transformed neural network is completely determined from the topology of the electrical network.
  • the weights are specified or result from the two-pole positions, which are generated by messages and determine the setting of the weights.
  • the network does not need a training phase, it shows no initialization behavior and does not work incrementally. It does not require recursiveness and determines the propagation of a 20th defining property in an optimally short time. The result is exact at all times.
  • Improvements in the calculation of the potential distribution of the energy network can also be achieved with an incompletely transformed neural network. Such improvements can be achieved by e.g. B. only the 25 edge neurons or transfer neurons or distribution neurons can be replaced in whole or in part in the manner described above.
  • a method for carrying out one of the arrangements described above is that the synaptic weights are set to the binary 30th states of the bipoles and that the states of the energy network are determined from the states of the neurons with assignment of physical quantities. Depending on the type of network and the degree of transformation, the states of the energy network can only be determined on the downstream neurons.
  • the neural networks described above can be designed as hardware networks or in software. In the case of hardware networks, the result is available more quickly in the event of one or more changes in the state of the bipoles.
  • the invention is described below with reference to an embodiment shown in a drawing, from which further details, features and advantages result.
  • FIG. 1 shows a circuit diagram of an electrical power supply network
  • FIG. 2 shows the energy supply network shown in FIG. 1 by means of a node-path graph; 10.
  • Fig. 3 shows a binary, feedback neural network as a model for the
  • Energy supply network 4a, b show a diagram to illustrate the transformation of bidirectionally connected edge neurons of the network into a feedforward
  • Network structure 15 Fig. 5a, b is a diagram to illustrate the transformation of bidirectionally coupled with uncoupled neighboring neurons
  • Transfer neurons of the neural network acc. 3 shows a feedforward network structure; 6a, b show a diagram to clarify the transformation of bidirectionally 20th coupled neighboring neurons
  • Transfer neurons of the neural network acc. 3 shows a feedforward network structure
  • Fig. 9 is a schematic representation of two network nodes through one
  • FIG. 10 shows a neural network constructed from several subnetworks.
  • FIG. 1 shows an example of an electrical power supply network which has an infeed 1 in the form of a generator which is arranged in series with a transformer 2 and a circuit breaker 3.
  • a busbar 4 is connected to the circuit breaker 3, from which two branches, not designated 35, emanate which have circuit breakers 5, 6.
  • the circuit breakers 5, 6 are connected together on their ends facing away from the busbar 4 to a circuit breaker 7.
  • Circuit breakers 5, 6 each have a further circuit breaker 8, 9 arranged.
  • the circuit breakers 8, 9 are each followed by consumers 10, 11.
  • An electrical power supply network such as.
  • Bipoles are resources that can change the potential profile, e.g. B. transformers, circuit breakers, disconnectors, earth electrodes, etc.
  • Nodes are the respective edge poles of the two-pole system and the connections between the two-pole system, for. B. lines and busbars. Nodes are loaded with potential. There are formative ones
  • nodes that bring a specific property "grounded”, “loaded”, “supplied” etc. into the network obtain the properties described above in that they are connected to distinctive nodes via a two-pole circuit that is closed. The properties mentioned above are distributed over the closed bipoles to the nodes of the network and thus determine the
  • the energy supply network shown in Fig. 1 can be represented schematically in the form of a node-path graph.
  • the node K0 corresponds to the infeed 1 and is connected to the node K1 via a path WO, which corresponds to the transformer 2
  • the node K1 symbolizes the connecting line between the transformer 2 and the circuit breaker 3.
  • the route W1 corresponds to the circuit breaker 3.
  • the busbar 4 is symbolized by the node K2.
  • the two circuit breakers 5, 6 are each arranged the paths W2, W3, which end in the node K3, K4, which are the lines between each
  • circuit breakers 5, 8 or 6, 9 are connected to one another by way W4.
  • the nodes K3, K4 are each connected through the paths W5, W6, which each symbolize one of the circuit breakers 8, 9, to the nodes K5, K6, to which the consumers 10, 11 correspond.
  • One way Wx e.g. B. one of the ways WO to W6, always acts bidirectionally between the two
  • the path Wx is feasible if the corresponding dipole transfers the property of a node connected to it to the other. Otherwise it is not feasible.
  • the 10th binary network is formed in the following manner, which was explained in connection with FIG. 2.
  • Each of the nodes K0 to K6 is replaced by a binary neuron NO to N6.
  • Each path between two nodes is established as a synaptic connection.
  • the paths G0 to G6 correspond to the paths WO to W6.
  • Formative nodes are simulated by formative neurons 1.
  • FIG. 3 A corresponding neural network is shown in FIG. 3, the circles symbolizing neurons, the rectangles, synaptic weights and the oval surfaces constant inputs.
  • Each path between two nodes is set up as a synaptic connection, which is labeled GO to G6 in FIG. 3. Because of the 20th bidirectionality of the paths, this results in a recurrent neural network with a symmetrical weight matrix.
  • each binary neuron forms the dot product of the weight vector and the input vector and compares it with the 30th threshold value "1". If the dot product is greater than or equal to this threshold, the neuron fires, ie outputs a "1", otherwise a "0".
  • Each defining neuron (equivalent to a defining node) of the property under consideration always fires, ie has an additional synaptic connection with weight "1" to a constant input "1".
  • the Hopfield network is transformed into a feedforward neural network in the manner described below:
  • All the neurons in the network are subjected to a transformation process in the order from the smallest number of connections to the largest number of connections, which successively reduces the recurrent network and generates a feedforward network. The process ends when the recurrent network is reduced to zero.
  • the neuron can either be formative or non-formative. If the neuron is formative, it always fires and can therefore be removed from the recurrent network without any effects. A non-formative neuron never fires and can therefore also be removed from the recurrent network.
  • the neural network is converted. This conversion is explained in more detail with reference to FIGS. 4a and 4b.
  • the neuron designated N x a so-called edge neuron, has only a bidirectional connection with the weight G x to a neighboring neuron N n , which is connected in any way to the recurrent remaining part of the network N r .
  • the original neuron N x is gem. Fig. 4b replaced by an additional neuron n x , which is coupled forward via the same weight G x to the only neighboring neuron N n 35.
  • the result of the recurrent residual network is also forward-fed via the same weight G x to a downstream neuron N x .
  • the states are on the neuron N x ' of the original neuron N x available.
  • the solution properties are retained, the computing time is reduced by reducing the recurrent network.
  • the first case is described below with reference to FIGS. 5a and 5b. It is a 10th neuron Ny, a so-called transfer neuron, connected via the two bidirectional synaptic connections with the weights G a and G D to a neighboring neuron Na and N D.
  • the two neighboring neurons N a , N D are connected to the recurrent residual network N r 'in a manner not shown in detail.
  • the original neuron Ny is replaced by an additional neuron ny, which is coupled forward to the two neighboring neurons N a , Nb via the same weights G a and Gb.
  • the two neighboring neurons N a and Nb which belong to the recurrent residual network N r , are also forwarded to a neuron Ny 'via the same weights G a and G D
  • the auxiliary neuron calculates the weight Gy on the basis of the weights G a and G D , which are each evaluated in half.
  • the states of the original neuron Ny can be tapped at the neuron Ny '.
  • FIGS. 6a, b which also have direct synaptic connections with one another.
  • a neuron labeled N 2 , a so-called transfer neuron according to 6 a , 35 is connected bidirectionally via synaptic weights G a ', Gb' to neighboring neurons N a ⁇ Nb ', which are connected directly to one another bidirectionally and synaptically according to the weight G a b.
  • the connection of the postbameurons N a ', N ⁇ with the recurrent residual network N r is retained in the corresponding manner.
  • the original neuron N z is replaced by an additional neuron n z , which is coupled forward to the two neighboring neurons N a ', N' via the same weights G a 'and Gb'.
  • the entire network was expanded by two additional neurons n z and H z .
  • the upstream and downstream subnetworks and the auxiliary network are linear.
  • the auxiliary neuron H z calculates the synaptic weight G z from the synaptic weights G a b, which is multiplied by one, and from the weights G a ⁇ Gb ', which are each multiplied by 1/2.
  • a transformation is performed based on the transformation of two synaptic connections described above. Every 20th synaptic connection to a neighboring neuron is treated or transformed separately, i.e. H. z. B. performed three transformations for three connections.
  • a forward-coupled binary neural network is obtained which is structured into the following three subnetworks: 1. An auxiliary weight network which calculates additional synaptic weights from known weights. 30. 2. An upstream network.
  • This transformed network is completely determined from the topology of the electrical network.
  • Such a neural network is shown in FIG. 7 and designated 12.
  • the weights of the network 12 are calculated or result from the two-pole positions, which determine the weights of the synaptic inputs of neurons as inputs.
  • the inputs are generally designated 13 in FIG. 7.
  • the outputs, generally designated 14 in FIG. 7, are made available by the neurons of the downstream network and represent the current potentials of the network nodes.
  • the neural network 14 does not require a training phase, it shows no initialization behavior and does not work incrementally. It does not require any recursiveness and determines the propagation of a defining property in an optimally short time. The result is exact at all times.
  • the neural network 14 can be implemented in hardware or software. The achievable advantages are mentioned again below:
  • the solution method (neural network) can be parallelized.
  • the neural network determines the result non-incrementally. In the event of faults, many switch position changes can be collected and then processed together.
  • the neural network determines results in real time even for very large electrical networks.
  • the neural network can be quasi "arbitrarily” accelerated on special neural hardware.
  • FIG. 8 shows a topological network node 15, which can have a number of properties, which are designated EO, E1, E2, E3 ... En.
  • the properties EO to E3 are also described above. These properties are e.g. B. in two ways 16, 17 with the
  • Network nodes 15 are connected, spread.
  • FIG. 9 shows two nodes 18, 19 which are connected by a path 20 which can have the various defining properties E0 to En.
  • the potential of an electrical network node depends on its connection to a formative network node.
  • This electrical network node then has the properties 5 - potential 20 kV, fed by generator 1 (property E2)
  • a yes / no statement is then to be determined for each network node and each property, which is carried out with the aid of binary neurons.
  • the electrical network is modeled by N neural networks, which independently determine yes / no statements for each node for each electrical property.
  • N is the number of different properties.
  • Such a neural network 21 is shown in FIG. 10. The independently determined results of the N binary neurons representing a node are then

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Abstract

L'invention concerne un procédé et un système qui permettent de déterminer les états de fonctionnement d'un réseau d'alimentation en énergie électrique qui transporte, distribue et met l'énergie électrique à disposition de consommateurs. Le système comprend un nombre de réseaux neuronaux qui dépend du nombre de propriétés électriques caractéristiques des n÷uds d'un réseau électrique. Les réseaux neuronaux ont la même structure que les n÷uds du réseau électrique et présentent pour chaque n÷ud du réseau électrique des neurones interconnectés par des synapses de manière correspondante aux connexions entre les n÷uds du réseau électrique. Les connexions passent à travers des dipôles et le poids des synapses peut être ajusté de l'extérieur du réseau sur 0 ou sur 1 selon la position correspondante des dipôles. Les produits scalaires du vecteur de poids et du vecteur d'entrée des neurones sont comparés à une valeur de seuil égale à 1 et selon que le résultat de la comparaison est supérieur, inférieur ou égal à 1, les neurones génèrent un 1 ou un 0. Les poids des sorties des neurones correspondants dans les réseaux neuronaux individuels sont superposés en fonction de la priorité des propriétés électriques caractéristiques.
EP96914197A 1995-05-17 1996-05-10 Procede et systeme permettant de determiner les etats de fonctionnement d'un reseau d'alimentation en energie electrique Withdrawn EP0826259A1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE19518030A DE19518030C1 (de) 1995-05-17 1995-05-17 Anordnung und Verfahren für die Bestimmung der Betriebszustände eines elektrischen Energieversorgungsnetzes
DE19518030 1995-05-17
PCT/EP1996/002005 WO1996037028A1 (fr) 1995-05-17 1996-05-10 Procede et systeme permettant de determiner les etats de fonctionnement d'un reseau d'alimentation en energie electrique

Publications (1)

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EP0826259A1 true EP0826259A1 (fr) 1998-03-04

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Application Number Title Priority Date Filing Date
EP96914197A Withdrawn EP0826259A1 (fr) 1995-05-17 1996-05-10 Procede et systeme permettant de determiner les etats de fonctionnement d'un reseau d'alimentation en energie electrique

Country Status (3)

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EP (1) EP0826259A1 (fr)
DE (1) DE19518030C1 (fr)
WO (1) WO1996037028A1 (fr)

Cited By (1)

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CN102946098A (zh) * 2012-10-23 2013-02-27 四川大学 基于网络拓扑聚类的电力系统主动解列方法

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112666423B (zh) * 2020-12-03 2021-08-17 广州电力通信网络有限公司 一种用于电力通信网络的测试装置

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JPH06106009B2 (ja) * 1988-11-16 1994-12-21 東京電力株式会社 ニューラルネットワークによる最適電力負荷配分システム
NL8900425A (nl) * 1989-02-21 1990-09-17 Rijksuniversiteit Informatieverwerkende module alsmede een informatieverwerkend netwerk omvattende een aantal van deze modules.
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FR2656791B1 (fr) * 1990-01-08 1996-12-13 Electricite De France Procede de traitement de surfaces dentaires ou osteo-articulaires, dispositifs pour sa mise en óoeuvre.

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102946098A (zh) * 2012-10-23 2013-02-27 四川大学 基于网络拓扑聚类的电力系统主动解列方法

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WO1996037028A1 (fr) 1996-11-21
DE19518030C1 (de) 1996-10-10

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