Patent Description:
An early warning device is a system used to prevent an equipment failure in advance, and has been widely introduced and used in military, aviation, and power generation fields.

The principle of the early warning device according to <CIT>, for instance, is a method in which a prediction model creates a prediction value for an operating state using previous normal operation data, calculates a residual by comparing the prediction value with an actual measurement value, and causes a warning to occur when the residual is out of the normal operation range.

Various mathematical methods are used to calculate prediction values for the operating states of equipment, and representative methods thereof include Kernel regression using a statistical method, Gaussian process regression, a neural network method, Kalman filter, etc..

The early warning device can have a practical effect only when the probability of false warnings is minimized. Otherwise, an operator may miss important warnings while analyzing a large number of warnings, and the operator's concentration may be degraded, failing to detect a warning that is caused by a real equipment problem.

Conventionally, a single prediction model has been used. This will be described with reference to <FIG>.

<FIG> is a diagram schematically showing the configuration of an early warning device employing a single prediction model in a related art.

As shown in <FIG>, a measured signal is transmitted to the single prediction model, and a prediction value is calculated in the single prediction model. This prediction value is transmitted to a comparison module, and the comparison module compares the prediction value and an actual measurement value to output a residual, and determines whether the determination module is in a normal range to determine whether there are defects.

In this case, the reliability of a detected defect was low and there was a defect that could not be detected due to characteristics of the prediction model, and accordingly, a method of applying the prediction model in multiple to increase the reliability of the prediction model is being studied.

From <CIT> a plant abnormality detection method and system, and more particularly, a plant abnormality detection method and system, are known which can collect plant data in real time, predict data in a normal state by learning the collected data, and diagnose abnormality by comparing the real-time plant data with the prediction data in the normal state.

The present disclosure is to solve the problems of the prior art as described above, and an object thereof is to increase the reliability for early warning by applying a multiple prediction model.

The present invention is defined by the subject matter of the appended claims.

In order to achieve the above object, the present disclosure provides a power plant early warning device employing a multiple prediction model, and the device includes: a multiple prediction model comprising a plurality of prediction models which receives information on operating states of equipment of a power plant and outputs prediction values and reliabilities of the prediction values; a reliability analysis module which quantifies and analyzes the reliabilities output from the multiple prediction model to determine ranks of the plurality of prediction models included in the multiple prediction model, and calculates a final prediction value; a comparison module which compares the final prediction value with an actual measurement value and outputs a residual; and a determination module which analyzes the residual and determines whether there are defects in the operating states of the equipment of the power plant.

Here, when calculating the final prediction value, the reliability analysis module may use a prediction value of a predicted model having a highest rank.

Further, when calculating the final prediction value, the reliability analysis module may use an arithmetic mean of two highest prediction values.

Further, when calculating the final prediction value, the reliability analysis module may apply weights according to the ranks.

In addition, all the plurality of prediction models in the multiple prediction model use a same algorithm, while the algorithm is applied differently for each operating condition.

Further, the multiple prediction model may apply different algorithms to the plurality of prediction models.

The early warning device and method according to the present disclosure has the following effects.

First, it is possible to overcome the weaknesses that occur in a single prediction model. The early warning device according to the present disclosure uses multiple prediction models rather than a single prediction model. Accordingly, a case in which a prediction value may be different from an actual value due to defects in each prediction model can be compensated for, thereby overcoming the vulnerability.

Second, it increases reliability. In the present disclosure, a multiple prediction model is applied to perform early warning for operating states of devices of a power plant. Accordingly, it is possible to enhance the capability of detecting defects than a single prediction model and to increase the reliability of early warning.

Hereinafter, preferred embodiments of the present disclosure will be described with reference to the accompanying drawings.

<FIG> is a block diagram of a power plant early warning device to which multiple prediction models are applied according to an embodiment of the present disclosure, and <FIG> is a diagram schematically illustrating a function performance between components of a power plant early warning device to which a multiple prediction models are applied according to an embodiment of the present disclosure.

As shown in <FIG> and <FIG>, a power plant early warning device employing multiple prediction model according to an embodiment of the present disclosure may include a signal measurement unit <NUM>, multiple prediction model <NUM>, a reliability analysis module <NUM>, a comparison module <NUM>, and a determination module <NUM>.

The signal measurement unit <NUM> is configured to receive monitoring signals related to operating states of devices of a power plant, calculate an actual measurement value, and transmit the actual measurement value. Monitoring signals related to the operating states of the devices of the power plant are related to pressure, temperature, flow data, etc.. The signal measurement unit <NUM> is attached to the power plant equipments or around them to measure and transmit the operating states of the equipment. A transmission interval may be set by a user. For example, it is possible to transmit the signal at an interval of one second.

The multiple prediction model <NUM> is configured to receive a monitoring signal received by the signal measurement unit <NUM> and generate a prediction value for an operating state of the equipment. In the multiple prediction model <NUM>, a plurality of prediction models of <NUM> to n number may be used.

That is, different algorithms may be applied to a first prediction model <NUM>, a second prediction model <NUM>,. , and an n-th prediction model, and each prediction model outputs a prediction value of a current monitoring signal and a reliability of the prediction value.

The reliability analysis module <NUM> is configured to receive a reliability of a prediction value from each prediction model and quantify and analyze the received reliabilities to determine ranks of the prediction models.

The comparison module <NUM> is configured to calculate a final prediction value according to a user's setting, compare the final prediction value with an actual measurement value, and output a residual. Here, for the final prediction value, a prediction value of a prediction model having a highest rank may be used, an arithmetic mean of two highest prediction values may be used, or a weighted average according to the ranks may be used.

The determination module <NUM> is configured to analyze a residual output from the comparison module <NUM> to determine whether there are defects in the operating states of the power plant equipment.

Hereinafter, a power plant early warning method employing the multiple prediction model <NUM> will be described with reference to the drawings.

<FIG> is a flowchart of a power plant early warning method employing a multiple prediction model provided for illustratory purposes.

As shown in <FIG>, in the power plant early warning method employing a multiple prediction model which does not partake in the claimed invention, a plurality of predictive model first receives information on the operating states of equipment of a power plant and outputs prediction values and reliabilities of the prediction values.

The signal measurement unit <NUM> receives a monitoring signal regarding the operating state of the equipment of the power plant. Since the received monitoring signal is an analog signal, the signal measurement unit <NUM> may process data to perform an analog-to-digital conversion, so that the monitoring signal can be converted into a digital signal. The signal measurement unit <NUM> may transmit actual measurement values of the monitoring signals converted into the digital signals to the comparison module <NUM>.

In addition, the multiple prediction model <NUM> composed of a plurality of prediction models outputs a prediction value of a corresponding equipment among the equipments of the power plant measured by the signal measurement unit <NUM>. Depending on the use, such a plurality of prediction models may be used from the first prediction model <NUM>, the second prediction model <NUM>,. , up to the n-th prediction model.

The first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model may output prediction values of equipment by applying different algorithms.

For example, the first prediction model <NUM> may use a data model using the Auto Associative Kernel Regression (AAKR) technique, and the second prediction model <NUM> may use a physical model using Kalman filter. Accordingly, the first prediction model <NUM> may output a prediction value using the AAKR technique, and the second prediction model <NUM> may output a prediction value using the Kalman filter.

Specifically, the AAKR technique generates a pattern model by learning previous data, determines a similarity by comparing a current actual measurement value and the pattern model, and generates a prediction value by weighting the pattern model based on the similarity. Therefore, the AAKR technique is an interpolation-type prediction model, which can be predicted within the learned pattern model and can be predicted only when a measurement value exists in a learned pattern model. In determining the similarity, it is possible to check whether the measurement value exists in a learned pattern region, and a reliability of a prediction value may be calculated by taking such similarities into account.

When a prediction model to which a plurality of AAKR techniques are applied is applied, each AAKR model may output a prediction value and a reliability. Here, the reliability refers to a similarity between an actual measurement value and a pattern model.

The Kalman filter is a statistical model based on a physical model and may output a prediction value and a covariance. Such a Kalman filter may be predicted even in a not-learned region, and an uncertainty (reliability) of a prediction value may be determined through the covariance.

After the prediction value is output, the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model may output a reliability of each prediction value. At this point, the size and meaning of reliability, and calculation methods may differ for each algorithm.

A reliability is an index that roughly indicates how accurate a prediction value output for a corresponding equipment from the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model.

In addition, similarly to that the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model all use the AAKR technique or the Kalman filter, the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model may all use the same algorithm. Even if the same algorithm is used, a plurality of prediction models may be used for each operating condition of the device.

For example, in consideration of changes in seawater temperature, prediction models for temperatures of four surface condensers each per season are composed of the first prediction model <NUM>, the second prediction model <NUM>, a third prediction model (not shown), and a fourth prediction model (not shown), and the first prediction model <NUM>, the second prediction model <NUM>, the third prediction model, and the fourth prediction model output prediction values by applying the AAKR technique only.

Here, a surface condenser is an equipment for cooling water vapor to return the vapor to water, and is a kind of generally well-known condenser.

Seawater is the ultimate heat sink of a power plant, and almost all of the heat generated in nuclear power plants is discharged into the seawater. Therefore, when the temperature of the seawater changes, the heat exchange process between the power plant and the seawater changes, and as a result, the operation pattern of almost all facilities changes. One of the facilities affected by the seawater temperature is the condenser.

While operating an early warning system at a nuclear power plant, there was a case where a large number of false warnings occurred in the summer of <NUM> due to an abnormal high temperature of the seawater (a temperature increase of <NUM> degrees compared to learning data of average year). Accordingly, in an existing single prediction model, a prediction range of the prediction model is increased by additionally learning data on the changed seawater temperature, or the warning sensitivity is reduced through a change that increases the allowable value of the determination module <NUM>.

However, this method is not desirable because the overall reliability and detection capability of the early warning device is reduced. Therefore, the multiple prediction model <NUM> based on the four AAKR techniques for each seawater temperature is applied, thereby learning each operation pattern of nuclear power plant variables according to the seawater temperature.

That is, a prediction model for spring, a prediction model for summer, a prediction model for autumn, and a prediction model for winter are generated. Thereafter, when a measurement value is input, it is determined which season-related prediction model is most similar, and a prediction value of the most similar prediction model is used.

As a result, since prediction is performed using a plurality of precise prediction models each having a narrow prediction range, the reliability of the early warning device is improved than before.

On the contrary, a prediction model to which the AAKR method is applied and a prediction model to which the Kalman filter is applied may be used together, and the prediction model to which the AAKR method is applied outputs a similarity as a reliability, and the prediction model to which the Kalman filter is applied outputs a covariance, and therefore, a prediction value and the reliability may be output.

Thereafter, the reliability analysis module <NUM> quantifies reliabilities output from the plurality of prediction models.

By using a normal distribution or the like of the reliabilities of the prediction values output from the first prediction model <NUM> and the second prediction model <NUM>, the reliability analysis module <NUM> quantifies the respective reliabilities, so that the reliabilities can be compared on the same basis.

Next, the reliability analysis module <NUM> determines ranks of the plurality of prediction models and calculates a final prediction value.

The reliability analysis module <NUM> determines the ranks of the prediction models based on the reliabilities output from the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model. This is to determine a prediction model that outputs a prediction value that the most matches a corresponding equipment among the first prediction model <NUM>, the second prediction model <NUM>,. the n-th prediction models by applying difference according to the value of the quantified reliability.

In the multiple prediction model <NUM> in which the AAKR technique is applied to all of the plurality of prediction models, since a similarity includes influences such as a signal magnitude or fluctuation range, it is not possible to directly compare similarities between prediction models. Therefore, the reliability analysis module <NUM> quantifies the similarity and then compares and analyzes the similarity to determine the ranks of the prediction models.

By applying a prediction model composed of data that is the most similar to the operating state of a nuclear power plant, the accuracy of the prediction values may be increased, thereby preventing a false warning. In addition, when an existing single prediction model is used, a work to modify and supplement the prediction model in response to a change in the environment is needed, and the work may be minimized by applying a plurality of prediction models.

In addition, the multiple prediction model <NUM> in which the AAKR technique and the Kalman filter are applied to the plurality of prediction models quantifies different reliabilities between algorithms to enable comparison, and then arrange the quantified reliabilities to determine ranks of the prediction models.

By applying these different algorithms, it is possible to prevent inaccuracy of a prediction value due to weakness of the algorithms.

In this way, when the ranks of the prediction models among the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model are determined, a final prediction value may be calculated according to a user's setting.

For example, a prediction value of a prediction model having a highest rank may be used to determine the final prediction value.

In addition, the final prediction value may be determined by using an arithmetic mean of two highest prediction values. That is, if a prediction value of the first prediction model <NUM> among the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model and a prediction value of the second prediction model <NUM> are determined the highest value and the second highest value, respectively, a value derived by calculating an arithmetic mean of the prediction value of the first prediction model <NUM> and the prediction value of the second prediction model <NUM> may be used as a final prediction value.

In addition, an average of prediction values to which weights are applied according to the rank determined for the first prediction model <NUM>, the second prediction model <NUM>,. , and the n-th prediction model may be determined as a final prediction value.

Next, the comparison module <NUM> compares the final prediction value with an actual measurement value and outputs a residual.

The residual is calculated using a difference between a current value (or the actual measurement value) and a prediction value of a corresponding equipment. That is, the residual may be calculated and output through |current value-prediction value|.

The determination module <NUM> analyzes the residual to determine whether there are defects in the operating states of equipment of the power plant.

The determination module <NUM> determines whether a monitoring signal for a corresponding equipment is included in a preset normal operation range based on the residual output from the comparison module <NUM>.

Here, the normal operation range may be determined differently according to a user's setting. Therefore, if the residual is a value outside the preset normal operation range, the monitoring signal for the corresponding equipment determines that the device is in an abnormal operating state and generates an early warning, and if the residual is a value that does not exceed the preset normal operation range, a warning is not generated.

At this point, a user may adjust the normal operation range for generating the warning.

For example, the normal operation range of a feed water pump is <NUM> (at minimum) to <NUM> (at maximum), and the deviation between the minimum value and maximum value is <NUM>.

Therefore, in a case where it is programmed to generate a warning when the residual exceeds <NUM>% (+<NUM>, -<NUM>) of the deviation (<NUM>) of the normal operating range, if an input measurement value is <NUM> and a calculated prediction value is <NUM>, the residual is <NUM> which exceeds <NUM>% of the deviation and thus a warning is generated.

On the contrary, if a measurement value is <NUM> and a prediction value is <NUM>, the residual is <NUM> and thus and a warning is not generated.

When a warning is generated, the determination module <NUM> may transmit warning status information to an integrated center equipment (not shown) for monitoring all power plants, so that a user can check it.

Claim 1:
A power plant early warning device employing a multiple prediction model, the device comprising:
a multiple prediction model (<NUM>) comprising a plurality of prediction models (<NUM>, <NUM>) which receives information on operating states of equipment of a power plant and outputs prediction values and reliabilities of the prediction values;
a reliability analysis module (<NUM>) for calculating a final prediction value;
a comparison module (<NUM>) which compares the final prediction value with an actual measurement value and outputs a residual; and
a determination module (<NUM>) which analyzes the residual and determines whether there are defects in the operating states of the equipment of the power plant,
characterized in that
calculating the final prediction value by the reliability analysis module (<NUM>) comprises:
quantifying and analyzing the reliabilities output from the multiple prediction model (<NUM>) to determine ranks of the plurality of prediction models (<NUM>, <NUM>) included in the multiple prediction model (<NUM>), and
calculating the final prediction value, by using prediction values output by the multiple prediction model weighted according to the ranks determined of the plurality of prediction models (<NUM>, <NUM>).