KR20220128785A - Digital-based offshore wind turbine maintenance system and method using failure history - Google Patents

Abstract

The present invention relates to a digital based offshore wind power generator maintenance management system using a failure history and a method thereof. The digital based offshore wind power generator maintenance management system according to the present invention comprises: a failure history data acquiring unit collecting the failure history of a wind power generator; a failure history analyzing unit which analyzes the failure history and an error code using data; a data correcting unit correcting the data; a failure predicting unit predicting failure occurrence by learning an analysis result; and an information providing unit providing the data and prediction information. The data correcting unit analyzes and reflects a weight and a frequency depending on whether the error code matches with a part and whether or not there is maintenance, thereby increasing reliability of the prediction information. An objective of the present invention is to provide the digital based offshore wind power generator maintenance management system, capable of predicting and preparing for efficient maintenance management and the failure occurrence, and the method thereof.

Description

Digital-based offshore wind turbine maintenance system and method using failure history}

The present invention relates to a system and method for maintenance of an offshore wind turbine, and a digital-based offshore wind turbine that collects failure history information (data) of a wind generator, corrects and analyzes the collected data, and uses it for maintenance and predicts failure occurrence. It relates to a generator maintenance system and method.

Recently, investment in new and renewable energy is increasing due to environmental problems such as depletion of fossil energy, climate change, and reduction of greenhouse gases. The domestic renewable energy market is rapidly growing due to the renewable energy expansion policy centered on solar and wind power.

In particular, wind power generation is a representative technology of new and renewable energy and is emerging as a technology that can reduce greenhouse gases. Wind power generation is generated by rotating a turbine equipped with a fan using wind power, and is divided into large, medium, and small according to the amount of power generation, and is divided into land wind power generation and offshore wind power generation according to the installation location. Such wind power generation is in the trend of large-scale, and wind power generation complexes or facilities are separately formed and operated due to the nature of the installation.

Due to the nature of this installation, it is not easy to manage it because it is far from the permit or a large-sized generator is used. .

In addition, the wind power generator is configured to continuously receive the wind whose wind speed and direction change from time to time, and damage or damage to each part of the wind generator occurs due to factors such as vibration, shock, and load deflection caused by the wind. At the moment when damage or breakage occurs, of course, the operation of the wind turbine is greatly affected, and accordingly, other parts in addition to the damaged or damaged parts are adversely affected.

Therefore, it is self-evident that it is preferable to repair or replace in advance when the service life is almost over before the damage or breakage occurs, rather than repair or replacement after the damage or breakage has already occurred. However, if it is not possible to correctly identify or predict the lifespan of a certain part, excessively frequent repairs or replacements are made, resulting in a problem that the cost of operating a wind power generator may increase unnecessarily.

Accordingly, various maintenance methods have been proposed, but most of them are based on the management of the amount of power generation and the determination of whether there is a simple abnormality. That is, according to the prior art, only the event situation of the power generation facility can be recognized through communication with a manager or a user, and services such as a method and a platform for comprehensive management are not provided. In addition, data analysis on the failure history of generators, parts supply and demand, and maintenance has not been conducted, and accordingly, efficient maintenance and learning and estimation of failure occurrence have not been reviewed.

Korean Registration Announcement No. 10-2097772 (2020.04.07. Announcement)

The present invention has been devised in view of the above-described problems, and an object of the present invention is to analyze failure history data and error codes of a wind power generator, but increase reliability through correction and learn this to predict and prepare for efficient maintenance and failure. It is to provide a digital-based offshore wind turbine maintenance system and method.

A digital-based offshore wind turbine maintenance system according to the present invention includes a failure history data acquisition unit that collects failure history of the wind turbine, a failure history analysis unit that analyzes failure history and error codes using the data, and corrects the data and a data correction unit that learns the analysis result to predict the occurrence of a failure, and an information provision unit that provides data and prediction information, wherein the data correction unit determines whether the error code matches the parts and whether maintenance is present. It is characterized in that the reliability of the prediction information is increased by analyzing and reflecting the weight and frequency of the prediction.

In addition, the failure history analysis unit analyzes the error code and the failure history for each part, but performs matching with the main parts for each error code that is different depending on the type of power generation complex and generator, and when an error occurs, the error code (Error code) ) according to Nacelle, PCS, Pitch system, Yaw system, Tower, Drive Train, Generator, and Hub.

In addition, the failure history data acquisition unit checks the state of the wind power generator using a plurality of sensors, and the confirmed information is transmitted to the user using a communication unit, and the communication unit uses 5G LoRa (Long Range) communication-based technology. It is characterized by securing network stability and operational data reliability by omitting the CDMA radio repeater and securing a communication range of up to 11 km.

In addition, the control unit; and cloud servers; Further comprising, the user transmits and receives data to and from the cloud server using a separate user terminal, and the data is processed into a database and stored or learned to predict and prepare for the state and failure of the wind power generator. characterized.

In addition, the communication unit transmits and receives a wireless signal transmitted remotely, modulates, demodulates, and amplifies the wireless signal and supplies it to the MCU of the system, and the control unit reflects the sunrise and sunset times of the area where the wind power generator is installed. It is characterized in that the wind power generator is automatically controlled by wirelessly remote control and outputting the control signal as an AC signal.

In addition, the information providing unit is an alarm unit for alerting the user by analyzing the urgency or necessity of data and prediction information; It further comprises, wherein the alarm unit is characterized in that it is possible to provide and control information from a distance or interlocked with a user's portable terminal using sound, sound, and lighting.

The digital-based offshore wind turbine maintenance method according to the present invention includes the steps of collecting failure history data of the wind turbine, classifying and analyzing the failure history by error code, correcting the data, replacing parts, and responding to the necessity of repair. It includes a learning and prediction step, and a replacement and inspection step of parts, and the data correction step increases the reliability of the prediction information by analyzing and reflecting the weight and frequency depending on whether the error code matches the parts and whether or not the maintenance is performed. characterized.

In addition, after the prediction step, the step of alarming the prediction information to an administrator or a user further includes, wherein the alarming step analyzes the urgency or necessity of data and prediction information and uses sound, sound, and lighting for the user's portable device. It is characterized in that information can be provided and controlled from a distance or interlocked with a terminal.

The wind power generator maintenance system and method according to the present invention analyzes the failure history data and error codes of the wind power generator, but increases the reliability through correction and learns it, thereby predicting and preparing for efficient maintenance and failure occurrence. A generator maintenance system and method are provided.

1 is a conceptual block diagram of a wind power generator maintenance system according to the present invention;
2 is a flowchart of a wind generator maintenance method according to the present invention;
3 is a conceptual diagram of a wind power generator maintenance method according to the present invention;
4 is a reference table regarding the failure history data and error codes of the wind power generator;
5 is a graph showing the frequency of occurrence of error codes of the wind power generator;
6 is an explanatory diagram matching the error code of the wind power generator and the main parts;
7 is a graph showing the frequency of occurrence of error codes for each major part of the wind power generator;
8 is a graph showing the frequency of inspection, repair, and replacement of major parts by month;
9 is a graph comparing the prediction and actual measurement of the failure occurrence by year of the wind power generator according to the present invention;
10 is a chart showing the average of frequency estimation accuracy for each major component.

Hereinafter, with reference to the accompanying drawings, a digital-based offshore wind turbine maintenance system and method according to an embodiment of the present invention will be described in detail so that those of ordinary skill in the art can easily implement it. do it with The present invention is not limited to the embodiments described herein, and may be implemented in several different forms.

In order to clearly describe the present invention, parts irrelevant to the description are omitted, and the same reference numerals are assigned to the same or similar components throughout the specification. The present invention is an invention related to a digital-based offshore wind turbine maintenance system and method, and in terms of the terms, units, devices, and systems can be understood as the same meaning with respect to specific configurations, and includes inventions related to methods, processes, and processes, The present invention can be understood by interpreting the terms in a reasonable common sense. In addition, data, information, etc. may refer to the same object in a similar meaning, and may be reasonably interpreted as far as technical implementation is possible.

The digital-based offshore wind power generator maintenance system 10 according to the present invention is a failure history data acquisition unit 100 that collects the failure history of the wind turbine, and a failure history analysis that analyzes the failure history and error codes using the data. It includes a unit 200, a data correction unit 300 for correcting the data, a failure prediction unit 400 for predicting the occurrence of a failure by learning the analysis result, and an information providing unit 500 for providing data and prediction information. .

As shown in FIG. 1 , the wind power generator maintenance system 10 according to the present invention acquires a failure history from the wind generator W and analyzes it, but performs correction to increase reliability, and accordingly, information of failure prediction As a means of providing or alarming, information can be provided through the portable terminal 700, computer, tablet, etc. of a user or administrator when necessary.

In particular, the data correction unit 300 increases the reliability of the prediction information by analyzing and reflecting the weight and frequency according to whether the error code matches the parts and whether or not to maintain. That is, the failure history of the acquired wind power generator is matched with the error code, and the results such as frequency and repair status are reflected by analyzing existing data.

For example, after checking the failure history and error codes of major parts, checking whether they match, and comparing the actual repair and replacement results, a weight for the need for repair or replacement can be obtained, and this is used in the process of analysis. In other words, if the frequency of actual repair or replacement is high, the need for repair or replacement increases in the maintenance process.

The failure history analysis unit 200 analyzes the error code and the failure history for each component, but performs a matching operation with the main components for each error code that is different according to the type of power generation complex and generator, and when an error occurs, the error code ( Error code), matching tasks and data with main components of Nacelle, PCS, Pitch system, Yaw system, Tower, Drive Train, Generator, and Hub can be classified. In other words, it is very important to predict failure occurrence through failure history and error codes of major parts.

If necessary, the failure history data acquisition unit 100 may use a plurality of sensors (not shown) to check the state of the wind power generator, and the confirmed information may be transmitted to the user using a communication unit (not shown). The sensor may be a sensor that compares applied and output current and voltage, and may be a sensor that senses illuminance, temperature, etc. or detects whether there is a physical failure.

The communication unit is not easy to use short-range communication means due to the locational characteristics of the wind power generator, and in some cases, by using 5G LoRa (Long Range) communication-based technology, the CDMA wireless repeater is omitted, and a communication range of up to 11 km is secured. stability and reliability of operation data can be secured.

The wind turbine maintenance system 10 according to the present invention further includes a control unit (not shown) and a cloud server, and a user can transmit and receive data to and from the cloud server by using a separate user terminal 700 . That is, the control unit may be provided inside the wind power generator, but is preferably provided in the maintenance system 10 or may be provided at a location where a user or a manager is located.

In particular, for the purpose of the present invention, the data is processed into a database and stored or learned, so that it is possible to compare with the prediction of the state of the wind power generator and the presence or absence of a failure. will be.

The communication unit transmits and receives a wireless signal transmitted remotely, modulates, demodulates, and amplifies the wireless signal and supplies it to the MCU of the system 10, and the control unit reflects the sunrise and sunset times of the area where the wind turbine is installed. to remotely control the wind turbine, and output the control signal as an AC signal so that the wind power generator is automatically controlled.

The information providing unit 500 further includes an alarm unit that alerts the user by analyzing the urgency or necessity of data and prediction information, and the alarm unit is linked to the user's portable terminal using sound, sound, and lighting or is remotely located. Information provision and control may be possible.

That is, the collected data, the analyzed value, the predicted value, the information data, etc. are provided to the administrator and the user, and at this time, the alarm can be accompanied by an alarm by following or selecting a preset standard. Accordingly, it is possible to judge the severity of maintenance in consideration of the urgency, necessity, priority, etc. of the occurrence of a failure, and to directly notify the user.

4 is a reference table regarding the failure history data and error codes of the wind power generator, and it is possible to compare and match the error codes according to the occurrence time and the type of failure history occurrence. That is, it is possible to classify and analyze the failure history of any part and what error code it is.

5 is a graph showing the frequency of occurrence of error codes of the wind power generator, and FIG. 6 is an explanatory diagram in which the error codes of the wind power generator are matched with the main parts, and the main parts according to the different error codes according to the power generation complex, the generator type, etc. Matching is performed, and when an error occurs, matching with main components such as Nacelle, PCS, Pitch system, Yaw system, Tower, Drive Train, Generator, Hub, etc. and data classification are performed according to the error code. It is possible.

7 is a graph showing the frequency of occurrence of error codes for each major part of the wind power generator, FIG. 8 is a graph showing the frequency of inspection, repair, and replacement of major parts by month, and FIG. 9 is a breakdown by year of the wind power generator according to the present invention. It is a graph comparing the occurrence prediction and actual measurement, and FIG. 10 is a chart showing the average of the frequency estimation accuracy for each major part.

Based on the data accumulated in this way, it is possible to compare and analyze the predictive accuracy of parts inspection, repair, and replacement frequency of major parts with the prediction accuracy of whether or not an actual error occurs. , it is possible to estimate the frequency of inspection, repair, and replacement of each part by applying weights according to their importance.

In other words, the biggest factor that hinders the stability and efficiency of wind farm operation is major failure of the wind power generator, which is a gearbox, generator, hub, and main bearing. ) and the failure of the main components constituting the main parts of the wind power generator, such as blades. Therefore, if the timing of inspection, repair, and replacement of major parts is predicted based on the failure history data of the wind farm, it can help to increase the operation rate by realizing the effective operation management of the wind turbine.

In addition, as 60~65% of the cost of wind power business improvement activity management (maintenance, O&M) cost is due to unplanned maintenance, and more than 40% of the failure causes of wind power generators are caused by parts failure, the status of wind power generation is monitored. The application of system and predictive maintenance technology can be applied as indispensable for cost reduction.

As expected effects according to the present invention, stable generator operation and downtime can be minimized through planned maintenance and intensive management of major parts, and failure of major parts of the wind power generation system can be predicted for replacement, repair, etc. By preparing spare parts, personnel and equipment in advance, it is possible to avoid the risk of accidents due to breakdown and reduce downtime.

In addition, by estimating the frequency of inspection, repair, and replacement of parts based on the failure history of wind power generators, it is possible to shorten O&M time and reduce input manpower and O&M costs by preemptively identifying the causes of component failures in the economic and industrial aspects, and fine dust in the social aspect. Through reduction, the living environment can be improved, and it can contribute to the government's policy toward denuclearization.

The digital-based offshore wind turbine maintenance method according to the present invention includes the steps of collecting failure history data of the wind turbine (S100), classifying and analyzing the failure history for each error code (S200), correcting the data, It includes a learning and predicting step (S600) of the need for replacement and repair and replacement of parts and a check step (S700).

As shown in FIG. 3 , the maintenance method according to the present invention largely consists of data acquisition, analysis, and estimation steps, and in particular, in the analysis and estimation steps, a correction step that considers weights using various indicators is applied.

Therefore, the wind turbine maintenance method according to the present invention, as shown in FIG. 2, analyzes the frequency of the failure history for correction (S300), confirms the matching between the error code and the parts (S400), reviews the weight, and analyzes the frequency (S500) ) to further analyze and predict.

In particular, the data correction step increases the reliability of the prediction information by analyzing and reflecting the weight and frequency according to whether the error code and parts match, and whether or not maintenance is performed. In the process of finding, estimating, and estimating the frequency of inspection, repair, and replacement of major parts, the accuracy is analyzed by comparing the actual repair and replacement history with the data, and then the results are applied as weights for more accurate inspection, repair, and replacement of parts. It is possible to predict and estimate the need and timing.

After the prediction step, further comprising the step of alarming the prediction information to an administrator or a user, wherein the alarming step analyzes the urgency or necessity of the data and prediction information to the user's portable terminal using sound, sound, and lighting It may be interlocked or it may be possible to provide and control information from a distance.

That is, the collected data, analyzed values, predicted values, information data, etc. are provided to managers and users by analyzing the urgency or necessity of data and prediction information to select and alert users. Alternatively, the alarm can be accompanied at the same time. Accordingly, it is possible to judge the severity of maintenance in consideration of the urgency, necessity, and priority of the occurrence of a failure and to directly notify the user, etc.

In addition, communication for the storage, transmission, reception and analysis of wind power generator status information, maintenance information, and remote control can be provided based on IoT or 5G LoRa communication. There is a disadvantage in that the function of the individual WIFI/Zigbee terminal connected to it is limited when a wireless repeater fails.

In addition, the communication method according to the prior art has a rather high communication cost burden when using CDMA and LTE radio repeaters/amplifiers (50,000 to 60,000 won per year), and the efficiency is reduced because only the group method can be installed. There is a problem of inefficiency in terms of purchase unit price and management.

Accordingly, the communication unit and communication method according to the present invention omit the CDMA radio repeater by using the 5G LoRa (Long Range) communication-based technology and secure the communication range of up to 11 km to secure the stability of the network and the reliability of the operation data. . In addition, the control performance through the communication unit uses a big data-based cloud server method, so network security, stability, and server reliability are very high compared to a general system.

For reference, in the sensing (detection) of failure, etc., the failure of a component is determined by measuring the supply (in/out) and current or voltage of the ACV LINE connected to the component, and transmitting the measured value to the MCU. can be designed as

Here, the input of ACV is confirmed by the sensor, but when it is confirmed that the supply of DCV is cut off, the MCU first determines the SMPS as a failure, and then measures the measured value by the temperature, illuminance sensor or various other sensors and the sensor measurement of ACV/DCV. It is designed to compare and analyze the values to make an accurate judgment about the faulty part. That is, the accuracy and reliability of the failure history data and information are improved.

Here, the failure determination of the communication unit is designed to enable the software algorithm self-diagnosis. For example, in the self-diagnosis contents, it is not possible to transmit/receive signals to and from the base station (repeater) due to the failure of the RSSI value to measure the reception sensitivity of wireless communication, the increase in the wireless communication noise level, the coverage problem due to simultaneous connection, and hardware such as LPWA. It can be confirmed that the grounding on the wiring is faulty.

The present invention described above is not limited by the above-described embodiments and the accompanying drawings, and simple substitutions, modifications and changes within the technical spirit of the present invention will be apparent to those of ordinary skill in the art.

The digital-based wind power generator maintenance system and method according to the present invention analyzes failure history data and error codes and corrects and learns the data to efficiently perform maintenance and predict and estimate the occurrence of failures; and method can be used.

10: wind power generator maintenance system
100: failure history data acquisition unit 200: failure history analysis unit
300: data correction unit 400: failure prediction unit
500: information providing unit 700: portable terminal
S100: Failure history data collection
S200: Classification of failure history by error code
S300: Failure history frequency analysis
S400: Check error code and parts matching
S500: Weight Review and Frequency Analysis
S600: Repair prediction, estimation
S700: Exchange, overhaul

Claims (8)

In the digital-based offshore wind turbine maintenance system,
Failure history data acquisition unit for collecting failure history of the wind power generator;
a failure history analysis unit that analyzes a failure history and an error code using the data;
a data correction unit for correcting the data;
a failure prediction unit for predicting failure occurrence by learning the analysis result; and
an information providing unit providing data and prediction information; includes.
The data compensator analyzes and reflects the weight and frequency according to whether the error code matches the parts and whether or not to maintain the wind turbine maintenance system, characterized in that it increases the reliability of the prediction information.
The method of claim 1,
The failure history analysis unit analyzes the error code and the failure history for each part, but performs matching with the main parts for each error code that is different depending on the power generation complex and the generator type, and when an error occurs, the error code is displayed. A wind power generator maintenance system, characterized in that it categorizes matching tasks and data with main components of Nacelle, PCS, Pitch system, Yaw system, Tower, Drive Train, Generator, and Hub accordingly.
The method of claim 1,
The failure history data acquisition unit uses a plurality of sensors to check the state of the wind turbine, and the verified information is transmitted to the user using a communication unit, and the communication unit uses a 5G LoRa (Long Range) communication-based technology to provide CDMA wireless A wind power generator maintenance system, characterized in that it secures network stability and operational data reliability by omitting a repeater and securing a communication range of up to 11 km.
4. The method of claim 3,
control unit; and cloud servers; Further comprising, the user transmits and receives data to and from the cloud server using a separate user terminal, and the data is processed into a database and stored or learned, so that prediction and preparation of the state of the wind power generator and the presence or absence of failure are possible A wind turbine maintenance system.
5. The method of claim 4,
The communication unit transmits/receives a wireless signal transmitted remotely, modulates, demodulates, and amplifies the wireless signal and supplies it to the MCU of the system, and the control unit reflects the sunrise and sunset times of the area where the wind power generator is installed to perform wireless remote control. A wind power generator maintenance system, characterized in that by outputting a control signal as an AC signal, the wind power generator is automatically controlled.
The method of claim 1,
The information providing unit may include an alarm unit for analyzing the urgency or necessity of data and prediction information and alerting the user; further comprising,
The alarm unit is a wind generator maintenance system, characterized in that it is linked to the user's portable terminal using sound, sound, and lighting, or it is possible to provide and control information from a distance.
In the maintenance method of the offshore wind power generator using the maintenance system according to any one of claims 1 to 6,
Collecting failure history data of the wind power generator;
Classifying and analyzing failure histories for each error code;
correcting the data;
learning and predicting the need for replacement and repair of parts; and
replacement and inspection of parts; includes.
The data correction step is a wind power generator maintenance method, characterized in that the reliability of the prediction information is increased by analyzing and reflecting the weight and frequency according to whether the error code matches the parts and the maintenance or not.
8. The method of claim 7,
after the prediction step, alarming an administrator or a user of prediction information; Wind power, characterized in that the alarming step analyzes the urgency or necessity of data and prediction information to be linked to the user's portable terminal using sound, sound, and lighting or to provide and control information from a distance How to maintain the generator.

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