Power equipment storage and transportation state monitoring method and system based on industrial internet

Abstract

The invention relates to the technical field of power equipment monitoring, in particular to a power equipment storage and transportation state monitoring method and system based on the industrial internet. Real-time transmission and sharing of data are achieved through the industrial internet technology, and it is ensured that monitoring of the storage and transportation state of the power equipment can reflect the actual situation in time. Meanwhile, a principal component analysis method is adopted to process and analyze the collected data, useful information is extracted, the storage and transportation state of the power equipment is accurately judged, and the problem of insufficient accuracy caused by incomplete data collection and limited analysis capability of a traditional monitoring method is solved. Through real-time analysis of the storage and transportation state of the power equipment, possible fault risks are predicted in advance. And a result verification module is introduced, and a plurality of verification methods are adopted to verify the data analysis result, so that the accuracy and reliability of the data analysis result are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to a method and system for monitoring the storage and operation status of power equipment based on industrial Internet. Background Art

[0002] During the storage and transportation of power equipment, it is crucial to ensure the safety and stability of the equipment. Traditional monitoring methods often have problems such as incomplete data collection, poor real-time performance, and limited analysis capabilities, and cannot meet the needs of modern power equipment storage and transportation management. With the development of industrial Internet technology, new solutions have been provided for efficient and accurate monitoring of the storage and transportation status of power equipment. However, the accuracy of data analysis results needs to be verified to ensure the reliability of the monitoring system. Summary of the invention

[0003] In view of the defects of the prior art, the present invention provides a method and system for monitoring the storage and transportation status of power equipment based on the industrial Internet, which uses industrial Internet technology to realize real-time transmission and sharing of data, ensures that the monitoring of the storage and transportation status of power equipment can timely reflect the actual situation, and uses the principal component analysis method to process and analyze the collected data to extract useful information, accurately judge the storage and transportation status of the power equipment, and overcome the problem of insufficient accuracy caused by incomplete data collection and limited analysis capabilities of traditional monitoring methods.

[0004] In order to solve the technical problem, the technical solution adopted by the present invention is: a method for monitoring the storage and transportation status of power equipment based on industrial Internet, comprising the following steps: S01, data acquisition, real-time acquisition of state parameters of power equipment through the sensor module, and transmission of the state parameters to the data transmission module; S02, data transmission, transmitting the collected state parameters to the data analysis module through the data transmission module; S03, data analysis, using principal component analysis algorithm to process and analyze the collected state parameters, extract the main features, and determine the storage and transportation status of the power equipment; S04. Status monitoring: Based on the data analysis results, the storage and transportation status of the power equipment is monitored in real time. If any abnormal situation is found, an alarm message will be automatically issued and corresponding decision support will be provided.

[0005] Furthermore, data analysis includes two parts: establishing a principal component model of normal working conditions and real-time data analysis. The process of establishing a principal component model of normal working conditions is as follows: S31, standardize the normal sample data and transform it into a standard data set with a mean of 0 and a variance of 1; S32, establishing a PCA principal component model for the standard data set in step S31, and extracting the principal component; The PCA principal component model is a Matrix X, m represents the feature dimension, n represents the number of samples, first calculate the covariance matrix A between the m features, and then find the eigenvalues ​​of the covariance matrix A With the eigenvector , and the eigenvalue Arrange the eigenvectors in descending order. Rearrange according to the corresponding eigenvalues; select the first k eigenvalues ​​according to the principle of proportion of the cumulative sum of eigenvalues Perform PCA dimensionality reduction and arrange the first k eigenvalues ​​from large to small Form a diagonal matrix , k corresponding eigenvectors Composition reduction moment ,Right now: ; After the PCA principal component model is reduced in dimension, the number of samples is still n, the number of features becomes k, and the dimensionality reduction formula is: , represents the matrix obtained after dimensionality reduction; The reconstruction formula is: ; represents the matrix obtained by dimensionality reduction and reconstruction of X, It is the extracted pivot element; S33, calculating the statistics of the PCA principal component model of the standard data set in step S31 and the corresponding control limits ; Statistics The calculation formula is: ; in Pair Matrix Each non-zero element in takes the -1 / 2 exponent, is the vector 2-norm; Control limits The calculation formula is: ; in, is the significance level, is the confidence level, It is an F distribution with the first degree of freedom k and the second degree of freedom nk; The real-time data analysis process is: S34, collect power equipment status parameters online and obtain new data from the collected data , and standardize; S35. Calculate the statistics of the standardized data : , S36. Monitor online data statistics Whether it exceeds the control limit of normal state , if not exceeded, repeat steps S34 and S35, otherwise go to step S37; S37. Calculate the statistics for each process variable The variable with the largest contribution rate is the variable that causes the fault. The fault type is determined based on the variable that causes the fault. Contribution rate calculation method: Check the standardized score of each monitoring data x , is mapped to The score of the observed value of the state parameter, is the corresponding eigenvalue, calculate each sensor data relatively Contribution rate: ,in Represents the dimension reduction matrix No. elements, yes The mean of the monitoring data, Calculate the Sensors Total contribution rate in .

[0006] Furthermore, z-score normalization is used for standardization.

[0007] Furthermore, the collected state parameters of the power equipment include an acceleration data set, an inclination angle data set, a speed data set, a temperature and humidity data set, and a gas pressure data set. The fault type caused by abnormal acceleration data is equipment acceleration abnormality, the fault type caused by abnormal inclination angle is equipment inclination abnormality, the fault type caused by abnormal speed data is equipment transportation abnormality, the fault type caused by abnormal temperature and humidity data is environmental abnormality, and the fault type caused by abnormal pressure data is transformer oil pressure abnormality.

[0008] Furthermore, it also includes step S05 fault warning. The fault warning is to construct an abnormal storage and transportation warning model for power equipment based on the results of data analysis. The input of the abnormal storage and transportation warning model for power equipment is the variables causing the fault and the corresponding fault type. The abnormal storage and transportation warning model for power equipment is trained. When warning, the power equipment status parameters collected by the sensor module are first transmitted to the abnormal storage and transportation warning model for power equipment for warning. If the warning indicates that a fault has occurred, the warning target is again subjected to data analysis, identification and verification.

[0009] Furthermore, it also includes step S06 result verification, in which a verification method is used to verify the data analysis results. The verification method includes comparing with historical data, conducting simulation experiments and introducing expert knowledge. If the verification fails, re-analysis or adjustment of parameters is performed.

[0010] Furthermore, the method further includes step S07 of user interaction, wherein the collected power equipment status parameters and alarm information are displayed through a user interface module, and the user operates through the user interface module.

[0011] The present invention also discloses an industrial Internet-based power equipment storage and transportation status monitoring system, comprising a sensor module, a data transmission module, a data analysis module, a user interface module and a result verification module; The sensor module is installed on the power equipment being stored and transported, and the user collects the status parameters of the power equipment during the storage and transportation process; The data transmission module is connected between the sensor module and the data analysis module, and uses industrial Internet technology to transmit the data collected by the sensor module to the data analysis module in real time; The data analysis module processes and analyzes the collected data, extracts useful information, and determines the storage and transportation status of power equipment; The user interface module is connected to the data transmission module and the data analysis module to display the storage and transportation status information of the power equipment and provide alarm and decision support functions; The fault warning module warns of possible faults based on data analysis results; The result verification module is used to verify the data analysis results.

[0012] Furthermore, the sensor module includes an acceleration sensor, an inclination angle sensor, a speed sensor, a temperature and humidity sensor, and a pressure sensor, which are respectively used to collect acceleration, inclination angle, speed, temperature and humidity, and pressure information during the storage and transportation of power equipment.

[0013] The beneficial effects of the present invention are as follows: the industrial Internet technology is adopted to realize the real-time transmission and sharing of data, and the real-time and accuracy of monitoring are improved.

[0014] Advanced data analysis algorithms, such as PCA, are used to process and analyze the collected data, extract useful information, and improve the level of intelligent monitoring.

[0015] It provides an intuitive user interface to facilitate user monitoring and management, thus improving user work efficiency.

[0016] The addition of a fault warning function can detect potential fault risks in advance and take timely measures to avoid equipment damage and accidents.

[0017] A result verification module is introduced to verify the data analysis results to ensure the accuracy and reliability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of the method described in the embodiment; Figure 2 This is a flowchart of fault warning. DETAILED DESCRIPTION

[0019] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0020] Example 1 This embodiment discloses a method for monitoring the storage and operation status of power equipment based on the industrial Internet. Figure 1 As shown, the following steps are included: S01, data acquisition, the sensor module collects the state parameters of the power equipment in real time and transmits the data to the data transmission module. In this embodiment, the sensor module includes an acceleration sensor, an inclination angle sensor, a speed sensor, a temperature and humidity sensor, and a pressure sensor, which are respectively used to collect acceleration, inclination angle, speed, temperature and humidity, and pressure information during the storage and transportation of the power equipment.

[0021] This step first collects N real-time data from each sensor to form a data set acceleration data set , tilt angle data set , speed data set , Temperature and humidity data collection , Gas pressure data set .

[0022] Get N raw data from the acceleration sensor , .

[0023] Get N raw data of the tilt angle sensor , .

[0024] Get N raw data of speed sensor , .

[0025] Get N raw data from temperature and humidity sensors , .

[0026] Get N raw data from pressure sensors , .

[0027] S02, data transmission, the data transmission module transmits the collected data to the data analysis module through the industrial Internet.

[0028] S03, Data Analysis: The data analysis module uses data analysis algorithms such as principal component analysis (PCA) to process and analyze the collected data, extract the main features, and determine the storage and transportation status of power equipment. For example, by analyzing the changing trends of parameters such as temperature and humidity, it can be determined whether the equipment has risks such as overheating and moisture.

[0029] S04, Status Monitoring: Based on the data analysis results, the storage and transportation status of power equipment is monitored in real time. If an abnormal situation is found, the system will automatically issue an alarm message and provide corresponding decision support.

[0030] S05, Fault warning: The fault warning module predicts and warns of possible faults based on the data analysis results. For example, when the temperature continues to rise or the vibration amplitude exceeds a certain threshold, the warning module determines that the equipment may have a risk of failure and issues a warning message in advance to notify relevant personnel to conduct inspections and maintenance.

[0031] S06. Result verification: The result verification module uses a variety of verification methods to verify the data analysis results. For example, the accuracy and reliability of the data analysis results can be verified by comparing with historical data, conducting simulation experiments, introducing expert knowledge, etc. If the verification result fails, the system will re-analyze the data or adjust the parameters to improve the accuracy of the results.

[0032] S07. User interaction: Users can view the storage and transportation status information of power equipment, receive alarm information and fault warning information, and perform corresponding operations and decisions through the user interface module.

[0033] In this embodiment, data analysis includes two parts: establishing a principal component model of normal working conditions and real-time data analysis. The process of establishing a principal component model of normal working conditions is as follows: S31, standardizing the normal sample data and transforming it into a standard data set with a mean of 0 and a variance of 1. In this embodiment, z-score normalization is used for standardization; S32, establishing a PCA principal component model for the standard data set in step S31, and extracting the principal component; The PCA principal component model is a The matrix X, m represents the feature dimension, n represents the number of samples, and there are 5 types of m, namely acceleration, tilt angle, speed, temperature and humidity, and gas pressure; , First calculate the covariance matrix A between the m features, and then find the eigenvalues ​​of the covariance matrix A With the eigenvector , and the eigenvalue Arrange the eigenvectors in descending order. Rearrange according to the corresponding eigenvalues; select the first k eigenvalues ​​according to the principle of proportion of the cumulative sum of eigenvalues Perform PCA dimensionality reduction and arrange the first k eigenvalues ​​from large to small Form a diagonal matrix , k corresponding eigenvectors Composition reduction moment ,Right now: ; After the PCA principal component model is reduced in dimension, the number of samples is still n, the number of features becomes k, and the dimensionality reduction formula is: , represents the matrix obtained after dimensionality reduction; The reconstruction formula is: ; represents the matrix obtained by dimensionality reduction and reconstruction of X, It is the extracted pivot element; S33, calculating the statistics of the PCA principal component model of the standard data set in step S31 and the corresponding control limits ; Statistics The calculation formula is: ; in Pair Matrix Each non-zero element in takes the -1 / 2 exponent, is the vector 2-norm; Control limits The calculation formula is: ; in, is the significance level, is the confidence level, It is an F distribution with the first degree of freedom k and the second degree of freedom nk; The real-time data analysis process is: S34, collect power equipment status parameters online and obtain new data from the collected data , and normalize (use z-score normalization for normalization, and use the parameters of training sample normalization to normalize new samples); S35. Calculate the statistics of the standardized data : , S36. Monitor online data statistics Whether it exceeds the control limit of normal state , if not exceeded, repeat steps S34 and S35, otherwise go to step S37; S37. Calculate the statistics for each process variable The contribution rate of the variable with the largest contribution rate is the variable that causes the fault. The fault type is determined based on the variable that causes the fault. The fault type caused by abnormal acceleration data is abnormal equipment acceleration, the fault type caused by abnormal inclination angle is abnormal equipment inclination, the fault type caused by abnormal speed data is abnormal equipment transportation, the fault type caused by abnormal temperature and humidity data is abnormal environment, and the fault type caused by abnormal pressure data is abnormal transformer oil pressure.

[0034] Contribution rate calculation method: Check the standardized score of each monitoring data x , is mapped to The score of the observed value of the state parameter, is the corresponding eigenvalue, calculate each sensor data relatively Contribution rate: ,in Represents the dimension reduction matrix No. elements, yes The mean of the monitoring data, Calculate the Sensors Total contribution rate in .

[0035] In this embodiment, Figure 2As shown, fault warning is to build an abnormal warning model for power equipment storage and transportation based on the results of data analysis. The input of the abnormal warning model for power equipment storage and transportation is the variables that cause the fault and the corresponding fault type. The abnormal warning model for power equipment storage and transportation is trained. When warning, the power equipment status parameters collected by the sensor module are first transmitted to the abnormal warning model for power equipment storage and transportation for warning. If the warning indicates that a fault has occurred, the warning target is again subjected to data analysis, identification and verification.

[0036] Example 2 This embodiment discloses an industrial Internet-based power equipment storage and transportation status monitoring system, including a sensor module, a data transmission module, a data analysis module, a user interface module and a result verification module; The sensor module is installed on the power equipment being stored and transported, and the user collects the status parameters of the power equipment during the storage and transportation process; in this embodiment, the sensor module includes an acceleration sensor, an inclination angle sensor, a speed sensor, a temperature and humidity sensor, and a pressure sensor, which are respectively used to collect the acceleration, inclination angle, speed, temperature and humidity, and pressure information of the power equipment during the storage and transportation process.

[0037] The data transmission module is connected between the sensor module and the data analysis module, and uses industrial Internet technology to transmit the data collected by the sensor module to the data analysis module in real time.

[0038] The data analysis module processes and analyzes the collected data, extracts useful information, and determines the storage and operation status of the power equipment. In this embodiment, the process of processing and analyzing the collected data by the data analysis module is the same as that in Embodiment 1, and will not be repeated here.

[0039] The user interface module is connected to the data transmission module and the data analysis module, and is used to display the storage and transportation status information of the power equipment and provide alarm and decision support functions.

[0040] The fault warning module warns of possible faults based on the data analysis results; the warning process is the same as that in Example 1 and will not be described again here.

[0041] The result verification module is used to verify the data analysis results.

[0042] The present invention can improve the real-time and accuracy of monitoring, use industrial Internet technology to achieve real-time data transmission and sharing, and ensure that the monitoring of the storage and transportation status of power equipment can reflect the actual situation in a timely manner. At the same time, advanced data analysis algorithms, such as principal component analysis (PCA), are used to process and analyze the collected data, extract useful information, and accurately judge the storage and transportation status of power equipment, overcoming the problem of insufficient accuracy caused by incomplete data collection and limited analysis capabilities of traditional monitoring methods.

[0043] Fault early warning is achieved, and possible fault risks can be predicted in advance through real-time analysis of the storage and transportation status of power equipment. When abnormal changes in parameters such as temperature, humidity, and vibration are detected, the fault early warning module can issue early warning information in a timely manner to notify relevant personnel to take measures to avoid equipment damage and accidents, thereby minimizing economic losses and safety hazards.

[0044] To ensure the reliability of data analysis results, a result verification module is introduced, and multiple verification methods are used to verify the data analysis results. For example, by comparing with historical data, conducting simulation experiments, and introducing expert knowledge, the accuracy and reliability of data analysis results are ensured, providing a scientific and accurate decision-making basis for the storage and transportation management of power equipment.

[0045] Improve user management efficiency and provide an intuitive user interface module to facilitate users to view the storage and transportation status information of power equipment, receive alarm information and fault warning information, and perform corresponding operations and decisions. Improve user work efficiency in the process of power equipment storage and transportation management and reduce management costs.

[0046] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the scope of protection. As used in the description in the text, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0047] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians may clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

Claims

1. A method for monitoring the storage and transportation status of power equipment based on the industrial Internet, characterized in that: The following steps are involved: S01, data acquisition, real-time acquisition of state parameters of power equipment through the sensor module, and transmission of the state parameters to the data transmission module; S02, data transmission, transmitting the collected state parameters to the data analysis module through the data transmission module; S03, data analysis, using principal component analysis algorithm to process and analyze the collected state parameters, extract the main features, and determine the storage and transportation status of the power equipment; S04. Status monitoring: Based on the data analysis results, the storage and transportation status of the power equipment is monitored in real time. If any abnormal situation is found, an alarm message will be automatically issued and corresponding decision support will be provided.

2. The method for monitoring the storage and transportation status of power equipment based on the industrial Internet according to claim 1 is characterized in that: Data analysis includes two parts: establishing the principal component model of normal working conditions and real-time data analysis. The process of establishing the principal component model of normal working conditions is as follows: S31, standardize the normal sample data and transform it into a standard data set with a mean of 0 and a variance of 1; S32, establishing a PCA principal component model for the standard data set in step S31, and extracting the principal component; The PCA principal component model is a Matrix X, m represents the feature dimension, n represents the number of samples, first calculate the covariance matrix A between the m features, and then find the eigenvalues ​​of the covariance matrix A With the eigenvector , and the eigenvalue Arrange the eigenvectors in descending order. Rearrange according to the corresponding eigenvalues; select the first k eigenvalues ​​according to the principle of proportion of the cumulative sum of eigenvalues Perform PCA dimensionality reduction and arrange the first k eigenvalues ​​from large to small Form a diagonal matrix , k corresponding eigenvectors Composition reduction moment ,Right now: ； After the PCA principal component model is reduced in dimension, the number of samples is still n, the number of features becomes k, and the dimensionality reduction formula is: ， represents the matrix obtained after dimensionality reduction; The reconstruction formula is: ； represents the matrix obtained by dimensionality reduction and reconstruction of X, It is the extracted pivot element; S33, calculating the statistics of the PCA principal component model of the standard data set in step S31 and the corresponding control limits ; Statistics The calculation formula is: ； in Pair Matrix Each non-zero element in takes the -1 / 2 exponent, is the vector 2-norm; Control limits The calculation formula is: ； in, is the significance level, is the confidence level, It is an F distribution with the first degree of freedom k and the second degree of freedom nk; The real-time data analysis process is: S34, collect power equipment status parameters online and obtain new data from the collected data , and standardize; S35. Calculate the statistics of the standardized data : ， S36. Monitor online data statistics Whether it exceeds the control limit of normal state , if not exceeded, repeat steps S34 and S35, otherwise go to step S37; S37. Calculate the statistics for each process variable The variable with the largest contribution rate is the variable that causes the fault. The fault type is determined based on the variable that causes the fault. Contribution rate calculation method: Check the standardized score of each monitoring data x , is mapped to The score of the observed value of the state parameter, is the corresponding eigenvalue, calculate each sensor data relatively Contribution rate: ,in Represents the dimension reduction matrix No. elements, yes The mean of the monitoring data, Calculate the Sensors Total contribution rate in .

3. The method for monitoring the storage and transportation status of power equipment based on the industrial Internet according to claim 2 is characterized in that: Standardization uses z-score normalization.

4. The method for monitoring the storage and transportation status of power equipment based on the industrial Internet according to claim 2 is characterized in that: The collected status parameters of the power equipment include acceleration data set, inclination angle data set, speed data set, temperature and humidity data set and gas pressure data set. The fault type caused by abnormal acceleration data is equipment acceleration abnormality, the fault type caused by abnormal inclination angle is equipment inclination abnormality, the fault type caused by abnormal speed data is equipment transportation abnormality, the fault type caused by abnormal temperature and humidity data is environmental abnormality, and the fault type caused by abnormal pressure data is transformer oil pressure abnormality.

5. The method for monitoring the storage and transportation status of power equipment based on the industrial Internet according to claim 2 is characterized in that: It also includes step S05 fault warning. The fault warning is to construct an abnormal storage and transportation warning model for power equipment based on the results of data analysis. The input of the abnormal storage and transportation warning model for power equipment is the variables causing the fault and the corresponding fault type. The abnormal storage and transportation warning model for power equipment is trained. When warning, the power equipment status parameters collected by the sensor module are first transmitted to the abnormal storage and transportation warning model for power equipment for warning. If the warning indicates that a fault has occurred, the warning target is again subjected to data analysis, identification and verification.

6. The method for monitoring the storage and transportation status of power equipment based on the industrial Internet according to claim 1 is characterized in that: The process also includes step S06 of result verification, in which the data analysis results are verified by a verification method, including comparison with historical data, simulation experiments, and introduction of expert knowledge. If the verification fails, the analysis is repeated or the parameters are adjusted.

7. The method for monitoring the storage and transportation status of power equipment based on the industrial Internet according to claim 1 is characterized in that: The step S07 is also included, in which the collected power equipment status parameters and alarm information are displayed through the user interface module, and the user operates through the user interface module.

8. An industrial Internet-based power equipment storage and transportation status monitoring system, characterized by: It includes a sensor module, a data transmission module, a data analysis module, a user interface module and a result verification module; The sensor module is installed on the power equipment being stored and transported, and the user collects the status parameters of the power equipment during the storage and transportation process; The data transmission module is connected between the sensor module and the data analysis module, and uses industrial Internet technology to transmit the data collected by the sensor module to the data analysis module in real time; The data analysis module processes and analyzes the collected data, extracts useful information, and determines the storage and transportation status of power equipment; The user interface module is connected to the data transmission module and the data analysis module to display the storage and transportation status information of the power equipment and provide alarm and decision support functions; The fault warning module warns of possible faults based on data analysis results; The result verification module is used to verify the data analysis results.

9. The power equipment storage and transportation status monitoring system based on industrial Internet according to claim 8 is characterized in that: The sensor module includes an acceleration sensor, an inclination angle sensor, a speed sensor, a temperature and humidity sensor, and a pressure sensor, which are used to collect acceleration, inclination angle, speed, temperature and humidity, and pressure information during the storage and transportation of power equipment.

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