Electric power measurement data security risk assessment method and system

Abstract

The invention relates to the technical field of power equipment monitoring, and discloses an electric power measurement data security risk assessment method and system, and the method comprises the steps: presetting a monitoring period, collecting the real-time current of power equipment through electric power measurement equipment in the monitoring period, and recognizing the current operation state of the power equipment; wherein the operation state of the power equipment current comprises an equipment current normal signal and an equipment current abnormal signal; based on the equipment current abnormal signal, obtaining a corresponding time abnormal unit, and performing data processing on the time abnormal unit to obtain an operation stable value of the power equipment in the monitoring period; the operation stability value of the power equipment in the monitoring period is compared with the operation stability limit interval of the power equipment in the monitoring period, so that the risk assessment of the power equipment is completed, the risk assessment is performed on the operation of the power equipment through multiple dimensions, and the reliability is high.

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 assessing the security risks of power metering data. Background Art

[0002] In the field of power system management, in order to ensure the safe and efficient operation of power system equipment, the effective collection and accurate evaluation of power metering data has become crucial.

[0003] Power system equipment management involves a large and complex network structure, which requires real-time monitoring and evaluation of power metering data at each node in order to promptly detect potential anomalies and fluctuations.

[0004] Due to the complexity and diversity of the existing power system equipment operation process, in the process of power system equipment operation risk assessment, the evaluation of power system equipment through single power metering data is often unconvincing.

[0005] Based on this, a method and system for security risk assessment of power metering data are proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for assessing the security risk of electric power metering data, which obtains the operating stability value of the electric power equipment during the monitoring period according to the degree of current anomaly in the time anomaly unit, the time proportion of the anomaly and the identification and processing of the operating behavior data of the electric power equipment under the current anomaly state, and compares the operating stability value of the electric power equipment during the monitoring period with the operating stability limit range of the electric power equipment during the monitoring period, thereby completing the risk assessment of the electric power equipment.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for assessing the security risk of electric power metering data comprises the following steps:

[0009] A monitoring cycle is preset, and during the monitoring cycle, the real-time current of the power equipment is collected by the power metering equipment to identify the current operation status of the power equipment;

[0010] The operating status of the electric power equipment current includes a normal equipment current signal and an abnormal equipment current signal;

[0011] Based on the abnormal current signal of the equipment, the corresponding time abnormal unit is obtained, and the data of the time abnormal unit is processed to obtain the operation stability value of the power equipment in the monitoring period;

[0012] The operational stability value of the power equipment during the monitoring period is compared with the operational stability limit range of the power equipment during the monitoring period, thereby completing the risk assessment of the power equipment.

[0013] As a further solution of the present invention: The process of identifying the current operating state of the power equipment is as follows:

[0014] Divide the operating time of the power equipment within the monitoring period into several time units, and extract the maximum and minimum values of the current for each time unit;

[0015] If the maximum value of the current is within the current threshold range and the minimum value of the current is also within the current threshold range, it indicates that all currents in this time unit are within the current threshold range, and a normal equipment current signal is generated;

[0016] If the duration during which the maximum value of the current is not within the current threshold range or the minimum value of the current is not within the current threshold range exceeds the preset duration, it indicates that there is a current not within the current threshold range in this time unit, and an abnormal equipment current signal is generated.

[0017] As a further solution of the present invention: The data processing of the time abnormal unit includes processing the time occupancy value of the abnormal unit in the monitoring period, the abnormal value of the sound fluctuation in the monitoring period, and the abnormal value of the vibration frequency deviation in the monitoring period;

[0018] Record the time occupancy value of the abnormal unit in the monitoring period as Dt;

[0019] Record the abnormal value of the sound fluctuation in the monitoring period as Js;

[0020] Record the abnormal value of the vibration frequency deviation in the monitoring period as Jz;

[0021] Process the time occupancy value Dt of the abnormal unit in the monitoring period, the abnormal value Js of the sound fluctuation in the monitoring period, and the abnormal value Jz of the vibration frequency deviation in the monitoring period, that is, calculate the operation stability value Di of the power equipment in the monitoring period through the formula Di = k * Dt * (Zi + Ti), where k is a preset proportionality coefficient.

[0022] As a further solution of the present invention: Preset the limit values of the operation stability value of the power equipment in the monitoring period as Di1 and Di2, where Di1 < Di2;

[0023] When Di < Di1, it indicates that the overall operation state of the power equipment is good, and the operation risk of the power equipment in the monitoring period is low;

[0024] When Di1 ≤ Di < Di2, it indicates that the overall operation state of the power equipment is average, and the operation risk of the power equipment in the monitoring period is medium;

[0025] When Di ≥ Di2, it indicates that the overall operation state of the power equipment is poor, and the operation risk of the power equipment in the monitoring period is high.

[0026] As a further solution of the present invention: the process of obtaining the abnormal unit time occupation value of the monitoring period is:

[0027] Based on the abnormal current signal of the equipment, the corresponding time unit is recorded as a time abnormality unit;

[0028] The ratio of the number of time anomaly units to the total number of time units in the monitoring period is calculated to obtain the time anomaly unit ratio;

[0029] The abnormal unit time value of the monitoring period is obtained by multiplying the time abnormal unit number ratio and the interval deviation base.

[0030] The interval deviation base is obtained by processing the time interval values ​​corresponding to adjacent time anomaly units.

[0031] As a further solution of the present invention: the process of obtaining the abnormal value of the sound fluctuation in the monitoring period is:

[0032] By processing the maximum sound decibel value of the power equipment during operation in each time unit within the monitoring period, the abnormal rate of the power equipment sound decibel value group is obtained;

[0033] Based on the normal current signal of the device, the corresponding time unit is recorded as a normal time unit;

[0034] Processing the decibel behavior value corresponding to the time abnormal unit and the decibel behavior value corresponding to the time normal unit to obtain a decibel difference ratio;

[0035] The sound fluctuation abnormality value of the sound data of the power equipment during the monitoring period is obtained by multiplying the decibel difference ratio of the power equipment by the abnormality rate of the sound decibel value group of the power equipment.

[0036] As a further solution of the present invention: the process of obtaining the abnormal rate of the sound decibel value group of the power equipment is:

[0037] The maximum sound decibel values ​​of several electric power equipments during operation are integrated to obtain a sound decibel value group of the electric power equipments;

[0038] The variance of the sound decibel value group of the power equipment is calculated according to the variance calculation formula;

[0039] Performing difference processing on the maximum sound decibel value and the minimum sound decibel value in the sound decibel value group of the electric equipment, and comparing the obtained difference with the minimum sound decibel value to obtain the amplitude of the sound decibel value group of the electric equipment;

[0040] The variance of the electric equipment sound decibel value group is multiplied by the amplitude of the electric equipment sound decibel value group to obtain the fluctuation rate of the electric equipment sound decibel value group.

[0041] As a further solution of the present invention: the process of obtaining the decibel difference ratio is:

[0042] The decibel behavior value of the time abnormal unit is calculated by difference with the decibel behavior value of the time normal unit to obtain the decibel difference value of the power equipment within the monitoring period;

[0043] The decibel difference ratio is calculated by ratioing the decibel behavior value of the time normal unit to obtain the decibel difference ratio.

[0044] As a further solution of the present invention: the process of obtaining the abnormal value of the vibration frequency deviation of the monitoring period is:

[0045] The difference between the mean frequency of the abnormal time unit and the mean frequency of the normal time unit is calculated to obtain the frequency deviation value of the monitoring period;

[0046] The frequency deviation value of the monitoring period is calculated by ratio with the frequency mean value of the normal time unit to obtain the abnormal value of the frequency deviation of the power equipment in the monitoring period.

[0047] As a further solution of the present invention: a power metering data security risk assessment system, comprising:

[0048] The data monitoring module has a preset monitoring period. During the monitoring period, the data monitoring module collects the real-time current of the power equipment through the power metering equipment and identifies the current operation status of the power equipment;

[0049] The operating status of the device current includes a normal device current signal and an abnormal device current signal;

[0050] The abnormality analysis module is used to obtain the corresponding time abnormality unit based on the abnormal current signal of the equipment, perform data processing on the time abnormality unit, and obtain the operation stability value of the power equipment in the monitoring period;

[0051] The risk assessment module is used to compare the operating stability value of the power equipment during the monitoring period with the operating stability limit range of the power equipment during the monitoring period, thereby completing the risk assessment of the power equipment.

[0052] Beneficial effects of the present invention: The present invention collects and processes the current data of the power equipment through the power metering equipment during the monitoring period, obtains the time unit (time abnormality unit) corresponding to the abnormal operation of the power equipment, and obtains the operation stability value of the power equipment during the monitoring period according to the degree of current abnormality in the time abnormality unit, the proportion of abnormal time and the identification and processing of the power equipment operation behavior data under the current abnormality state, and compares and processes the operation stability value of the power equipment during the monitoring period with the operation stability limit range of the power equipment during the monitoring period, thereby completing the risk assessment of the power equipment, and conducting risk assessment on the operation of the power equipment through multiple dimensions, with strong reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a flow chart of a method for security risk assessment of electric power metering data according to an embodiment of the present invention;

[0055] Figure 2 It is a program flowchart of a power metering data security risk assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Example 1

[0058] See also Figure 1 As shown, the present invention is a method for assessing the security risk of power metering data, comprising the following steps:

[0059] A monitoring cycle is preset, and during the monitoring cycle, the real-time current of the power equipment is collected by the power metering equipment to identify the current operation status of the power equipment;

[0060] The operating status of the electric power equipment current includes a normal equipment current signal and an abnormal equipment current signal;

[0061] Based on the abnormal current signal of the equipment, the corresponding time abnormal unit is obtained, and the data of the time abnormal unit is processed to obtain the operation stability value of the power equipment in the monitoring period;

[0062] The operational stability value of the power equipment during the monitoring period is compared with the operational stability limit range of the power equipment during the monitoring period, thereby completing the risk assessment of the power equipment.

[0063] Among them, the identification process of the current operating state is:

[0064] Preset a monitoring cycle, divide the operation time of the power equipment in the monitoring cycle into a number of time units, extract the maximum and minimum current values ​​of each time unit, and compare the maximum and minimum current values ​​with the current threshold range respectively;

[0065] Among them, the current threshold range is summarized by the staff in this field based on historical experimental data;

[0066] If the maximum current value is within the current threshold range and the minimum current value is also within the current threshold range, it means that all currents in the time unit are within the current threshold range, and a normal device current signal is generated;

[0067] If the duration during which the maximum current value is not within the current threshold range or the minimum current value is not within the current threshold range exceeds the preset duration, it indicates that the current is not within the current threshold range within the time unit, and an abnormal current signal of the device is generated.

[0068] Among them, the power metering equipment includes a current sensor.

[0069] Based on the abnormal current signal of the equipment, the corresponding time unit is recorded as a time abnormality unit;

[0070] Based on the normal current signal of the device, the corresponding time unit is recorded as a normal time unit;

[0071] Get the number of time anomaly units within the monitoring period;

[0072] The ratio of the number of time anomaly units to the total number of time units in the monitoring period is calculated to obtain the time anomaly unit ratio;

[0073] Obtain the time interval values ​​corresponding to adjacent time anomaly units, sum and average all the time interval values ​​to obtain the interval mean, calculate the difference between each time interval value and the interval mean and take the absolute value to obtain the interval deviation value, calculate the ratio of the interval deviation value to the interval deviation threshold to obtain the interval deviation ratio, sum all the interval deviation ratios to obtain the interval deviation cardinality;

[0074] The abnormal unit time value of the monitoring period is obtained by multiplying the ratio of the number of time abnormal units with the interval deviation base.

[0075] During the monitoring period, the maximum sound decibel value of the power equipment during operation in each time unit is obtained, that is, the maximum sound decibel values ​​of several power equipment during operation are obtained, and the maximum sound decibel values ​​of several power equipment during operation are integrated to obtain a power equipment sound decibel value group;

[0076] The variance of the sound decibel value group of the power equipment is calculated according to the variance calculation formula;

[0077] Performing difference processing on the maximum sound decibel value and the minimum sound decibel value in the sound decibel value group of the electric equipment, and comparing the obtained difference with the minimum sound decibel value to obtain the amplitude of the sound decibel value group of the electric equipment;

[0078] The variance of the electric equipment sound decibel value group is multiplied by the amplitude of the electric equipment sound decibel value group to obtain the abnormality rate of the electric equipment sound decibel value group;

[0079] The maximum sound decibel values ​​corresponding to all time anomaly units within the monitoring period are summed and averaged to obtain the decibel behavior value of the time anomaly unit;

[0080] The maximum sound decibel values ​​corresponding to all normal time units in the monitoring period are summed and averaged to obtain the decibel behavior value of the normal time unit;

[0081] The decibel behavior value of the time abnormal unit is calculated by difference with the decibel behavior value of the time normal unit to obtain the decibel difference value of the power equipment within the monitoring period;

[0082] The decibel difference ratio is calculated by ratio calculation with the decibel behavior value of the time normal unit to obtain the decibel difference ratio;

[0083] The decibel difference ratio of the power equipment is multiplied by the abnormal fluctuation rate to obtain the abnormal value of the sound fluctuation of the power equipment sound data during the monitoring period;

[0084] Among them, the sound decibel value is collected by a sound sensor;

[0085] Obtain the maximum vibration frequency value corresponding to the power equipment in each time anomaly unit, sum and average the maximum vibration frequency values ​​of the time anomaly unit, and obtain the vibration frequency mean value of the time anomaly unit;

[0086] Obtain the maximum vibration frequency value corresponding to the power equipment in each normal time unit, sum and average the maximum vibration frequency values ​​of the normal time unit, and obtain the vibration frequency mean of the normal time unit;

[0087] The difference between the mean frequency of the abnormal time unit and the mean frequency of the normal time unit is calculated to obtain the frequency deviation value of the monitoring period;

[0088] The frequency deviation value of the monitoring period is calculated by ratio with the frequency mean value of the normal time unit to obtain the abnormal value of the frequency deviation of the power equipment in the monitoring period.

[0089] The abnormal unit time occupation value of the monitoring period is recorded as Dt;

[0090] Denote the abnormal value of sound fluctuation in the monitoring period as Js;

[0091] Denote the abnormal value of vibration frequency deviation in the monitoring period as Jz;

[0092] Process the abnormal unit time occupancy value Dt in the monitoring period, the abnormal value Js of sound fluctuation in the monitoring period, and the abnormal value Jz of vibration frequency deviation in the monitoring period. That is, calculate the operation stability value Di of the power equipment in the monitoring period through the formula Di = k * Dt * (Zi + Ti), where k is a preset proportionality coefficient;

[0093] Among them, the process of obtaining the preset proportionality coefficient is as follows:

[0094] There are m groups of historical data. Each group of historical data includes the abnormal unit time occupancy value Dt in the monitoring period, the abnormal value Js of sound fluctuation in the monitoring period, the abnormal value Jz of vibration frequency deviation in the monitoring period, and the operation stability value Di of the power equipment;

[0095] Use a linear model to fit the m groups of historical data. Substitute the prepared historical data into the selected fitting model for fitting, and obtain the average value of the fitting coefficients as the preset proportionality coefficient k.

[0096] The limit values of the operation stability value of the preset power equipment in the monitoring period are Di1 and Di2, where Di1 < Di2;

[0097] Among them, the limit values Di1 and Di2 of the operation stability value in the monitoring period are an empirical value, obtained according to experience:

[0098] In the actual obtaining process, there are many groups of abnormal unit time occupancy values, abnormal sound fluctuation values, and abnormal vibration frequency deviation values in the monitoring period. Process these many groups of abnormal unit time occupancy values, abnormal sound fluctuation values, and abnormal vibration frequency deviation values in the monitoring period to obtain the operation stability value in the monitoring period. The staff identify the operation state level of the power equipment based on these many groups of operation stability values in the monitoring period, so as to obtain the corresponding relationship between the operation stability value in a monitoring period and the operation state level of the power equipment. Furthermore, deduce and divide the operation stability value in the monitoring period according to the operation state of the power equipment, so as to obtain the limit values Di1 and Di2 of the operation stability value in the monitoring period. By comparing the limit values of the operation stability value in the monitoring period, the identification of the operation state level of the power equipment corresponding to the operation stability value in the monitoring period is completed;

[0099] When Di < Di1, it indicates that the overall operation state of the power equipment is good and the operation risk of the power equipment in the monitoring period is low;

[0100] When Di1 ≤ Di < Di2, it indicates that the overall operating state of the power equipment is average, and the operating risk of the power equipment during the monitoring period is medium;

[0101] When Di ≥ Di2, it indicates that the overall operating state of the power equipment is poor, and the operating risk of the power equipment during the monitoring period is high;

[0102] Thus, the effective monitoring of the safety risk of the power equipment during operation is completed.

[0103] Embodiment 2

[0104] Refer to Figure 2 , a power metering data security risk assessment system, including:

[0105] A data monitoring module, presetting a monitoring period, the data monitoring module collects the real-time current of the power equipment through the power metering equipment during the monitoring period, and identifies the current operating state of the power equipment;

[0106] Among them, the operating state of the equipment current includes a normal signal of the equipment current and an abnormal signal of the equipment current;

[0107] An abnormal analysis module, based on the abnormal signal of the equipment current, the abnormal analysis module is used to obtain the corresponding time abnormal unit, and perform data processing on the time abnormal unit to obtain the operating stability value of the power equipment during the monitoring period;

[0108] A risk assessment module, the risk assessment module is used to compare and process the operating stability value of the power equipment during the monitoring period with the operating stability limit interval of the power equipment during the monitoring period, so as to complete the risk assessment of the power equipment.

[0109] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A method for assessing the security risk of electric power metering data, characterized in that: It includes the following steps: Preset a monitoring period, collect the real-time current of the power equipment through the power metering equipment within the monitoring period, and identify the current operation status of the power equipment; Among them, the operation status of the power equipment current includes the equipment current normal signal and the equipment current abnormal signal; Based on the equipment current abnormal signal, obtain the corresponding time abnormal unit, process the data of the time abnormal unit, and obtain the operation stability value of the power equipment within the monitoring period; Compare and process the operation stability value of the power equipment within the monitoring period with the operation stability limit interval of the power equipment within the monitoring period, so as to complete the risk assessment of the power equipment.

2. A method for assessing the security risk of electric power metering data according to claim 1, characterized in that: The process of identifying the current operation status of the power equipment is as follows: Divide the operation time of the power equipment within the monitoring period into several time units, and extract the maximum and minimum current values of each time unit; If the maximum current value is within the current threshold range and the minimum current value is also within the current threshold range, it means that all currents in this time unit are within the current threshold range, and an equipment current normal signal is generated; If the duration during which the maximum current value is not within the current threshold range or the minimum current value is not within the current threshold range exceeds the preset duration, it means that there is a current not within the current threshold range within this time unit, and an equipment current abnormal signal is generated.

3. The method for assessing the security risk of electric power metering data according to claim 1 is characterized in that: The data processing of the time abnormal unit includes processing the time occupancy value of the abnormal unit in the monitoring period, the sound fluctuation abnormal value in the monitoring period, and the vibration frequency deviation abnormal value in the monitoring period; Record the time occupancy value of the abnormal unit in the monitoring period as Dt; Record the sound fluctuation abnormal value in the monitoring period as Js; Record the vibration frequency deviation abnormal value in the monitoring period as Jz; Process the time occupancy value Dt of the abnormal unit in the monitoring period, the sound fluctuation abnormal value Js in the monitoring period, and the vibration frequency deviation abnormal value Jz in the monitoring period, that is, calculate the operation stability value Di of the power equipment within the monitoring period through the formula Di = k * Dt * (Zi + Ti), where k is a preset proportionality coefficient.

4. A method for assessing the security risk of electric power metering data according to claim 3, characterized in that: Preset the limit values of the operation stability value of the power equipment within the monitoring period as Di1 and Di2, where Di1 < Di2; When Di < Di1, it means that the overall operation status of the power equipment is good, and the operation risk of the power equipment within the monitoring period is low; When Di1 ≤ Di < Di2, it means that the overall operation status of the power equipment is average, and the operation risk of the power equipment within the monitoring period is medium; When Di ≥ Di2, it means that the overall operation status of the power equipment is poor, and the operation risk of the power equipment within the monitoring period is high.

5. A method for assessing the security risk of electric power metering data according to claim 3, characterized in that: The process of obtaining the time occupancy value of the abnormal unit in the monitoring period is as follows: Based on the equipment current abnormal signal, record the corresponding time unit as the time abnormal unit; Calculate the ratio of the number of time abnormal units to the total number of time units in the monitoring period to obtain the time abnormal unit ratio; Multiply the time abnormal unit ratio by the interval deviation base number to obtain the time occupancy value of the abnormal unit in the monitoring period; Among them, the interval deviation base number is obtained by processing the time interval value corresponding to adjacent time abnormal units.

6. A method for assessing the security risk of electric power metering data according to claim 3, characterized in that: The process of obtaining the sound fluctuation abnormal value in the monitoring period is as follows: By processing the maximum sound decibel value of the power equipment during operation in each time unit within the monitoring period, the abnormal rate of the power equipment sound decibel value group is obtained; Based on the normal current signal of the device, the corresponding time unit is recorded as a normal time unit; Processing the decibel behavior value corresponding to the time abnormal unit and the decibel behavior value corresponding to the time normal unit to obtain a decibel difference ratio; The sound fluctuation abnormality value of the sound data of the power equipment during the monitoring period is obtained by multiplying the decibel difference ratio of the power equipment by the abnormality rate of the sound decibel value group of the power equipment.

7. A method for assessing the security risk of electric power metering data according to claim 6, characterized in that: The process of obtaining the abnormal rate of the sound decibel value group of power equipment is as follows: The maximum sound decibel values ​​of several electric power equipments during operation are integrated to obtain a sound decibel value group of the electric power equipments; The variance of the sound decibel value group of the power equipment is calculated according to the variance calculation formula; Performing difference processing on the maximum sound decibel value and the minimum sound decibel value in the sound decibel value group of the electric equipment, and comparing the obtained difference with the minimum sound decibel value to obtain the amplitude of the sound decibel value group of the electric equipment; The variance of the electric equipment sound decibel value group is multiplied by the amplitude of the electric equipment sound decibel value group to obtain the fluctuation rate of the electric equipment sound decibel value group.

8. A method for assessing the security risk of electric power metering data according to claim 6, characterized in that: The process of obtaining the decibel difference ratio is: The decibel behavior value of the time abnormal unit is calculated by difference with the decibel behavior value of the time normal unit to obtain the decibel difference value of the power equipment within the monitoring period; The decibel difference ratio is calculated by ratioing the decibel behavior value of the time normal unit to obtain the decibel difference ratio.

9. A method for assessing the security risk of electric power metering data according to claim 3, characterized in that: The process of obtaining the abnormal value of the vibration frequency deviation in the monitoring period is as follows: The difference between the mean frequency of the abnormal time unit and the mean frequency of the normal time unit is calculated to obtain the frequency deviation value of the monitoring period; The frequency deviation value of the monitoring period is calculated by ratio with the frequency mean value of the normal time unit to obtain the abnormal value of the frequency deviation of the power equipment in the monitoring period.

10. A power metering data security risk assessment system, characterized in that: include: The data monitoring module has a preset monitoring period. During the monitoring period, the data monitoring module collects the real-time current of the power equipment through the power metering equipment and identifies the current operation status of the power equipment; The operating status of the device current includes a normal device current signal and an abnormal device current signal; The abnormality analysis module is used to obtain the corresponding time abnormality unit based on the abnormal current signal of the equipment, perform data processing on the time abnormality unit, and obtain the operation stability value of the power equipment in the monitoring period; The risk assessment module is used to compare the operating stability value of the power equipment during the monitoring period with the operating stability limit range of the power equipment during the monitoring period, thereby completing the risk assessment of the power equipment.

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