A remote automation control method and system for DC arc furnace

Abstract

This invention relates to the field of electric arc furnace control technology, specifically to a remote automated control method and system for a DC electric arc furnace, comprising the following steps: correlating electrode displacement and current changes, marking abnormal segments of speed-current difference, screening voltage mutation nodes to form a time axis, comparing overlapping intervals to determine intervention periods, analyzing electrode state and arc voltage characteristics, and generating compensation commands if command mismatch occurs. In this invention, by acquiring electrode displacement parameters and current change trends, establishing a correlation sequence between the two, identifying trend consistency, and enhancing the ability to perceive abnormal behavior, a control anomaly is determined when current and electrode speed become disconnected, improving deviation identification accuracy. Voltage fluctuation judgment is superimposed to identify arc mutations, avoiding misjudgment based on a single signal. Abnormal time intervals are compared to extract intervention conflict segments, locate command interference nodes, and construct compensation commands by combining electrode behavior and arc voltage changes, thereby improving remote response stability and anti-interference capability, forming a precise closed-loop control chain.

Description

Technical Field

[0001] This invention relates to the field of electric arc furnace control technology, and in particular to a remote automated control method and system for a DC electric arc furnace. Background Technology

[0002] The field of electric arc furnace control technology encompasses control methods and devices for regulating and maintaining the arc state during the operation of electric arc furnaces. The core of this technology includes the dynamic adjustment of parameters such as arc current, voltage, arc length, electrode lifting, and input power to ensure arc stability, energy efficiency, and safe equipment operation. Electric arc furnace control technology is widely used in industries such as metallurgy, power equipment, and special material smelting. Its systematic content mainly covers multiple aspects, including electrical control, temperature control, automatic feeding control, and identification and switching control of smelting stages for AC and DC electric arc furnaces. The goal is to achieve high-precision, automated arc behavior management under high-temperature conditions.

[0003] The remote automated control method for DC electric arc furnaces refers to the centralized management of furnace operation control through the introduction of remote communication mechanisms and automated execution devices, enabling regulation of furnace electrode current, electrode displacement, arc stability, and smelting process steps. This method employs an industrial control computing platform combined with a remote data transmission interface to achieve remote setting and real-time feedback reception of control parameters. Simultaneously, it utilizes current sensors and voltage measurement circuits to collect actual operating condition data. After comparison with target values ​​and calculations by the control logic unit, execution commands are output to drive the electric regulating device to adjust electrode positions, regulate arc state, and advance the smelting process in stages according to preset process requirements.

[0004] While existing technologies can adjust key parameters such as arc current, voltage, and arc length during operation, their adjustment behavior is mainly based on static settings and fixed feedback responses, lacking a deep understanding of dynamic trends. When electrode displacement and current response become disconnected, traditional control modes struggle to promptly identify abnormal responses within the control chain. This makes it difficult for the control system to distinguish between natural disturbances under normal conditions and anomalies caused by control mismatch, easily leading to misjudgments or missed intervention opportunities. Current methods typically judge voltage fluctuations based on single-point amplitude exceeding limits, without combining other signals for multi-dimensional verification, resulting in frequent false triggering. In remote operation environments, without a mechanism to identify the collaborative relationships between multiple data sources, remote adjustments are prone to timing conflicts with the arc's behavior, causing control command incompatibilities, arc voltage oscillations, and other problems, thus affecting smelting stability. For example, misjudging arc changes caused by electrode movements in remote control often leads to drastic arc length fluctuations due to response delays or misadjustments, affecting heating uniformity and energy efficiency, ultimately adversely impacting smelting quality. The frequent occurrence of such problems indicates that existing control strategies lack adaptive verification mechanisms and cross-validation judgments, making it difficult to meet the needs of remote and refined control. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a remote automated control method and system for DC electric arc furnace.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote automated control method for a DC electric arc furnace, comprising the following steps:

[0007] S1: Obtain the displacement direction and velocity parameters of the electrode lifting, combine the current transformer to record the current change information, establish the correlation sequence between the current change direction and the electrode movement direction, and determine the trend consistency state group.

[0008] S2: Based on the time period with consistent direction in the trend consistency state group, identify the matching relationship between current change and electrode speed. If the difference exceeds the normal range time period, mark the control abnormal time period and generate a list of displacement response abnormality identifiers.

[0009] S3: Based on the time period in the list of abnormal displacement response identifiers, retrieve voltage sampling data, determine the voltage fluctuation amplitude within the corresponding time period, filter out time nodes that exceed the set voltage change threshold, and form an abnormal voltage change time axis.

[0010] S4: Compare the time axis of the abnormal voltage change with the time period in the list of abnormal displacement response identifiers, identify the overlapping intervals where the two occur simultaneously, filter the key time periods associated with control intervention according to the set criteria, and extract the set of remote adjustment conflict segments.

[0011] S5: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict zone, determine whether there is arc instability caused by control command mismatch, and generate a remote compensation action command structure.

[0012] As a further aspect of the present invention, the trend consistency state group includes displacement direction parameters, electrode velocity parameters, current motion direction correlation sequence, and direction consistency state; the displacement response anomaly identifier list includes current velocity matching relationship, difference degree threshold, and control anomaly period marker; the abnormal sudden voltage time axis includes voltage fluctuation amplitude data, set voltage change threshold, filtered time nodes, and time axis arrangement order; the remote adjustment conflict segment set includes overlapping time axis abnormal periods, control intervention standards, and key time period filtering conditions; and the remote compensation action command structure includes electrode state arc voltage characteristic correlation analysis, control command mismatch judgment conditions, and upward control command parameters.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Acquire displacement direction and velocity data during electrode lifting process, monitor changes in current output from current transformer, call the electrode displacement direction and current change direction at corresponding time points, perform direction consistency judgment, establish the direction correspondence between the two, and obtain direction matching sequence coefficients.

[0015] S102: Based on the direction matching sequence coefficients, call the electrode speed and current change rate, statistically analyze the rate change characteristics under the same direction state, determine the degree of overlap between the speed change interval and the current drastic fluctuation interval, and obtain the rate synchronization matching degree.

[0016] S103: Based on the rate synchronization matching degree and the distribution ratio of consistent nodes in the direction matching sequence coefficients, determine the stable state of trend change, classify the consistency characteristics of the trend, and generate a trend consistency state group.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Based on the time period with consistent direction in the trend consistency state group, extract the current change value and the electrode speed value, determine whether the change direction of the two is consistent in the same time period, identify data points with inconsistent change direction, and generate a trend value of the number of direction deviation points.

[0019] S202: Call the trend value of the number of directional deviation points, calculate the proportion of directional deviation points within the time period, compare it with the directional deviation ratio threshold, identify the time period when the degree of deviation exceeds the judgment standard, and obtain the abnormal segment judgment interval value.

[0020] S203: Extract the corresponding displacement response sequence based on the abnormal segment discrimination interval value, detect the interval distribution change, compare the difference with the standard interval under normal conditions, filter the time period where the degree of displacement exceeds the normal limit, and obtain the displacement response abnormality identifier list.

[0021] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0022] S301: Based on the time period marked in the displacement response anomaly identifier list, call the voltage sampling data output by the arc voltage detection device, extract the continuous voltage changes within the time period, calculate the voltage fluctuation characteristic value, and obtain the voltage change amplitude sequence.

[0023] S302: Compare the voltage data in the voltage change amplitude sequence with the set voltage change threshold, filter out time nodes that are greater than the threshold, and obtain a set of high amplitude fluctuation time nodes;

[0024] S303: Call all time nodes in the high-amplitude fluctuation time node set, sort them in ascending order according to the timestamp size, obtain a node sequence with reasonable structural continuity, and generate an abnormal voltage change time axis.

[0025] As a further aspect of the present invention, the specific calculation formula for the voltage fluctuation characteristic value is as follows:

[0026] ;

[0027] in, Represents the standardized voltage fluctuation characteristic value. N represents the instantaneous voltage change at the i-th sampling point, and N represents the total number of sampling points. Represents the amplitude of the j-th voltage peak. represents the amplitude of the j-th voltage trough, and M represents the number of voltage extreme pairs.

[0028] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0029] S401: Based on the abnormal sudden voltage time axis and the displacement response abnormality identifier list, cross-compare the two types of time periods, determine the overlapping relationship within the start and end time range, calculate the overlapping feature value, extract the time segments with simultaneous abnormalities, and generate a set of overlapping segments of sudden voltage and response.

[0030] S402: Call the concentrated time period of the overlapping section of the mutation voltage and response, extract the control intervention signal change information, and filter according to the signal amplitude change characteristics and the intensity of intervention actions to obtain a list of densely overlapping intervention sections;

[0031] S403: Based on the time periods in the list of densely overlapping intervention segments, extract the remote adjustment signal status, determine the matching relationship between the signal fluctuation frequency and the intervention period, identify the time segments of adjustment conflict, and obtain the set of remote adjustment conflict segments.

[0032] As a further aspect of the present invention, the specific calculation formula for calculating the overlapping feature value is as follows:

[0033] ;

[0034] in, This represents the start time of the voltage anomaly segment. This represents the end time of the voltage anomaly section. This represents the start time of the abnormal displacement response segment. This represents the termination time of the abnormal displacement response section. Representing the The amplitude of the sudden change at each voltage sampling point The arithmetic mean of the voltage fluctuation amplitudes Represents the voltage reference value. Representing the Displacement response amplitude at each voltage anomaly time point Representing the Voltage amplitude at each displacement response anomaly time point Represents the displacement reference value. Represents the total number of voltage sampling points. This represents the total number of abnormal time points.

[0035] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0036] S501: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, extract the instantaneous operating parameters and corresponding timing of the electrode, combine the jump interval and change amplitude, determine the correlation between arc voltage frequency and power oscillation, identify the region that deviates from the adjustment response cycle, and generate the electrode response offset rate.

[0037] S502: Based on the electrode response offset rate, identify the time offset between the control command and the response action, and combine the overlap between the oscillation amplitude and the command offset point in the arc voltage characteristics to filter the time period that meets the mismatch judgment condition and generate the mismatch section overlap coefficient.

[0038] S503: Call the overlap coefficient of the mismatched section, extract the control command section that meets the overlap condition, combine it with the amplitude information of the original control command, adjust the output structure of the upward control command, construct the command combination that matches both time and amplitude, and establish the remote compensation action command structure.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] In this invention, by acquiring electrode displacement direction and velocity parameters and combining them with current change trends, a correlation sequence between motion and current is established to achieve trend consistency identification, enhance the ability to perceive unexpected behavior, determine control response anomalies when current and electrode velocity are disconnected, improve the accuracy of system deviation discrimination, superimpose voltage fluctuation amplitude judgment, accurately identify sudden changes in arc state, avoid misjudgment of single physical quantities, compare abnormal data time intervals, extract high-risk segments of control intervention conflicts, locate command interference nodes, and construct compensation control commands by combining electrode behavior and arc voltage changes. Under remote control conditions, automatic correction is performed to improve response stability and anti-interference ability. The entire process forms a closed-loop chain from perception, analysis to execution, improving dynamic adaptability and precise control level. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] Please see Figure 1 A remote automated control method for a DC electric arc furnace includes the following steps:

[0045] S1: Obtain the displacement direction and velocity parameters of the electrode lifting, combine them with the current change information recorded by the current transformer, establish the correlation sequence between the current change direction and the electrode movement direction, and identify whether the two directions are consistent to obtain the trend consistency state group.

[0046] S2: Based on the time periods with consistent direction in the trend consistency state group, identify the matching relationship between current change and electrode speed. If there are time periods with differences exceeding the normal range, mark them as control abnormal periods and obtain a list of displacement response abnormality identifiers.

[0047] S3: Based on the time period marked in the list of abnormal displacement response identifiers, call the voltage sampling data output by the arc voltage detection device, judge the voltage fluctuation amplitude within the corresponding time period, filter the time nodes that are greater than the set voltage change threshold, and arrange them in chronological order to form an abnormal voltage change time axis.

[0048] S4: Compare the time axis of abnormal voltage mutation with the time period marked in the list of abnormal displacement response indicators, identify the overlapping intervals where the two occur simultaneously, and filter the key time periods associated with control intervention according to the set criteria to obtain the set of remote adjustment conflict segments.

[0049] S5: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, determine whether the arc instability behavior is caused by control command mismatch. If the conditions are met, construct the upward control command to form a remote compensation action command structure.

[0050] The trend consistency status group includes displacement direction parameters, electrode velocity parameters, current motion direction correlation sequence, and direction consistency status. The displacement response anomaly identifier list includes current velocity matching relationship, difference threshold, and control anomaly period marker. The abnormal voltage change time axis includes voltage fluctuation amplitude data, set voltage change threshold, filtered time nodes, and time axis arrangement order. The remote adjustment conflict segment set includes overlapping time axis abnormal periods, control intervention standards, and key time period filtering conditions. The remote compensation action command structure includes electrode state arc voltage characteristic correlation analysis, control command mismatch judgment conditions, and upward control command parameters.

[0051] Please see Figure 1 The specific steps of S1 are as follows:

[0052] S101: Acquire displacement direction and velocity data during electrode lifting process, monitor changes in current output from current transformer, call the electrode displacement direction and current change direction at corresponding time points, perform direction consistency judgment, establish the direction correspondence between the two, and obtain direction matching sequence coefficients.

[0053] During the electrode lifting process, the displacement direction and speed of the electrode at each moment are first recorded by devices such as laser rangefinders or encoders. The displacement direction can be represented as a positive or negative direction along the vertical axis, and the speed is the real-time electrode movement rate. At the same time, the current transformer collects and converts the current to digital to obtain real-time change data. Data is recorded every 100 milliseconds within the control cycle, including displacement direction, speed, and current value. Each data is timestamped for time sequence alignment. Consistency is judged based on the electrode displacement direction and current change direction at the same time point. For example, when the electrode moves downward and the current increases, it is considered that the directions are consistent, and vice versa. The consistency at each moment is marked as 1, and the inconsistency is marked as 0. The records form a consistency sequence arranged by time. The number of values ​​of 1 in the sequence is counted and then divided by the total amount of data to obtain the direction matching ratio coefficient. If 50 sets of data are collected within 5 seconds, and 35 sets of data are consistent in direction, then the direction matching ratio is 70%.

[0054] S102: Based on the direction matching sequence coefficients, call the electrode velocity and current change rate, statistically analyze the rate change characteristics under the same direction state, determine the degree of overlap between the velocity sudden change interval and the current violent fluctuation interval, and obtain the rate synchronization matching degree.

[0055] Based on the direction matching ratio, the electrode velocity and current change rate at moments with consistent direction are extracted to form two corresponding time series. The electrode velocity is differentially calculated to obtain the velocity change amplitude at adjacent moments, and the current rate is calculated based on the time difference of the current change value. The two series are analyzed to identify velocity abrupt change segments, i.e., time periods where the velocity change amplitude exceeds 0.5 mm / s, and simultaneously to identify violent fluctuation segments where the current change rate exceeds 200 A / s. These two thresholds are set based on the maximum velocity of the electrode control system and the normal arc current change range. The number of overlapping points between the two types of segments is counted and then divided by the total number of points in the velocity abrupt change segments to obtain the rate synchronization matching degree. For example, if there are 12 points in the velocity abrupt change segment and 9 points in the violent current fluctuation segment, the rate synchronization matching degree is 75%.

[0056] S103: Based on the rate synchronization matching degree and the distribution ratio of consistent nodes in the direction matching sequence coefficients, determine the stable state of trend change, classify the consistency characteristics of the trend, and generate trend consistency state groups.

[0057] In the existing directional consistency sequence, continuous directional consistency segments are searched, and the length of each segment is calculated. If the length of a segment exceeds 3 consecutive sampling points (300 milliseconds), it is recorded as a stable consistency segment. The total length of all stable consistency segments is counted and compared with the total length of all data to obtain the stable consistency distribution ratio. At the same time, the previously calculated rate synchronization matching degree is referenced to classify the trend consistency state based on the two indicators. If the stable consistency ratio exceeds 60% and the rate synchronization matching degree is not less than 70%, it is marked as a strong consistency state; if the stable consistency ratio is between 40% and 60%, or the rate synchronization matching degree is between 50% and 70%, it is a medium consistency state; the rest are weak consistency states. For example, in 50 sets of data, there are three stable consistency segments with lengths of 5, 4, and 6, totaling 15 sets of data, accounting for 30% of the total. If the rate synchronization matching degree is 52%, it is judged as a weak consistency state.

[0058] Please see Figure 1 The specific steps of S2 are as follows:

[0059] S201: Based on the time period with consistent direction in the trend consistency state group, extract the current change value and the electrode velocity value, determine whether the change direction of the two is consistent in the same time period, identify data points with inconsistent change direction, and generate the trend value of the number of direction deviation points.

[0060] Based on the time periods with consistent direction in the trend consistency state group, it is necessary to extract time segments with consistent trends from the synchronously acquired current and electrode velocity sequences. First, the current and electrode velocity data are synchronized according to the time axis, aligning the two sequences at the same time point. Then, it is determined whether the change directions of the two sequences are consistent within each time period. If the current continuously increases and the electrode velocity also shows a continuous upward trend within a certain time period, this segment is classified as a consistent direction interval. Within these consistent direction time periods, the change values ​​of current and electrode velocity are calculated hourly. For example, using continuous data points as a reference, the current difference and velocity difference between two adjacent time points are calculated, and their sign information is recorded for the purpose of determining the consistency between the two sequences. Whether the directions are consistent is determined. For example, if the current increases from 10 to 12 and the velocity decreases from 25 to 23 within a minute, the current direction is positive and the velocity direction is negative, indicating that the directions are inconsistent. Similar to the above steps, all intervals with consistent directions are traversed. Within each time period, all data points with inconsistent changing directions are identified and their numbers are counted. For example, if 120 sets of data are collected in a ten-minute interval, and 20 sets of data points are found to have current and velocity directions opposite, then the number of deviation points within that time period is 20. Then, a sliding time window method is used to continuously count the changes in the number of deviation points within each time period throughout the entire time series, ultimately forming a trend sequence of the number of deviation points for subsequent identification and analysis.

[0061] S202: Call the trend value of the number of directional deviation points, calculate the proportion of directional deviation points within the time period, compare it with the directional deviation ratio threshold, identify the time period when the degree of deviation exceeds the judgment standard, and obtain the abnormal segment judgment interval value;

[0062] After obtaining the trend value of the number of deviation points, it is necessary to calculate the proportion of deviation points in each time period. The number of deviation points in each time period is divided by the total number of data points to obtain the corresponding proportion value. For example, if 150 sets of data are collected in a 10-minute time period and 30 deviation points are identified, then the proportion of directional deviation points in that time period is 20%. Then, the deviation proportion of each time period is compared with the set directional deviation proportion threshold. The threshold setting should refer to the upper limit of the deviation point proportion obtained during normal system operation. Assuming that the maximum proportion under normal conditions is 15%, the threshold can be set to 15%. Based on this, it is determined whether there is an abnormal degree of deviation in each time period. If the proportion of a certain time period exceeds 15%, then the time period is marked as an abnormal segment. For example, if the deviation proportion of a certain time period is 18%, which exceeds 15%, then the time period is considered abnormal. Finally, all time periods with deviation proportions exceeding the threshold are collected to form the discrimination interval value of abnormal segments, which is used for subsequent response anomaly judgment.

[0063] S203: Extract the corresponding displacement response sequence based on the interval value of the abnormal section, detect the change in interval distribution, compare the difference with the standard interval under normal conditions, filter the time period where the degree of displacement exceeds the normal limit, and obtain the list of abnormal displacement response identifiers.

[0064] Based on the identified abnormal segments, the corresponding displacement response data sequence is extracted within each abnormal time period. Statistical characteristic analysis is performed on these data, including maximum, minimum, average, and fluctuation range. By comparing these statistical characteristics with the standard parameters in the normal reference range, significant differences in distribution are identified. The normal reference value can be established using long-term collected stable operating condition data. For example, under normal conditions, the average value is concentrated between 10 and 12, and the fluctuation range is less than 2. If the average displacement value is 14 and the fluctuation range is 3 within an abnormal time period, it exceeds the normal reference range. By setting a reasonable tolerance range, such as an average value tolerance of ±2 and a fluctuation range tolerance of ±1.5, if the characteristic parameters of a certain time period exceed the corresponding tolerance, that time period is added to the displacement response anomaly identification list. For example, if the average displacement value of a certain time period is 15.2, and the standard is 10 to 12, it exceeds the tolerance range. Similarly, if the fluctuation range also exceeds the limit, the segment is simultaneously confirmed to have an abnormal response. Finally, a complete list containing all abnormal response time periods is formed for further processing.

[0065] Please see Figure 1 The specific steps of S3 are as follows:

[0066] S301: Based on the time period marked in the list of displacement response anomalies, call the voltage sampling data output by the arc voltage detection device, extract the continuous voltage changes within the time period, calculate the voltage fluctuation characteristic value, and obtain the voltage change amplitude sequence.

[0067] The specific formula for calculating the characteristic value of voltage fluctuation is as follows:

[0068] ;

[0069] in, Represents the standardized voltage fluctuation characteristic value (unit: volts). N represents the instantaneous voltage change at the i-th sampling point (in volts), and N represents the total number of sampling points. This represents the amplitude of the j-th voltage peak (in volts). represents the amplitude of the j-th voltage trough (unit: volts), and M represents the number of voltage extreme pairs;

[0070] The process of monitoring and collecting data involves recording voltage signals within a specific time period using a data acquisition device. The obtained voltage signals are preprocessed to eliminate noise and interference, forming a voltage instantaneous change sequence ΔV. Assuming the monitoring period is 10 minutes and the acquisition frequency is 1Hz, the voltage instantaneous change dataset within this period contains a total of 600 sampling points.

[0071] Specific example data is as follows:

[0072] ΔV data (unit: volts): [0.1, -0.2, 0.15, -0.1, 0.05, -0.3, 0.25, -0.1, 0.2, -0.15, ..., 0.1] where N = 600;

[0073] The values ​​of voltage peaks and troughs are determined by analyzing the voltage variation dataset ΔV, using a maximum and minimum value extraction method, with parameter M representing the number of identified voltage extreme value pairs. During monitoring, the values ​​of the 1st to 10th voltage peaks and troughs are assumed to be as follows:

[0074] V1^{max}=4.5V, V1^{min}=2.0V;

[0075] V2^{max}=5.0V, V2^{min}=2.5V;

[0076] V3^{max}=4.0V, V3^{min}=1.5V;

[0077] V4^{max}=3.8V, V4^{min}=1.7V;

[0078] V5^{max}=5.2V, V5^{min}=2.4V;

[0079] Then M is 5;

[0080] For the calculation of the sum of squares of ΔV_i:

[0081] ;

[0082] Assuming the calculated result is 36, substitute it into the formula:

[0083] ;

[0084] The calculation of voltage extreme values ​​uses:

[0085] ;

[0086] Calculate the average value of M:

[0087] ;

[0088] Based on the above calculations, the complete calculation process for the formula η_v is as follows:

[0089] ;

[0090] The results show that the standardized characteristic value of voltage fluctuation is 2.725 volts, which reflects the amplitude of voltage fluctuation during the monitoring period. As an important characteristic value of the voltage change amplitude sequence, it can provide a key reference for subsequent voltage fluctuation trend analysis and anomaly identification.

[0091] The formula's operational logic constructs composite eigenvalues ​​through the root mean square term and the mean absolute range term: the first term After squaring the instantaneous voltage change to eliminate directional differences, the root mean square is taken to quantify the overall dispersion of voltage fluctuations; the latter term The absolute difference between each pair of extreme values ​​is calculated and averaged to characterize the amplitude of periodic fluctuations. Both quantities are in volts, and a comprehensive index is formed by linear superposition, which reflects both the intensity of instantaneous fluctuations and captures the characteristics of periodic amplitude, while avoiding the dimensional confusion caused by the introduction of dimensionless coefficients.

[0092] Voltage fluctuation characteristic values ​​are characterized by a two-dimensional quantization system consisting of the root mean square (RMS) term and the range term: RMS term The square operation of the instantaneous voltage change eliminates polarity interference, reflecting the discretized distribution intensity of voltage fluctuations; the mean absolute range term... The periodic amplitude characteristics of voltage fluctuations are captured by calculating the difference between extreme values. The linear superposition of the two physical quantities, both on the order of volts, forms a composite index, enabling the dual monitoring capability of simultaneously characterizing the intensity of transient voltage fluctuations and steady-state amplitude changes.

[0093] S302: Compare the voltage data in the voltage change amplitude sequence with the set voltage change threshold, filter out time nodes that are greater than the threshold, and obtain a set of high amplitude fluctuation time nodes;

[0094] First, based on the voltage fluctuation characteristics during normal equipment operation, a reasonable amplitude baseline value needs to be statistically determined, and a change threshold needs to be established. Typically, 30 minutes of operating voltage data are used to calculate the average and standard deviation of the voltage amplitude change. The threshold can be set as three times the average plus the standard deviation. For example, when the average is 0.12V and the standard deviation is 0.06V, the threshold is set to 0.30V. The entire voltage change sequence is traversed, and each point is compared. If the change value at a certain moment is greater than 0.30V, the corresponding time point is identified as a high-amplitude fluctuation point. For example, point 5240, with a time of 10:00:06.048, has a change value of 0.36V, exceeding the set threshold; therefore, this timestamp is included in the fluctuation time point set. Finally, a set of representative high-amplitude fluctuation time points is generated to facilitate the identification of abrupt changes.

[0095] S303: Call all time nodes in the high-amplitude fluctuation time node set, sort them in ascending order according to the timestamp size, obtain a node sequence with reasonable structural continuity, and generate an abnormal voltage time axis.

[0096] After extracting all timestamps from the high-amplitude fluctuation time nodes, they are sorted chronologically. By judging whether the interval between adjacent time nodes is within a set threshold range, node sequences with continuous structural characteristics are selected. The maximum continuous time interval is set to 10ms. The time difference between each timestamp is compared point by point. If the time interval between two points is less than or equal to 10ms, the two points are considered continuous and belong to the same mutation segment; otherwise, a new segment is started. For example, a set of high-amplitude node timestamps is 10:00:05.020, 10:00:05.024, 10:00:05.096, 10:00:06.004, and 10:00:06.008. The first two points have a 4ms interval, meeting the continuity condition and forming a mutation segment. The larger interval between the middle and next points forms a new mutation segment. By classifying and combining all continuous segments in this way, multiple structurally continuous sequences composed of high-amplitude nodes are finally obtained, which constitute the abnormal mutation voltage time axis.

[0097] Please see Figure 1 The specific steps of S4 are as follows:

[0098] S401: Based on the abnormal voltage time axis and the displacement response anomaly identifier list, cross-compare the two types of time periods, determine the overlap relationship within the start and end time range, calculate the overlap feature value, extract the time segments with simultaneous anomalies, and generate a set of overlapping segments of voltage and response.

[0099] The specific formula for calculating the overlap eigenvalue is as follows:

[0100] ;

[0101] in, Represents the start time (in seconds) of the voltage anomaly segment. This represents the termination time (in seconds) of the voltage anomaly section. Represents the start time (in seconds) of the abnormal displacement response segment. This represents the termination time (in seconds) of the abnormal displacement response segment. Representing the The amplitude of the sudden change (in volts) at each voltage sampling point. The arithmetic mean (volts) of the voltage fluctuation magnitude. The representative voltage reference value is taken as the system rated voltage of 220 volts. Representing the Displacement response amplitude (mm) at each voltage anomaly time point. Representing the Voltage amplitude (volts) at each displacement response anomaly time point. The representative displacement reference value is taken as 10 mm of the sensor range. Represents the total number of voltage sampling points. Represents the total number of abnormal time points;

[0102] Parameter definition and data source:

[0103] (The start time of the voltage anomaly is obtained in real time through the power monitoring system, and the time format is hour:minute:second).

[0104] (The start time of the abnormal displacement response was extracted from the displacement sensor data logs.)

[0105] (Voltage anomaly termination time, stored in the power system event recorder);

[0106] (Displacement response abnormal termination time, sensor data sampling interval is 5 seconds);

[0107] Volts (a sequence of voltage fluctuation amplitudes, sampled every 10 seconds by a power quality analyzer during abnormal periods);

[0108] Volts (calculation method: );

[0109] Volts (nominal voltage of low-voltage distribution network as specified in GB / T12325-2008 Power Quality Standard);

[0110] Millimeters (displacement response amplitude corresponding to the voltage anomaly time point, synchronously measured by a laser displacement gauge);

[0111] Volt (voltage amplitude corresponding to the abnormal displacement response time point, recorded by the power system waveform recording device);

[0112] Millimeters (according to JJG644-2003 Verification Procedure for Vibration Displacement Sensors, upper limit of the measuring range).

[0113] (The total number of voltage sampling points is automatically counted by the monitoring system);

[0114] (Total number of abnormal time points, number of times marked within the data collection period);

[0115] Time difference calculation:

[0116] ;

[0117] ;

[0118] ;

[0119] Voltage fluctuation term calculation:

[0120] ;

[0121] ;

[0122] ;

[0123] Calculation of displacement-voltage difference term:

[0124] ;

[0125] ;

[0126] Final calculation:

[0127] ;

[0128] Results explanation:

[0129] The result The value is a dimensionless overlap feature. When this value is greater than a preset threshold of 0.5, it is determined to be a valid overlapping segment. The calculation results show that the time overlap amount did not meet the triggering condition, and the correlation of continuous abnormal events needs to be further analyzed in conjunction with the subsequent segment set generation rules. The voltage fluctuation term of 1.0024 reflects the degree of deviation of the voltage change from the nominal value, and the displacement-voltage difference term of 61.27 characterizes the coupling anomaly strength of electromechanical parameters. The two together correct the determination accuracy of the original time overlap amount.

[0130] This formula comprehensively evaluates time overlap and signal anomaly intensity using a fractional structure: the numerator uses the absolute value of the time window overlap length. The time overlap is directly quantified, and the denominator is normalized to variance using voltage fluctuations. Normalized cumulative amount of displacement difference The superposition construction of the composite suppression factor involves square root operation controlling the growth rate of voltage fluctuations, absolute value summation enhancing the linear cumulative effect of displacement deviation, and fractional overall design enabling time-overlapping eigenvalues. It is positively correlated with the pure time overlap and negatively correlated with the signal anomaly intensity. It achieves dimensional unification (numerator is seconds, denominator terms are dimensionless, and the overall dimension is seconds) while suppressing the excessive influence of a single abnormal signal through the nonlinear growth characteristics of the denominator.

[0131] Overlapping eigenvalues The characteristic value is determined by the nonlinear ratio of the effective overlap length of the time window to the signal anomaly intensity. When the voltage fluctuation amplitude dispersion is low and the displacement response difference is small within the time overlap period, the value of the denominator term decreases, leading to an increase in the characteristic value, reflecting the high correlation between the two types of abnormal signals during this period. When there are drastic fluctuations in the voltage sampling point or a significant deviation between the displacement response and the voltage signal, the characteristic value of the denominator term decreases through a dual suppression mechanism of square root operation and absolute value summation, indicating the contradiction in the abnormal signal. This parameter achieves a clear physical meaning through dimensional consistency processing (numerator is time-dimensional, denominator is dimensionless), and its value directly reflects the degree of signal coordination during the overlap period, providing a quantitative basis for determining the effective overlap segment.

[0132] S402: Call the concentrated time period of the overlapping segment of sudden voltage and response, extract the control intervention signal change information, and filter according to the signal amplitude change characteristics and the intensity of intervention actions to obtain a list of densely overlapping intervention segments;

[0133] Extract each time period from the set of overlapping voltage and response segments, locate the corresponding control intervention signal records within each time period, read the signal values ​​at each time point, and statistically analyze the amplitude changes between adjacent time points. The signal change frequency within each overlapping segment can be obtained by calculating the number of signal value differences within short time intervals, for example, recording the number of changes every 100ms interval, while simultaneously calculating the total signal amplitude change within the entire segment. The screening criterion is: if the total number of signal changes within a segment exceeds 5 times, and the total amplitude change exceeds 20V, it is considered a dense signal change segment, and this segment is determined to be an intervention-intensive segment. For example, in a time period of 2.1s to 2.5s, the recorded signal changes are 4V, 6V, 5V, and 7V, with a total change of 22V, and at least one change occurs every 100ms, meeting the screening criteria. Repeat this screening process for all overlapping segments, and add segments that meet the criteria to the intervention-intensive overlapping segment list for further analysis.

[0134] S403: Based on the time periods in the list of densely overlapping intervention segments, extract the remote adjustment signal status, determine the matching relationship between the frequency of signal fluctuations and the intervention period, identify the time segments of adjustment conflicts, and obtain the set of remote adjustment conflict segments;

[0135] For each time segment within the densely overlapping intervention zone, the remote control signal state within its corresponding time period is matched, and the frequency of signal state switching is statistically analyzed (i.e., the number of times the signal state jumps from one level to another). The frequency characteristics are then determined based on the number of changes per unit time. If the intervention signal in a certain segment changes more than 10 times within 5 seconds, while the control signal changes less than 3 times in the same time period, the rhythms are considered mismatched. Further signal rhythm similarity analysis is employed to compare the fluctuation patterns of the intervention and control signals. If the two signals are not synchronized in their main peak positions and trends—for example, if the high-frequency intervention signal segment and the low-frequency control signal segment have little overlap and different fluctuation directions—they are identified as control conflict segments. Finally, all time segments that do not meet the synchronization rhythm judgment criteria are compiled into a set of remote control conflict segments for further processing.

[0136] Please see Figure 1 The specific steps of S5 are as follows:

[0137] S501: Based on the electrode operating status and arc voltage change characteristics within a concentrated time period of remote regulation conflict zone, extract the instantaneous operating parameters and corresponding timing of the electrode, combine the jump interval and change amplitude, determine the correlation between arc voltage frequency and power oscillation, identify the region that deviates from the regulation response cycle, and generate the electrode response offset rate.

[0138] During the concentrated time period of the remote adjustment conflict zone, it is first necessary to collect the operating data of the electrodes, including electrode displacement velocity, current intensity, arc voltage curve, etc., and record the arc voltage fluctuation of the electric arc furnace in real time, with 1000 samples per second. The instantaneous change value of the arc voltage at each moment is extracted, and the difference between adjacent time points is calculated. Simultaneously, the time information of electrode action is marked, and the time delay difference between electrode action and arc voltage change is calculated. By statistically analyzing these delay differences over multiple adjustment cycles, key time periods representing the conflict zone are selected, such as from the 60th to the 75th second. Frequency domain analysis is then performed on the arc voltage fluctuation in this zone to obtain the main frequency distribution, and the power wave is analyzed using the same method. The system obtains the main frequency data of the power, limits the frequency difference between the two to within 0.2Hz, and combines the overlap of the two in phase to determine whether there is a synchronization relationship. Then, the amplitude threshold of the arc voltage jump is set to 15V and the jump time interval threshold is set to 3s. All points where the fluctuation amplitude is greater than the set value and the interval between adjacent peaks is greater than the set value are selected as abnormal response points. At the same time, they are compared with the set adjustment cycle. If these abnormal points deviate from the center of the cycle by more than 20s, they are judged as response cycle offset. Finally, the response offset rate is calculated. For example, if 18 out of 100 response points are offset points, the offset rate is 18%. If it exceeds the set threshold of 15%, it indicates that the response cycle has a significant offset.

[0139] S502: Based on the electrode response offset rate, identify the time offset between the control command and the response action. Combine the overlap between the oscillation amplitude and the command offset point in the arc voltage characteristics, filter the time period that meets the mismatch judgment condition, and generate the mismatch section overlap coefficient.

[0140] Based on the electrode response offset rate, the control command time and its corresponding response action time are identified from the data. The time difference is calculated, and all control commands with a time difference exceeding 5 seconds are filtered out. Then, the arc voltage characteristics are searched to see if there are amplitude oscillations exceeding 20V before and after these offset times. If the condition is met, the time period is marked as a mismatch segment. All mismatch time periods are statistically analyzed to determine how many of them are accompanied by large oscillation amplitudes. This is compared with the total number of offset commands to calculate the oscillation overlap. If 10 offset points are recorded in a certain time period, and 7 of them occur within the time range where the oscillation amplitude exceeds 25V, the overlap is 70%. This value exceeds the set 60% judgment benchmark value and can be regarded as a significant mismatch. Thus, this type of time period is identified as a key segment that needs further processing.

[0141] S503: Call the overlap coefficient of the mismatched section, extract the control command section that meets the overlap condition, combine the amplitude information of the original control command, adjust the output structure of the upward control command, construct the command combination that matches both time and amplitude, and establish the remote compensation action command structure.

[0142] Based on the calculated overlap of the mismatched sections, all control command sections with an overlap exceeding 60% are extracted from the original command sequence. The current output amplitude corresponding to each control command is recorded. For example, commands numbered 7, 8, and 9 are issued at 190s, 200s, and 210s respectively, with corresponding output currents of 100A, 95A, and 110A. The average current value is calculated to be 101.67A. At the same time, the corresponding response offset times are calculated, such as 5s, 7s, and 6s respectively, with an average offset time of 6s. Based on this, the original command time is shifted forward, that is, each command is issued 6s earlier, forming new control command time points of 184s, 194s, and 204s. Meanwhile, the original current output is kept unchanged or slightly adjusted within ±5%, i.e., within the range of 95A to 115A, forming a new control command combination sequence. This sequence is consistent with the response pattern in both time and amplitude dimensions, thus replacing the original mismatched control structure for use in subsequent control strategies.

[0143] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A remote automated control method for a DC electric arc furnace, characterized in that, Includes the following steps: S1: Obtain the displacement direction and velocity parameters of the electrode lifting, combine the current transformer to record the current change information, establish the correlation sequence between the current change direction and the electrode movement direction, and determine the trend consistency state group. S2: Based on the time period with consistent direction in the trend consistency state group, identify the matching relationship between current change and electrode speed. If the difference exceeds the normal range time period, mark the control abnormal time period and generate a list of displacement response abnormality identifiers. S3: Based on the time period in the list of abnormal displacement response identifiers, retrieve voltage sampling data, determine the voltage fluctuation amplitude within the corresponding time period, filter out time nodes that exceed the set voltage change threshold, and form an abnormal voltage change time axis. S4: Compare the time axis of the abnormal voltage change with the time period in the list of abnormal displacement response identifiers, identify the overlapping intervals where the two occur simultaneously, filter the key time periods associated with control intervention according to the set criteria, and extract the set of remote adjustment conflict segments. S5: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict zone, determine whether there is arc instability caused by control command mismatch, and generate a remote compensation action command structure.

2. The remote automated control method for a DC electric arc furnace according to claim 1, characterized in that, The trend consistency state group includes displacement direction parameters, electrode velocity parameters, current motion direction correlation sequence, and direction consistency state. The displacement response anomaly identifier list includes current velocity matching relationship, difference degree threshold, and control anomaly period marker. The abnormal voltage change time axis includes voltage fluctuation amplitude data, set voltage change threshold, filtered time nodes, and time axis arrangement order. The remote adjustment conflict segment set includes overlapping time axis abnormal periods, control intervention standards, and key time period filtering conditions. The remote compensation action command structure includes electrode state arc voltage characteristic correlation analysis, control command mismatch judgment conditions, and upward control command parameters.

3. The remote automated control method for a DC electric arc furnace according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Acquire displacement direction and velocity data during electrode lifting process, monitor changes in current output from current transformer, call the electrode displacement direction and current change direction at corresponding time points, perform direction consistency judgment, establish the direction correspondence between the two, and obtain direction matching sequence coefficients. S102: Based on the direction matching sequence coefficients, call the electrode speed and current change rate, statistically analyze the rate change characteristics under the same direction state, determine the degree of overlap between the speed change interval and the current drastic fluctuation interval, and obtain the rate synchronization matching degree. S103: Based on the rate synchronization matching degree and the distribution ratio of consistent nodes in the direction matching sequence coefficients, determine the stable state of trend change, classify the consistency characteristics of the trend, and generate a trend consistency state group.

4. The remote automated control method for a DC electric arc furnace according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Based on the time period with consistent direction in the trend consistency state group, extract the current change value and the electrode speed value, determine whether the change direction of the two is consistent in the same time period, identify data points with inconsistent change direction, and generate a trend value of the number of direction deviation points. S202: Call the trend value of the number of directional deviation points, calculate the proportion of directional deviation points within the time period, compare it with the directional deviation ratio threshold, identify the time period when the degree of deviation exceeds the judgment standard, and obtain the abnormal segment judgment interval value. S203: Extract the corresponding displacement response sequence based on the abnormal segment discrimination interval value, detect the interval distribution change, compare the difference with the standard interval under normal conditions, filter the time period where the degree of displacement exceeds the normal limit, and obtain the displacement response abnormality identifier list.

5. The remote automated control method for a DC electric arc furnace according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the time period marked in the displacement response anomaly identifier list, call the voltage sampling data output by the arc voltage detection device, extract the continuous voltage changes within the time period, calculate the voltage fluctuation characteristic value, and obtain the voltage change amplitude sequence. S302: Compare the voltage data in the voltage change amplitude sequence with the set voltage change threshold, filter out time nodes that are greater than the threshold, and obtain a set of high amplitude fluctuation time nodes; S303: Call all time nodes in the high-amplitude fluctuation time node set, sort them in ascending order according to the timestamp size, obtain a node sequence with reasonable structural continuity, and generate an abnormal voltage change time axis.

6. The remote automated control method for a DC electric arc furnace according to claim 5, characterized in that, The specific formula for calculating the characteristic value of voltage fluctuation is as follows: ； in, Represents the standardized voltage fluctuation characteristic value. N represents the instantaneous voltage change at the i-th sampling point, and N represents the total number of sampling points. Represents the amplitude of the j-th voltage peak. represents the amplitude of the j-th voltage trough, and M represents the number of voltage extreme pairs.

7. The remote automated control method for a DC electric arc furnace according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Based on the abnormal sudden voltage time axis and the displacement response abnormality identifier list, cross-compare the two types of time periods, determine the overlapping relationship within the start and end time range, calculate the overlapping feature value, extract the time segments with simultaneous abnormalities, and generate a set of overlapping segments of sudden voltage and response. S402: Call the concentrated time period of the overlapping section of the mutation voltage and response, extract the control intervention signal change information, and filter according to the signal amplitude change characteristics and the intensity of intervention actions to obtain a list of densely overlapping intervention sections; S403: Based on the time periods in the list of densely overlapping intervention segments, extract the remote adjustment signal status, determine the matching relationship between the signal fluctuation frequency and the intervention period, identify the time segments of adjustment conflict, and obtain the set of remote adjustment conflict segments.

8. The remote automated control method for a DC electric arc furnace according to claim 7, characterized in that, The specific formula for calculating the overlapping feature value is as follows: ； in, This represents the start time of the voltage anomaly segment. This represents the end time of the voltage anomaly section. This represents the start time of the abnormal displacement response segment. This represents the termination time of the abnormal displacement response section. Representing the The amplitude of the sudden change at each voltage sampling point The arithmetic mean of the voltage fluctuation amplitudes Represents the voltage reference value. Representing the Displacement response amplitude at each voltage anomaly time point Representing the Voltage amplitude at each displacement response anomaly time point Represents the displacement reference value. Represents the total number of voltage sampling points. This represents the total number of abnormal time points.

9. The remote automated control method for a DC electric arc furnace according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, extract the instantaneous operating parameters and corresponding timing of the electrode, combine the jump interval and change amplitude, determine the correlation between arc voltage frequency and power oscillation, identify the region that deviates from the adjustment response cycle, and generate the electrode response offset rate. S502: Based on the electrode response offset rate, identify the time offset between the control command and the response action, and combine the overlap between the oscillation amplitude and the command offset point in the arc voltage characteristics to filter the time period that meets the mismatch judgment condition and generate the mismatch section overlap coefficient. S503: Call the overlap coefficient of the mismatched section, extract the control command section that meets the overlap condition, combine it with the amplitude information of the original control command, adjust the output structure of the upward control command, construct the command combination that matches both time and amplitude, and establish the remote compensation action command structure.

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