Intelligent line loss treatment method for low-voltage transformer area

Abstract

The invention belongs to the technical field of power system line loss treatment, and particularly relates to a low-voltage transformer area line loss intelligent treatment method. The method comprises the following steps: data acquisition and model construction: acquiring electric power, equipment and environment data through multiple types of equipment such as an intelligent electric meter and a distribution transformer monitoring terminal, and integrating a circuit structure and a user file to construct a transformer area data model; abnormal diagnosis: calculating theoretical line loss and actual line loss based on a model, identifying high loss, negative loss and sudden change loss abnormal transformer areas by adopting a 3 sigma principle, and positioning abnormal reasons through special algorithms such as metering faults, electricity stealing and three-phase imbalance; carrying out field checking, generating an electronic work order containing abnormal information, sending the order through a mobile terminal, and feeding back checking evidence in real time; precise treatment is carried out, and targeted measures such as fault equipment replacement and three-phase load adjustment are taken according to a checking result; effect evaluation and continuous optimization are carried out, a line loss rate decline value and economic benefits are quantified, and a diagnosis model and a treatment strategy are iterated in combination with cases. The intelligent, precise and efficient level of low-voltage transformer area line loss management can be remarkably improved, the line loss rate is reduced, the power supply economy and reliability are improved, and the method is suitable for line loss management scenes of various urban and rural low-voltage transformer areas.

Description

Technical Field

[0001] This invention belongs to the field of power system line loss management technology, and particularly relates to an intelligent management method for low-voltage distribution area line losses. Background Technology

[0002] In power systems, the low-voltage distribution area line loss rate is a key indicator for measuring the operational management level and economic efficiency of power supply companies. Currently, due to various factors such as metering faults, user electricity theft, aging lines, and load imbalance, the low-voltage distribution area line loss rate remains high, causing huge economic losses to power supply companies and affecting the stability and reliability of power supply. Traditional line loss management methods rely on manual experience and simple data analysis, making it difficult to quickly and accurately locate the root causes of line loss problems and failing to meet the needs of efficient and intelligent management in modern power systems. Summary of the Invention

[0003] This invention aims to provide an intelligent management method for line losses in low-voltage distribution areas. Through advanced algorithms and data processing technologies, it achieves accurate calculation, analysis, and management of line losses, effectively reducing the line loss rate and improving the economic benefits and management level of power supply companies.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A method for intelligent management of line losses in low-voltage distribution areas includes the following steps:

[0006] S1 Data Acquisition and Model Building: Utilizing various acquisition devices deployed in the low-voltage distribution area, comprehensive power data, equipment data, and environmental parameters are collected. Filtering algorithms are used to eliminate data noise, and the circuit topology and user profile information of the distribution area are integrated to build a complete and dynamically updated distribution area data model, providing a data foundation for subsequent analysis.

[0007] S2 Anomaly Diagnosis: Based on the aforementioned transformer area data model, the theoretical and actual line loss values ​​of the transformer area are calculated. Through comparative analysis, abnormal transformer areas such as high loss, negative loss, and sudden loss are identified. Furthermore, various intelligent diagnostic algorithms are applied to conduct a preliminary diagnosis of the causes of the anomalies, locating the type and possible location of the anomaly source.

[0008] S3 On-site Verification: Based on the diagnostic results of S2, an electronic work order is automatically generated, including the anomaly type, suspected fault location, key verification points, and safety precautions. The work order is dispatched to on-site professionals via mobile terminals. Professionals conduct on-site verification according to the work order instructions and upload photos, videos, test data, and final conclusions of the verification process in real time via mobile terminals.

[0009] S4 Precision Management: Based on the results of on-site inspections, develop and implement targeted management measures to eliminate the root causes of abnormal line loss.

[0010] S5 Effectiveness Evaluation and Continuous Optimization: After the governance measures are implemented, the system continuously monitors the line loss indicators of the transformer area. It automatically compares the line loss data before and after the governance to quantitatively evaluate the governance effect. At the same time, the entire process data of this governance (including diagnosis, verification, governance, and evaluation) is stored as a case in the knowledge base for iterative optimization of the diagnostic algorithm model and governance strategy, realizing the system's self-learning and continuous improvement.

[0011] Preferably, in S1, the data acquisition equipment includes a smart meter, a distribution transformer monitoring terminal, a concentrator, a current and voltage transformer, a temperature sensor, a humidity sensor, a power factor analyzer, a line parameter acquisition instrument, and a distribution area GIS positioning device; wherein, the smart meter is used to collect real-time electricity consumption data from the user end, the distribution transformer monitoring terminal is used to obtain transformer operating parameters, the current and voltage transformer is used to accurately collect line current and voltage signals, and the line parameter acquisition instrument is used to measure basic line parameters such as conductor resistance and reactance.

[0012] Preferably, in S1, the filtering algorithm is as follows:

[0013]

[0014] in: : The fused data value at time k; State transition matrix, representing the temporal correlation of data, with a value of 0.95 to 0.98; Gain, dynamically adjusted based on data error, ranging from 0.1 to 0.3; : Raw data collected at time k; : Observation matrix, matching data dimensions, residential transformer areas take 1, commercial transformer areas take 1.2; : Scenario dynamic correction coefficient, a correction coefficient that is dynamically adjusted according to the power consumption scenario and load fluctuation characteristics of the transformer area, ranging from 0.8 to 1.2.

[0015] Preferably, in S1, the power data includes: total power supply of the distribution area, total electricity sales of users, real-time line current / voltage / power, power factor, transformer load rate, and time-of-use load curve; the equipment data includes: transformer parameters, smart meter information, line parameters, and switchgear status; the environmental parameters include: distribution area ambient temperature, relative humidity, light intensity, and wind speed.

[0016] Preferably, in S2, the theoretical line loss calculation formula is:

[0017]

[0018] No. Section line current; Line equivalent resistance; Temperature correction factor Temperature coefficient of resistance Conductor temperature; Number of line segments; Altitude correction factor; Line aging factor; considering the impact of temperature, altitude, and line aging on line loss;

[0019] The actual line loss calculation formula is:

[0020]

[0021] Total input power of the transformer area; Reverse power is provided by distributed power sources; No. User electricity meters measure power;

[0022] Actual line loss rate = (power supply in the transformer area - power sales in the transformer area) / power supply in the transformer area × 100%, where power supply in the transformer area is the total electrical energy input to the high-voltage side of the transformer, and power sales in the transformer area is the total electrical energy measured by all users' smart meters.

[0023] Preferably, in S2, the abnormal transformer area identification algorithm is as follows:

[0024]

[0025]

[0026] In the formula: This represents the upper limit of the normal range for line loss rate. This represents the lower limit of the normal range for line loss rate. This represents the average line loss rate for transformer substations of the same type. The standard deviation of line loss rate for similar transformer substations; Seasonal correction factor; Dynamic confidence level adjustment factor; Current front-end load rate; Typical load factor; Correction factor for the impact of load rate on line loss; when the actual line loss rate of the transformer area... The area was identified as a high-damage transformer area. The area was initially determined to be a negative loss area. It was determined to be a mutation-damaged area. The line loss rate for time period t. This represents the line loss rate for the t-1 time period.

[0027] Preferably, in S2, the causes of the abnormality include: metering failure, electricity theft, three-phase imbalance, overload operation, line aging, harmonic interference, and abnormal access of distributed power sources.

[0028] Preferably, in S2, the specific algorithm for anomaly diagnosis includes:

[0029] Metering fault diagnosis algorithm:

[0030]

[0031] Parameter meaning: Measurement deviation rate; when this value exceeds a threshold, a measurement fault is determined. Total electricity collected by the metering device; Total number of users within the district; No. The actual electricity load of each user; Statistical time period; The measurement deviation rate threshold is set at 0.05.

[0032] Electricity theft diagnosis algorithm:

[0033]

[0034]

[0035] Parameter meaning: Line loss deviation rate; Theoretical line loss power calculated based on the transformer area data model; Actual monitored line loss power; Line loss deviation rate threshold; The user electricity consumption characteristic coefficient is calculated as the ratio of the user's actual electricity consumption to the average electricity consumption of users of the same type. The upper limit of the normal range for the user's electricity consumption characteristic coefficient is 0.6. If the value is lower than this and the line loss deviation rate exceeds the threshold, it is considered that there is suspicion of electricity theft.

[0036] Three-phase imbalance diagnosis algorithm:

[0037]

[0038]

[0039]

[0040] Parameter meaning: Three-phase current imbalance; The maximum current in a three-phase circuit; Minimum current in a three-phase circuit; Average value of three-phase current; Three-phase voltage imbalance; The maximum voltage in a three-phase circuit; Minimum voltage in a three-phase circuit; Average value of three-phase voltage; Overall imbalance; The three-phase imbalance threshold is set at 15%. If the value exceeds this threshold for 5 minutes, it is considered an abnormal three-phase imbalance.

[0041] Overload operation diagnostic algorithm:

[0042]

[0043] Parameter meaning: Current overload factor; The actual operating current of the line or equipment; The rated current of a line or equipment is determined based on the line cross-section and the equipment model. Temperature correction factor; The time weighting coefficient is determined based on the duration of the overload. The overload factor threshold is set at 1.1. Exceeding this value is considered an overload operation abnormality.

[0044] Line aging diagnosis algorithm:

[0045]

[0046] Parameter meaning: Line resistance deviation rate; Actual measured resistance of the circuit; The standard resistor for the new circuit; Temperature coefficient of resistance of circuit material; The difference between the actual ambient temperature and the standard temperature of 20℃; The line resistance deviation rate threshold is set at 20%. If it exceeds this value, it is considered an abnormal aging of the line.

[0047] Preferably, in S3, the mobile terminal is a smartphone or tablet computer with a dedicated APP installed, which has functions such as work order reception, navigation and positioning, on-site photo / video recording, data entry, voice recording, and real-time uploading to ensure that the verification process is traceable.

[0048] Preferably, in S4, the governance measures include: replacing faulty meters or transformers, investigating and handling electricity theft, adjusting the phase sequence of user load access to improve three-phase balance, adding or optimizing reactive power compensation devices, increasing capacity or changing loads on overloaded lines, replacing or partially upgrading aging lines, installing harmonic filtering devices, and configuring intelligent distributed power supplies.

[0049] Preferably, in S5, the effect evaluation includes indicators such as: absolute reduction in line loss rate (line loss rate before treatment - line loss rate after treatment), relative reduction in line loss rate ((absolute reduction value / line loss rate before treatment) × 100%), and annualized economic benefits (absolute reduction value × annual power supply × electricity price).

[0050] The present invention has the following beneficial effects:

[0051] 1. Precise Diagnosis: By using multiple devices such as smart meters and distribution transformer monitoring terminals, comprehensive data on power, equipment, and environment are collected and a data model of the distribution area is constructed. Combined with anomaly identification algorithms and specialized diagnostic algorithms, line loss is accurately calculated, abnormal distribution areas are identified, and specific causes are located. This solves the problems of fragmented traditional data and misjudgment or omission by manual judgment, providing accurate basis for subsequent management and reducing the cost of ineffective investigation.

[0052] 2. Highly efficient governance: Based on the diagnostic results, work orders containing the anomaly type and suspected location are generated. Work orders are dispatched via mobile terminals and verification evidence is fed back in real time, improving verification efficiency. At the same time, specific measures are matched for different anomaly causes to achieve targeted governance, which not only significantly reduces the line loss rate, but also reduces power supply failures caused by overload and line aging, thus improving power supply reliability.

[0053] 3. Continuous optimization: After governance, the system automatically compares the data, evaluates the effect by measuring changes in line loss rate and economic benefits, and then iterates and optimizes the diagnostic model and governance strategy by combining case data, thus building a closed loop of "governance-evaluation-optimization" to improve long-term governance capabilities. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] The invention will be further explained below in conjunction with its practical application in a low-voltage distribution area of ​​a residential community in a certain city.

[0057] 1. Data Acquisition and Model Building

[0058] 512 smart meters, 1 distribution transformer monitoring terminal, 12 sets of current and voltage transformers, 6 temperature and humidity sensors, and 3 line parameter acquisition instruments were installed. Power data (total power supply, electricity consumption of each user, current, voltage, power factor, etc.), equipment data (transformer load rate, line resistance, etc.), and environmental data (temperature, humidity) were continuously collected for 30 days. A filtering algorithm was used to smooth the raw data, and a data model of the distribution area was constructed, including circuit topology, user electricity consumption behavior profiles, and environmental influencing factors.

[0059] 2. Abnormal diagnosis

[0060] The theoretical line loss was calculated to be approximately 4.2%, while the actual line loss was 8.5%, showing a significant discrepancy. The system determined this area to be a "high-loss area" and preliminarily diagnosed it as being caused by a combination of "three-phase imbalance" and "metering failure".

[0061] 3. On-site verification

[0062] The system automatically generated a work order, indicating that Phase B was overloaded and some meters in Building 3 had metering abnormalities. Staff received the work order using a mobile app and conducted on-site inspections, finding that Phase B accounted for 52% of the total load, and that five older meters in Building 3 had errors exceeding 10%. Staff uploaded on-site photos, test data, meter numbers, and other information.

[0063] 4. Precise governance

[0064] Some B-phase users were relocated to A and C phases to achieve basic three-phase current balance. Five meters with large errors were replaced with new meters. A reactive power compensation device was installed on the transformer outlet side to improve the power factor to above 0.95.

[0065] 5. Effectiveness Evaluation and Continuous Optimization

[0066] After the remediation was completed, the system continuously monitored the line loss indicators of the transformer area. The line loss rate decreased to 4.8%, an absolute decrease of 3.7% and a relative decrease of 43.5%. The annualized economic benefit was approximately 126,000 yuan. The data from this remediation was entered into the knowledge base to optimize the correlation judgment logic between three-phase imbalance and metering faults in the diagnostic model.

[0067] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent management of line losses in low-voltage distribution areas, characterized in that: Includes the following steps: S1 Data Acquisition and Model Building: Using acquisition equipment, comprehensively collect power data, equipment data and environmental parameters of low-voltage distribution areas, use filtering algorithms to eliminate data noise, and integrate distribution area circuit structure and user files to build distribution area data models; S2 Anomaly Diagnosis: Based on the aforementioned transformer area data model, calculate the theoretical line loss and actual line loss, identify abnormal transformer areas with high loss, negative loss, and sudden loss, conduct a preliminary diagnosis of the cause of the anomaly, and locate the source of the anomaly; S3 On-site Verification: Based on the diagnostic results, a task work order is generated that includes the type of abnormality, suspected location and key points of verification. The work order is then dispatched to professional personnel via mobile terminal. Professional personnel conduct on-site verification according to the work order instructions and provide feedback on the verification results and evidence via mobile terminal. S4 Precision Governance: Based on the feedback from on-site inspections, corresponding governance measures are taken; S5 Effectiveness Evaluation and Continuous Optimization: After the treatment is completed, the line loss index of the transformer area is continuously monitored, the data before and after the treatment is automatically compared, and the treatment effect is quantitatively evaluated; based on the evaluation results and case data, the diagnostic model and treatment strategy are iteratively optimized.

2. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S1, the filtering algorithm is as follows: in: : The fused data value at time k; State transition matrix, representing the temporal correlation of data, with a value of 0.95 to 0.98; Gain, dynamically adjusted based on data error, ranging from 0.1 to 0.3; : Raw data collected at time k; : Observation matrix, matching data dimensions, residential transformer areas take 1, commercial transformer areas take 1.2; : Scenario dynamic correction coefficient, a correction coefficient that is dynamically adjusted according to the power consumption scenario and load fluctuation characteristics of the transformer area, ranging from 0.8 to 1.

2.

3. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S1, the power data includes: total power supply of the distribution area, total electricity sales of users, real-time line current / voltage / power, power factor, transformer load rate, and time-of-use load curves; the equipment data includes: transformer parameters, smart meter information, line parameters, and switchgear status; the environmental parameters include: distribution area ambient temperature, relative humidity, light intensity, and wind speed.

4. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S2, the theoretical line loss calculation formula is: No. Section line current; Line equivalent resistance; Temperature correction factor Temperature coefficient of resistance Conductor temperature; Number of line segments; Altitude correction factor; Circuit aging factors; The actual line loss calculation formula is: Total input power of the transformer area; Reverse power is provided by distributed power sources; No. User electricity meters measure power; Actual line loss rate = (power supply in the transformer area - power sales in the transformer area) / power supply in the transformer area × 100%, where power supply in the transformer area is the total electrical energy input to the high-voltage side of the transformer, and power sales in the transformer area is the total electrical energy measured by all users' smart meters.

5. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S2, the algorithm for identifying abnormal transformer areas is as follows: In the formula: This represents the upper limit of the normal range for line loss rate. This represents the lower limit of the normal range for line loss rate. This represents the average line loss rate for transformer substations of the same type. The standard deviation of line loss rate for similar transformer substations; Seasonal correction factor; Dynamic confidence level adjustment factor; Current front-end load rate; Typical load factor; Correction factor for the impact of load rate on line loss; when the actual line loss rate of the transformer area... The area was identified as a high-damage transformer area. The area was initially determined to be a negative loss area. It was determined to be a mutation-damaged area. The line loss rate for time period t. This represents the line loss rate for the t-1 time period.

6. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S2, the causes of the abnormality include: metering failure, electricity theft, three-phase imbalance, overload operation, line aging, harmonic interference, and abnormal access of distributed power sources.

7. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S2, the specific diagnostic algorithm for the cause of the anomaly includes: Metering fault diagnosis algorithm: Parameter meaning: Measurement deviation rate; when this value exceeds a threshold, a measurement fault is determined. Total electricity collected by the metering device; Total number of users within the district; No. The actual electricity load of each user; Statistical time period; The measurement deviation rate threshold is set at 0.

05. Electricity theft diagnosis algorithm: Parameter meaning: Line loss deviation rate; Theoretical line loss power calculated based on the transformer area data model; Actual monitored line loss power; Line loss deviation rate threshold; The user electricity consumption characteristic coefficient is calculated as the ratio of the user's actual electricity consumption to the average electricity consumption of users of the same type. The upper limit of the normal range for the user's electricity consumption characteristic coefficient is 0.

6. If the value is lower than this and the line loss deviation rate exceeds the threshold, it is considered that there is suspicion of electricity theft. Three-phase imbalance diagnosis algorithm: Parameter meaning: Three-phase current imbalance; The maximum current in a three-phase circuit; Minimum current in a three-phase circuit; Average value of three-phase current; Three-phase voltage imbalance; The maximum voltage in a three-phase circuit; Minimum voltage in a three-phase circuit; Average value of three-phase voltage; Overall imbalance; The three-phase imbalance threshold is set at 15%. If the value exceeds this threshold for 5 minutes, it is considered an abnormal three-phase imbalance. Overload operation diagnostic algorithm: Parameter meaning: Current overload factor; The actual operating current of the line or equipment; The rated current of a line or equipment is determined based on the line cross-section and the equipment model. Temperature correction factor; The time weighting coefficient is determined based on the duration of the overload. The overload factor threshold is set at 1.

1. Exceeding this value is considered an overload operation abnormality. Line aging diagnosis algorithm: Parameter meaning: Line resistance deviation rate; Actual measured resistance of the circuit; The standard resistor for the new circuit; Temperature coefficient of resistance of circuit material; The difference between the actual ambient temperature and the standard temperature of 20℃; The line resistance deviation rate threshold is set at 20%. If it exceeds this value, it is considered an abnormal aging of the line.

8. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S3, the mobile terminal is a smartphone or tablet computer with a dedicated APP installed, used to receive work orders and upload on-site photos, videos and data.

9. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S4, the governance measures include: replacing faulty equipment, investigating and dealing with electricity theft, adjusting three-phase loads, optimizing reactive power compensation, upgrading old lines, installing harmonic filtering devices, and configuring intelligent distributed power sources.

10. The intelligent management method for line loss in low-voltage distribution areas according to claim 1, characterized in that: In S5, the effectiveness evaluation includes: the absolute decrease in line loss rate, the relative percentage decrease, and economic benefit indicators.

Images