Substation energy consumption diagnosis method and system based on subentry measurement data

Abstract

The invention relates to the technical field of energy efficiency management of a power system, in particular to a substation energy consumption diagnosis method and system based on subentry measurement data. Acquiring real-time electrical parameters of each metering point, and calculating accumulated energy consumption data corresponding to each independent metering area or loop in a preset period according to the subentry metering data in the preset period; carrying out anomaly detection analysis according to the real-time electrical parameters of the metering points to obtain anomaly detection analysis values, carrying out anomaly classification identification according to the anomaly detection analysis values, and judging the anomaly conditions of the corresponding independent metering areas or loops; and performing energy consumption analysis according to the subentry measurement data in the preset period and the corresponding accumulated energy consumption data to obtain a visual energy consumption characteristic spectrum. Refined energy consumption management of each device of the transformer substation is realized, abnormal power consumption devices or loops are found in time, the energy efficiency is improved, and unnecessary energy consumption is reduced.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency management technology for power systems, and in particular to a method and system for diagnosing substation energy consumption based on sub-item metering data. Background Technology

[0002] Under the existing substation operation model, energy consumption management mainly relies on a global power monitoring system and extensive data analysis methods. This approach has monitoring blind spots, only able to grasp the overall power consumption of the substation, making it difficult to accurately quantify the individual energy consumption of each load device. For key indicators such as transformer power loss and air conditioning operating efficiency, the lack of precise monitoring methods makes it difficult to identify and locate abnormal points in power consumption in real time, resulting in significant obstacles to the formulation and implementation of energy-saving solutions. Summary of the Invention

[0003] The main objective of this invention is to provide a substation energy consumption diagnosis method and system based on sub-item metering data, which aims to accurately measure the electricity consumption of different load equipment, monitor energy consumption in real time, automatically identify abnormal consumption, analyze high-energy-consuming equipment, and optimize energy saving.

[0004] To achieve the above objectives, this invention proposes a substation energy consumption diagnosis method based on sub-item metering data, applied to substations, comprising the following steps:

[0005] The real-time electrical parameters of each metering point are obtained. The substation divides the station's power load into different categories based on the operating topology and the functional attributes of the electrical equipment. For each category of electrical equipment, an independent metering area or circuit is defined, and the metering points are set on the independent metering area or circuit.

[0006] Based on the sub-metering data within the preset period, calculate the cumulative energy consumption data corresponding to each independent metering area or loop within the preset period;

[0007] Anomaly detection and analysis are performed based on the real-time electrical parameters of the metering points to obtain anomaly detection and analysis values. Anomalies are then classified and identified based on these values ​​to determine the anomalies in the corresponding independent metering areas or circuits.

[0008] Energy consumption analysis is performed based on the sub-metering data and corresponding cumulative energy consumption data within a preset period to obtain a visualized energy consumption characteristic map.

[0009] In the above-mentioned substation energy consumption diagnosis method based on sub-item metering data, the anomaly detection and analysis includes:

[0010] The actual instantaneous power is calculated based on the real-time electrical parameters of the metering point;

[0011] The relative deviation rate is obtained by calculating the relative deviation between the actual instantaneous power and the reference power; the reference power is obtained from the reference energy consumption model.

[0012] The number of consecutive periods in which the relative deviation rate exceeds the abnormal threshold is obtained to obtain the continuous deviation value;

[0013] A volatility analysis was performed on the time series of the equipment's power, and the coefficient of variation was calculated.

[0014] The above-mentioned substation energy consumption diagnosis method based on sub-item metering data includes anomaly classification and identification based on anomaly detection analysis values, which includes:

[0015] If the relative deviation rate is greater than the abnormal threshold and the continuous deviation value exceeds the preset value, it is identified as an abnormally high state.

[0016] If the relative deviation rate is less than the abnormal threshold and the continuous deviation value exceeds the preset value, it is identified as an abnormal decrease state.

[0017] If the coefficient of variation is greater than the set threshold, it is identified as an unplanned operation.

[0018] The energy consumption analysis described in the above-mentioned substation energy consumption diagnosis method based on sub-item metering data includes:

[0019] The time dimension analysis results include daily load curves, cycle energy consumption comparisons, and seasonal energy consumption trends.

[0020] The equipment dimension analysis results are obtained from the equipment dimension analysis, which include equipment operating efficiency offset and equipment energy consumption ranking.

[0021] The energy consumption Sankey diagram was obtained from the composition ratio analysis;

[0022] An energy consumption characteristic map was established based on the analysis results of the time dimension, the analysis results of the equipment dimension, and the energy consumption Sankey diagram.

[0023] The time dimension analysis mentioned in the above-mentioned substation energy consumption diagnosis method based on sub-item metering data includes:

[0024] Daily load curve modeling:

[0025]

[0026] In the formula: N represents the average load power in the h-th hour. d P represents the number of days within the statistical period, ΔT represents the data sampling interval, and ΔT ≤ 5 / 60 hours; i(t) represents the actual measured instantaneous power value of device i at time t, S represents the target device set; h represents the hourly time period number, which is an integer from 0 to 23;

[0027] Comparison of energy consumption over cycles:

[0028]

[0029] K represents the energy consumption deviation coefficient; n represents the total number of sampling points (or time steps); P i (t) represents the actual measured instantaneous power value of device i at time t, P model (t) represents the baseline power predicted by the baseline energy consumption model at time t, E1 represents the total energy consumption of a certain period, E2 represents the total energy consumption of another period, T1 represents the set of time points of a certain period, T2 represents the set of time points of another period, M represents the total number of devices, and Δt is the data acquisition interval.

[0030] Seasonal trend analysis:

[0031] E ac (m)=α·HDD(m)+β·CDD(m)+γ;

[0032]

[0033] E ac (m) represents the total energy consumption of the air conditioning system in month m; HDD(m) represents the number of heating days in month m; CDD(m) represents the number of cooling days; T avg,d D represents the average temperature on day d; m α represents the number of days in month m; β represents the regression coefficients; and γ is the regression constant term.

[0034] In the above substation energy consumption diagnosis method based on sub-item metering data, the equipment dimension analysis includes:

[0035] The operating efficiency offset of the calculation equipment is calculated using the following formula:

[0036]

[0037] Δη i P represents the runtime efficiency offset. i (t) The actual measured instantaneous power value of device i at time t, P 额定 Indicates the rated power of the equipment, ζ opt,i This indicates the optimal operating efficiency value of the equipment under rated operating conditions;

[0038] Calculate the energy consumption ratio index of each type of load in the total power consumption of the station to obtain the equipment energy consumption ranking. The formula for calculating the energy consumption ratio index is as follows:

[0039]

[0040] R i P represents the energy consumption ratio of device i. i (t) represents the actual measured instantaneous power value of device i at time t, M represents the total number of devices, T represents the set of time points within the period, and P j (t) represents the actual measured instantaneous power value of device j at time t; Δt is the data acquisition interval.

[0041] In the above substation energy consumption diagnosis method based on sub-item metering data, the composition ratio analysis includes establishing a time-varying energy consumption composition matrix:

[0042]

[0043] Π(t) represents the time-varying energy consumption composition matrix, which is a multi-row, single-column matrix. Each row represents the composition ratio of an energy consumption component at time t and the sum of actual power; π trans (t) represents the proportion of the transformer system's energy consumption to the total energy consumption at time t; π ac (t) represents the proportion of the total energy consumption of the air conditioning system at time t; π light (t) represents the proportion of the lighting system's energy consumption to the total energy consumption at time t; This indicates that at time t, all entities belonging to transformer group G... trans The sum of the power of the equipment, that is, the total real-time power of the transformer system; This indicates that at time t, all elements belonging to air conditioning group G... ac The sum of the power of the equipment, that is, the total real-time power of the air conditioning system; This indicates that at time t, all items belonging to lighting group G... light The sum of the power of the devices, that is, the total real-time power of the lighting system.

[0044] The above-mentioned substation energy consumption diagnosis method based on sub-item metering data also includes: assessing equipment operating efficiency based on energy consumption data and equipment operating status, identifying abnormally high energy consumption equipment; performing energy-saving optimization and adjustment on abnormally high energy consumption equipment, and calculating the energy saving rate and the proportion of energy consumption that can be optimized based on the energy consumption changes before and after the energy-saving optimization and adjustment.

[0045] This invention also provides a substation energy consumption diagnosis system based on sub-item metering data, applied to the aforementioned substation energy consumption diagnosis method based on sub-item metering data, comprising:

[0046] The data acquisition module is used to obtain real-time electrical parameters of each metering point. The substation divides the station's electrical load into different categories (such as main transformer cooling system, air conditioning and ventilation system, DC power supply system, lighting system, secondary equipment system and auxiliary system) based on the operating topology and the functional attributes of the electrical equipment. For each type of electrical equipment, an independent metering area or circuit is divided, and the metering point is set on the independent metering area or circuit.

[0047] The data processing module is used to calculate the cumulative energy consumption data corresponding to each independent metering area or loop within the preset period based on the sub-metering data within the preset period.

[0048] The anomaly detection module is used to perform anomaly detection and analysis based on the real-time electrical parameters of the metering point, obtain anomaly detection and analysis values, and classify and identify anomalies based on the anomaly detection and analysis values ​​to determine the anomaly situation of the corresponding independent metering area or circuit.

[0049] The energy consumption analysis module is used to perform energy consumption analysis based on the sub-metering data and corresponding cumulative energy consumption data within a preset period, and to obtain a visualized energy consumption characteristic map.

[0050] The technical solution provided by this invention may include the following beneficial effects:

[0051] The substation energy consumption diagnosis method and system proposed in this invention, based on sub-item metering data, enables refined energy consumption management of various substation equipment, timely detection of abnormal power-consuming equipment or circuits, improved energy efficiency, and reduction of unnecessary energy consumption. Furthermore, by combining energy consumption analysis with energy-saving strategies, it can dynamically adjust equipment operating modes, optimize energy consumption structure, reduce overall substation energy consumption, and ultimately achieve energy conservation and emission reduction goals. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the substation energy consumption diagnosis process based on sub-item metering data according to the present invention;

[0054] Figure 2 This is a schematic diagram of the energy consumption analysis process according to an embodiment of the present invention;

[0055] Figure 3 This is a matrix diagram illustrating an alarm mechanism according to an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of an anomaly detection process according to an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] The following is combined with Figure 1 This invention describes a substation energy consumption diagnosis method based on sub-item metering data, according to an embodiment of the present invention. It includes the following steps:

[0059] Step S1: Obtain the real-time electrical parameters of each metering point. The substation divides the station's power load into different categories based on the operating topology and the functional attributes of the electrical equipment. For each category of electrical equipment, an independent metering area or circuit is defined, and the metering points are set on the independent metering area or circuit.

[0060] For example, based on the operating topology and the functional attributes of the electrical equipment, the substation's power load is divided into the following six core systems:

[0061] (a) Main transformer cooling system: including fans, oil pumps, water-cooled units and their control circuits;

[0062] (b) Air conditioning and ventilation system: including precision air conditioning, ventilation equipment and temperature control device;

[0063] (c) DC power supply system: including charging device, battery pack, relay protection panel and operating power circuit;

[0064] (d) Lighting system: including indoor and outdoor lighting fixtures and their power distribution circuits;

[0065] (e) Secondary equipment system: including monitoring host, communication equipment and measurement and control device;

[0066] (f) Auxiliary systems: including fire protection, security, dehumidifiers and maintenance power supplies;

[0067] For each type of equipment mentioned above, separate metering areas or circuits should be rationally defined. For example, this could be done according to all equipment on the circuit connected to the station service transformer. Intelligent energy metering devices should be deployed at key access nodes to achieve accurate itemization. For instance, a data acquisition terminal with metering functions can be connected to the distribution box of the substation's air conditioning equipment.

[0068] In practice, the metering node settings in the metering point deployment criteria must simultaneously meet the following requirements:

[0069] The principle of coverage integrity: ensure that the energy consumption data of each classification system is independent and traceable;

[0070] Topology optimization principle: The electrical distance between the metering point and the monitored equipment shall be ≤50 meters, and the point shall be located upstream of the power distribution branch;

[0071] Maintainability principle: The installation location of the metering device shall have a reserved maintenance passage and an operating space of ≥0.6m×0.8m.

[0072] At each sub-metering node, deploy data acquisition terminals with voltage, current, power, and energy metering functions, such as three-phase energy meters, smart circuit breakers, or multi-functional meters. These terminals have communication capabilities, supporting the uploading of collected data to the central monitoring platform or energy management system. For three-phase loads, three-phase current, voltage, power factor, active power, and reactive power should be collected; for DC systems, voltage, current, and total energy consumption should be collected.

[0073] Step S2: Calculate the cumulative energy consumption data corresponding to each independent metering area or loop within the preset period based on the sub-metering data within the preset period;

[0074] Specifically, for AC equipment, the formula for calculating instantaneous power is:

[0075]

[0076] Where P(t) is the active power at a certain moment; U(t) is the line voltage; I(t) is the line current; and cos(φ) is the power factor.

[0077] For DC equipment, the formula for calculating DC power is:

[0078] P DC (t)=U DC (t)×I DC (t);

[0079] The cumulative energy consumption is calculated based on the corresponding instantaneous power or DC power. The calculation formula is as follows:

[0080]

[0081] In the formula: E i (t) represents the cumulative energy consumption of device i during period T; P i (t) represents the actual measured instantaneous power value of device i at time t; Δt is the sampling period.

[0082] All cumulative energy consumption data is periodically collected and stored at set time intervals (e.g., 1 minute, 5 minutes, 15 minutes, etc.), and uploaded to a cloud platform or local SCADA system for unified management. Specifically, standardized communication protocols (e.g., Modbus, DL / T 645, IEC 61850, MQTT, etc.) are used to upload data from each collection terminal to the cloud platform or local SCADA system. Data can be transmitted through various communication methods, including: wired methods such as fiber optic Ethernet and RS485 bus; wireless methods such as Wi-Fi, NB-IoT, and 4G / 5G communication modules; and hybrid network architecture suitable for remote or complex field environments. To ensure data integrity and real-time performance, breakpoint resume, redundant data storage, and edge computing caching mechanisms are supported.

[0083] Step S3: Perform anomaly detection and analysis based on the real-time electrical parameters of the metering point to obtain anomaly detection and analysis values, and classify and identify anomalies based on the anomaly detection and analysis values ​​to determine the anomaly situation of the corresponding independent metering area or circuit.

[0084] For example, anomaly detection analysis includes:

[0085] Step S31: Calculate the actual instantaneous power based on the real-time electrical parameters of the metering point;

[0086] Step S32: Calculate the relative deviation rate based on the actual instantaneous power and the reference power; the reference power is obtained from the reference energy consumption model.

[0087] Specifically, the baseline energy consumption model consists of a historical statistical model method and a rule template method. The historical statistical model method calculates the historical average power per unit time based on the operating data of a device over a past period (e.g., the last 30 days or 90 days). Standard deviation σ i Maximum value P max and minimum value P min The following model is established as the baseline energy consumption model:

[0088]

[0089] In the formula: P model (t) represents the baseline power predicted by the baseline energy consumption model at time t, and k is a control coefficient (such as 1.5 or 2) used to adjust the sensitivity.

[0090] For equipment with well-defined load characteristics and significant periodic patterns (such as air conditioners, lighting, and cooling devices), a benchmark energy consumption model can be constructed using a rule-based template method. For example, an air conditioning system can have its expected operating power curve fitted based on the relationship between external temperature and load.

[0091] Based on a power curve fitted using a temperature-power fitting function, the baseline power at a given time t and external temperature can be predicted from the temperature-power fitting curve. The expression for the baseline energy consumption model of this type of equipment is as follows:

[0092] P model (t)=f(T ext (t));

[0093] P model (t) represents the baseline power predicted by the baseline energy consumption model at time t, where T ext (t) represents the external temperature at time t, and f(·) represents the temperature-power fitting curve.

[0094] The formula for calculating relative deviation is:

[0095]

[0096] D i (t) represents the relative deviation value, P i (t) represents the actual measured instantaneous power value of device i at time t, which is derived from real-time acquired data. model (t) represents the baseline power predicted by the baseline energy consumption model at time t. When D i (t)>D th D th To set an abnormal threshold, typically 10% to 30%, when the instantaneous power deviates from the reference range, the device is judged to have an abnormal current energy consumption.

[0097] Step S33: Obtain the number of consecutive periods in which the relative deviation rate exceeds the abnormal threshold to obtain the continuous deviation value; for example, if the number of consecutive periods is 2, it means that the relative deviation in the two consecutive periods exceeds the abnormal threshold. If N consecutive periods (N is a preset value, for example, N=3, within 5 minutes) exceed the abnormal threshold, it is judged as "continuous abnormality" and the alarm mechanism is activated.

[0098] Step S34: Perform volatility analysis on the time series of the equipment's power and calculate the coefficient of variation. The calculation formula is:

[0099]

[0100] When C Vi >CV th At that time, CV th To set a threshold, such as CV th =0.5 indicates abnormal power fluctuations in the equipment, which may indicate intermittent faults or unstable power supply.

[0101] For example, the following three types of abnormal patterns are considered for classification, identification, and judgment:

[0102] If the relative deviation rate is greater than the abnormal threshold and the continuous deviation value exceeds the preset value, it is identified as an abnormally high state. A relative deviation rate greater than the abnormal threshold means that the power is significantly higher than the baseline, such as a stalled fan or an air conditioner operating under overload for a long time. A continuous deviation value exceeding the preset value indicates that the equipment is operating at high power continuously.

[0103] If the relative deviation rate is less than the abnormal threshold, but the continuous deviation value exceeds the preset value, it is identified as an abnormally low state. A relative deviation rate less than the abnormal threshold indicates that the equipment is in the operating state but the power is low, which may be due to system failure or load shedding. A continuous deviation value exceeding the preset value indicates that the equipment is in the operating state but the power is low.

[0104] If the coefficient of variation is greater than the set threshold, it is identified as an unplanned operating state. That is, the equipment is running during non-operational periods or has not started according to the set operating logic.

[0105] For example, when any of the above types of abnormal events are detected, the following actions can be performed:

[0106] Event Log: Records the time of the anomaly, the device number, the anomaly type, and the measured and model values.

[0107] Intelligent alarms: Alarm notifications are sent to maintenance personnel via SCADA / HMI interface pop-ups, SMS, WeChat, APP push, email, etc.

[0108] Multi-level processing strategy:

[0109] Level 1 Alarm: Slight deviation, record and observe; occasional deviation, such as: a single Di(t) > Dth; CV slightly higher than CVth (critical).

[0110] Level 2 Alarm: Moderate deviation, inspection recommended; meets the following criteria: continuous deviation identification is established (multiple exceedances of the threshold) or CV is significantly higher than the threshold (drastic fluctuations).

[0111] Level 3 alarm: Severe deviation or persistent anomaly, recommending shutdown or emergency maintenance. Simultaneously, the following conditions must be met: the deviation significantly exceeds Dth; the persistent anomaly lasts for a long time or CV is extremely high / the unstable state is severe.

[0112] Step S4: Perform energy consumption analysis based on the sub-metering data and corresponding cumulative energy consumption data within the preset period to obtain a visualized energy consumption characteristic map. This allows maintenance personnel to easily review the overall energy consumption situation. An energy consumption characteristic map is a graphical representation that reflects the energy consumption behavior, change patterns, operating status, and abnormal characteristics of a system or equipment, constructed through data modeling, visualization, and classification analysis. The energy consumption characteristic map includes time-series energy consumption curves, power load profiles, energy consumption classification structures, multi-dimensional energy efficiency indicators, anomaly identification features, and seasonal / climate coupling relationships.

[0113] The substation energy consumption diagnosis method based on sub-item metering data proposed in this invention enables refined energy consumption management of various equipment in the substation, timely detection of abnormal power-consuming equipment or circuits, improved energy efficiency, and reduction of unnecessary energy consumption. Simultaneously, by combining energy consumption analysis with energy-saving strategies, it can dynamically adjust equipment operating modes, optimize energy consumption structure, reduce the overall energy consumption of the substation, and ultimately achieve energy conservation and emission reduction goals.

[0114] The energy consumption analysis includes: obtaining time-dimensional analysis results from time-dimensional analysis, which includes daily load curves, periodic energy consumption comparisons, and seasonal energy consumption trends; obtaining equipment-dimensional analysis results from equipment-dimensional analysis, which includes equipment operating efficiency offsets and equipment energy consumption rankings; obtaining an energy consumption Sankey diagram from composition ratio analysis; and establishing an energy consumption characteristic map based on the time-dimensional analysis results, equipment-dimensional analysis results, and energy consumption Sankey diagram.

[0115] For example, the time dimension analysis includes:

[0116] Daily load curve modeling (average power per hour):

[0117]

[0118] In the formula: N represents the average load power in the h-th hour. d P represents the number of days within the statistical period, ΔT represents the data sampling interval, and ΔT ≤ 5 / 60 hours; i (t) represents the actual measured instantaneous power value of device i at time t, S represents the target device set; h represents the hourly time period number, which is an integer from 0 to 23;

[0119] Cyclic energy consumption comparison, such as comparing energy consumption on weekdays and holidays, or comparing daytime energy consumption to nighttime energy consumption. Specifically, cyclical energy consumption comparison is as follows:

[0120]

[0121] K represents the energy consumption deviation coefficient; n represents the total number of sampling points (or time steps); P i (t) represents the actual measured instantaneous power value of device i at time t, P model (t) represents the baseline power predicted by the baseline energy consumption model at time t, E1 represents the total energy consumption of a certain period, E2 represents the total energy consumption of another period, T1 represents the set of time points of a certain period, T2 represents the set of time points of another period, M represents the total number of devices, and Δt is the data acquisition interval.

[0122] Seasonal trend analysis, such as the significant increase in energy consumption of air conditioners in summer:

[0123] E ac (m)=α·HDD(m)+β·CDD(m)+γ;

[0124]

[0125] E ac (m) represents the total energy consumption of the air conditioning system in month m; HDD(m) represents the number of heating days in month m; CDD(m) represents the number of cooling days; T avg,d D represents the average temperature on day d; m α represents the number of days in month m; β represents the regression coefficients; and γ is the regression constant term.

[0126] For example, equipment-level analysis includes calculating the operating efficiency offset of equipment, calculating the energy consumption ratio index of each type of load in the total electricity consumption of the station, obtaining the equipment energy consumption ranking, and identifying the main energy-consuming loads.

[0127] The operating efficiency offset of the calculated equipment is used to assess the degree of energy efficiency degradation. The formula for calculating the operating efficiency offset is:

[0128]

[0129] Δη i P represents the runtime efficiency offset. i (t) The actual measured instantaneous power value of device i at time t (which can be obtained from monitoring data), P 额定 This indicates the rated power of the equipment, that is, the maximum output power of the equipment under standard operating conditions. opt,i This represents the optimal operating efficiency value of the equipment under rated operating conditions, which is usually determined by the equipment's factory parameters or historical health status data.

[0130] Calculate the energy consumption ratio index of each type of load in the total power consumption of the station to obtain the equipment energy consumption ranking. The formula for calculating the energy consumption ratio index is as follows:

[0131]

[0132] R i P represents the energy consumption ratio of device i. i (t) represents the actual measured instantaneous power value of device i at time t, M represents the total number of devices, T represents the set of time points within the period, and P j (t) represents the actual measured instantaneous power value of device j at time t; Δt is the data acquisition interval.

[0133] For example, the composition analysis includes establishing a time-varying energy consumption composition matrix:

[0134]

[0135] ∏(t) represents the time-varying energy consumption composition matrix, which is a multi-row, single-column matrix. Each row represents the composition ratio of an energy consumption component at time t and the sum of actual power; π trans (t) represents the proportion of the transformer system's energy consumption to the total energy consumption at time t; π ac (t) represents the proportion of the total energy consumption of the air conditioning system at time t; π light (t) represents the proportion of the lighting system's energy consumption to the total energy consumption at time t; This indicates that at time t, all entities belonging to transformer group G... trans The sum of the power of the equipment, that is, the total real-time power of the transformer system; This indicates that at time t, all elements belonging to air conditioning group G... ac The sum of the power of the equipment, that is, the total real-time power of the air conditioning system; This indicates that at time t, all items belonging to lighting group G... light The sum of the power of the devices, that is, the total real-time power of the lighting system.

[0136] The system visually presents the energy consumption composition of systems such as air conditioning, lighting, DC systems, and cooling systems.

[0137] Preferably, the present invention further includes: evaluating equipment operating efficiency and identifying abnormally high-energy-consuming equipment based on energy consumption data and equipment operating status (such as start / stop signals, load rate, etc.). For example, by calculating the load rate:

[0138]

[0139] η i P represents the load factor. 实际 P represents the actual power of the device. 额定 Indicates the rated power of the equipment;

[0140] After energy-saving optimization and adjustment of abnormally high energy-consuming equipment, the energy-saving rate and the proportion of energy consumption that can be optimized are calculated based on the changes in energy consumption before and after the energy-saving optimization and adjustment.

[0141] The formula for calculating the energy saving rate is:

[0142]

[0143] ΔE represents the energy saving rate, E before E represents the energy consumption of equipment with abnormally high energy consumption before energy saving. after This indicates the energy consumption of equipment with abnormally high energy consumption after energy saving.

[0144] The formula for calculating the optimizable energy consumption ratio is as follows:

[0145]

[0146] ρoptim Indicates the proportion of energy consumption that can be optimized; U represents the set of adjustable devices (such as air conditioning, non-critical lighting, etc.), P i (t) represents the actual measured instantaneous power value of device i at time t, T represents the analysis period, and P j (t) represents the actual measured instantaneous power value of device j at time t, and M represents the total number of devices.

[0147] For example, energy-saving analysis reports can be generated periodically, including: trends of high-energy-consuming equipment; abnormal electricity consumption records and handling; the effectiveness of energy-saving measures; and subsequent energy-saving recommendations. All data and strategies form a closed-loop feedback mechanism to continuously optimize the station's electricity consumption management level.

[0148] For equipment that operates under low load for extended periods, such as air conditioners and fans, prompts are given to adjust equipment capacity configuration or optimize start-stop strategies to achieve energy-saving optimization. Energy-saving optimization also includes optimized operation scheduling, intelligent linkage control, and energy efficiency behavior reminders.

[0149] Operation scheduling optimization refers to implementing time-sharing operation control strategies to rationally arrange operation plans based on electricity prices, grid load, and equipment priorities. For example, "off-peak operation" is adopted for air conditioning equipment, and "regional sensor linkage" is adopted for lighting systems to reduce energy consumption during unnecessary periods.

[0150] Intelligent linkage control refers to start-stop logic based on multi-dimensional data linkage. For example, if the external temperature and humidity are higher than the threshold and the main transformer temperature is higher than the set value, the cooling device will be activated. Another example is that if the ambient light intensity is sufficient, daytime lighting will be turned off. If the low-load operation time exceeds a preset time, the system will automatically enter standby mode or shut down unloaded equipment.

[0151] Energy efficiency behavior alerts refer to data-driven energy efficiency prompts provided to operation and maintenance personnel, such as: "The air conditioner set temperature is too low, it is recommended to increase it by 2℃"; "The fan operating frequency is too high, it is recommended to enable intermittent operation mode".

[0152] This invention also provides a substation energy consumption diagnosis system based on sub-item metering data, applied to the aforementioned substation energy consumption diagnosis method based on sub-item metering data, comprising:

[0153] The data acquisition module is used to obtain real-time electrical parameters of each metering point. The substation divides the station's electrical load into different categories (such as main transformer cooling system, air conditioning and ventilation system, DC power supply system, lighting system, secondary equipment system and auxiliary system) based on the operating topology and the functional attributes of the electrical equipment. For each type of electrical equipment, an independent metering area or circuit is divided, and the metering point is set on the independent metering area or circuit.

[0154] The data processing module is used to calculate the cumulative energy consumption data corresponding to each independent metering area or loop within the preset period based on the sub-metering data within the preset period.

[0155] The anomaly detection module is used to perform anomaly detection and analysis based on the real-time electrical parameters of the metering point, obtain anomaly detection and analysis values, and classify and identify anomalies based on the anomaly detection and analysis values ​​to determine the anomaly situation of the corresponding independent metering area or circuit.

[0156] The energy consumption analysis module is used to perform energy consumption analysis based on the sub-metering data and corresponding cumulative energy consumption data within a preset period, and to obtain a visualized energy consumption characteristic map.

[0157] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made under the concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A substation energy consumption diagnosis method based on sub-item metering data, applied to substations, characterized in that: Includes the following steps: The real-time electrical parameters of each metering point are obtained. The substation divides the station's power load into different categories based on the operating topology and the functional attributes of the electrical equipment. For each category of electrical equipment, an independent metering area or circuit is defined, and the metering points are set on the independent metering area or circuit. Based on the sub-metering data within the preset period, calculate the cumulative energy consumption data corresponding to each independent metering area or loop within the preset period; Anomaly detection and analysis are performed based on the real-time electrical parameters of the metering points to obtain anomaly detection and analysis values. Anomalies are then classified and identified based on these values ​​to determine the anomalies in the corresponding independent metering areas or circuits. Energy consumption analysis is performed based on the sub-metering data and corresponding cumulative energy consumption data within a preset period to obtain a visualized energy consumption characteristic map.

2. The substation energy consumption diagnosis method based on sub-item metering data according to claim 1, characterized in that: The anomaly detection and analysis includes: The actual instantaneous power is calculated based on the real-time electrical parameters of the metering point; The relative deviation rate is obtained by calculating the relative deviation between the actual instantaneous power and the reference power; the reference power is obtained from the reference energy consumption model. The number of consecutive periods in which the relative deviation rate exceeds the abnormal threshold is obtained to obtain the continuous deviation value; A volatility analysis was performed on the time series of the equipment's power, and the coefficient of variation was calculated.

3. The substation energy consumption diagnosis method based on sub-item metering data according to claim 2, characterized in that: Anomaly classification and identification based on anomaly detection analysis values ​​includes: If the relative deviation rate is greater than the abnormal threshold and the continuous deviation value exceeds the preset value, it is identified as an abnormally high state. If the relative deviation rate is less than the abnormal threshold and the continuous deviation value exceeds the preset value, it is identified as an abnormal decrease state. If the coefficient of variation is greater than the set threshold, it is identified as an unplanned operation.

4. The substation energy consumption diagnosis method based on sub-item metering data according to claim 1, characterized in that: The energy consumption analysis includes: The time dimension analysis results include daily load curves, cycle energy consumption comparisons, and seasonal energy consumption trends. The equipment dimension analysis results are obtained from the equipment dimension analysis, which include equipment operating efficiency offset and equipment energy consumption ranking. The energy consumption Sankey diagram was obtained from the composition ratio analysis; An energy consumption characteristic map was established based on the analysis results of the time dimension, the analysis results of the equipment dimension, and the energy consumption Sankey diagram.

5. The substation energy consumption diagnosis method based on sub-item metering data according to claim 4, characterized in that: The time dimension analysis includes: Daily load curve modeling: In the formula: N represents the average load power in the h-th hour. d P represents the number of days within the statistical period, ΔT represents the data sampling interval, and ΔT ≤ 5 / 60 hours; i (t) represents the actual measured instantaneous power value of device i at time t, S represents the target device set; h represents the hourly time period number, which is an integer from 0 to 23; Comparison of energy consumption over cycles: K represents the energy consumption deviation coefficient; n represents the total number of sampling points (or time steps); P i (t) represents the actual measured instantaneous power value of device i at time t, P model (t) represents the baseline power predicted by the baseline energy consumption model at time t, E1 represents the total energy consumption of a certain period, E2 represents the total energy consumption of another period, T1 represents the set of time points of a certain period, T2 represents the set of time points of another period, M represents the total number of devices, and Δt is the data acquisition interval. Seasonal trend analysis: E ac (m)=α·HDD(m)+β·CDD(m)+γ; E ac (m) represents the total energy consumption of the air conditioning system in month m; HDD(m) represents the number of heating days in month m; CDD(m) represents the number of cooling days; T avg,d D represents the average temperature on day d; m α represents the number of days in month m; β represents the regression coefficients; and γ is the regression constant term.

6. The substation energy consumption diagnosis method based on sub-item metering data according to claim 4, characterized in that: Device-level analysis includes: The operating efficiency offset of the calculation equipment is calculated using the following formula: Δη i P represents the runtime efficiency offset. i (t) The actual measured instantaneous power value of device i at time t, P 额定 Indicates the rated power of the equipment, ζ opt,i This indicates the optimal operating efficiency value of the equipment under rated operating conditions; Calculate the energy consumption ratio index of each type of load in the total power consumption of the station to obtain the equipment energy consumption ranking. The formula for calculating the energy consumption ratio index is as follows: R i P represents the energy consumption ratio of device i. i (t) represents the actual measured instantaneous power value of device i at time t, M represents the total number of devices, T represents the set of time points within the period, and P j (t) represents the actual measured instantaneous power value of device j at time t; Δt is the data acquisition interval.

7. The substation energy consumption diagnosis method based on sub-item metering data according to claim 4, characterized in that: Compositional analysis includes establishing a time-varying energy consumption composition matrix: Π(t) represents the time-varying energy consumption composition matrix, which is a multi-row, single-column matrix. Each row represents the composition ratio of an energy consumption component at time t and the sum of actual power; π trans (t) represents the proportion of the transformer system's energy consumption to the total energy consumption at time t; π ac (t) represents the proportion of the total energy consumption of the air conditioning system at time t; π light (t) represents the proportion of the lighting system's energy consumption to the total energy consumption at time t; This indicates that at time t, all entities belonging to transformer group G... trans The sum of the power of the equipment, that is, the total real-time power of the transformer system; This indicates that at time t, all elements belonging to air conditioning group G... ac The sum of the power of the equipment, that is, the total real-time power of the air conditioning system; This indicates that at time t, all items belonging to lighting group G... light The sum of the power of the devices, that is, the total real-time power of the lighting system.

8. The substation energy consumption diagnosis method based on sub-item metering data according to claim 2, characterized in that: Also includes: Evaluate equipment operating efficiency based on energy consumption data and equipment operating status, and identify abnormally high energy consumption equipment; Energy-saving optimization and adjustment are carried out for abnormally high energy-consuming equipment, and the energy-saving rate and the proportion of energy consumption that can be optimized are calculated based on the changes in energy consumption before and after the energy-saving optimization and adjustment.

9. A substation energy consumption diagnosis system based on sub-item metering data, applied to the substation energy consumption diagnosis method based on sub-item metering data as described in any one of claims 1-8, comprising: The data acquisition module is used to obtain real-time electrical parameters of each metering point. The substation divides the station's electrical load into different categories (such as main transformer cooling system, air conditioning and ventilation system, DC power supply system, lighting system, secondary equipment system and auxiliary system) based on the operating topology and the functional attributes of the electrical equipment. For each type of electrical equipment, an independent metering area or circuit is divided, and the metering point is set on the independent metering area or circuit. The data processing module is used to calculate the cumulative energy consumption data corresponding to each independent metering area or loop within the preset period based on the sub-metering data within the preset period. The anomaly detection module is used to perform anomaly detection and analysis based on the real-time electrical parameters of the metering point, obtain anomaly detection and analysis values, and classify and identify anomalies based on the anomaly detection and analysis values ​​to determine the anomaly situation of the corresponding independent metering area or circuit. The energy consumption analysis module is used to perform energy consumption analysis based on the sub-metering data and corresponding cumulative energy consumption data within a preset period, and to obtain a visualized energy consumption characteristic map.

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