Collaborative optimization control method for economic operation of cable transformer

Abstract

The invention discloses a collaborative optimization control method for economic operation of a cable transformer, and relates to the technical field of electric power. In the prior art, only the economic operation strategy of a traditional transformer is concerned, and the loss coupling effect of a cable line is not considered. The method specifically comprises the following steps: hardware deployment and communication network establishment; establishing a collaborative operation model; information field acquisition and regulation are carried out; and maintaining and optimizing the model according to stages. According to the method, the cable-transformer cooperative operation model is established, the real-time load and loss of the cable transformer are monitored, and economic cooperative operation regulation and control are performed in a targeted manner, so that the energy efficiency maximization is realized, and the purposes of improving the average current-carrying capacity of the cable, reducing the comprehensive loss of the transformer and avoiding the instantaneous overload risk are achieved; the cable transformer is ensured to run in the optimal energy efficiency state all the time, meanwhile, the service life of equipment is prolonged, and economical running of the cable transformer can be achieved on the premise that safe running is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a cable transformer economic operation collaborative optimization control method. Background Art

[0002] In power systems, cable transformers are key devices for energy transmission and distribution, and their operating efficiency is directly related to the energy consumption and operating costs of the entire system. Traditionally, the operation and management of cable transformers has relied heavily on regular maintenance and manual judgment, making it difficult to achieve real-time monitoring and precise control of the transformer's operating status. With the development of smart grids, although some systems have begun to use sensors to monitor transformer parameters, most of these systems remain at the data collection level and lack in-depth analysis and intelligent optimization and control strategies. Furthermore, traditional transformer economic operation strategies fail to consider the loss coupling effect of cable lines, resulting in low overall system efficiency.

[0003] Therefore, how to achieve economic operation of cable transformers while ensuring safe operation has become a key research direction in current power system optimization control. Based on this, we propose a coordinated optimization control method for economic operation of cable transformers. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a cable transformer economic operation collaborative optimization control method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A cable transformer economic operation collaborative optimization control method comprises the following steps:

[0007] S1: Hardware deployment and communication network construction;

[0008] S2: Establish collaborative operation model;

[0009] S3: Information field collection and control;

[0010] S4: Maintain and optimize the model by stage;

[0011] The hardware deployment includes setting up a cable monitoring unit and a transformer monitoring unit;

[0012] The communication network is a ring topology network constructed using industrial-grade ARM processors and industrial-grade TSN switches;

[0013] The collaborative operation model includes the following contents:

[0014] ① Operation status database;

[0015] ② Preset the prerequisites for collaborative control based on intelligent algorithms;

[0016] ③Build a mirror virtual model.

[0017] Preferably, the cable monitoring unit is a plurality of distributed optical fiber temperature sensors provided at the cable joints for measuring the cable temperature, and the distance between two adjacent distributed optical fiber temperature sensors is 150-250m;

[0018] The transformer monitoring unit is a temperature sensor for monitoring the hot spots of the windings and a dissolved gas analyzer arranged at the transformer oil pillow for analyzing the gas content of the oil pillow.

[0019] Preferably: the database includes cable parameters, transformer nameplate data and environmental factors;

[0020] The cable parameters include resistance per unit length, inductance, capacitance and allowable current carrying capacity;

[0021] The transformer nameplate data includes rated capacity, no-load loss / load loss, short-circuit impedance and voltage regulation range;

[0022] The environmental factors include the cable laying method and the soil thermal resistance coefficient.

[0023] Preferably, the intelligent algorithm includes an artificial intelligence algorithm and an improved NSGA-II multi-objective genetic algorithm.

[0024] Preferably, the coordinated control includes voltage and reactive power coordinated control, load rate balancing control and over-temperature load reduction curve;

[0025] The coordinated control also includes emergency treatment of cable transformers, including:

[0026] 1) Dynamic derating of cable current carrying capacity;

[0027] 2) When the transformer has a light gas alarm, it will automatically switch to the conservative operation mode;

[0028] 3) When the partial discharge of the cable is greater than 20pC, start segmented insulation monitoring and positioning.

[0029] Preferably, the mirror image virtual model is constructed by importing a single-line diagram of a power distribution system circuit, and laying out a one-to-one mapping association between cables, transformers and the mirror image virtual model;

[0030] The mirror virtual model includes an estimation simulation module, which calls an LSTM network to predict the cable transformer load within the next 1-60 minutes.

[0031] Preferably, the on-site collection and control of information includes the following aspects:

[0032] A1: Real-time data collection parameters;

[0033] A2: Analyze and collect data and make adjustments;

[0034] A3: Control instruction feedback.

[0035] Preferably, the data parameters include impedance, current, voltage, temperature, insulation resistance of the cable, iron loss / copper loss coefficient of the transformer, gas, and temperature and humidity of the environment;

[0036] The current and voltage are collected at a frequency of 5-15 ms / time, wherein when a current mutation of ≥20% is detected, cross-data verification and correction are required; the temperature and gas are collected at a frequency of 1-2 min / time.

[0037] Preferably, the content of the regulation includes the following aspects:

[0038] B1: When the voltage exceeds the limit, the transformer OLTC tap is controlled to adjust the gear, such as downshifting to reduce copper loss and lowering I 2 R loss;

[0039] B2: When reactive power is insufficient, the SVG device is controlled to start outputting capacitive reactive power;

[0040] B3: When overtemperature occurs, control the load transfer switch to cut off non-critical loads;

[0041] B4: When the cable temperature is greater than 80℃, the load is forced to be reduced and the standby cooling pump is started.

[0042] Preferably, the time limit for the instruction feedback is 2-5 minutes after the control instruction is issued, and the changes in data parameters need to be repeatedly reviewed multiple times.

[0043] The beneficial effects of the present invention are:

[0044] The present invention establishes a cable-transformer cooperative operation model, combines it with the real-time load and loss monitoring of the cable transformer, and conducts targeted economic cooperative operation regulation to maximize energy efficiency, thereby achieving the purpose of improving the average current carrying capacity of the cable, reducing the comprehensive loss of the transformer, and avoiding the risk of instantaneous overload, ensuring that the cable transformer always operates in the optimal energy efficiency state, while also extending the service life of the equipment. It can achieve economic operation of the cable transformer while ensuring safe operation, and avoid the chain tripping reaction that may be caused by the independent action of the cable overtemperature protection and the transformer overload protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a collaborative optimization control method for economic operation of a cable transformer proposed in the present invention. DETAILED DESCRIPTION

[0046] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0047] The following describes in detail embodiments of the present invention, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0048] Example 1:

[0049] A cable transformer economic operation collaborative optimization control method, such as Figure 1 As shown, the following steps are included:

[0050] S1: Hardware deployment and communication network construction;

[0051] Furthermore, the hardware deployment includes setting up a cable monitoring unit and a transformer monitoring unit;

[0052] The cable monitoring unit is a plurality of distributed fiber optic temperature sensors (DTS) installed at the cable joints for measuring the cable temperature, and the distance between two adjacent distributed fiber optic temperature sensors is 150-250m;

[0053] In addition, the transformer monitoring unit is a temperature sensor for monitoring the hot spots of the windings, which monitors the highest temperature point of the windings in real time during transformer operation, and a dissolved gas analyzer (DGA) installed at the transformer oil conservator to analyze the gas content of the oil conservator;

[0054] Preferably, the gas at the oil pillow includes but is not limited to H2, CH4 and C2H2, etc.

[0055] Furthermore, the communication network is a ring topology network constructed using an industrial-grade ARM processor and an industrial-grade TSN switch; preferably, its control instruction transmission delay is ≤10ms, and the clock synchronization accuracy is ≤1μs.

[0056] S2: Establish a collaborative operation model; use big data analysis technology to build a cable transformer collaborative operation status model, so as to accurately judge the health status, energy efficiency level and potential failure risks of the cable transformer.

[0057] Furthermore, the collaborative operation model includes the following contents:

[0058] ① Operation status database;

[0059] Preferably, the database includes cable parameters, transformer nameplate data, and environmental factors;

[0060] Further preferably, the cable parameters include resistance per unit length, inductance, capacitance and allowable current carrying capacity;

[0061] Further preferably, the transformer nameplate data includes rated capacity, no-load loss / load loss, short-circuit impedance and voltage regulation range;

[0062] Further preferably, the environmental factor includes the cable laying method (such as direct burial / bridge) and soil thermal resistance coefficient, etc.

[0063] ② Preset the preconditions for collaborative control based on intelligent algorithms; design a collaborative optimization control strategy for the economic operation of cable transformers, set the control preconditions for the collaborative operation of cable transformers, and automatically control the collaborative operation when a certain precondition is triggered.

[0064] Preferably, the intelligent algorithm includes artificial intelligence algorithm (such as deep learning, reinforcement learning, etc.) and improved NSGA-II multi-objective genetic algorithm, etc.;

[0065] Preferably, the coordinated control includes voltage and reactive power coordinated control, load rate balancing control and over-temperature load reduction curve, wherein the over-temperature load reduction curve is controlled such that the current limit is reduced by 5% for every 10K increase in temperature;

[0066] Furthermore, coordinated control also includes emergency response measures for cable transformers, specifically: 1) dynamic derating of cable current carrying capacity, such as an 8% current reduction for every 10°C increase in temperature; 2) automatic switching to conservative operation mode when a transformer light gas alarm sounds; and 3) activating segmented insulation monitoring and positioning when cable partial discharge exceeds 20pC. An emergency interruption strategy for cable transformers is implemented to ensure normal system operation.

[0067] ③ Build a mirrored virtual model as an intuitive reference so that operators can understand the status of the real equipment at any time through the background.

[0068] Preferably, the mirror image virtual model is constructed by importing a single-line diagram of a power distribution system circuit, and laying out a one-to-one mapping association between cables and transformers and the mirror image virtual model.

[0069] Further preferably, the mirror virtual model includes an estimation simulation module, which can call the LSTM network to predict the cable transformer load within the next 1-60 minutes; simulate the actual cable transformer load situation in advance so that people can initiate corresponding measures in advance to reduce economic losses.

[0070] S3: Information field collection and control;

[0071] Furthermore, the on-site collection and control of information includes the following aspects:

[0072] A1: Real-time data collection parameters;

[0073] Furthermore, the data parameters include cable impedance, current, voltage, temperature, insulation resistance and other parameters, transformer iron loss / copper loss coefficient, gas, environmental temperature and humidity, etc.; laying a solid foundation for subsequent analysis and regulation.

[0074] The frequency of collecting current and voltage is 5-15ms / time, and the frequency of collecting temperature and gas is 1-2min / time, thus avoiding unnecessary energy loss.

[0075] Preferably, when a current mutation of ≥20% is detected, cross-data verification and correction are required;

[0076] Among them, the cross-data verification method is redundant sensors; the correction method is to use Kalman filtering to eliminate outliers and ensure the accuracy of information.

[0077] A2: Analyze and collect data and make adjustments; comprehensively consider factors such as load changes, ambient temperature, energy efficiency targets, and dynamically adjust the working state of the cable transformer to maximize energy efficiency and ensure that the cable transformer always operates at the best energy efficiency state.

[0078] Furthermore, the content of the regulation includes the following aspects:

[0079] B1: When the voltage exceeds the limit, the transformer OLTC tap is controlled to adjust the gear, such as downshifting to reduce copper loss and lowering I 2 R loss; when the load current is large, reducing the ratio will slightly increase the low-voltage side voltage, thereby reducing the load current and further reducing the resistance loss of the transformer and line (I 2 R), while also reducing equipment heat loss.

[0080] B2: When reactive power is insufficient, the SVG device is controlled to start outputting capacitive reactive power to reduce line reactive current, balance grid demand, and suppress temperature rise.

[0081] B3: When overtemperature occurs, control the load transfer switch to cut off non-critical loads such as non-main operating equipment, reduce unnecessary load expenditure, and provide preventive protection to avoid overload, voltage abnormality, temperature increase, etc.

[0082] B4: When the cable temperature is greater than 80℃, the load is forced to be reduced and the standby cooling pump is started.

[0083] A3: Control command feedback;

[0084] Preferably, the time limit for instruction feedback is 2-5 minutes after the control instruction is issued, and the changes in data parameters need to be repeatedly reviewed multiple times.

[0085] S4: Maintain and optimize the model by stage.

[0086] Compared with traditional methods such as the existing standard IEEEC57.91-2011, the present invention reduces the total system loss by 10%-18%, reduces the comprehensive transformer loss by 8%-12%, increases the voltage qualification rate to more than 99.8%, increases the cable current carrying capacity utilization rate by ≥15%, extends the equipment life by 20%-30%, avoids the risk of instantaneous overload, and achieves millisecond-level risk interception.

[0087] Example 2:

[0088] A cable transformer economic operation collaborative optimization control method, such as Figure 1 As shown, in order to achieve normal operation processing of data in each stage; this embodiment makes the following supplements on the basis of embodiment 1: the control instructions rely on the main controller with edge computing capabilities to be issued.

[0089] Test Example 1:

[0090] 10kV power distribution system of a data center (cable YJV22-8.7 / 15kV-3×400mm 2 , length 1.8km; transformer SCB11-2500kVA).

[0091] Control results:

[0092] The peak temperature of the cable dropped from 78°C to 65°C, and the average daily loss of the transformer was reduced by 14.7kWh.

[0093] Test Example 2:

[0094] 10kV distribution station in an industrial park (transformer 2×1600kVA, cable YJV22-3×300mm 2 , length 1.2km).

[0095] Control results:

[0096] 1. Peak power consumption is reduced from 24.3kW to 20.1kW, with a power saving rate of 17.3%;

[0097] 2. The off-peak power loss is reduced from 8.7kW to 7.2kW, with a power saving rate of 14.7%.

[0098] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A cable transformer economic operation collaborative optimization control method, characterized in that: The following steps are involved: S1: Hardware deployment and communication network construction; S2: Establish collaborative operation model; S3: Information field collection and control; S4: Maintain and optimize the model by stage; The hardware deployment includes setting up a cable monitoring unit and a transformer monitoring unit; The communication network is a ring topology network constructed using industrial-grade ARM processors and industrial-grade TSN switches; The collaborative operation model includes the following contents: ① Operation status database; ② Preset the prerequisites for collaborative control based on intelligent algorithms; ③Build a mirror virtual model.

2. A cable transformer economic operation collaborative optimization control method according to claim 1, characterized in that: The cable monitoring unit is a plurality of distributed optical fiber temperature sensors arranged at the cable joints for measuring the cable temperature, and the distance between two adjacent distributed optical fiber temperature sensors is 150-250m; The transformer monitoring unit is a temperature sensor for monitoring the hot spots of the windings and a dissolved gas analyzer arranged at the transformer oil pillow for analyzing the gas content of the oil pillow.

3. A cable transformer economic operation collaborative optimization control method according to claim 2, characterized in that: The database includes cable parameters, transformer nameplate data and environmental factors; The cable parameters include resistance per unit length, inductance, capacitance and allowable current carrying capacity; The transformer nameplate data includes rated capacity, no-load loss / load loss, short-circuit impedance and voltage regulation range; The environmental factors include the cable laying method and the soil thermal resistance coefficient.

4. A cable transformer economic operation collaborative optimization control method according to claim 1, characterized in that: The intelligent algorithms include artificial intelligence algorithms and improved NSGA-II multi-objective genetic algorithms.

5. A cable transformer economic operation collaborative optimization control method according to claim 4, characterized in that: The coordinated control includes voltage and reactive power coordinated control, load factor balancing control and over-temperature load reduction curve; The coordinated control also includes emergency treatment of cable transformers, including: 1) Dynamic derating of cable current carrying capacity; 2) When the transformer has a light gas alarm, it will automatically switch to the conservative operation mode; 3) When the partial discharge of the cable is greater than 20pC, start segmented insulation monitoring and positioning.

6. A cable transformer economic operation collaborative optimization control method according to claim 5, characterized in that: The mirror image virtual model is constructed by importing a single-line diagram of a power distribution system circuit and laying out a one-to-one mapping association between cables and transformers and the mirror image virtual model; The mirror virtual model includes an estimation simulation module, which calls an LSTM network to predict the cable transformer load within the next 1-60 minutes.

7. A cable transformer economic operation collaborative optimization control method according to claim 1, characterized in that: The on-site collection and control of the information include the following aspects: A1: Real-time data collection parameters; A2: Analyze and collect data and make adjustments; A3: Control instruction feedback.

8. A cable transformer economic operation collaborative optimization control method according to claim 7, characterized in that: The data parameters include cable impedance, current, voltage, temperature, insulation resistance, transformer iron loss / copper loss coefficient, gas, and ambient temperature and humidity; The current and voltage are collected at a frequency of 5-15 ms / time, wherein when a current mutation of ≥20% is detected, cross-data verification and correction are required; the temperature and gas are collected at a frequency of 1-2 min / time.

9. A cable transformer economic operation collaborative optimization control method according to claim 8, characterized in that: The content of the regulation includes the following aspects: B1: When the voltage exceeds the limit, the transformer OLTC tap is controlled to adjust the gear, such as downshifting to reduce copper loss and lowering I 2 R loss; B2: When reactive power is insufficient, the SVG device is controlled to start outputting capacitive reactive power; B3: When overtemperature occurs, control the load transfer switch to cut off non-critical loads; B4: When the cable temperature is greater than 80℃, the load is forced to be reduced and the standby cooling pump is started.

10. A cable transformer economic operation collaborative optimization control method according to claim 9, characterized in that: The time limit for the instruction feedback is 2-5 minutes after the control instruction is issued, and the changes in data parameters need to be repeatedly reviewed multiple times.

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