Temperature and humidity regulation and control method and system for new energy transformer substation

Abstract

The invention relates to the technical field of new energy transformer substation temperature and humidity regulation and control, in particular to a new energy transformer substation temperature and humidity regulation and control method and system. Through a complete control flow of data acquisition, fuzzy control premise variable calculation, membership value calculation, control law synthesis, layered start-stop control strategy, fan fault diagnosis, fan fault processing and closed loop circulation, deep fusion of temperature and humidity regulation and fault tolerance is realized. And the strong coupling characteristic of the multi-room environment of the new energy transformer substation can be accurately adapted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature and humidity regulation of new energy substations, and in particular to a temperature and humidity regulation method and system for new energy substations. BACKGROUND

[0002] The key devices such as secondary protection, control and communication in new energy substations have strict requirements on the temperature and humidity of the operating environment, and the accurate control of the temperature and humidity is directly related to the stability and service life of the devices. At present, the traditional decentralized PID control or simple threshold start-stop control scheme is generally used in the industry, and such a scheme has a core defect: lack of adaptation to the strong coupling characteristics of the multi-chamber environment and fault-tolerant regulation mechanism after the failure of the fan.

[0003] In the traditional scheme, the temperature and humidity control of the transformer room, the high-voltage room and the low-voltage room are independent of each other, the mutual influence of the environmental parameters of the chambers is not considered, it is difficult to cope with complex and variable operating conditions, resulting in insufficient regulation accuracy, and local overheating or condensation problems often occur. At the same time, the fan failure detection only relies on thermal relays or air switches, which cannot accurately locate the fault type, and once a fan fails, it will cause the power failure of the entire circuit, causing the shutdown of other normal fans in the same circuit, and further causing the paralysis of the temperature and humidity control system, and in severe cases, it may cause the high-temperature trip of the protection system of the substation, affecting the overall power supply safety.

[0004] Therefore, there is an urgent need for a temperature and humidity intelligent control scheme that can adapt to the strong coupling characteristics of multiple chambers, has intelligent diagnosis and fault-tolerant regulation functions of the fan, to solve the core problems of insufficient regulation accuracy and system paralysis after the failure of the fan in the traditional control method. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a temperature and humidity regulation method and system for new energy substations, which can solve the core problems of insufficient regulation accuracy and system paralysis after the failure of the fan in the traditional control method in a multi-chamber strong coupling environment, and realize accurate regulation of the temperature and humidity in multiple chambers and fault-tolerant operation.

[0006] To solve the above technical problems, the first technical scheme adopted by the present application is: A temperature and humidity regulation method for a new energy substation, comprising the following steps: S1, collecting temperature and humidity values of multiple electrical chambers in the substation; S2, calculating multiple fuzzy control premise variables for a T-S fuzzy model according to the temperature and humidity values of the multiple electrical chambers collected in step S1; S3, calculating the membership values of each fuzzy control premise variable on each fuzzy set in the T-S fuzzy model according to the fuzzy control premise variables calculated in step S2; S4, the membership degree value calculated according to step S3 is synthesized into a global control law through a T-S fuzzy model, and a control instruction for cooperatively regulating the fans of each electrical room is generated; S5, the control instruction generated according to step S4 is used to execute a hierarchical start-stop control strategy for the fans at different positions in each electrical room; S6, the current value and the voltage value of the fans in each electrical room are collected, and fault diagnosis is performed on each fan according to the collected current value and voltage value to determine whether the fan has failed; S7, if the fan has not failed, return to step S1 to continue the next cycle; if the fan has failed, execute step S8; S8, the faulty fan is cut off and the T-S fuzzy model is reconstructed online; S9, the running time of the remaining normal fans is readjusted based on the reconstructed T-S fuzzy model; S10, return to step S1 to continue the next control cycle.

[0007] The second technical solution adopted by the application is: A temperature and humidity regulation system for a new energy substation, for realizing the temperature and humidity regulation method described above, the temperature and humidity regulation system comprising: A plurality of temperature and humidity sensors arranged in a plurality of electrical rooms respectively, for collecting the temperature and humidity values of each electrical room; A plurality of fans arranged in a plurality of electrical rooms respectively; A plurality of current transformers and a plurality of voltage transformers arranged in the power supply circuit of each fan respectively, for collecting the current value and voltage value of each fan; An edge controller for running a multivariable T-S fuzzy control algorithm, electrically connected with the plurality of temperature and humidity sensors, the plurality of fans, the plurality of current transformers and the plurality of voltage transformers respectively, and executing the steps of the method described above.

[0008] The application has the following advantages: The scheme realizes the deep integration of temperature and humidity regulation and fault tolerance through data acquisition, fuzzy control premise variable calculation, membership value calculation, control law synthesis, hierarchical start-stop control strategy, fan fault diagnosis, fan fault handling and complete control flow of closed loop circulation, can accurately adapt to the strong coupling characteristics of the multi-chamber environment of the new energy substation; the global control law synthesized by the T-S fuzzy model realizes the cooperative operation of the multi-chamber fan, greatly improves the accuracy and consistency of temperature and humidity regulation, and effectively avoids the problem of local overheating or condensation; the hierarchical start-stop control strategy can control the fan operation according to the structural characteristics and temperature and humidity demand differences of different electrical chambers, reduces the phenomenon of frequent start-stop of the fan, reduces energy consumption and prolongs the service life of the fan; the fault diagnosis and fault tolerance control link can quickly identify the fan fault and only remove the faulty fan, through model reconstruction and remaining fan operation time adjustment, avoids the risk of system paralysis caused by traditional control mode of one fault all shutdown, ensures the continuity and stability of temperature and humidity control, and improves the adaptive ability and operation reliability of the system as a whole, provides a strong guarantee for the safe and stable operation of key equipment of the new energy substation. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 The step flow chart of the temperature and humidity regulation method of the new energy substation of the present application is shown in the figure. Figure 2 The overall connection block diagram of the temperature and humidity regulation system of the new energy substation of the present application is shown in the figure. Figure 3 The multivariable T-S fuzzy controller design flow chart of the temperature and humidity regulation method of the new energy substation of the present application is shown in the figure. Figure 4 The hierarchical start-stop strategy flow chart of the temperature and humidity regulation method of the new energy substation of the present application is shown in the figure. Figure 5 The fault diagnosis and only removal of the faulty fan of the temperature and humidity regulation method of the new energy substation of the present application is shown in the figure. Figure 6 The software flow chart of the temperature and humidity regulation method of the new energy substation of the present application is shown in the figure. Figure 7 The connection block diagram of the temperature and humidity regulation system of the new energy substation of the present application is shown in the figure. KEY 1, temperature and humidity sensor; 2, fan; 3, current transformer; 4, voltage transformer; 5, edge controller. DETAILED DESCRIPTION

[0010] To explain the technical content, purposes and effects of the present application in detail, the following will be described in conjunction with the embodiments and the accompanying drawings.

[0011] Please refer to Figure 1The first technical solution adopted by the present application is: A temperature and humidity regulation method for a new energy substation, comprising the following steps: S1, collecting temperature values and humidity values of multiple electrical rooms in the substation; S2, calculating multiple fuzzy control premise variables for a T-S fuzzy model according to the temperature values and humidity values of the multiple electrical rooms collected in step S1; S3, calculating membership values of each fuzzy control premise variable on each fuzzy set in the T-S fuzzy model according to the fuzzy control premise variables calculated in step S2; S4, synthesizing a global control law through the T-S fuzzy model according to the membership values calculated in step S3, and generating a control instruction for cooperatively regulating fans of each electrical room; S5, executing a hierarchical start-stop control strategy for the fans at different positions in each electrical room according to the control instruction generated in step S4; S6, collecting current values and voltage values of the fans of each electrical room, and performing fault diagnosis on each fan according to the collected current values and voltage values to determine whether each fan has failed; S7, if the fan has not failed, returning to step S1 to continue the next cycle; if the fan has failed, executing step S8; S8, removing the failed fan and reconstructing the T-S fuzzy model online; S9, readjusting the operation time of the remaining normal fans based on the reconstructed T-S fuzzy model; S10, returning to step S1 to continue the next control cycle.

[0012] From the above description, the beneficial effects of the present application are: The scheme realizes the deep integration of temperature and humidity regulation and fault tolerance through data acquisition, fuzzy control premise variable calculation, membership value calculation, control law synthesis, hierarchical start-stop control strategy, fan fault diagnosis, fan fault handling and complete control flow of closed loop circulation, can accurately adapt to the strong coupling characteristics of the multi-chamber environment of the new energy substation; the global control law synthesized by the T-S fuzzy model realizes the cooperative operation of the multi-chamber fan, greatly improves the accuracy and consistency of temperature and humidity regulation, and effectively avoids the problem of local overheating or condensation; the hierarchical start-stop control strategy can control the fan operation according to the structural characteristics and temperature and humidity demand differences of different electrical chambers, reduce the phenomenon of frequent start-stop of the fan, reduce the energy consumption and prolong the service life of the fan; the fault diagnosis and fault tolerance control link can quickly identify the fan fault and only remove the faulty fan, avoid the system paralysis risk caused by the traditional control mode of one fault all shutdown, ensure the continuity and stability of temperature and humidity control, and improve the self-adaptive ability and operation reliability of the system, and provide a strong guarantee for the safe and stable operation of the key equipment of the new energy substation.

[0013] Further, the plurality of fuzzy control premise variables in step S2 include a global temperature deviation maximum value, a global humidity deviation maximum value and a transformer room temperature rise rate. The calculation formula of the global temperature deviation maximum value is as follows: ; Wherein, the global temperature deviation maximum value, the transformer room temperature deviation, the high-voltage room temperature deviation, the low-voltage room temperature deviation; The calculation formula of the global humidity deviation maximum value is as follows: ; Wherein, the global humidity deviation maximum value, the transformer room humidity deviation, the high-voltage room humidity deviation, the low-voltage room humidity deviation; The calculation formula of the transformer room temperature rise rate is as follows: ; Wherein, the transformer room temperature rise rate, the temperature rise value of the transformer room per unit time, per unit time.

[0014] From the above description, the three fuzzy control premise variables comprehensively reflect the temperature and humidity state of the substation from different dimensions, among which the global temperature deviation maximum value and the global humidity deviation maximum value focus on the most serious situation of temperature and humidity deviation from the set value in each electrical room, ensuring that the control instruction can solve the key problems and avoid affecting the equipment operation due to local temperature and humidity exceeding the standard; the transformer room temperature rise rate accurately captures the temperature change trend of the main heating area, enabling the controller to predict temperature change risks in advance and effectively inhibit the over-temperature problem caused by rapid temperature rise; the combination of the three fuzzy control premise variables provides comprehensive, accurate and targeted input information for the T-S fuzzy model, ensuring the rationality and effectiveness of the subsequent control law synthesis, and further improving the response speed and control accuracy of the entire regulation system to complex environmental changes.

[0015] Further, in step S6, the fault diagnosis of each fan according to the collected current value and voltage value includes: comparing the collected current value with the preset rated current value, and comparing the collected voltage value with the preset rated voltage value, and judging the fault type of the fan according to the preset four-classification criterion; the four-classification criterion includes: If and , it is diagnosed as a circuit break; If and , it is diagnosed as overload if the drop is less than 5%; If and , it is diagnosed as short circuit if the rapid rise and the sudden drop are more than 10%; If and , and the state of the upper power supply is normal, it is diagnosed as a fan contactor fault; Wherein, is the current value of the fan, is the voltage value of the fan, is the rated current value, is the rated voltage value.

[0016] From the above description, the five-class fault diagnosis method based on current-voltage characteristics can accurately distinguish five different types of faults, i.e., circuit breaking, overload, short circuit, fan failure, and power supply voltage loss, compared with the traditional fault detection method relying only on thermal relays or air switches. This provides clear and specific basis for on-site fault troubleshooting and maintenance, avoiding time and cost waste caused by blind maintenance. The criteria for each fault type are based on the quantitative characteristics of current and voltage, and the judgment logic is clear and responsive. The diagnosis can be quickly completed after the fan failure occurs, providing a guarantee for timely removal of the faulty fan and preventing the expansion of the fault. By accurately identifying the fault type, unnecessary downtime caused by misjudgment can be avoided, and precise fault information support is provided for the operation adjustment of the remaining fans in subsequent fault-tolerant control, ensuring that the air volume distribution and control strategy adjustment are more targeted, and the stability of the temperature and humidity control under fault conditions is ensured.

[0017] Further, in step S3, the membership values of each fuzzy control premise variable on each fuzzy set in the T-S fuzzy model are calculated by a triangular membership function.

[0018] From the above description, the triangular membership function has the advantages of simple calculation process and small amount of calculation, which can quickly process the fuzzy control premise variables and meet the needs of real-time control of the temperature and humidity in the new energy substation. It ensures that the control command can be generated and executed in time, avoiding the regulation and control lag caused by calculation delay. The fuzzy boundary of the membership function is smooth, which can effectively avoid the problem of sudden change of control command caused by fuzzy set division, so that the fan operating state and temperature and humidity change remain stable, reducing temperature and humidity oscillation and further improving the regulation and control accuracy. At the same time, the triangular membership function has good sensitivity to the change of input variables, which can accurately capture the subtle fluctuations of the premise variables, ensuring that the T-S fuzzy model can quickly respond to the slight changes of the environment temperature and humidity, and timely adjust the control strategy, so that the temperature and humidity is always stable within the set range, ensuring the stability of the equipment operating environment.

[0019] Further, the calculation process of the triangular membership function includes: The actual value of each fuzzy control premise variable is normalized to a pre-set standard domain; In the pre-set standard domain, three triangular membership functions are defined for each fuzzy control premise variable, which cover the negative value interval, the interval around zero and the positive value interval of the premise variable in turn; The normalized fuzzy control premise variables are respectively substituted into the corresponding three triangular membership functions to obtain three membership values.

[0020] From the above description, it can be known that the normalization processing of the fuzzy control premise variable can effectively eliminate the influence of the dimension difference between different variables, make the global temperature deviation maximum value, the global humidity deviation maximum value and the transformer room temperature rise rate have equal weights and influences in the T-S fuzzy model, avoid the control deviation caused by the difference in variable numerical range, and ensure the rationality of the control logic; defining three triangular membership functions for each premise variable, which cover different intervals, can comprehensively cover all possible states of the variable, whether the premise variable is in the negative value, near zero or in the positive value interval, the corresponding fuzzy set can be found and the membership value can be accurately calculated, which greatly improves the coverage and adaptability of the model to complex environment.

[0021] Further, the normalization standard domain of the fuzzy control premise variable is [-1, 1]; The vertices of the three triangular membership functions are located near the negative side boundary, zero and positive side boundary of the preset standard domain, respectively.

[0022] From the above description, it can be known that the normalization standard domain is set to [-1, 1], which is moderate, can fully reflect the deviation degree and change trend of the premise variable, and is convenient for the design and calculation of the membership function, reduces the algorithm complexity and improves the calculation efficiency; the vertices of the three triangular membership functions are located near the negative side boundary, zero and positive side boundary of the standard domain, respectively, so that the membership function can uniformly and symmetrically cover the entire domain, ensuring the consistency and rationality of the fuzzy set division, and avoiding the control deviation caused by uneven fuzzy set division.

[0023] Further, step S4 is specifically: According to the membership value calculated in step S3, the normalized activation degree of each fuzzy rule in the plurality of T-S fuzzy models is calculated; According to the normalized activation degree, the local linear control law corresponding to the plurality of fuzzy rules is weighted and averaged to synthesize a global control law; the output of the global control law is the duty cycle of each fan.

[0024] From the above description, it can be known that the calculation of the normalized activation degree can accurately reflect the matching degree of the current temperature and humidity state and each fuzzy rule, providing a scientific and reasonable basis for the weighted fusion of the local linear control law, ensuring that the global control law can adapt to the current environmental state and avoiding the regulation limitation caused by single rule control; the global control law is synthesized by weighted average, which can organically combine the control effects of the plurality of fuzzy rules, fully play the advantages of T-S fuzzy model in processing nonlinear and strong coupling system, realize the collaborative regulation of the multi-chamber fan, and effectively solve the problems of energy waste or inaccurate regulation caused by uncoordinated operation of each fan in traditional decentralized control; The global control law output is the duty ratio of each fan, and the continuous adjustment of the fan speed can be realized through the duty ratio adjustment. Compared with the traditional on-off control, the air volume output can be more accurately controlled, which not only improves the temperature and humidity control accuracy, but also avoids the mechanical wear caused by frequent start-stop of the fan, prolongs the service life of the fan and reduces the energy consumption.

[0025] Further, in step S5, a layered start-stop control strategy for the fans at different positions in each electrical room is executed, specifically including the following steps: S51, according to the control instruction and the temperature and humidity values of the multiple electrical rooms collected in step S1, identifying the target electrical room type; S52, if the target electrical room is a transformer room, a first layered strategy is executed: According to the temperature deviation of the transformer room, the bottom cross-flow fan of the transformer room is controlled in a duty ratio modulation mode; if the ambient temperature exceeds a higher level of temperature set value, the wall surface axial flow fan and the top axial flow fan of the transformer room are started in turn according to the preset priority; S53, if the target electrical room is a high-voltage room or a low-voltage room, a second layered strategy is executed: When the cabinet temperature exceeds the standard, the cabinet fan is started preferentially; When the indoor humidity exceeds the standard, the cabinet fan and the heater are started synchronously, and the wall surface fan is started after a delay of a preset time.

[0026] As can be seen from the above description, in view of the different structural characteristics and temperature and humidity control requirements of the transformer room, the high-voltage room and the low-voltage room, differentiated layered start-stop strategies are designed to achieve precise control according to local conditions and avoid unreasonable control caused by unified control strategies; the transformer room uses a duty ratio modulation mode to control the bottom cross-flow fan, effectively reducing the number of fan start-stop and prolonging the service life of the fan, the wall surface axial flow fan and the top axial flow fan are started in turn according to the priority to ensure that the air volume is distributed as needed and unnecessary energy waste is avoided; the start priority of the cabinet fan in the high-voltage room and the low-voltage room is placed before the wall surface fan, which can quickly solve the local heat dissipation problem of the equipment in the cabinet and avoid the influence of high temperature in the cabinet on the performance of the equipment; when the humidity exceeds the standard, the fan and the heater are started synchronously and the wall surface fan is started after a delay, which effectively prevents the conflict between heat and humidity and avoids the influence of condensation on the insulation performance of the equipment, further improving the scientificity and rationality of the control.

[0027] Further, in step S8, the T-S fuzzy model is reconstructed online, specifically including: From the input matrix corresponding to each fuzzy rule of the T-S fuzzy model, remove the matrix row corresponding to the faulty fan to obtain the fault input matrix; According to the obtained fault input matrix, the normalized activation degree and the global control law of each fuzzy rule in the T-S fuzzy model are recalculated.

[0028] From the above description, the T-S fuzzy model online reconstruction method is simple and efficient, only needs to remove the matrix column corresponding to the fault wind turbine in the input matrix, does not need to change the overall structure and other parameters of the model, ensures the continuity of control, avoids the interruption problem in the model reconstruction process; the reconstructed input matrix can accurately adapt to the number and layout of the remaining normal wind turbines, and through the recalculation of the normalized activation and the global control law, the control command can quickly adapt to the system state after the fault, ensures the accuracy and stability of the temperature and humidity control, and avoids the local temperature and humidity exceeding the standard caused by the wind turbine fault.

[0029] Please refer to Figure 7 The second technical solution adopted by the present application is: A temperature and humidity control system of a new energy substation is used to realize the temperature and humidity control method, and the temperature and humidity control system comprises: A plurality of temperature and humidity sensors 1 are arranged in a plurality of electrical chambers respectively, and are used to collect temperature values and humidity values of the electrical chambers; A plurality of fans 2 are arranged in a plurality of electrical chambers respectively; A plurality of current transformers 3 and a plurality of voltage transformers 4 are arranged in the power supply circuit of each fan respectively, and are used to collect current values and voltage values of the fans; An edge controller 5 is used to run a multivariable T-S fuzzy control algorithm, and is electrically connected with the plurality of temperature and humidity sensors 1, the plurality of fans 2, the plurality of current transformers 3 and the plurality of voltage transformers 4 respectively, and executes the steps of the above method.

[0030] From the above description, the present application has the following beneficial effects: The temperature and humidity sensors arranged at multiple positions can comprehensively and accurately collect temperature and humidity data of different regions of each electrical chamber, provide reliable and comprehensive basic data support for the control algorithm, and avoid control deviation caused by incomplete data collection; the current transformer and the voltage transformer independently configured for each fan can accurately collect fan operation data in real time, provide an accurate data source for fault diagnosis, and ensure the accuracy of fault type judgment.

[0031] Please refer to Figures 1 to 6 The first embodiment of the present application is: Please refer to Figure 1 and Figure 6 A temperature and humidity control method of a new energy substation comprises the following steps: S1, collecting temperature values and humidity values of a plurality of electrical chambers in a substation; S2, calculating a plurality of fuzzy control premise variables for a T-S fuzzy model according to the temperature values and humidity values of the plurality of electrical chambers collected in step S1; The plurality of fuzzy control premise variables in step S2 include a global temperature deviation maximum value, a global humidity deviation maximum value, and a transformer room temperature rise rate; The calculation formula of the global temperature deviation maximum value is as follows: ; Wherein, is the global temperature deviation maximum value, is a transformer room temperature deviation, is a high-voltage room temperature deviation, is a low-voltage room temperature deviation; The calculation formula of the global humidity deviation maximum value is as follows: ; Wherein, is the global humidity deviation maximum value, is a transformer room humidity deviation, is a high-voltage room humidity deviation, is a low-voltage room humidity deviation; The calculation formula of the transformer room temperature rise rate is as follows: ; Wherein, is the transformer room temperature rise rate, is a temperature rise value of the transformer room per unit time, is a unit of time.

[0032] S3, according to the fuzzy control premise variables calculated in step S2, the membership values of each fuzzy control premise variable on each fuzzy set in the T-S fuzzy model (TS stands for Takagi-Sugeno) are calculated. In step S3, the membership values of each fuzzy control premise variable on each fuzzy set in the T-S fuzzy model are calculated by a triangular membership function.

[0033] The calculation process of the triangular membership function includes: The actual value of each fuzzy control premise variable is normalized to a preset standard universe of discourse; In the preset standard universe of discourse, three triangular membership functions are defined for each fuzzy control premise variable, which cover the negative value interval, the zero point interval and the positive value interval of the fuzzy control premise variable in turn; The normalized fuzzy control premise variables are respectively substituted into the corresponding three triangular membership functions to obtain three membership values.

[0034] The normalization standard universe of discourse of the fuzzy control premise variable is [-1, 1]; The vertices of the three triangular membership functions are located near the negative side boundary, zero point and positive side boundary of the preset standard argument domain, respectively.

[0035] S4, synthesizing the global control law through the T-S fuzzy model according to the membership values calculated in step S3, to generate the control instruction for the coordinated control of the fans in each electrical room; Step S4 specifically includes the following steps: According to the membership values calculated in step S3, the normalized activation degrees of each fuzzy rule in the plurality of T-S fuzzy models are calculated. According to the normalized activation degrees, the local linear control laws corresponding to the plurality of fuzzy rules are weighted and averaged to synthesize the global control law; the output of the global control law is the duty cycle of each fan.

[0036] S5, executing the hierarchical start-stop control strategy for the fans at different positions in each electrical room according to the control instruction generated in step S4; In step S5, the hierarchical start-stop control strategy for the fans at different positions in each electrical room is executed, specifically including the following steps: S51, identifying the target electrical room type according to the control instruction and the temperature and humidity values of the plurality of electrical rooms collected in step S1; S52, if the target electrical room is a transformer room, a first hierarchical strategy is executed: According to the temperature deviation of the transformer room, the bottom cross-flow fan of the transformer room is controlled in a duty cycle modulation manner; if the ambient temperature exceeds a higher temperature set value, the wall surface axial fan and the top axial fan of the transformer room are started in a preset priority order; S53, if the target electrical room is a high-voltage room or a low-voltage room, a second hierarchical strategy is executed: When the cabinet temperature exceeds the standard, the cabinet fan is preferentially started; When the indoor humidity exceeds the standard, the cabinet fan and the heater are started synchronously, and the wall surface fan is started after a preset time delay.

[0037] S6, collecting the current and voltage values of the fans in each electrical room, and performing fault diagnosis on each fan according to the collected current and voltage values to determine whether each fan has failed; In step S6, the fault diagnosis on each fan according to the collected current and voltage values includes comparing the collected current value with the preset rated current value, and comparing the collected voltage value with the preset rated voltage value, and determining the fault type of the fan according to a preset four-classification criterion; the four-classification criterion includes: If and , it is diagnosed as a circuit break; If and If the drop is less than 5%, it is diagnosed as overload; If the drop is less than 5%, it is diagnosed as overload; If the drop is less than 5%, it is diagnosed as overload; If the drop is less than 5%, it is diagnosed as overload; If the drop is less than 5%, it is diagnosed as overload; If the drop is less than 5%, it is diagnosed as overload; If the drop is less than 5%, it is diagnosed as overload; Wherein, is the current value of the fan, is the voltage value of the fan, is the rated current value, is the rated voltage value.

[0038] S7, if the fan fails, return to step S1 to continue the next cycle; if the fan fails, execute step S8; S8, remove the fault fan and reconstruct the T-S fuzzy model online; In step S8, the T-S fuzzy model is reconstructed online, specifically including: From the input matrix corresponding to each fuzzy rule of the T-S fuzzy model, remove the matrix row corresponding to the fault fan to obtain the fault input matrix; According to the obtained fault input matrix, the normalization activation degree and global control law of each fuzzy rule in the T-S fuzzy model are recalculated.

[0039] S9, based on the reconstructed T-S fuzzy model, readjust the operation time of the remaining normal fan; S10, return to step S1 to continue the next control cycle.

[0040] The embodiment provides a temperature and humidity regulation method for a new energy substation, which is suitable for a photovoltaic power station booster station comprising a transformer room, a high-voltage room and a low-voltage room.

[0041] Please refer to Figure 2 System hardware configuration: 1. Temperature and humidity sensor deployment: High-voltage room: 3 groups of temperature and humidity sensors are deployed, respectively installed in the cabinet, the central room and the wall; Low-voltage room: 3 groups of temperature and humidity sensors are deployed, respectively installed in the cabinet, the central room and the wall; Transformer room: 1 group of temperature and humidity sensors are installed on the wall and in the center, 3 groups of PT100 thermal resistance sensors are installed on the transformer winding for accurate collection of the transformer winding temperature.

[0042] 2. Fan deployment: High-voltage room: 1 cooling fan is installed in the cabinet, and 1 axial flow fan is installed on the wall; Low pressure chamber: 1 cooling fan is installed in the cabinet, and 1 axial fan is installed on the wall. Transformer room: 6 cross-flow cooling fans are installed at the bottom (controlled by independent contactors), and 1 axial fan is installed at the top and on the wall.

[0043] 3. Current transformer: Current and voltage transformers are installed in the power supply circuit of each fan to collect real-time current and voltage values. .

[0044] 4. Edge controller: Dual-core ARM + DSP architecture is used, supporting IEC-61850 and Modbus-TCP communication interfaces, and electrically connected with sensors, fans, and current transformers for operation control algorithm and data processing.

[0045] I. Related design of T-S fuzzy model as follows (please refer to Figure 3 ): 1. Selection of fuzzy control premise variables: Global maximum temperature deviation : ; Global maximum humidity deviation : ; Transformer room temperature rise rate : .

[0046] 2. Fuzzy membership function determination: For each fuzzy control premise variable, define 3 fuzzy sets: {N (negative), Z (zero), P (positive)}, use triangular membership function, and normalize to standard domain [-1, 1]; The basic domain of global maximum temperature deviation is [-10℃, 10℃], normalized to standard domain [-1, 1]; The basic domain of global maximum humidity deviation is [-20% RH, 20% RH], normalized to standard domain [-1, 1]; The basic domain of transformer room temperature rise rate is [-2℃ / s, 2℃ / s], normalized to standard domain [-1, 1].

[0047] Global maximum temperature deviation is ±10℃, basic domain is {-10, 10}, fuzzy subset domain is {-10, 0, 10}, and corresponding fuzzy subsets are {N (negative), Z (zero), P (positive)}.

[0048] Global maximum humidity deviation ​It is ±20RH, the basic domain is {-20, 20}, the fuzzy subset domain is {-20, 0, 20}, and the corresponding fuzzy subsets are {N (negative), Z (zero), P (positive)}.

[0049] Transformer room temperature rise rate It is ±2℃ / 1s, the basic domain is {-2, 2}, the fuzzy subset domain is {-2, 0, 2}, and the corresponding fuzzy subsets are {N (negative), Z (zero), P (positive)}.

[0050] After regional normalization, it corresponds to a triangular membership function. For example, the maximum value of the global temperature deviation The height of the triangular vertex of the triangular membership function of the fuzzy subset Z (zero) is equal to 1 (membership degree is 100%), corresponding to the maximum value of the global temperature deviation being equal to zero. When the deviation is -10℃ < T < 0℃, the membership degree corresponds to the hypotenuse on the left side of the triangle. When the deviation is -10℃, the membership degree is 0 (0%). When the deviation is a positive value, the situation is similar. For the temperature deviation of -10℃ < T < 10℃, the corresponding membership degree can be calculated.

[0051] Extract the temperature and humidity values sampled at each moment according to the maximum value of the global temperature deviation, the maximum value of the global humidity deviation, and the transformer room temperature rise rate for control (if the maximum value is within the control target range, the other temperature and humidity values are also within the control target range). Use the triangular membership function for easy calculation. Normalize these three input variables to the range [-1, 1], and the corresponding membership degree is 0 - 100%. For example, define the Z (zero) fuzzy set: when the measured maximum temperature deviation is a positive deviation of +10℃ or a negative deviation of -10℃, the membership degree is 0 (i.e., 0%). When the temperature deviation is 0℃, the membership degree is 1 (i.e., 100%). When the maximum temperature deviation is 0℃ < T < 10℃ (or -10℃ < T < 0℃), calculate the membership degree according to the two hypotenuses of the triangle. Each temperature and humidity sampled in real time corresponds to a membership degree value in a corresponding fuzzy set. It is used to predict the next temperature and humidity state value , the output value of the global temperature and humidity state at the current moment 、Real-time weight etc. (see the following three formulas). And calculate the duty cycle control amount of the fan based on this .

[0052] is the prediction of the control system for the future, which enables the controller to "foresee" the future consequences of the current actions, so as to make more intelligent and accurate decisions. This is one of the key technologies for this invention to overcome the shortcomings of traditional methods such as "poor adaptability and inaccurate regulation", achieving the leap from "passive reaction" to "active prediction and optimization".

[0053] 3. T-S Fuzzy Rules (Total 27 rules) Rule 1: If is N (negative), and is N (negative), and is N (negative), then: ; ; Rule 2: If is N (negative), and is N (negative), and is P (positive), then: ; ; Similarly, up to Rule 27: If is N (negative), and is N (negative), and is Z (zero), then: ; ; where, is the transpose matrix, belonging to 6x1 column state vector; is the duty ratio speed command of each fan.

[0054] These 27 fuzzy rules are all permutations and combinations of fuzzy control premise variables; for any temperature and humidity measurement value within the above target control range, the specific membership values of the three fuzzy control premise variables can be calculated, and it is determined which one of the 27 rules it belongs to; after determining the specific rule, the state space equation can be used to predict the next step temperature and humidity state value and the global temperature and humidity state output value at the current time, and then realize output control.

[0055] 4. Global output of fuzzy system: The local linear model described by the above 27 rules is weighted and averaged by fuzzy weight to obtain the final global state space equation for control and prediction, which is updated in real time, for subsequent prediction of the next step temperature and humidity state value (temperature and humidity prediction values of 6 rooms), calculation of global state feedback control law (that is, speed / duty ratio control amount of each fan), and online reconstruction , after failure of a fan, to realize air volume redistribution. The global temperature and humidity state output value at the current time can also be calculated (Generally , i.e. measured temperature and humidity), and real-time weights (for synthesizing 27 local matrices , , into a global matrix).

[0056] ; ; ; ; The physical meaning of each symbol in the above equations is shown in Table 1: Table 1. Table of physical meaning of symbols

[0057] In the equations, , , , These four matrices together form the core of a typical state-space model, which is used to describe the dynamic behavior of a control system. Under the T-S fuzzy control framework, a complex nonlinear temperature and humidity system in a new energy substation is represented as a fuzzy fusion of multiple linear models (each rule corresponds to a linear model).

[0058] Each linear model is defined by its own set of , , , matrices, which are mathematical model parameters used to describe the dynamic characteristics of the subsystem in the design of T-S fuzzy controllers.

[0059] The state matrix (i.e. matrix) describes the dynamic characteristics of the evolution of the internal state variables of the system over time.

[0060] The input matrix (i.e. matrix) defines how the external control input of the system affects the change of the internal state; m represents the dimension of the input variable.

[0061] The affine term (or offset term ) represents a constant offset in the system model, which is used to describe the static characteristics near the equilibrium point or linearization point of the system.

[0062] The output matrix (i.e. matrix) defines how the internal state of the system is mapped to the measurable output; it is usually a unit matrix, which means that all state variables can be directly observed as output.

[0063] According to the definition of three room's 6 humidity deviation variables 、 、 、 、 、 , the corresponding 6 state variables are: 、 、 、 、 、 , where 、 、 represent the air humidity of the three rooms, and 、 、 can be converted by the formula. In addition, the system uses two external action control variables to adjust: the air flow corresponding to the fan speed / duty ratio control variable ; the heating power of the jth heater .

[0064] Take the transformer room as an example, the temperature change rate The nonlinear equation form of the heat balance of the transformer room is established as: ; Similarly, take the transformer room as an example, the humidity change rate The nonlinear equation form of the moisture balance of the transformer room is established as: ; Where: and represent the outdoor temperature and outdoor humidity, respectively.

[0065] The heat and moisture balance equations are established for the three rooms (transformer room, high-voltage room, and low-voltage room), resulting in 6 first-order nonlinear differential equations. Arranging them into matrix form, we get the embryonic nonlinear continuous-time state space equation: ; Where: is the derivative of the state vector; is a nonlinear vector function; is the control input vector (fan, heater).

[0066] This equation clearly shows that the dynamic change of system state (how temperature and humidity change) depends on the current state itself, control command and external environment disturbance, and there is a complex nonlinear coupling relationship among them.

[0067] A complex nonlinear system is approximated as a simple linear system near a certain operating point (equilibrium point). For each T-S fuzzy rule L, a representative equilibrium point is selected, and the nonlinear function is expanded to the first order Taylor series near the equilibrium point, and the high-order terms in the nonlinear function are ignored, only the first-order term of the deviation from the operating point is retained, to obtain a local linear model: , and thus the matrix , , , is calculated; this linear model can accurately describe the system dynamics in a small range near the operating point, and linear system theory is used for analysis and control design.

[0068] This is the basis for subsequent linearization to solve the matrix , , , in the T-S fuzzy model. The core of the entire T-S fuzzy control framework is the weighted fusion of the local linear models , , , obtained by linearizing the nonlinear model at different operating points (corresponding to different fuzzy rules).

[0069] 5. Fuzzy state feedback control: For each local linear model corresponding to the aforementioned 27 T-S fuzzy rules, an optimal control law is designed, which adopts the zero-error control characteristics of the PI regulator to achieve smooth control of the environment temperature and humidity. These local control laws are weighted and fused according to the real-time state of the system to ultimately generate a global stable control command that can adapt to the nonlinear characteristics of the system, i.e., the duty ratio or speed command of each fan. The design process can be summarized as "decomposition first, design second, fusion third, and enhancement last".

[0070] a. Decomposition: design an LQR controller for each local subsystem; Since the temperature and humidity system of a new energy substation is a complex nonlinear system, we have linearized it at 27 different operating points (corresponding to 27 rules) through the T-S fuzzy model, obtaining 27 local linear models, each of which is described by the state equation .

[0071] To ensure that each local subsystem is not only stable, but also has good dynamic performance (e.g. fast response, low overshoot), a linear quadratic regulator (abbreviated as LQR in English) is designed for each rule.

[0072] The goal of LQR is to find the optimal control gain such that the following performance index is minimized: ; where, is the state vector (i.e. temperature and humidity of the three chambers); is the control input vector (i.e. the instructions of the fans); is the state weight matrix (positive definite matrix), which determines the importance of the system to the deviation of temperature and humidity; increasing means that the system will work harder to reduce the deviation and improve control accuracy, but may cause the control action to be too drastic and increase energy consumption.

[0073] is the control weight matrix (positive definite matrix), which determines the importance of the system to the control amount (fan energy consumption); increasing means that the system will tend to use smaller control actions to save energy, but may sacrifice some regulation speed.

[0074] By solving the famous algebraic Riccati equation: ; the positive definite matrix is obtained, and the local LQR gain is given by: ; Substituting the state equation, the closed-loop system is obtained: ; By reasonably selecting and , the eigenvalues (i.e. system poles) of can be guaranteed to be located in the stable region, thus ensuring the stability and excellent dynamic performance of the local subsystem.

[0075] b, fusion: weighted to get the global fuzzy control instruction; The running state of the system at any time may activate multiple T-S fuzzy rules (i.e. belong to multiple fuzzy sets) at the same time. Therefore, it cannot simply use the control law of only one rule.

[0076] the final global control instruction is the weighted average of the 27 local LQR control commands: ; is the normalized activation of the Lth rule (calculated in the previous section), which reflects the degree of matching of the current system state to the Lth rule; is the control amount suggested by the Lth rule; This weighted fusion process enables the controller to smoothly transition between different operating points and adaptively handle the nonlinear characteristics of the system.

[0077] c. Enhancement: Introduce an integral term to eliminate steady-state error (form a T-S-Fuzzy-PI controller); Although the above fuzzy state feedback controller performs well, for temperature and humidity control processes that require high precision, there may be a steady-state error (steady-state error), i.e., a small deviation between the actual value and the set value after long-term operation. In order to completely eliminate the steady-state error, this scheme adds an integral term based on the fuzzy state feedback, forming a more advanced T-S-Fuzzy-PI controller.

[0078] Step 1: Integral term design: Define the integral state , as the tracking error; where is the set value, and the actual output value.

[0079] Define the integral state , which is the cumulative sum of all historical errors: ; Introduce it into the state vector or design an integral gain separately ; Step 2: Fusion of PI control logic: At this point, the controller has both "proportional term " and "integral term ", retaining the adaptability of fuzzy state feedback to nonlinear systems, and forcibly eliminating steady-state error through the integral term, ultimately achieving the combined control effect of "T-S fuzzy and PI control".

[0080] This new control law contains: Proportional term : Provides fast, proportional control actions to the current deviation, adjusted adaptively by T-S fuzzy logic.

[0081] Integral term : By accumulating the historical deviation, a continuous control action is generated until the error is eliminated.

[0082] This "T-S fuzzy and PI regulation" compound structure not only retains the powerful ability of fuzzy control to handle nonlinearity, but also introduces the static error tracking characteristics of the integral element, ultimately achieving high-precision temperature and humidity regulation.

[0083] After the above series of steps, the control command finally calculated is a vector, each element of which directly corresponds to the duty cycle of a fan (if it is on-off control) or the speed command (if it is variable frequency control). This command is sent to the corresponding frequency converter or contactor of each fan, thereby achieving intelligent, precise, and fault-tolerant regulation of temperature and humidity in the substation.

[0084] II. The hierarchical start-stop control strategy is as follows (please refer to Figure 4 ): In order to achieve high-precision, static error-free regulation of multi-chamber temperature and humidity, a PI regulator is added. The previous steps are methods to determine the control parameters of the PI regulator; and using these methods, the fan duty cycle is calculated, thereby achieving fan control.

[0085] This step only uses the aforementioned output to control the fan. In terms of control strategy, the duty cycle period can be set longer, for example, 30s, to avoid frequent starting; by comparing the temperature and humidity sensor sampling values with the target values, the bottom cross-flow fan start-stop temperature difference is obtained; to avoid "hot and humid conflict", the transformer room uses wall / top axial flow fans to gradually put in; the high / low voltage room uses cabinet / wall step-by-step input method.

[0086] Transformer room: Bottom cross-flow fan start-stop temperature difference (target value default 8 K): , using duty cycle modulation to avoid frequent start-stop; wall / top axial flow fans are gradually put in according to "ambient temperature ", with priority wall -> top.

[0087] High / low voltage room: The cabinet fan is directly started by the cabinet temperature measuring PT100 (thermistor), with priority over the wall fan; when the humidity is high, the cabinet fan and heater are started simultaneously, and the wall fan is started with a 5-minute delay to prevent "hot and humid conflict".

[0088] Fault diagnosis and removal of faulty fans, as follows: a. Four-classification criteria, as shown in Table 2: Table 2 Four classification criterion table

[0089] Through the current transformer (CT) and voltage transformer (PT), real-time sampling current voltage value current Ij, voltage Uj; current rating In, voltage rating Un can be set in the system in advance.

[0090] The judgment basis of this table is the characteristic combination of current and voltage, which is used to distinguish different fan fault types.

[0091] a1, fan internal circuit break: When the control contactor is on, it is detected that the fan current ≈ 0, indicating that there is no current in the circuit; and it is detected that the fan voltage = rated value (such as 380VAC), indicating that the power supply is normal, but the fan working current is zero, indicating that the fan is internally disconnected somewhere.

[0092] a2, fan overload: When the control contactor is on, it is detected that the fan current > 1.5 times the rated current In, indicating that the current exceeds the normal working range, but has not reached the degree of short circuit. For example, bearing blockage / impeller jamming, etc. And it is detected that the fan voltage has a slight drop, which is caused by the slight drop of the fan voltage caused by the overload, but it will not drop sharply like a short circuit; the overload can be detected by comparing the actual current I_j with the rated current curve in real time.

[0093] a3, fan winding internal short circuit: When the control contactor is on, it is detected that the fan current > 2 times the rated current In, and the fan current rises steeply; because when the fan circuit is short-circuited, the current will rise sharply, more than twice the normal working current; At the same time, it will cause the fan voltage to drop > 10%, which is because the short circuit will cause the voltage to drop sharply, because the resistance of the short circuit path is very low; In this case, the contactor will be immediately disconnected to cut off the circuit to protect the equipment.

[0094] a4, contactor not attracted or power supply open phase: If it is detected that the fan current ≈ 0, the fan voltage = 0, and the upper power supply state is normal; It is indicated that the contactor on command has been issued, but the contactor main contact is not actually on; The fan itself has no problem, but the contactor is faulty, causing the fan to fail to start.

[0095] Summary: This table can quickly and accurately judge the fault type of the fan by monitoring the current and voltage of each fan, combined with specific characteristic combinations; This monitoring method can timely find problems and avoid the impact of faulty fans on the normal operation of other equipment.

[0096] b. Remove the failed fan: After detecting the fault, the edge controller immediately disconnects the fan-specific contactor, and the remaining fans in the same room are not affected. Update T-S fuzzy rules: delete the jth column from to get the input matrix of the Lth rule after the fan failure Recalculate the real-time weights and the global state feedback control law to achieve online weight reconstruction.

[0097] Next step temperature and humidity state values , current global temperature and humidity state output values , etc. require the use of matrices , , etc. Among them is the natural drift matrix, related to the sensor input. is the fan gain matrix, related to the fan control output, and each column corresponds to a fan, used to define the running duty cycle of each fan. When a fan fails and is removed, the corresponding column in is deleted to obtain . Then re-global control law and online reconstruction output to ensure the original control requirements.

[0098] is the real-time weight in the global output of the fuzzy system, used to combine the 27 local matrices into a global matrix output.

[0099] c. Remaining fan running time self-tuning (see Figure 5 ): Convert the original required air volume of the failed fan to running time increment according to the "air volume-speed-power" curve, and distribute it to the normal fans in the same room according to the weight = (available air volume i) / (total remaining air volume) through frequency conversion or duty cycle.

[0100] The above technical solutions can bring the following advantages: A. Compared with the existing decentralized PID, the regulation time is shortened by more than 40%, and the overshoot is less than 0.5°C / 2%RH; B. After the fan fails, the removal and reconstruction are completed within 1s, and the indoor temperature overshoot is less than 1°C and the humidity is less than 3%RH; C. The number of start-stop times of the cross-flow fan at the bottom of the transformer room is reduced from the traditional 120 times / day to less than 30 times / day, extending the life by 2.5 times. D, the fan energy consumption is reduced by 20-30% throughout the year, the fault positioning accuracy is greater than 98%, and the risk of high temperature tripping of the protection system caused by "one fault all stop" is avoided.

[0101] Please refer to Figure 2 and Figure 3 , embodiment two of the present application is: Please refer to Figure 2 , a temperature and humidity control system of a new energy substation is used to realize the temperature and humidity control method, and the temperature and humidity control system comprises: A plurality of temperature and humidity sensors 1 are arranged in a plurality of electrical chambers respectively, and are used to collect temperature values and humidity values of the electrical chambers; A plurality of fans 2 are arranged in a plurality of electrical chambers respectively; A plurality of current transformers 3 and a plurality of voltage transformers 4 are arranged in the power supply circuit of each fan respectively, and are used to collect current values and voltage values of each fan; An edge controller 5 is used to run a multivariable T-S fuzzy control algorithm, and is electrically connected with the plurality of temperature and humidity sensors 1, the plurality of fans 2, the plurality of current transformers 3 and the plurality of voltage transformers 4 respectively, and executes the steps of the above method.

[0102] Please refer to Figure 3 , the temperature and humidity sensor deployment: High-voltage chamber: 3 groups of temperature and humidity sensors are arranged, and are installed in the cabinet, the central chamber and the wall respectively; Low-voltage chamber: 3 groups of temperature and humidity sensors are arranged, and are installed in the cabinet, the central chamber and the wall respectively; Transformer chamber: 1 group of temperature and humidity sensors are installed on the wall and the center, 3 groups of PT100 thermal resistance sensors are installed on the transformer winding, and are used to accurately collect the temperature of the transformer winding.

[0103] Fan deployment: High-voltage chamber: 1 cooling fan is installed in the cabinet, and 1 axial flow fan is installed on the wall; Low-voltage chamber: 1 cooling fan is installed in the cabinet, and 1 axial flow fan is installed on the wall; Transformer chamber: 6 cross-flow cooling fans (independent contactor control) are installed at the bottom, and 1 axial flow fan is installed at the top and on the wall.

[0104] Transformer: current transformer and voltage transformer are installed in the power supply circuit of each fan, and are used to collect current values and voltage values of the fan in real time.

[0105] Edge controller: dual-core ARM+DSP architecture is adopted, IEC-61850 and Modbus-TCP communication interfaces are supported, each sensor, fan and transformer is electrically connected, and the edge controller is used to run a control algorithm and process data.

[0106] In summary, the application provides a new energy substation temperature and humidity control method and system, through data acquisition, fuzzy control premise variable calculation, membership value calculation, control law synthesis, hierarchical start-stop control strategy, fan fault diagnosis, fan fault handling and closed-loop complete control process, the depth of temperature and humidity control and fault tolerance is realized. Fusion, it can accurately adapt to the strong coupling characteristics of the multi-chamber environment of the new energy substation; the global control law synthesized by the T-S fuzzy model realizes the cooperative operation of the multi-chamber fan, greatly improves the accuracy and consistency of temperature and humidity control, and effectively avoids the problem of local overheating or condensation; The hierarchical start-stop control strategy can control the fan operation according to the structural characteristics and temperature and humidity demand differences of different electrical rooms, reduce the frequent start-stop phenomenon of the fan, reduce energy consumption and prolong the service life of the fan; The fault diagnosis and fault tolerance control link can quickly identify the fan fault and only remove the faulty fan, through model reconstruction and remaining fan operation time adjustment, avoid the system paralysis risk caused by the traditional control mode of one fault all shutdown, guarantee the continuity and stability of temperature and humidity control, improve the self-adaptive ability and operation reliability of the system, and provide strong guarantee for the safe and stable operation of the key equipment of the new energy substation. The temperature and humidity sensors arranged in multiple positions can comprehensively and accurately collect the temperature and humidity data of different areas of each electrical room, providing reliable and comprehensive basic data support for the control algorithm, avoiding the control deviation caused by incomplete data acquisition; The current transformer and voltage transformer independently configured for each fan can accurately collect the fan operation data in real time, providing an accurate data source for fault diagnosis, and ensuring the accuracy of fault type judgment.

[0107] The above is only an embodiment of the application, and does not limit the patent scope of the application. Any equivalent transformation or direct or indirect application in related technical fields based on the content of the specification and drawings is also included in the patent protection scope of the application.

Claims

1. A method for temperature and humidity control in a new energy substation, characterized in that, Includes the following steps: S1. Collect temperature and humidity values ​​from multiple electrical rooms within the substation; S2. Based on the temperature and humidity values ​​of multiple electrical rooms collected in step S1, calculate multiple fuzzy control prerequisite variables for the TS fuzzy model. S3. Based on the fuzzy control premise variables calculated in step S2, calculate the membership values ​​of each fuzzy control premise variable on each fuzzy set in the TS fuzzy model. S4. Based on the membership values ​​calculated in step S3, synthesize the global control law through the TS fuzzy model to generate control commands for the coordinated control of the fans in each electrical room. S5. Based on the control instructions generated in step S4, execute the layered start-stop control strategy for fans in different locations in each electrical room; S6. Collect the current and voltage values ​​of the fans in each electrical room, and perform fault diagnosis on each fan based on the collected current and voltage values ​​to determine whether each fan has malfunctioned. S7. If the fan does not malfunction, return to step S1 and continue to the next cycle; If the fan malfunctions, proceed to step S8; S8. Disconnect the faulty fan and reconstruct the TS fuzzy model online; S9. Based on the reconstructed TS fuzzy model, readjust the operating time of the remaining normal fans; S10. Return to step S1 and continue executing the next control loop.

2. The temperature and humidity control method for a new energy substation according to claim 1, characterized in that, The multiple fuzzy control prerequisite variables in step S2 include the maximum global temperature deviation, the maximum global humidity deviation, and the transformer room temperature rise rate; The formula for calculating the maximum global temperature deviation is as follows: ； in, This represents the maximum global temperature deviation. For transformer room temperature deviation, For the temperature deviation in the high-pressure chamber, Temperature deviation in the low-pressure chamber; The formula for calculating the maximum global humidity deviation is as follows: ； in, This represents the maximum global humidity deviation. For humidity deviation in the transformer room, For humidity deviation in the high-pressure room, Humidity deviation in the low-pressure chamber; The formula for calculating the transformer's room temperature rise rate is as follows: ； in, For the transformer room temperature rise rate, This represents the temperature rise of the transformer room per unit time. Unit of time.

3. The temperature and humidity control method for a new energy substation according to claim 1, characterized in that, In step S6, fault diagnosis of each fan based on the collected current and voltage values ​​includes: comparing the collected current value with a preset rated current value, and comparing the collected voltage value with a preset rated voltage value, and determining the fault type of the fan according to a preset four-category criterion; the four-category criterion includes: like and If so, the diagnosis is an open circuit; like and If the decrease is less than 5%, it is diagnosed as overload; like And it rose rapidly, at the same time If the sudden drop exceeds 10%, it is diagnosed as a short circuit; like and If the upstream power supply is normal, the diagnosis is a fan contactor fault. in, This represents the current value of the fan. This is the voltage value of the fan. This is the rated current value. This is the rated voltage value.

4. The temperature and humidity control method for a new energy substation according to claim 1, characterized in that, In step S3, the membership values ​​of each fuzzy control premise variable on each fuzzy set in the TS fuzzy model are calculated using the triangular membership function.

5. The temperature and humidity control method for a new energy substation according to claim 4, characterized in that, The calculation process of the membership function of the triangle includes: The actual value of each of the fuzzy control premise variables is normalized to a preset standard universe of discourse. Within the predefined standard universe of discourse, three triangular membership functions are defined for each fuzzy control premise variable, successively covering its negative value range, the range near zero, and the positive value range. Substituting the normalized fuzzy control premise variables into their corresponding three triangular membership functions yields three membership values.

6. The temperature and humidity control method for a new energy substation according to claim 5, characterized in that, The normalized standard universe of discourse for the fuzzy control premise variables is [-1, 1]; The vertices of the three membership functions of the triangle are located near the negative boundary, zero, and positive boundary of the preset standard universe of discourse, respectively.

7. The temperature and humidity control method for a new energy substation according to claim 1, characterized in that, Step S4 is as follows: Based on the membership value calculated in step S3, the normalized activation degree of each fuzzy rule in multiple TS fuzzy models is calculated. Based on the normalized activation degree, the local linear control laws corresponding to multiple fuzzy rules are weighted and averaged to synthesize a global control law. The output of the global control law is the duty cycle of each wind turbine.

8. The temperature and humidity control method for a new energy substation according to claim 1, characterized in that, In step S5, a tiered start-stop control strategy is implemented for fans in different locations within each electrical room, specifically including the following steps: S51. Identify the type of the target electrical room based on the control command and the temperature and humidity values ​​of multiple electrical rooms collected in step S1; S52. If the target electrical room is a transformer room, then execute the first layering strategy: Based on the temperature deviation of the transformer room, the bottom crossflow fan of the transformer room is controlled by duty cycle modulation; if the ambient temperature exceeds the temperature setting value of a higher level, the wall axial flow fan and the top axial flow fan of the transformer room are started in sequence according to the preset priority. S53. If the target electrical room is a high-voltage room or a low-voltage room, then execute the second stratification strategy: When the temperature inside the cabinet exceeds the standard, the cabinet fan should be activated first. When the indoor humidity exceeds the standard, the cabinet fan and heater will start simultaneously, and the wall fan will start after a preset delay.

9. The temperature and humidity control method for a new energy substation according to claim 1, characterized in that, In step S8, the TS fuzzy model is reconstructed online, specifically including: Remove the matrix column corresponding to the faulty wind turbine from the input matrix corresponding to each fuzzy rule of the TS fuzzy model to obtain the input matrix after the fault. Based on the obtained input matrix after the fault, the normalized activation degree and global control law of each fuzzy rule in the TS fuzzy model are recalculated.

10. A temperature and humidity control system for a new energy substation, used to implement the temperature and humidity control method according to any one of claims 1-9, characterized in that, The temperature and humidity control system includes: Multiple temperature and humidity sensors are arranged in multiple electrical rooms to collect temperature and humidity values ​​in each electrical room; Multiple fans are located in multiple electrical rooms; Multiple current transformers and multiple voltage transformers are installed in the power supply circuit of each wind turbine to collect the current and voltage values ​​of each wind turbine. An edge controller, used to run a multivariable TS fuzzy control algorithm, is electrically connected to multiple temperature and humidity sensors, multiple fans, multiple current transformers and multiple voltage transformers, and performs the steps of the method described in any one of claims 1-9.

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