Intelligent electrical power distribution cabinet

Abstract

An intelligent electrical power distribution cabinet disclosed by the present invention comprises a cabinet body, an MCU control module is integrated in the cabinet body, and the MCU control module is connected with a power distribution assembly, an intelligent monitoring module, a control execution module and a communication module. The MCU control module is used for analyzing data collected by the intelligent monitoring module and controlling the whole power distribution cabinet according to an analysis result, and the control execution module is matched with the MCU control module to control the power distribution assembly according to an analysis processing result; the intelligent monitoring module is used for collecting electrical parameters and environmental parameters in the power distribution cabinet in real time, and the communication module is used for realizing data interaction between the power distribution cabinet and external equipment. Through the arrangement of the power distribution assembly, the intelligent monitoring module, the control execution module and the communication module, the whole power distribution cabinet can operate normally, and the intelligent monitoring module can monitor current, voltage, temperature, humidity and smoke concentration in the cabinet body.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution cabinets, and particularly to an intelligent electrical distribution cabinet. Background Art

[0002] The safety maintenance of traditional electrical distribution cabinets mainly relies on manual inspections and passive protection devices (such as circuit breakers and relays). This method has many drawbacks and it is very difficult to issue early warnings in time when the distribution cabinet fails, thus causing significant economic losses. Therefore, some intelligent electrical distribution cabinets have emerged on the market.

[0003] For example, the invention patent with the publication number CN117712845B discloses an intelligent electrical distribution cabinet, which includes a cabinet body. A cabinet cover is clamped on the front of the cabinet body. A voltage monitoring mechanism is fixedly installed on the left side of the bottom of the cabinet body. Two fixed seats are fixedly connected to the middle of the cabinet body. Four circuit breakers arranged linearly and equidistantly are fixedly installed on the front of the two fixed seats. Through the setting of the current monitoring mechanism, the main coil and the secondary coil will generate induced current due to the alternating current in the circuit. The induced current generates a magnetic field in the vertical direction through the deflection coil. This magnetic field attracts the magnetic field of the U-shaped magnet, causing the rotating shaft to rotate counterclockwise and compress the spiral spring. The greater the induced current passing through the deflection coil, the greater the suction force between the magnetic field and the U-shaped magnet. In this way, the current in the circuit can be detected without accessing the circuit, thus avoiding the characteristics that the damage of the ammeter in the existing detection method will affect the normal operation of the entire circuit and the power-off maintenance.

[0004] The intelligent electrical distribution cabinet provided by the above patent mainly monitors the distribution cabinet through the current mechanism. However, the monitoring data of this intelligent distribution cabinet is relatively single, unable to truly and comprehensively reflect the situation inside the distribution cabinet, and it is not convenient to issue an alarm in time when the distribution cabinet is abnormal, which is not conducive to the staff to quickly process the abnormal distribution cabinet in time. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent electrical distribution cabinet, aiming to improve the problems that the monitoring data of the existing intelligent distribution cabinet is relatively single, unable to truly and comprehensively reflect the situation inside the distribution cabinet, not convenient to issue an alarm in time when the distribution cabinet is abnormal, and not conducive to the staff to quickly process the abnormal distribution cabinet in time.

[0006] The present invention is implemented as follows:

[0007] An intelligent electrical distribution cabinet, comprising a cabinet body, inside which an MCU control module is integrated. The MCU control module is connected to a power distribution component, an intelligent monitoring module, a control execution module and a communication module. The MCU control module is used to analyze the data collected by the intelligent monitoring module and control the entire distribution cabinet according to the analysis results. The control execution module cooperates with the MCU control module to control the power distribution component according to the analysis and processing results. The intelligent monitoring module is used to collect electrical parameters and environmental parameters inside the distribution cabinet in real time, and the communication module is used to realize data interaction between the distribution cabinet and external devices.

[0008] Further, a cabinet door is provided on the front of the cabinet body. The cabinet door is hinged to the cabinet body through a hinge, and a handle is provided on the side of the cabinet door that is not hinged to the cabinet body. An intelligent control host is provided at the top of the cabinet body, and an operation panel is provided on the front of the intelligent control host. The intelligent control host is used to cooperate with the MCU control module, the intelligent monitoring module and the communication module to control the entire distribution cabinet.

[0009] Further, the intelligent monitoring module includes a current sensor, a voltage sensor, a temperature sensor, a humidity sensor and a smoke sensor. The current sensor is used to detect the current of the internal circuit of the cabinet body, the voltage sensor is used to monitor the voltage of the internal circuit of the cabinet body, the temperature sensor is used to monitor the temperature inside the cabinet body, the humidity sensor is used to monitor the humidity inside the cabinet body, and the smoke sensor is used to give a timely feedback when smoke appears inside the cabinet body.

[0010] Further, the control execution module includes a plurality of circuit breakers, contactors and relays. The plurality of circuit breakers, contactors and relays are all connected to the MCU control module and are used to realize quick on-off operations according to the control instructions issued by the MCU control module.

[0011] Further, the MCU control module uses an edge computing chip to quickly analyze and process the collected data. The MCU control module is built-in with a fault diagnosis algorithm, which is based on support vector machine (SVM). Through learning and training of historical fault data and normal operation data, it realizes accurate diagnosis of distribution cabinet faults. The MCU control module is also built-in with a load optimization scheduling algorithm, which is based on the improved particle swarm optimization (IPSO) algorithm.

[0012] Further, the communication module includes a Modbus communication protocol unit, a Profibus communication protocol unit and an Ethernet communication protocol unit. The communication module can upload the operation data and fault information of the distribution cabinet to the remote monitoring center in real time, and at the same time receive the control instructions issued by the remote monitoring center.

[0013] Further, it also includes a heat pipe radiator and a cooling fan. A heat dissipation box is provided at the bottom of the cabinet body. Heat dissipation holes are provided at the front and rear ends of the heat dissipation box, and feet are provided at the bottom corners of the bottom surface of the heat dissipation box. A groove is provided at the bottom of the heat dissipation box, a plurality of threaded holes are provided at the top of the groove, a plurality of first connection slots are provided at the bottom of the heat dissipation box, the heat pipe radiator is arranged inside the heat dissipation box, the cooling fan is connected to the threaded holes, and the cooling fan is electrically connected to the heat dissipation box.

[0014] Further, fixing plates are provided on both sides of the heat pipe radiator. A plurality of fixing holes are provided on the fixing plates. Bolts pass through the fixing holes to fix the heat pipe radiator at the inner top end of the heat dissipation box. A row of heat absorption pipes is provided on the heat pipe radiator. The heat absorption surface of the heat absorption pipe faces the inner cavity of the cabinet body, and a row of heat dissipation fins is provided at the bottom of the heat absorption pipe.

[0015] Further, an adapter ring is provided at the top of the cooling fan. The adapter ring is threadedly connected to the threaded hole. A first connection line is provided on the side of the cooling fan. A first connection plug is provided at the end of the first connection line. The first connection plug is connected to the first connection slot, and the cooling fan is connected to the control execution module for the control execution module to control the rotation speed of the cooling fan.

[0016] Further, it also includes an audible and visual alarm. The MCU control module inside the cabinet body is connected to an alarm module. The alarm module is used to give an alarm when the intelligent monitoring module detects abnormal data. A supporting plate is provided at the top of one side of the cabinet body. Mounting holes are provided on the supporting plate. A second connection slot is provided at a position near the supporting plate on one side of the cabinet body. A stud is provided at the bottom end of the audible and visual alarm. The stud is threadedly connected to the mounting hole. A second connection line is provided on one side of the audible and visual alarm. A second connection plug is provided at the end of the second connection line. The second connection plug is connected to the second connection slot, and the audible and visual alarm is connected to the alarm module for controlling the operation of the audible and visual alarm.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0018] 1. By setting a power distribution component, an intelligent monitoring module, a control execution module and a communication module, this structure can enable the normal operation of the entire power distribution cabinet. The intelligent monitoring module can monitor the current, voltage, temperature, humidity and smoke concentration inside the cabinet body, facilitating timely handling when the data inside the cabinet body is abnormal. At the same time, the monitored data is transmitted to the remote control center through the communication module, facilitating the remote control center to handle according to the abnormal situation in a timely manner. The control execution module can also receive the instructions transmitted by the MCU control module, facilitating the control of the operation of each component of the power distribution cabinet according to the instructions.

[0019] 2. By providing a heat pipe radiator and an exhaust fan, the present invention can effectively discharge the heat generated by the components inside the cabinet. Moreover, the heat pipe radiator seals the bottom of the cabinet, so that the air flow inside the heat dissipation cavity does not flow into the inner cavity of the cabinet, thereby preventing dust in the air from accumulating on the electrical components inside the cabinet.

[0020] 3. By providing an alarm, the present invention can give an alarm when an abnormality occurs in the power distribution cabinet, so as to enable the staff to quickly handle the abnormality of the power distribution cabinet and prevent serious faults from occurring in the power distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic structural diagram of the whole of the present invention;

[0022] Figure 2 is a schematic structural diagram of the cabinet of the present invention;

[0023] Figure 3 is a structural block diagram of the interior of the cabinet of the present invention;

[0024] Figure 4 is a structural block diagram of the intelligent monitoring module of the present invention;

[0025] Figure 5 is a structural block diagram of the control execution module of the present invention;

[0026] Figure 6 is a structural block diagram of the communication module of the present invention;

[0027] Figure 7 is a schematic structural diagram of the heat pipe radiator of the present invention;

[0028] Figure 8 is a schematic structural diagram of the cooling fan of the present invention;

[0029] Figure 9 is a schematic structural diagram of the sound and light alarm of the present invention.

[0030] In the figure: 1. Cabinet; 11. Cabinet door; 12. Hinge; 13. Handle; 14. Intelligent control host; 15. Control panel; 16. Heat dissipation box; 161. Heat dissipation holes; 162. Support feet; 163. Grooves; 164. Threaded holes; 165. First communication slot; 17. Support plate; 171. Mounting holes; 172. Second communication slot; 2. Heat pipe radiator; 21. Fixing plate; 22. Fixing holes; 23. Heat absorption pipe; 24. Heat dissipation fins; 3. Cooling fan; 31. Adapter ring; 32. First communication line; 33. First communication plug; 4. Sound and light alarm; 41. Stud; 42. Second communication line; 43. Second communication plug. DETAILED DESCRIPTION OF THE INVENTION

[0031] In the present invention, unless otherwise clearly defined and limited, terms such as "installation", "connection", "linkage", "fixation", etc. shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral body; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0032] The following will be further described in conjunction with the accompanying drawings and specific embodiments:

[0033] Embodiment 1

[0034] As Figure 1 and Figure 3 shown, an intelligent electrical distribution cabinet includes a cabinet body 1. An MCU control module is integrated inside the cabinet body 1. The MCU control module is connected to a power distribution component, an intelligent monitoring module, a control execution module, and a communication module. The MCU control module is used to analyze the data collected by the intelligent monitoring module and control the entire distribution cabinet according to the analysis results. The power distribution component is used to cooperate with the entire distribution cabinet to perform power distribution operations on the power supply system. The control execution module cooperates with the MCU control module to control the power distribution component according to the analysis and processing results, facilitating the operation of the entire distribution cabinet. The intelligent monitoring module is used to collect electrical parameters and environmental parameters inside the distribution cabinet in real time, so as to issue an abnormal alarm in time when the electrical parameters and environmental parameters are abnormal. The communication module is used to realize data interaction between the distribution cabinet and external devices, facilitating the feedback of various data of the monitored distribution cabinet to the remote control center.

[0035] As Figure 2 and Figure 3 shown, a cabinet door 11 is provided on the front of the cabinet body 1. The cabinet door 11 is hinged to the cabinet body 1 through a hinge 12. A handle 13 is provided on the side of the cabinet door 11 that is not hinged to the cabinet body 1, facilitating the opening and closing and use of the cabinet door 11, and also facilitating the maintenance of the inside of the cabinet body 1 by the staff. An intelligent control host 14 is provided at the top of the cabinet body 1. An operation panel 15 is provided on the front of the intelligent control host 14. The intelligent control host 14 and the operation panel 15 cooperate to facilitate the operation of the distribution cabinet by the staff. The intelligent control host 14 is used to cooperate with the MCU control module, the intelligent monitoring module, and the communication module to control the entire distribution cabinet.

[0036] As Figure 4As shown in the figure, the intelligent monitoring module includes a current sensor, a voltage sensor, a temperature sensor, a humidity sensor, and a smoke sensor. The current sensor is used to detect the current of the internal circuit of the cabinet 1. The current sensor adopts a Rogowski coil current sensor, which can achieve wide-band and high-precision current measurement. The measurement accuracy reaches ±0.1%, and the measurement range is 0 - 5000A. The voltage sensor is used to monitor the voltage of the internal circuit of the cabinet 1. The voltage sensor adopts a capacitive voltage transformer, which has good linearity and stability. The measurement accuracy reaches ±0.2%, and the measurement range is 0 - 10kV. The data monitored by the current sensor and the voltage sensor are real-time fed back to the MCU control module. When the monitored current and voltage are abnormal, the MCU control module will send instructions to the control execution module to conveniently cut off the corresponding circuit, and at the same time feed back the data to the remote control center. The temperature sensor is used to monitor the temperature inside the cabinet 1. The temperature sensor adopts an infrared thermal imaging sensor, which can real-time monitor the temperature distribution of key parts inside the power distribution cabinet. The temperature resolution reaches 0.1°C, and the measurement range is -20°C - 200°C. The humidity sensor is used to monitor the humidity inside the cabinet 1. The temperature and humidity can reflect the environmental state inside the cabinet 1, which is convenient to make corresponding treatments in time when the environmental state inside the cabinet 1 is abnormal. The smoke sensor is used to make a timely feedback when there is smoke inside the cabinet 1.

[0037] As Figure 5 shown, the control execution module includes multiple circuit breakers, contactors, and relays. The multiple circuit breakers, contactors, and relays are all connected to the MCU control module and are used to realize fast opening and closing operations according to the control instructions issued by the MCU control module, which is convenient to connect and disconnect some circuits and is conducive to ensuring the safe operation of the entire power distribution cabinet. The opening and closing time is less than 100ms.

[0038] As Figure 3 shown, the MCU control module adopts an edge computing chip, which is used to quickly analyze and process the collected data. The processing speed reaches more than 100,000 data operations per second. The MCU control module is built-in with a fault diagnosis algorithm. The fault diagnosis algorithm is based on the support vector machine (SVM). Through the learning and training of historical fault data and normal operation data, it realizes the accurate diagnosis of power distribution cabinet faults. The fault diagnosis accuracy rate reaches more than 95%. The feature vector of the fault diagnosis algorithm is:

[0039] X = [I a , V a , THD I , T a , PF, D a T

[0040] Among them, I a is the effective value current, V​a is the effective value voltage, THD I is the total harmonic distortion rate of current, T a is the cabinet temperature, PF is the power factor, D a is the load rate; The feature vectors of the fault diagnosis algorithm are mainly used to extract the time-domain, frequency-domain, and time-frequency domain features (such as root mean square, spectral entropy, wavelet coefficients, etc.) of multi-source monitoring data such as current, voltage, temperature, and humidity, construct multi-dimensional feature vectors, and accurately characterize the operating state (normal / abnormal / fault) of the power distribution cabinet. For example, the contact badness fault is identified through the singularity index (such as the Hurst index) of the current signal, and the abnormal heat dissipation is judged through the temperature gradient feature. At the same time, the feature vectors of the guzheng diagnosis algorithm can improve the diagnosis accuracy, enhance the real-time performance and robustness, support predictive maintenance, reduce false alarms and missed alarms, and optimize the operation and maintenance decision-making. The radial basis kernel function (RBF) is adopted:

[0041] K(x i ,x j )=exp(-γ||x i -x j || 2 )(γ>0)

[0042] The uses of the radial basis kernel function are mainly divided into the following aspects:

[0043] 1. Nonlinear feature mapping: Map the low-dimensional nonlinearly separable feature vectors (such as current distortion rate, temperature gradient, vibration spectrum) to the high-dimensional Hilbert space through RBF to solve the nonlinear classification problem of fault modes (such as distinguishing contact aging and instantaneous overvoltage).

[0044] 2. The kernel trick reduces the computational complexity, avoids explicit high-dimensional mapping, and directly calculates the high-dimensional inner product through the kernel function, which is suitable for the lightweight deployment of edge computing modules.

[0045] 3. Multi-source heterogeneous data fusion uniformly measures heterogeneous feature vectors such as current (time domain), temperature (spatial distribution), and vibration (frequency domain), and enhances the correlation of different physical quantities.

[0046] The effects of the radial basis kernel function include the following aspects:

[0047] 1. Improve the accuracy of nonlinear fault classification. Compared with the linear kernel function, the RBF kernel increases the recognition accuracy of SVM for nonlinear faults (such as insulation aging gradual faults) from 82% to 95%. For example: For a contact oxidation fault in a power distribution cabinet, the nonlinear relationship between the current singularity index and the contact resistance is mapped through the RBF kernel, and early contact badness is successfully distinguished (the misjudgment rate is reduced by 40%).

[0048] 2. Enhance the model generalization ability, adaptively adjust the γ parameter, and balance the model complexity and noise robustness. The generalization error of the optimized model for unknown faults decreases by 18%.

[0049] 3. Support the processing of high-dimensional sparse features. For the high-dimensional feature vectors after wavelet packet decomposition, the RBF kernel reduces the computational complexity through sparse mapping, and the edge-side diagnosis delay is reduced from 120 ms to 50 ms.

[0050] The decision function is:

[0051]

[0052] where α i is the Lagrange multiplier, y i ∈ -1, +1 is the fault label (+1: normal, -1: fault), and b is the bias term.

[0053] The specific uses of the decision function are as follows:

[0054] 1. Fault classification decision, the SVM decision function based on the RBF kernel outputs the fault category

[0055] 2. Threshold warning, set multi-level warning thresholds in combination with the output value of the decision function (non-binarized confidence) (e.g., |f(x)| < 0.5 is normal, 0.5 ≤ |f(x)| < 1.0 is warning, |f(x)| ≥ 1.0 is fault).

[0056] 3. Confidence-assisted decision, output continuous values (such as [-2, 2]) to reflect the severity of the fault, and support the sorting of operation and maintenance priorities (e.g., a short-circuit fault with f(x) = 1.8 takes precedence over a slight overheating with f(x) = 0.7).

[0057] The specific effects of the decision function are as follows:

[0058] 1. Real-time and accurate classification

[0059] 2. Reduce false alarms and missed detections, introduce soft margin decision (C parameter adjustment), and allow a small number of samples to be misclassified to improve the overall robustness. The actual measurement of a substation shows that the false alarm rate of the decision function is reduced from 12% to 3% (compared with the fixed threshold rule)

[0060] 3. Support hierarchical maintenance strategies, and the confidence of the decision function drives the maintenance process:

[0061] Low confidence (0.5 ≤ |f| < 1.0): Trigger enhanced online monitoring (increase the sampling frequency);

[0062] High confidence (|f| ≥ 1.0): Start the circuit breaker to trip and push the fault code.

[0063] The MCU control module also incorporates a load optimization scheduling algorithm, which is based on the Improved Particle Swarm Optimization (IPSO) algorithm and uses active power loss, load priority, and voltage deviation as multi-objective functions:

[0064]

[0065] Among them, ω1, ω2, and ω3 are weight coefficients, and ω1 + ω2 + ω3 = 1. The weight coefficients are dynamically adjusted; is the system active power loss, S k is the priority penalty of the kth type of load (S b = 0: high priority, S b = 1: interruptible), U t is the voltage deviation at time t (U t = |V t - V nom | / V nom );

[0066] The specific uses of the load optimization scheduling algorithm based on the Improved Particle Swarm Optimization (IPSO) algorithm are as follows:

[0067] 1. Target collaborative optimization: Reduce P by load transfer (such as switching high-energy-consuming loads to low-resistance branches) b .

[0068] 2. Real-time dynamic scheduling: The IPSO algorithm is embedded in the edge controller of the distribution cabinet, and the scheduling strategy is updated every 500 ms based on real-time collected data (current, voltage, load status).

[0069] The specific effects of the load optimization scheduling algorithm based on the Improved Particle Swarm Optimization (IPSO) algorithm are as follows:

[0070] 1. Nonlinear multi-objective optimization ability: It can handle the mixed optimization problem of active power loss (nonlinear), priority (discrete constraint), and voltage deviation (continuous variable), which cannot be solved by traditional linear programming (comparison case: genetic algorithm calculation time 2.1 s → IPSO 0.8 s).

[0071] 2. Real-time performance and robustness: Edge-side optimization: Single-cycle scheduling time < 80 ms (meeting the real-time requirements of GB / T 3797-2016 for distribution systems), introducing a particle memory bank (retaining the historical optimal solution), and the recovery time < 200 ms when the load mutates (±30%).

[0072] Solve through chaotic perturbation particle swarm optimization, meeting the triple constraints of capacity, temperature, and voltage:

[0073]

[0074] T cabinet(t) ≤ T max (Temperature constraint)

[0075] V min ≤ V(t) ≤ V max (Voltage constraint)

[0076] Implement the optimal scheduling of the loads in the power distribution cabinet to improve the power utilization efficiency;

[0077] The specific uses of the triple constraints of capacity, temperature, and voltage are as follows:

[0078] 1. Resource optimal scheduling under multiple constraints: Capacity constraint: Ensure that the current in each branch of the power distribution cabinet does not exceed the rated capacity to avoid overload tripping or equipment damage; Temperature constraint: Optimize heat dissipation and load distribution by real-time monitoring the temperature of equipment (such as circuit breakers, busbars) to prevent insulation aging or fire risks caused by overheating; Voltage constraint: Maintain the voltage at each node within the allowable deviation range (such as ±5% of the rated voltage) to ensure the stable operation of sensitive loads (such as precision instruments).

[0079] 2. Dynamic load balancing and energy efficiency optimization: Integrate the real-time data (current, temperature, voltage) of the intelligent power distribution cabinet, and dynamically adjust the load distribution through the CPSO algorithm to reduce the active power loss (such as line resistance loss) and improve the energy utilization efficiency.

[0080] 3. Improve the robustness and reliability of the system: Handle non-linear and strongly coupled constraint conditions (such as the non-linear relationship between temperature and current) to ensure stable operation under complex working conditions (such as seasonal load fluctuations, sudden failures).

[0081] 4. Support intelligent operation and predictive maintenance: The optimization results provide data support for equipment life assessment (such as the impact of temperature rise on insulation materials) and maintenance strategies (such as the priority maintenance of overloaded branches).

[0082] The specific effects of the triple constraints of capacity, temperature, and voltage are as follows:

[0083] 1. Enhanced global optimization ability: The ergodicity and randomness of the chaotic sequence (such as the Logistic map) can break the local aggregation of the particle swarm, expand the search space, and improve the solution accuracy for multi-constraint non-convex optimization problems; Adjust the particle velocity and position through chaotic perturbation to enhance global search (exploration) in the initial stage of iteration and focus on local optimization (exploitation) in the later stage to meet the multi-objective optimization requirements under complex constraints.

[0084] 2. Improved constraint satisfaction: Ensure that all solutions meet the hard limits of capacity, temperature, and voltage, and avoid the safety risks caused by traditional algorithms due to relaxed constraints. On the premise of meeting the hard constraints, minimize the voltage deviation and temperature gradient (balance the equipment temperature rise) to extend the equipment life.

[0085] 3. Improvement in multi-objective optimization performance. By optimizing the load distribution path, the line resistance loss is reduced, and the loss can be reduced by 5% - 15% compared with the traditional PSO algorithm. Combining with the user-defined load priority (such as power supply to first-level loads first), the power supply stability of critical loads is maximized under constraints.

[0086] Such as Figure 6 As shown in the figure, the communication module includes a Modbus communication protocol unit, a Profibus communication protocol unit, and an Ethernet communication protocol unit. The communication module supports the Modbus communication protocol, the Profibus communication protocol, and the Ethernet communication protocol simultaneously. The communication module can upload the operation data and fault information of the power distribution cabinet to the remote monitoring center in real time, and at the same time receive the control instructions sent by the remote monitoring center. The communication module of the present invention supports multiple communication protocols simultaneously, ensuring the effectiveness and applicability of communication.

[0087] Embodiment 2

[0088] Such as Figure 1 And Figure 3 As shown in the figure, an intelligent electrical power distribution cabinet includes a cabinet body 1. An MCU control module is integrated inside the cabinet body 1. The MCU control module is connected to a power distribution component, an intelligent monitoring module, a control execution module, and a communication module; the MCU control module is used to analyze the data collected by the intelligent monitoring module and control the entire power distribution cabinet according to the analysis results. The power distribution component is used to cooperate with the entire power distribution cabinet to perform power distribution operations on the power supply system. The control execution module cooperates with the MCU control module to control the power distribution component according to the analysis and processing results, facilitating the operation of the entire power distribution cabinet; the intelligent monitoring module is used to collect electrical parameters and environmental parameters inside the power distribution cabinet in real time, so as to send out an abnormal alarm in time when the electrical parameters and environmental parameters are abnormal. The communication module is used to realize data interaction between the power distribution cabinet and external devices, facilitating the feedback of various data of the monitored power distribution cabinet to the remote control center.

[0089] Such as Figure 2 And Figure 3 As shown in the figure, a cabinet door 11 is provided on the front of the cabinet body 1. The cabinet door 11 is hinged to the cabinet body 1 through a hinge 12. A handle 13 is provided on the side of the cabinet door 11 that is not hinged to the cabinet body 1, facilitating the opening and closing and use of the cabinet door 11, and at the same time facilitating the maintenance of the inside of the cabinet body 1 by the staff. An intelligent control host 14 is provided at the top of the cabinet body 1. An operation panel 15 is provided on the front of the intelligent control host 14. The intelligent control host 14 cooperates with the operation panel 15 to facilitate the operation of the power distribution cabinet by the staff. The intelligent control host 14 is used to cooperate with the MCU control module, the intelligent monitoring module, and the communication module to control the entire power distribution cabinet.

[0090] Such as Figure 4As shown in the figure, the intelligent monitoring module includes a current sensor, a voltage sensor, a temperature sensor, a humidity sensor, and a smoke sensor; the current sensor is used to detect the current of the internal circuit of the cabinet 1. The current sensor adopts a Rogowski coil current sensor, which can achieve wide-band and high-precision current measurement, with a measurement accuracy of ±0.1% and a measurement range of 0-5000A. The voltage sensor is used to monitor the voltage of the internal circuit of the cabinet 1. The voltage sensor adopts a capacitive voltage transformer, which has good linearity and stability, with a measurement accuracy of ±0.2% and a measurement range of 0-10kV. The data monitored by the current sensor and the voltage sensor are real-time fed back to the MCU control module. When the monitored current and voltage are abnormal, the MCU control module will send an instruction to the control execution module to facilitate cutting off the corresponding circuit, and at the same time, the data will be fed back to the remote control center. The temperature sensor is used to monitor the temperature inside the cabinet 1. The temperature sensor adopts an infrared thermal imaging sensor, which can monitor the temperature distribution of key parts inside the power distribution cabinet in real time, with a temperature resolution of 0.1°C and a measurement range of -20°C - 200°C. The humidity sensor is used to monitor the humidity inside the cabinet 1. The temperature and humidity can reflect the environmental state inside the cabinet 1, which is convenient for making corresponding treatments in time when the environmental state inside the cabinet 1 is abnormal. The smoke sensor is used to give a timely feedback when there is smoke inside the cabinet 1.

[0091] As Figure 5 shown, the control execution module includes multiple circuit breakers, contactors, and relays. The multiple circuit breakers, contactors, and relays are all connected to the MCU control module and are used to achieve fast opening and closing operations according to the control instructions issued by the MCU control module, which is convenient for turning on and off some circuits and ensuring the safe operation of the entire power distribution cabinet. The opening and closing time is less than 100ms.

[0092] As Figure 3 shown, the MCU control module adopts an edge computing chip, which is used to quickly analyze and process the collected data, and the processing speed reaches more than 100,000 data operations per second; the MCU control module is built-in with a fault diagnosis algorithm. The fault diagnosis algorithm is based on support vector machine (SVM). Through the learning and training of historical fault data and normal operation data, it realizes the accurate diagnosis of power distribution cabinet faults, and the fault diagnosis accuracy rate reaches more than 95%; the eigenvector of the fault diagnosis algorithm is:

[0093] X = [I a , V a , THD I , T a , PF, D a T

[0094] Among them, I a is the effective value current, V​a is the effective value voltage, THD I is the total harmonic distortion rate of current, T a is the cabinet temperature, PF is the power factor, D a is the load rate; The feature vectors of the fault diagnosis algorithm are mainly used to extract the time-domain, frequency-domain, and time-frequency domain features (such as root mean square, spectral entropy, wavelet coefficients, etc.) of multi-source monitoring data such as current, voltage, temperature, and humidity, construct multi-dimensional feature vectors, and accurately characterize the operating state (normal / abnormal / fault) of the power distribution cabinet. For example, the poor contact fault of the contact is identified through the singularity index (such as the Hurst index) of the current signal, and the abnormal heat dissipation is judged through the temperature gradient feature. At the same time, the feature vectors of the guzheng diagnosis algorithm can improve the diagnosis accuracy, enhance the real-time performance and robustness, support predictive maintenance, reduce false alarms and missed alarms, and optimize the operation and maintenance decision-making. The radial basis kernel function (RBF) is adopted:

[0095] K(x i ,x j ) = exp(-γ||x i -x j || 2 )(γ > 0)

[0096] The uses of the radial basis kernel function are mainly divided into the following aspects:

[0097] 1. Nonlinear feature mapping: Map the low-dimensional nonlinearly separable feature vectors (such as current distortion rate, temperature gradient, vibration spectrum) to the high-dimensional Hilbert space through RBF to solve the nonlinear classification problem of fault modes (such as distinguishing contact aging and instantaneous overvoltage).

[0098] 2. The kernel trick reduces the computational complexity, avoids explicit high-dimensional mapping, and directly calculates the high-dimensional inner product through the kernel function, which is suitable for the lightweight deployment of edge computing modules.

[0099] 3. Multi-source heterogeneous data fusion, uniformly measuring heterogeneous feature vectors such as current (time domain), temperature (spatial distribution), and vibration (frequency domain), and enhancing the correlation of different physical quantities.

[0100] The effects of the radial basis kernel function include the following aspects:

[0101] 1. Improve the accuracy of nonlinear fault classification. Compared with the linear kernel function, the RBF kernel increases the recognition accuracy of SVM for nonlinear faults (such as insulation aging gradual faults) from 82% to 95%. For example: For a power distribution cabinet contact oxidation fault, the nonlinear relationship between the current singularity index and the contact resistance is mapped through the RBF kernel, and early poor contact is successfully distinguished (the misjudgment rate is reduced by 40%).

[0102] 2. Enhance the model's generalization ability, adaptively adjust the γ parameter, and balance the model complexity and noise robustness. The generalization error of the optimized model for unknown faults is reduced by 18%.

[0103] 3. Support the processing of high-dimensional sparse features. For the high-dimensional feature vectors after wavelet packet decomposition, the RBF kernel reduces the computational complexity through sparse mapping, and the edge-side diagnosis delay is reduced from 120 ms to 50 ms.

[0104] The decision function is:

[0105]

[0106] where α i is the Lagrange multiplier, y i ∈{-1, +1} is the fault label (+1: normal, -1: fault), and b is the bias term.

[0107] The specific uses of the decision function are as follows:

[0108] 1. Fault classification decision: The SVM decision function based on the RBF kernel outputs the fault category.

[0109] 2. Threshold warning: Combine the output value of the decision function (non-binarized confidence) to set multi-level warning thresholds (e.g., |f(x)| < 0.5 is normal, 0.5 ≤ |f(x)| < 1.0 is a warning, |f(x)| ≥ 1.0 is a fault).

[0110] 3. Confidence-assisted decision: Output a continuous value (e.g., [-2, 2]) to reflect the severity of the fault and support the prioritization of maintenance operations (e.g., a short-circuit fault with f(x) = 1.8 has a higher priority than a minor overheating with f(x) = 0.7).

[0111] The specific effects of the decision function are as follows:

[0112] 1. Real-time and accurate classification

[0113] 2. Reduce false alarms and missed detections: Introduce soft margin decision (C parameter adjustment) to allow a small number of samples to be misclassified to improve the overall robustness. The measured data of a substation shows that the false alarm rate of the decision function is reduced from 12% to 3% (compared with the fixed threshold rule).

[0114] 3. Support hierarchical maintenance strategies: The confidence of the decision function drives the maintenance process:

[0115] Low confidence (0.5 ≤ |f| < 1.0): Trigger enhanced online monitoring (increase the sampling frequency);

[0116] High confidence (|f| ≥ 1.0): Trip the circuit breaker and push the fault code.

[0117] The MCU control module also has a built-in load optimization scheduling algorithm, which is based on the improved particle swarm optimization (IPSO) algorithm and takes active power loss, load priority, and voltage deviation as multi-objective functions:

[0118]

[0119] Among them, ω1, ω2, and ω3 are weight coefficients, and ω1 + ω2 + ω3 = 1, and the weight coefficients are dynamically adjusted; is the active power loss of the system, S k is the priority penalty of the kth type of load (S b = 0: high priority, S b = 1: interruptible), U t is the voltage deviation at time period t (U t = |V t - V nom | / V nom );

[0120] The specific uses of the load optimization scheduling algorithm based on the improved particle swarm optimization (IPSO) algorithm are as follows:

[0121] 1. Target collaborative optimization, reducing P through load transfer (such as switching high-energy-consuming loads to low-resistance branches) b .

[0122] 2. Real-time dynamic scheduling, the IPSO algorithm is embedded in the edge controller of the distribution cabinet, and the scheduling strategy is updated every 500 ms based on real-time collected data (current, voltage, load status).

[0123] The specific effects of the load optimization scheduling algorithm based on the improved particle swarm optimization (IPSO) algorithm are as follows:

[0124] 1. Nonlinear multi-objective optimization ability, dealing with the mixed optimization problem of active power loss (nonlinear), priority (discrete constraint), and voltage deviation (continuous variable), which cannot be solved by traditional linear programming (comparative case: genetic algorithm calculation time 2.1 s → IPSO 0.8 s).

[0125] 2. Real-time performance and robustness, edge-side optimization: single-cycle scheduling time < 80 ms (meeting the real-time requirements of GB / T 3797-2016 for distribution systems), introducing a particle memory bank (retaining the historical optimal solution), and the recovery stable time < 200 ms when the load changes suddenly (±30%).

[0126] Solving through chaotic perturbation particle swarm optimization, meeting the triple constraints of capacity, temperature, and voltage:

[0127]

[0128] T cabinet(t) ≤ T max (Temperature constraint)

[0129] V min V ≤ V(t) ≤ V max (Voltage constraint)

[0130] Optimize the scheduling of loads in the power distribution cabinet to improve the efficiency of power utilization;

[0131] The specific uses of the triple constraints of capacity, temperature, and voltage are as follows:

[0132] 1. Resource optimization scheduling under multiple constraints; Capacity constraint: Ensure that the current in each branch of the power distribution cabinet does not exceed the rated capacity to avoid overload tripping or equipment damage; Temperature constraint: Optimize heat dissipation and load distribution by monitoring the temperature of equipment (such as circuit breakers and busbars) in real time to prevent insulation aging or fire risks caused by overheating; Voltage constraint: Maintain the voltage at each node within the allowable deviation range (such as ±5% of the rated voltage) to ensure the stable operation of sensitive loads (such as precision instruments).

[0133] 2. Dynamic load balancing and energy efficiency optimization, combined with the real-time data (current, temperature, voltage) of the intelligent power distribution cabinet, dynamically adjust the load distribution through the CPSO algorithm to reduce the active power loss (such as line resistance loss) and improve the energy utilization efficiency.

[0134] 3. Improve the robustness and reliability of the system, handle non-linear and strongly coupled constraint conditions (such as the non-linear relationship between temperature and current), and ensure stable operation under complex working conditions (such as seasonal load fluctuations and sudden failures).

[0135] 4. Support intelligent operation and predictive maintenance, and the optimization results provide data support for equipment life assessment (such as the impact of temperature rise on insulating materials) and maintenance strategies (such as priority maintenance of overloaded branches).

[0136] The specific effects of the triple constraints of capacity, temperature, and voltage are as follows:

[0137] 1. Enhanced global optimization ability, the ergodicity and randomness of the chaotic sequence (such as the Logistic map) can break the local aggregation of the particle swarm, expand the search space, and improve the solution accuracy of multi-constraint non-convex optimization problems; Adjust the particle velocity and position through chaotic perturbation, enhance the global search (exploration) in the initial stage of iteration, and focus on local optimization (exploitation) in the later stage to meet the multi-objective optimization requirements under complex constraints.

[0138] 2. Improved constraint satisfaction, ensure that all solutions meet the hard limits of capacity, temperature, and voltage, and avoid the safety risks caused by traditional algorithms due to relaxed constraints. On the premise of meeting the hard constraints, minimize the voltage deviation and temperature gradient (balance the temperature rise of equipment) to extend the equipment life.

[0139] 3. Improvement in multi-objective optimization performance. By optimizing the load distribution path, the line resistance loss is reduced, and the loss can be reduced by 5% - 15% compared with the traditional PSO algorithm. Combining the user-defined load priorities (such as power supply to primary loads first), the power supply stability of critical loads is maximized under constraints.

[0140] As Figure 6 shown, the communication module includes a Modbus communication protocol unit, a Profibus communication protocol unit, and an Ethernet communication protocol unit. The communication module supports the Modbus communication protocol, the Profibus communication protocol, and the Ethernet communication protocol simultaneously. The communication module can upload the operation data and fault information of the power distribution cabinet to the remote monitoring center in real time, and at the same time receive the control instructions sent by the remote monitoring center. The communication module of the present invention supports multiple communication protocols simultaneously, ensuring the effectiveness and applicability of communication.

[0141] As Figure 1 and Figure 2 shown, it further includes a heat pipe radiator 2 and a cooling fan 3. A heat dissipation box 16 is provided at the bottom of the cabinet body 1. Heat dissipation holes 161 are provided at the front and rear ends of the heat dissipation box 16. The cooperation of the heat dissipation box 16 and the heat dissipation holes 161 facilitates the auxiliary heat dissipation of the entire power distribution cabinet and the operation of the power distribution cabinet. And support feet 162 are provided at the bottom corners of the heat dissipation box 16, which facilitate the stable operation of the power distribution cabinet. A groove 163 is provided at the bottom of the heat dissipation box 16, and a plurality of threaded holes 164 are provided at the top of the groove 163. The cooperation of the groove 163 and the threaded holes 164 facilitates the installation of the cooling fan 3 and the operation and use of the cooling fan 3. A plurality of first connection slots 165 are provided at the bottom of the heat dissipation box 16, which facilitate the connection between the cooling fan 3 and the heat dissipation box 16. A circuit for controlling the cooling fan 3 is provided inside the heat dissipation box 16, which facilitates the work and use of the cooling fan 3. The heat pipe radiator 2 is arranged inside the heat dissipation box 16. The cooling fan 3 is connected to the threaded holes 164 and is electrically connected to the heat dissipation box 16. This structure facilitates the dissipation of the heat generated inside the cabinet body 1 through the heat pipe radiator 2 and the cooling fan 3.

[0142] As Figure 7As shown in the figure, fixing plates 21 are provided on both sides of the heat pipe radiator 2. Multiple fixing holes 22 are provided on the fixing plates 21. Bolts pass through the fixing holes 22 to fix the heat pipe radiator 2 at the inner top end of the heat dissipation box 16. This structure facilitates the fixing of the heat pipe radiator 2 to the bottom of the cabinet body 1, ensuring the stable installation and use of the heat pipe radiator 2. A row of heat absorption pipes 23 is provided on the heat pipe radiator 2. The heat absorption surface of the heat absorption pipes 23 faces the inner cavity of the cabinet body 1. This structure facilitates the heat absorption pipes 23 to absorb the heat inside the cabinet body 1. When installing electrical components, it is best to arrange the electrical components with the highest heat generation in contact with the heat absorption surface of the heat absorption pipes 23 to ensure the best heat dissipation effect of the heat pipe radiator 2. And a row of heat dissipation fins 24 is provided at the bottom of the heat absorption pipes 23. The heat dissipation fins 24 facilitate the rapid discharge of the heat absorbed by the heat absorption pipes 23.

[0143] As Figure 8 shown, a transfer ring 31 is provided at the top of the cooling fan 3. The transfer ring 31 is threadedly connected to the threaded hole 164, facilitating the disassembly, assembly, and use of the cooling fan 3. A first connection line 32 is provided on the side of the cooling fan 3. A first connection plug 33 is provided at the end of the first connection line 32. The first connection plug 33 is connected to the first connection slot 165. This structure facilitates the power connection of the cooling fan 3 and the operation and use of the cooling fan 3. And the cooling fan 3 is connected to the control execution module, and the control execution module is used to control the rotation speed of the cooling fan 3.

[0144] Embodiment 3

[0145] As Figure 1 and Figure 3 shown, an intelligent electrical distribution cabinet includes a cabinet body 1. An MCU control module is integrated inside the cabinet body 1. The MCU control module is connected to a power distribution component, an intelligent monitoring module, a control execution module, and a communication module; the MCU control module is used to analyze the data collected by the intelligent monitoring module and control the entire distribution cabinet according to the analysis results. The power distribution component is used to cooperate with the entire distribution cabinet to perform power distribution operations on the power supply system. The control execution module cooperates with the MCU control module to control the power distribution component according to the analysis and processing results, facilitating the operation of the entire distribution cabinet; the intelligent monitoring module is used to collect electrical parameters and environmental parameters inside the distribution cabinet in real time, facilitating the timely issuance of an abnormal alarm when the electrical parameters and environmental parameters are abnormal. The communication module is used to realize data interaction between the distribution cabinet and external devices, facilitating the feedback of various data of the monitored distribution cabinet to the remote control center.

[0146] As Figure 2 and Figure 3As shown in the figure, a cabinet door 11 is provided on the front of the cabinet body 1. The cabinet door 11 is hinged to the cabinet body 1 through a hinge 12. A handle 13 is provided on the side of the cabinet door 11 that is not hinged to the cabinet body 1, which facilitates the opening and closing and use of the cabinet door 11, and also facilitates the staff to perform maintenance on the inside of the cabinet body 1. An intelligent control host 14 is provided at the top of the cabinet body 1. An operation panel 15 is provided on the front of the intelligent control host 14. The intelligent control host 14 and the operation panel 15 cooperate to facilitate the staff to control the power distribution cabinet. The intelligent control host 14 is used to cooperate with the MCU control module, intelligent monitoring module and communication module to control the entire power distribution cabinet.

[0147] As Figure 4 shown, the intelligent monitoring module includes a current sensor, a voltage sensor, a temperature sensor, a humidity sensor and a smoke sensor; the current sensor is used to detect the current of the internal circuit of the cabinet body 1. The current sensor adopts a Rogowski coil current sensor, which can achieve wide-band and high-precision current measurement, with a measurement accuracy of ±0.1%, and a measurement range of 0 - 5000A. The voltage sensor is used to monitor the voltage of the internal circuit of the cabinet body 1. The voltage sensor adopts a capacitive voltage transformer, which has good linearity and stability, with a measurement accuracy of ±0.2%, and a measurement range of 0 - 10kV. The data monitored by the current sensor and voltage sensor are real-time fed back to the MCU control module. When the monitored current and voltage are abnormal, the MCU control module will send an instruction to the control execution module to facilitate cutting off the corresponding circuit, and at the same time feed the data back to the remote control center. The temperature sensor is used to monitor the temperature inside the cabinet body 1. The temperature sensor adopts an infrared thermal imaging sensor, which can monitor the temperature distribution of key parts inside the power distribution cabinet in real time, with a temperature resolution of 0.1°C, and a measurement range of -20°C - 200°C. The humidity sensor is used to monitor the humidity inside the cabinet body 1. The temperature and humidity can reflect the environmental state inside the cabinet body 1, which is convenient for making corresponding treatments in time when the environmental state inside the cabinet body 1 is abnormal. The smoke sensor is used to give a timely feedback when there is smoke inside the cabinet body 1.

[0148] As Figure 5 shown, the control execution module includes multiple circuit breakers, contactors and relays. The multiple circuit breakers, contactors and relays are all connected to the MCU control module, and are used to perform fast on-off operations according to the control instructions issued by the MCU control module, which is convenient for turning on and off some circuits, and is convenient for ensuring the safe operation of the entire power distribution cabinet. The on-off time is less than 100ms.

[0149] As Figure 3As shown, the MCU control module uses an edge computing chip to quickly analyze and process the collected data, with a processing speed reaching over 100,000 data operations per second; the MCU control module is built-in with a fault diagnosis algorithm, and the fault diagnosis algorithm is based on Support Vector Machine (SVM). Through the learning and training of historical fault data and normal operation data, accurate diagnosis of the power distribution cabinet faults is achieved, and the fault diagnosis accuracy rate reaches over 95%; the feature vector of the fault diagnosis algorithm is:

[0150] X = [I a , V a , THD I , T a , PF, D a T

[0151] Among them, I a is the effective value current, V a is the effective value voltage, THD I is the total harmonic distortion rate of current, T a is the cabinet temperature, PF is the power factor, D a is the load rate; the feature vector of the fault diagnosis algorithm is mainly used to extract the time-domain, frequency-domain, and time-frequency domain features (such as root mean square, spectral entropy, wavelet coefficients, etc.) of multi-source monitoring data such as current, voltage, temperature, and humidity, construct a multi-dimensional feature vector, and accurately characterize the operating state (normal / abnormal / fault) of the power distribution cabinet. For example, the poor contact fault of the contact is identified through the singularity index (such as the Hurst index) of the current signal, and the abnormal heat dissipation is judged through the temperature gradient feature. At the same time, the feature vector of the fault diagnosis algorithm can improve the diagnosis accuracy rate, enhance the real-time performance and robustness, support predictive maintenance, reduce false alarms and missed alarms, and optimize the operation and maintenance decision-making. The Radial Basis Function (RBF) is adopted:

[0152] K(x i , x j ) = exp(-γ||x i - x j || 2 )(γ > 0)

[0153] The uses of the Radial Basis Function are mainly divided into the following aspects:

[0154] 1. Nonlinear feature mapping: Map the low-dimensional non-linearly separable feature vectors (such as current distortion rate, temperature gradient, vibration spectrum) to the high-dimensional Hilbert space through RBF to solve the non-linear classification problem of fault modes (such as distinguishing contact aging and instantaneous overvoltage).

[0155] 2. The kernel trick reduces the computational complexity, avoids explicit high-dimensional mapping, and directly calculates the high-dimensional inner product through the kernel function, which is suitable for the lightweight deployment of the edge computing module.​

[0156] 3. Multi-source heterogeneous data fusion, unifying the measurement of heterogeneous feature vectors such as current (time domain), temperature (spatial distribution), and vibration (frequency domain), and enhancing the correlation of different physical quantities.

[0157] The effects of the radial basis kernel function include the following aspects:

[0158] 1. Improve the accuracy of non-linear fault classification. Compared with the linear kernel function, the RBF kernel increases the recognition accuracy of SVM for non-linear faults (such as insulation aging gradual faults) from 82% to 95%. For example, for the contact oxidation fault of a distribution cabinet, by mapping the non-linear relationship between the current singularity index and the contact resistance through the RBF kernel, early poor contact is successfully distinguished (the misjudgment rate is reduced by 40%).

[0159] 2. Enhance the generalization ability of the model, adaptively adjust the γ parameter, and balance the model complexity and noise robustness. The generalization error of the optimized model for unknown faults is reduced by 18%.

[0160] 3. Support the processing of high-dimensional sparse features. For the high-dimensional feature vectors after wavelet packet decomposition, the RBF kernel reduces the computational amount through sparse mapping, and the edge-side diagnosis delay is reduced from 120ms to 50ms.

[0161] The decision function is:

[0162]

[0163] where α i is the Lagrange multiplier, y i ∈ -1, +1 is the fault label (+1: normal, -1: fault), and b is the bias term.

[0164] The specific uses of the decision function are as follows:

[0165] 1. Fault classification decision, the SVM decision function based on the RBF kernel outputs the fault category

[0166] 2. Threshold warning, set multi-level warning thresholds by combining the output value of the decision function (non-binarized confidence) (e.g., |f(x)| < 0.5 is normal, 0.5 ≤ |f(x)| < 1.0 is warning, |f(x)| ≥ 1.0 is fault).

[0167] 3. Confidence-assisted decision, output continuous values (such as [-2, 2]) to reflect the severity of the fault, and support the prioritization of operation and maintenance (e.g., a short-circuit fault with f(x) = 1.8 takes precedence over a slight overheating with f(x) = 0.7).

[0168] The specific effects of the decision function are as follows:

[0169] 1. Real-time and accurate classification

[0170] 2. Reduce false alarms and missed detections, introduce soft margin decision (C parameter adjustment), and allow a small number of samples to be misclassified to improve overall robustness. The actual measurement of a distribution substation shows that the false alarm rate of the decision function has decreased from 12% to 3% (compared with the fixed threshold rule).

[0171] 3. Support hierarchical maintenance strategies, and the confidence of the decision function drives the maintenance process:

[0172] Low confidence (0.5 ≤ |f| < 1.0): Trigger enhanced online monitoring (increase the sampling frequency);

[0173] High confidence (|f| ≥ 1.0): Initiate circuit breaker tripping and push the fault code.

[0174] The MCU control module is also built-in with a load optimization scheduling algorithm. The load optimization scheduling algorithm is based on the improved particle swarm optimization (IPSO) algorithm, with active power loss, load priority, and voltage deviation as multi-objective functions:

[0175]

[0176] Among them, ω1, ω2, and ω3 are weight coefficients, and ω1 + ω2 + ω3 = 1, and the weight coefficients are dynamically adjusted; is the active power loss of the system, S k is the priority penalty of the kth type of load (S b = 0: high priority, S b = 1: interruptible), U t is the voltage deviation at time t (U t = |V t - V nom | / V nom );

[0177] The specific uses of the load optimization scheduling algorithm based on the improved particle swarm optimization (IPSO) algorithm are as follows:

[0178] 1. Target collaborative optimization, reduce P by load transfer (such as switching high-energy-consuming loads to low-resistance branches) b .

[0179] 2. Real-time dynamic scheduling, the IPSO algorithm is embedded in the edge controller of the distribution cabinet, and the scheduling strategy is updated every 500 ms based on real-time collected data (current, voltage, load status).

[0180] The specific effects of the load optimization scheduling algorithm based on the improved particle swarm optimization (IPSO) algorithm are as follows:

[0181] 1. Nonlinear multi-objective optimization ability to handle the mixed optimization problem of active power loss (nonlinear), priority (discrete constraint), and voltage deviation (continuous variable), which cannot be solved by traditional linear programming (comparison case: genetic algorithm calculation time 2.1s → IPSO 0.8s).

[0182] 2. Real-time performance and robustness, edge-side optimization: single-cycle scheduling time < 80ms (meeting the real-time requirements of GB / T 3797-2016 distribution system), introducing a particle memory bank (retaining the historical optimal solution), and the recovery stable time < 200ms when the load suddenly changes (±30%).

[0183] Solve by chaotic perturbation particle swarm optimization, meeting the triple constraints of capacity, temperature, and voltage:

[0184]

[0185] T cabinet T(t) ≤ T max (Temperature constraint)

[0186] V min V ≤ V(t) ≤ V max (Voltage constraint)

[0187] Realize the optimal scheduling of the loads in the distribution cabinet and improve the power utilization efficiency;

[0188] The specific uses of the triple constraints of capacity, temperature, and voltage are as follows:

[0189] 1. Resource optimal scheduling under multiple constraint conditions; Capacity constraint: Ensure that the current of each branch in the distribution cabinet does not exceed the rated capacity to avoid overload tripping or equipment damage; Temperature constraint: Optimize heat dissipation and load distribution by real-time monitoring of the temperature of equipment (such as circuit breakers, busbars) to prevent insulation aging or fire risks caused by overheating; Voltage constraint: Maintain the voltage of each node within the allowable deviation range (such as ±5% rated voltage) to ensure the stable operation of sensitive loads (such as precision instruments).

[0190] 2. Dynamic load balancing and energy efficiency optimization, integrating the real-time data (current, temperature, voltage) of intelligent distribution cabinets, and dynamically adjusting the load distribution through the CPSO algorithm to reduce active power loss (such as line resistance loss) and improve energy utilization efficiency.

[0191] 3. Improve the robustness and reliability of the system, handle nonlinear and strongly coupled constraint conditions (such as the nonlinear relationship between temperature and current), and ensure stable operation under complex working conditions (such as seasonal load fluctuations, sudden failures).

[0192] 4. Support intelligent operation and maintenance and predictive maintenance. The optimization results provide data support for equipment life assessment (such as the impact of temperature rise on insulating materials) and maintenance strategies (such as priority maintenance of overloaded branches).

[0193] The specific effects of the triple constraints of capacity, temperature, and voltage are as follows:

[0194] 1. Enhanced global optimization ability. The ergodicity and randomness of chaotic sequences (such as Logistic mapping) can break the local aggregation of particle swarms, expand the search space, and improve the solution accuracy for multi-constrained non-convex optimization problems. By adjusting the particle velocity and position through chaotic perturbation, global search (exploration) is enhanced in the initial stage of iteration, and local optimization (exploitation) is focused in the later stage to meet the multi-objective optimization requirements under complex constraints.

[0195] 2. Improved constraint satisfaction. Ensure that all solutions meet the hard limits of capacity, temperature, and voltage, avoiding the safety risks caused by relaxed constraints in traditional algorithms. On the premise of meeting the hard constraints, minimize voltage deviation and temperature gradient (balance equipment temperature rise) to extend equipment life.

[0196] 3. Enhanced multi-objective optimization performance. By optimizing the load distribution path, reduce line resistance loss. Compared with the traditional PSO algorithm, the loss can be reduced by 5% - 15%. Combining user-defined load priorities (such as power supply to first-level loads first), maximize the power supply stability of critical loads under constraints.

[0197] As Figure 6 shown, the communication module includes a Modbus communication protocol unit, a Profibus communication protocol unit, and an Ethernet communication protocol unit. The communication module supports the Modbus communication protocol, Profibus communication protocol, and Ethernet communication protocol simultaneously. The communication module can upload the operation data and fault information of the power distribution cabinet to the remote monitoring center in real time, and at the same time receive the control instructions issued by the remote monitoring center. The communication module of the present invention supports multiple communication protocols simultaneously, ensuring the effectiveness and applicability of communication.

[0198] As Figure 1 and Figure 2As shown in the figure, it also includes a heat pipe radiator 2 and a cooling fan 3. A heat dissipation box 16 is provided at the bottom of the cabinet body 1. Heat dissipation holes 161 are provided at the front and rear ends of the heat dissipation box 16. The cooperation of the heat dissipation box 16 and the heat dissipation holes 161 facilitates the auxiliary heat dissipation of the entire power distribution cabinet and the operation of the power distribution cabinet. And feet 162 are provided at the bottom corners of the heat dissipation box 16, which facilitate the stable operation of the power distribution cabinet. A groove 163 is provided at the bottom of the heat dissipation box 16, and a plurality of threaded holes 164 are provided at the top of the groove 163. The cooperation of the groove 163 and the threaded holes 164 facilitates the installation of the cooling fan 3 and the operation and use of the cooling fan 3. A plurality of first connection slots 165 are provided at the bottom of the heat dissipation box 16, which facilitate the connection between the cooling fan 3 and the heat dissipation box 16. A circuit for controlling the cooling fan 3 is provided inside the heat dissipation box 16, which facilitates the work and use of the cooling fan 3. The heat pipe radiator 2 is arranged inside the heat dissipation box 16. The cooling fan 3 is connected to the threaded holes 164 and is electrically connected to the heat dissipation box 16. This structure facilitates the dissipation of the heat generated inside the cabinet body 1 through the heat pipe radiator 2 and the cooling fan 3.

[0199] As Figure 7 shown, fixing plates 21 are provided on both sides of the heat pipe radiator 2, and a plurality of fixing holes 22 are provided on the fixing plates 21. Bolts pass through the fixing holes 22 to fix the heat pipe radiator 2 at the inner top end of the heat dissipation box 16. This structure facilitates the fixing of the heat pipe radiator 2 at the bottom of the cabinet body 1 and ensures the stable installation and use of the heat pipe radiator 2. A row of heat absorption pipes 23 is provided on the heat pipe radiator 2, and the heat absorption surface of the heat absorption pipes 23 faces the inner cavity of the cabinet body 1. This structure facilitates the heat absorption pipes 23 to absorb the heat inside the cabinet body 1. When installing electrical components, it is best to arrange the electrical components with the highest heat generation in contact with the heat absorption surface of the heat absorption pipes 2 to ensure the best heat dissipation effect of the heat pipe radiator 2. And a row of heat dissipation fins 24 is provided at the bottom of the heat absorption pipes 23, which facilitates the rapid discharge of the heat absorbed by the heat absorption pipes 23.

[0200] As Figure 8 shown, an adapter ring 31 is provided at the top of the cooling fan 3. The adapter ring 31 is threadedly connected to the threaded holes 164, which facilitates the disassembly and assembly and use of the cooling fan 3. A first connection wire 32 is provided on the side of the cooling fan 3, and a first connection plug 33 is provided at the end of the first connection wire 32. The first connection plug 33 is connected to the first connection slots 165. This structure facilitates the power connection of the cooling fan 3 and the operation and use of the cooling fan 3. And the cooling fan 3 is connected to the control execution module, and the control execution module is used to control the rotation speed of the cooling fan 3.

[0201] As Figure 1 , Figure 2 and Figure 9As shown in the figure, it also includes an audible and visual alarm 4. The MCU control module inside the cabinet body 1 is connected to an alarm module, which is used to give an alarm when the intelligent monitoring module detects abnormal data. At the top of one side of the cabinet body 1, there is a supporting plate 17, and there are mounting holes 171 on the supporting plate 17. The cooperation of the supporting plate 17 and the mounting holes 171 facilitates the installation of the audible and visual alarm 4 and is convenient for the operation and use of the cooling fan 3. At a position on one side of the cabinet body 1 close to the supporting plate 17, there is a second connection slot 172, which facilitates the electrical connection between the audible and visual alarm 4 and the cabinet body 1. At the bottom end of the audible and visual alarm 4, there are studs 41, and the studs 41 are threadedly connected to the mounting holes 171. On one side of the audible and visual alarm 4, there is a second connection wire 42, and at the end of the second connection wire 42, there is a second connection plug 43, and the second connection plug 43 is connected to the second connection slot 172. This structure facilitates the electrical connection between the audible and visual alarm 4 and the cabinet body 1 and is convenient for the audible and visual alarm 4 to understand and operate after receiving an instruction. The audible and visual alarm 4 is connected to the alarm module and is used to control the operation of the audible and visual alarm 4.

[0202] Working principle: After the power distribution cabinet is powered on and operates, the power distribution components will perform power distribution work according to the settings, and the intelligent monitoring module will monitor the current, voltage, temperature, and humidity of the internal circuit of the cabinet body 1 in real time, and set the thresholds of current, voltage, temperature, and humidity through the intelligent control host 14. When the current and voltage are abnormal, the data will be fed back to the MCU control module. The MCU control module will process and analyze the data to determine whether the data of the current and voltage are greater than the thresholds. When the data of the current and voltage are greater than the thresholds, the MCU control module will issue an instruction to the control execution module, causing the circuit breaker of the corresponding circuit to cut off the specified circuit, avoiding the problem of electrical components being burned out due to abnormal current or voltage, and at the same time feeding back the data to the remote control center. When the temperature and humidity are abnormal, the data will also be fed back to the MCU control module. The MCU control module will determine whether the temperature and humidity are greater than the thresholds. If they are greater than the thresholds, the MCU control module will issue an instruction to the control execution module, causing the cooling fan 3 to operate, assisting in reducing the temperature inside the cabinet body 1, and the higher the temperature, the faster the operating speed of the cooling fan 3 needs to be controlled to ensure good heat dissipation effect. When the humidity is too high, the staff needs to be notified to deal with it in time. When the MCU control module determines that the data is greater than the threshold, the MCU control module will also issue an instruction to the alarm module. The alarm module will send an alarm to the remote control center through the communication module, and at the same time the alarm module will control the audible and visual alarm 4 to give an alarm to remind the staff to deal with the abnormal situation in time.

[0203] In summary, compared with the prior art, in the present application, by providing a power distribution component, an intelligent monitoring module, a control execution module, and a communication module, this structure enables the normal operation of the entire power distribution cabinet. Moreover, the intelligent monitoring module can monitor the current, voltage, temperature, humidity, and smoke concentration inside the cabinet 1, facilitating timely handling when the data inside the cabinet 1 is abnormal. Meanwhile, the monitored data is transmitted to the remote control center through the communication module, facilitating the remote control center to handle it in a timely manner according to the abnormal situation. The control execution module can also receive the instructions transmitted by the MCU control module, facilitating the control of the operation of each component of the power distribution cabinet according to the instructions.

[0204] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent electrical distribution cabinet, comprising a cabinet body (1), characterized in that, The interior of the cabinet body (1) is integrated with an MCU control module, which is connected to a power distribution component, an intelligent monitoring module, a control execution module, and a communication module; the MCU control module is used to analyze the data collected by the intelligent monitoring module and control the entire power distribution cabinet according to the analysis results. The control execution module cooperates with the MCU control module to control the power distribution component according to the analysis and processing results; the intelligent monitoring module is used to collect electrical parameters and environmental parameters in the power distribution cabinet in real time, and the communication module is used to realize data interaction between the power distribution cabinet and external devices.

2. An intelligent electrical distribution cabinet according to claim 1, characterized in that, A cabinet door (11) is provided on the front of the cabinet body (1). The cabinet door (11) is hinged to the cabinet body (1) through a hinge (12). A handle (13) is provided on the side of the cabinet door (11) that is not hinged to the cabinet body (1); an intelligent control host (14) is provided at the top of the cabinet body (1). A control panel (15) is provided on the front of the intelligent control host (14). The intelligent control host (14) is used to cooperate with the MCU control module, the intelligent monitoring module, and the communication module to control the entire power distribution cabinet.

3. An intelligent electrical distribution cabinet according to claim 1, characterized in that, The intelligent monitoring module includes a current sensor, a voltage sensor, a temperature sensor, a humidity sensor, and a smoke sensor; the current sensor is used to detect the current of the internal circuit of the cabinet body (1), the voltage sensor is used to monitor the voltage of the internal circuit of the cabinet body (1), the temperature sensor is used to monitor the temperature inside the cabinet body (1), the humidity sensor is used to monitor the humidity inside the cabinet body (1), and the smoke sensor is used to give a timely feedback when smoke appears inside the cabinet body (1).

4. An intelligent electrical distribution cabinet according to claim 1, characterized in that, The control execution module includes multiple circuit breakers, contactors, and relays. The multiple circuit breakers, contactors, and relays are all connected to the MCU control module and are used to perform fast opening and closing operations according to the control instructions issued by the MCU control module.

5. An intelligent electrical distribution cabinet according to claim 1, characterized in that, The MCU control module uses an edge computing chip to quickly analyze and process the collected data; the MCU control module is built-in with a fault diagnosis algorithm, which is based on the support vector machine (SVM). Through the learning and training of historical fault data and normal operation data, it realizes the accurate diagnosis of power distribution cabinet faults; the MCU control module is also built-in with a load optimization scheduling algorithm, which is based on the improved particle swarm optimization (IPSO) algorithm.

6. An intelligent electrical distribution cabinet according to claim 1, characterized in that, The communication module includes a Modbus communication protocol unit, a Profibus communication protocol unit, and an Ethernet communication protocol unit; the communication module can upload the operation data and fault information of the power distribution cabinet to the remote monitoring center in real time, and at the same time receive the control instructions issued by the remote monitoring center.

7. An intelligent electrical distribution cabinet according to any one of claims 1-6, characterized in that, The cabinet (1) further comprises a heat pipe radiator (2) and a cooling fan (3). A cooling box (16) is provided at the bottom of the cabinet (1). Cooling holes (161) are provided at the front and rear ends of the cooling box (16). Support legs (162) are provided at the corners of the bottom surface of the cooling box (16). A groove (163) is provided at the bottom of the cooling box (16). A plurality of threaded holes (164) are provided at the top of the groove (163). A plurality of first communication slots (165) are provided at the bottom of the cooling box (16). The heat pipe radiator (2) is arranged inside the cooling box (16). The cooling fan (3) is connected to the threaded holes (164), and the cooling fan (3) is electrically connected to the cooling box (16).

8. An intelligent electrical distribution cabinet according to claim 7, characterized in that, The heat pipe radiator (2) is provided with fixing plates (21) on both sides, and a plurality of fixing holes (22) are provided on the fixing plates (21). Bolts pass through the fixing holes (22) to fix the heat pipe radiator (2) to the top inner side of the heat dissipation box (16). The heat pipe radiator (2) is provided with a row of heat absorbing pipes (23), the heat absorbing surfaces of the heat absorbing pipes (23) are arranged toward the inner cavity of the cabinet (1), and a row of heat dissipating fins (24) are provided at the bottom of the heat absorbing pipes (23).

9. An intelligent electrical distribution cabinet according to claim 7, characterized in that, The top of the heat dissipation fan (3) is provided with an adapter ring (31), the adapter ring (31) is threadedly connected to the threaded hole (164), the side of the heat dissipation fan (3) is provided with a first communication line (32), the end of the first communication line (32) is provided with a first communication plug (33), the first communication plug (33) is connected to the first communication slot (165), and the heat dissipation fan (3) is connected to the control execution module, which is used for the control execution module to control the rotation speed of the heat dissipation fan (3).

10. An intelligent electrical distribution cabinet according to any one of claims 1-6, characterized in that, The cabinet (1) further comprises an audible and visual alarm (4), wherein the MCU control module inside the cabinet (1) is connected to the alarm module, and the alarm module is used to issue an alarm when the intelligent monitoring module detects data abnormality. A support plate (17) is provided on the top of one side of the cabinet (1), and a mounting hole (171) is provided on the support plate (17). A second communication slot (172) is provided on one side of the cabinet (1) near the support plate (17). A stud (41) is provided at the bottom end of the audible and visual alarm (4), and the stud (41) is threadedly connected to the mounting hole (171). A second communication line (42) is provided on one side of the audible and visual alarm (4), and a second communication plug (43) is provided at the end of the second communication line (42), and the second communication plug (43) is connected to the second communication slot (172). The audible and visual alarm (4) is connected to the alarm module for controlling the operation of the audible and visual alarm (4).

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