Intelligent power management device

Abstract

This invention proposes an intelligent power management device, comprising a power monitoring module for monitoring the status of mains input power, a battery module for providing backup power, a power conversion module for providing power conversion capability, a switch module for switching power supply modes, and a control module electrically connected to the power monitoring module, the battery module, the power conversion module, and the switch module. The control module monitors the status of mains input power through the power monitoring module, predicts and evaluates the probability of abnormal situations occurring in the mains input power status, and controls the battery module, the power conversion module, and the switch module to switch between mains power supply mode, battery inverter power supply mode, and hybrid power supply mode based on the evaluation results, thereby effectively ensuring the stability of the power supply at the input of electronic devices.

Description

Technical Field

[0001] This invention relates to the field of power management technology, and in particular to an intelligent power management device. Background Technology

[0002] The stability of the input power supply is crucial during the operation of electronic devices. For electronic devices directly powered by mains electricity, many factors can affect their voltage stability, such as power grid failures, lightning strikes, the starting or stopping of large-capacity equipment like large motors or transformers, and the connection of numerous impulsive or nonlinear loads. These factors can cause problems with the mains power supply voltage, such as voltage dips, voltage imbalances, voltage fluctuations, and excessive harmonic voltages, which can seriously affect the normal operation of electronic devices. Summary of the Invention

[0003] Based on the above-mentioned problems, this invention proposes an intelligent power management device that can effectively ensure the stability of the power supply at the input end of electronic devices.

[0004] In view of this, the present invention proposes an intelligent power management device, including a power monitoring module for monitoring the status of mains input power, a battery module for providing backup power, a power conversion module for providing power conversion capability, a switch module for switching power supply modes, and a control module electrically connected to the power monitoring module, the battery module, the power conversion module, and the switch module. The control module is used to monitor the status of mains input power through the power monitoring module, predict and evaluate the probability of abnormal situations occurring in the mains input power status, and control the battery module, the power conversion module, and the switch module to switch between mains power supply mode, battery inverter power supply mode, and hybrid power supply mode based on the evaluation results.

[0005] Furthermore, the battery module includes a battery pack and a charging circuit. The input terminal of the charging circuit is connected to the AC power input, and the output terminal is connected to the battery pack, for charging the battery pack through the AC power input in AC power supply mode.

[0006] The switching module includes a first switching switch with one end connected to the mains input power and the other end connected to the output terminal of the intelligent power management device, and a second switching switch with one end connected to the power conversion module and the other end connected to the output terminal of the intelligent power management device.

[0007] The output terminal of the battery module is connected to the input terminal of the power conversion module, and is used to boost and invert the battery voltage through the power conversion module to supply power to the load.

[0008] The power conversion module includes a boost circuit connected to the battery module and an inverter circuit connected to the second switching switch. When the second switching switch is turned on, the boost circuit boosts the battery voltage of the battery module and provides it to the subsequent inverter circuit. The inverter circuit converts the boosted voltage into a stable inverter voltage for output.

[0009] Furthermore, the input terminal of the power monitoring module is connected to the mains power input to monitor the power status of the mains power input. The output terminal of the power monitoring module is connected to the control module to provide monitoring data to the control module. The control module is configured to:

[0010] Obtain power status data of the AC power input;

[0011] The power state data is input into a pre-trained power state prediction model to predict power state data for a future period of time.

[0012] Use the predicted power state data to assess whether an abnormal power state is about to occur in the mains input power supply.

[0013] The operating mode decision is made based on the assessment of whether an abnormal power supply state is about to occur in the mains input power supply, so as to control the battery module, the power conversion module and the switching module to switch between mains power supply mode, battery inverter power supply mode and hybrid power supply mode according to the operating mode decision.

[0014] Furthermore, in the step of assessing whether an abnormal power supply state is imminent based on the predicted power state data, the control module is configured to:

[0015] Based on the predicted power state data for a future period, extract or calculate the set of input variables corresponding to each power anomaly state:

[0016] ,

[0017] in This refers to the voltage sag depth. The duration of the voltage sag. For voltage imbalance, This refers to the voltage fluctuation amplitude. The total harmonic voltage distortion rate;

[0018] The fuzzy variable set corresponding to the input variable set is determined based on the membership function of each input variable:

[0019] ,

[0020] The fuzzy set is used to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set:

[0021] ,

[0022] in The number of output variables in the output fuzzy set;

[0023] Obtain the basic score corresponding to each output element in the output fuzzy set. ;

[0024] Calculate the comprehensive prediction score for power supply anomalies occurring within a future period:

[0025] ,

[0026] in From 1 to Positive integers between;

[0027] Based on the comprehensive prediction score of power supply anomalies occurring in the near future Determine if the mains power input is about to experience a power abnormality.

[0028] Furthermore, in the step of determining the fuzzy variable set corresponding to the input variable set based on the membership function of each input variable, the control module is configured as follows:

[0029] Configure computational variables with values ​​ranging from 1 to 5 for iterating through the set of input variables. ;

[0030] The input variable values ​​are calculated by iterating through each input variable in the set of input variables as follows:

[0031] The currently iterated input variable is determined as the target input variable;

[0032] Configure the target input variable corresponding to Membership threshold ,in satisfy:

[0033] ;

[0034] use This represents the fuzzy state number in the fuzzy set of the target input variable. A fuzzy state, whose fuzzy variables satisfy:

[0035] when hour: ;

[0036] when hour: ;

[0037] when hour: .

[0038] Furthermore, in the step of using the fuzzy variable set to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set, the control module is configured as follows:

[0039] The fuzzy rules in the fuzzy rule base are divided into categories corresponding to each output variable. A subset of rules, where the first rule is... The rule subset contains A fuzzy rule;

[0040] Configure a counter variable to iterate through each fuzzy rule in each rule subset. For the first A subset of rules From 1 to Positive integers between;

[0041] The rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. ;

[0042] Rule strength based on fuzzy rules in each rule subset Calculate each output variable of the output fuzzy set:

[0043] .

[0044] Furthermore, the rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. In the following steps, the control module is configured as follows:

[0045] Iterate through each fuzzy rule in each rule subset;

[0046] The traversed to the th The first rule subset The fuzzy rule was determined as the target rule;

[0047] Determine the associated input variables of the target rule Association membership degree And the associated fuzzy state, the associated membership degree For the associated input variables The fuzzy variable values ​​corresponding to the associated fuzzy state are in the fuzzy variable set, where From 1 to Positive integers between The number of associated input variables in the target rule;

[0048] Calculate the rule strength of the target rule:

[0049] .

[0050] Furthermore, in the step of making a working mode decision based on the assessment result of whether an abnormal power supply state is about to occur in the mains input power supply, the control module is configured to:

[0051] Obtain the pre-configured comprehensive score The corresponding score ranges for each working mode;

[0052] Determine the comprehensive score The working mode corresponding to the score range that the score falls into;

[0053] When the comprehensive score When the value falls within the range corresponding to the mains power supply mode, the first switching switch is controlled to enter the on state, and the second switching switch is controlled to enter the off state so that the intelligent power management device supplies power to the load through the mains power input.

[0054] Furthermore, in determining the comprehensive score... After the steps of the working mode corresponding to the score range that the value falls into, the control module is configured as follows:

[0055] When the comprehensive score When the value falls within the range corresponding to the battery inverter power supply mode, the first switching switch is controlled to enter the off state, and the second switching switch is controlled to enter the on state so that the intelligent power management device supplies power to the load through the battery module.

[0056] Furthermore, in determining the comprehensive score... After the steps of the working mode corresponding to the score range that the value falls into, the control module is configured as follows:

[0057] When the comprehensive score When the value falls within the range corresponding to the hybrid power supply mode, the first switching switch and the second switching switch are controlled to enter the on state, and the output voltage of the battery module is adjusted by the power conversion module to compensate for the supply voltage of the mains input power.

[0058] This invention proposes an intelligent power management device, comprising a power monitoring module for monitoring the status of mains input power, a battery module for providing backup power, a power conversion module for providing power conversion capability, a switch module for switching power supply modes, and a control module electrically connected to the power monitoring module, the battery module, the power conversion module, and the switch module. The control module monitors the status of mains input power through the power monitoring module, predicts and evaluates the probability of abnormal situations occurring in the mains input power status, and controls the battery module, the power conversion module, and the switch module to switch between mains power supply mode, battery inverter power supply mode, and hybrid power supply mode based on the evaluation results, thereby effectively ensuring the stability of the power supply at the input of electronic devices. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of an intelligent power management device provided in one embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of an intelligent power management device provided in another embodiment of the present invention;

[0061] Figure 3 This is a flowchart of an intelligent power management device control method provided in one embodiment of the present invention. Detailed Implementation

[0062] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0063] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0064] In the description of this invention, the term "multiple" refers to two or more. Unless otherwise explicitly defined, the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. The terms "connect," "install," "fix," etc., should be interpreted broadly. For example, "connect" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "multiple" means two or more.

[0065] In the description of this specification, the terms "one embodiment," "some implementations," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] The intelligent power management device according to some embodiments of the present invention will now be described with reference to the accompanying drawings.

[0067] like Figure 1 As shown, the first aspect of the present invention proposes an intelligent power management device, including a power monitoring module for monitoring the status of mains input power, a battery module for providing backup power, a power conversion module for providing power conversion capability, a switch module for switching power supply modes, and a control module electrically connected to the power monitoring module, the battery module, the power conversion module, and the switch module. The control module is used to monitor the status of mains input power through the power monitoring module, predict and evaluate the probability of abnormal situations occurring in the mains input power status, and control the battery module, the power conversion module, and the switch module to switch between mains power supply mode, battery inverter power supply mode, and hybrid power supply mode according to the evaluation results.

[0068] Furthermore, such as Figure 2As shown, the battery module includes a battery pack and a charging circuit. The input terminal of the charging circuit is connected to the AC power input, and the output terminal is connected to the battery pack. It is used to charge the battery pack through the AC power input in AC power supply mode.

[0069] The switching module includes a first switching switch with one end connected to the mains input power and the other end connected to the output terminal of the intelligent power management device, and a second switching switch with one end connected to the power conversion module and the other end connected to the output terminal of the intelligent power management device.

[0070] The output terminal of the battery module is connected to the input terminal of the power conversion module, and is used to boost and invert the battery voltage through the power conversion module to supply power to the load.

[0071] The power conversion module includes a boost circuit connected to the battery module and an inverter circuit connected to the second switching switch. When the second switching switch is turned on, the boost circuit boosts the battery voltage of the battery module and provides it to the subsequent inverter circuit. The inverter circuit converts the boosted voltage into a stable inverter voltage for output.

[0072] Specifically, the input terminal of the intelligent power management device is connected to the mains power input, used to directly supply power to the load via mains power in mains power supply mode, and to charge the battery pack in the battery module via the mains power input when charging is required. The input terminal of the intelligent power management device is also connected to the load, used to output power voltage to the load via mains power input or the battery pack in different power supply modes.

[0073] Preferably, in the switching module, both the first switching switch and the second switching switch are SCR (Silicon Controlled Rectifier) ​​switches or IGBT (Insulated Gate Bipolar Transistor) switches.

[0074] The boost circuit in the power conversion module is a DC / DC (Direct Current / Direct Current) boost circuit. The output of this boost circuit is connected to the input of the inverter circuit. It boosts the DC power supplied by the battery pack and outputs it to the inverter circuit as DC power. The inverter circuit in the power conversion module is a DC / AC (Direct Current / Alternating Current) inverter circuit, which converts the DC power output from the boost circuit into AC power for supplying the load.

[0075] Furthermore, such as Figure 3 As shown, the input terminal of the power monitoring module is connected to the mains power input to monitor the power status of the mains power input. The output terminal of the power monitoring module is connected to the control module to provide monitoring data to the control module. The control module is configured as follows:

[0076] Obtain power status data of the AC power input;

[0077] The power state data is input into a pre-trained power state prediction model to predict power state data for a future period of time.

[0078] Use the predicted power state data to assess whether an abnormal power state is about to occur in the mains input power supply.

[0079] The operating mode decision is made based on the assessment of whether an abnormal power supply state is about to occur in the mains input power supply, so as to control the battery module, the power conversion module and the switching module to switch between mains power supply mode, battery inverter power supply mode and hybrid power supply mode according to the operating mode decision.

[0080] Preferably, the power monitoring module includes a high-precision voltage sensor for real-time acquisition of the voltage signal of the mains input power supply, and a high-precision current sensor for real-time acquisition of the current signal of the mains input power supply. The power status data acquired by the power monitoring module includes, but is not limited to, the amplitude data, phase data, and frequency data of the voltage and current of the mains input power supply, so that the control module can fully grasp the operating status of the mains input power supply.

[0081] The power state prediction model is obtained by using historical power state data of the mains input power at the input terminal of the intelligent power management device monitored by the power monitoring module as sample data, and training it with machine learning algorithms such as neural networks and support vector machines.

[0082] Preferably, the power state prediction model is a data sequence prediction model. This involves supervised training of the power state prediction model by dividing a continuous segment of historical power state data into input sample data and output sample data. After training, the model can be used as input data, consisting of a segment of power state data of equal length to the input sample data obtained from the power monitoring module, to predict the power state data of the mains input power source over a future period. The lengths of the input and output sample data can be configured according to actual implementation needs. Correspondingly, the acquisition time of the input data for the power state prediction model, and the duration of the predicted "future period," are determined based on the configured lengths of the input and output sample data.

[0083] The abnormal power supply state refers to a state in which the mains input power supply experiences abnormalities such as voltage dips, voltage imbalances, voltage fluctuations, and excessive harmonic voltages.

[0084] Voltage sag refers to the phenomenon where the effective value of the mains input voltage suddenly drops for a short period of time and then returns to the normal level. It is mainly caused by short circuit faults in the power system, the start-up of large-capacity equipment, lightning strikes, etc.

[0085] Voltage imbalance refers to a situation where the amplitudes of the three-phase voltages in the mains input power supply are not equal, which is usually caused by uneven distribution of the three-phase load.

[0086] Voltage fluctuation refers to the rapid, periodic or non-periodic change in the supply voltage of the mains input power source over a period of time, and its amplitude variation range usually exceeds ±10% of the rated voltage. It is usually caused by the instantaneous change of the grid load due to the start-up or shutdown of large loads in the grid, or by the large current fluctuation caused by the operation of impulsive loads.

[0087] When there is a nonlinear load in the power grid, the voltage waveform will be distorted, resulting in harmonic voltage. When the harmonic voltage exceeds the standard, it will increase the loss of electrical equipment, aggravate the heat generation of the equipment, and shorten the service life of the equipment.

[0088] Furthermore, in the step of assessing whether an abnormal power supply state is imminent based on the predicted power state data, the control module is configured to:

[0089] Based on the predicted power state data for a future period, extract or calculate the set of input variables corresponding to each power anomaly state:

[0090] ,

[0091] in This refers to the voltage sag depth. This refers to the duration of the voltage sag. For voltage imbalance, This refers to the voltage fluctuation amplitude. The total harmonic voltage distortion rate;

[0092] The fuzzy variable set corresponding to the input variable set is determined based on the membership function of each input variable:

[0093] ,

[0094] The fuzzy set is used to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set:

[0095] ,

[0096] in The number of output variables in the output fuzzy set;

[0097] Obtain the basic score corresponding to each output element in the output fuzzy set. ;

[0098] Calculate the comprehensive prediction score for power supply anomalies occurring within a future period:

[0099] ,

[0100] in From 1 to Positive integers between;

[0101] Based on the comprehensive prediction score of power supply anomalies occurring in the near future Determine if the mains power input is about to experience a power abnormality.

[0102] In the technical solution of the above implementation, the set of input variables is constructed using abnormal power supply states such as voltage sag, voltage imbalance, voltage fluctuation, and excessive harmonic voltage as examples.

[0103] The voltage sag depth in the input variable set The value is the percentage of the difference between the real-time measured voltage value and the rated voltage relative to the rated voltage, and its range is between 0% and 100%. In the technical solution of the above embodiment, This represents the number of fuzzy states in the voltage sag depth. These represent the fuzzy variable values ​​for each fuzzy state corresponding to the voltage sag depth.

[0104] Voltage sag duration in the set of input variables The duration of the voltage sag is typically measured in seconds, and its value is configured based on empirical data; for example, it can be configured to be 0-10 seconds. In the technical solution of the above implementation, The number of fuzzy states for the duration of the voltage dip. These are the fuzzy variable values ​​for each fuzzy state corresponding to the duration of the voltage sag.

[0105] The voltage imbalance The negative-sequence voltage component can be represented by the percentage of the positive-sequence voltage component, ranging from 0% to 10%. The positive-sequence voltage component is a set of three-phase voltages of equal magnitude, with a phase difference of 120° and the same phase sequence as a normal three-phase system. When the three-phase system is perfectly symmetrical, only the positive-sequence voltage component exists. The negative-sequence voltage component is also a set of three-phase voltages of equal magnitude, with a phase difference of 120°, but its phase sequence is opposite to the positive sequence. Under normal circumstances, the three-phase voltages of a power system are symmetrical, and the negative-sequence voltage component is theoretically zero. However, when a fault occurs in the system (such as a single-phase ground fault, a two-phase short circuit, etc.) or the three-phase load is severely unbalanced, a negative-sequence voltage component will be generated. In the technical solution of the above implementation method, The number of fuzzy states representing the voltage imbalance. These represent the fuzzy variable values ​​for each fuzzy state corresponding to the voltage imbalance.

[0106] The voltage fluctuation amplitude It represents the magnitude of the monitored real-time voltage, with the unit being volts, and its value range is determined according to the power supply environment of the actual application. In the technical solution of the above implementation method, The number of fuzzy states representing the voltage fluctuation amplitude. These are the fuzzy variable values ​​for each fuzzy state corresponding to the voltage fluctuation amplitude.

[0107] The total distortion rate of harmonic voltage , which is the ratio of the square root of the sum of the squares of the effective values ​​of all harmonic voltages in a voltage signal to the effective value of the fundamental voltage, is used to measure the severity of harmonic content in a voltage signal. It is usually expressed as a percentage, and its value ranges from 0% to 20%. In the technical solution of the above implementation, The number of fuzzy states representing the voltage fluctuation amplitude. These are the fuzzy variable values ​​for each fuzzy state corresponding to the voltage fluctuation amplitude.

[0108] In the technical solutions of the above embodiments, From 1 to Positive integers between [a certain range].

[0109] Based on the comprehensive prediction score of power supply anomalies occurring in the near future. In the step of determining whether the mains input power supply is about to experience a power abnormality, the comprehensive prediction score is used... The score is compared with a pre-configured scoring threshold to determine whether an abnormal power supply condition is imminent at the mains input. When the comprehensive prediction score... If the score exceeds the threshold, it is determined that a power supply anomaly is likely to occur within the predicted future period.

[0110] Furthermore, in the step of determining the fuzzy variable set corresponding to the input variable set based on the membership function of each input variable, the control module is configured as follows:

[0111] Configure computational variables with values ​​ranging from 1 to 5 for iterating through the set of input variables. ;

[0112] The input variable values ​​are calculated by iterating through each input variable in the set of input variables as follows:

[0113] The currently iterated input variable is determined as the target input variable;

[0114] Configure the target input variable corresponding to Membership threshold ,in satisfy:

[0115] ;

[0116] use This represents the fuzzy state number in the fuzzy set of the target input variable. A fuzzy state, whose fuzzy variables satisfy:

[0117] when hour: ;

[0118] when hour: ;

[0119] when hour: .

[0120] Specifically, each input variable corresponds to a fuzzy set, which consists of several fuzzy states that describe the degree to which the abnormal power supply state corresponds to the input variable.

[0121] For example, the fuzzy set of voltage sag depth can be defined as: “Slight” (VS), “Moderate” (MS), and “Severe” (SS), in order to... The number of its fuzzy states is then ;

[0122] Similarly, the fuzzy set of voltage sag durations can be defined as: "Short" (ST), "Medium" (MT), and "Long" (LT), in order to... The number of its fuzzy states is then ;

[0123] The fuzzy set of voltage imbalance can be defined as: "low" (L), "medium" (M), and "high" (H), with... The number of its fuzzy states is then ;

[0124] The fuzzy set of voltage fluctuation amplitude can be defined as: “small” (VM), “small” (SM), “large” (LM), and “large” (GM). The number of its fuzzy states is then ;

[0125] The fuzzy set of harmonic voltage THD can be defined as: "Good" (G), "Average" (F), and "Poor" (P), in order to... The number of its fuzzy states is then .

[0126] It should be understood that the fuzzy sets listed above are merely examples, and the fuzzy states they contain and the number of fuzzy states do not constitute a limitation on the present invention. In actual implementation, they can be configured as fuzzy sets composed of other fuzzy states according to implementation needs.

[0127] Furthermore, in the step of using the fuzzy variable set to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set, the control module is configured as follows:

[0128] The fuzzy rules in the fuzzy rule base are divided into categories corresponding to each output variable. A subset of rules, where the first rule is... The rule subset contains A fuzzy rule;

[0129] Configure a counter variable to iterate through each fuzzy rule in each rule subset. For the first A subset of rules From 1 to Positive integers between;

[0130] The rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. ;

[0131] Rule strength based on fuzzy rules in each rule subset Calculate each output variable of the output fuzzy set:

[0132] .

[0133] Specifically, each rule subset corresponds to an output variable. For example, the output fuzzy set can be defined as "Slight" (SL), "Moderate" (MOD), and "Severe" (SEV), then the output variable set contains three output variables. In this implementation, the fuzzy rules in the fuzzy rule base can be divided into three rule subsets, using... This represents the number of fuzzy rules in the fuzzy rule base. The number of fuzzy rules in each rule subset satisfy:

[0134] .

[0135] Furthermore, the rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. In the following steps, the control module is configured as follows:

[0136] Iterate through each fuzzy rule in each rule subset;

[0137] The traversed number The first rule subset The fuzzy rule was determined as the target rule;

[0138] Determine the associated input variables of the target rule Association membership degree And the associated fuzzy state, the associated membership degree For the associated input variables The fuzzy variable values ​​corresponding to the associated fuzzy state are in the fuzzy variable set, where From 1 to Positive integers between The number of associated input variables in the target rule;

[0139] Calculate the rule strength of the target rule:

[0140] .

[0141] Furthermore, in the step of making a working mode decision based on the assessment result of whether an abnormal power supply state is about to occur in the mains input power supply, the control module is configured to:

[0142] Obtain the pre-configured comprehensive score The corresponding score ranges for each working mode;

[0143] Determine the comprehensive score The working mode corresponding to the score range that the score falls into;

[0144] When the comprehensive score When the value falls within the range corresponding to the mains power supply mode, the first switching switch is controlled to enter the on state, and the second switching switch is controlled to enter the off state so that the intelligent power management device supplies power to the load through the mains power input.

[0145] Specifically, in AC power supply mode, the intelligent power management device supplies power to the load via AC power input, and its power output terminal outputs AC power to the load. Furthermore, in AC power supply mode, the control module periodically acquires the remaining power of the battery pack, and when the remaining power is less than a threshold, it charges the battery pack via AC power input.

[0146] Furthermore, in determining the comprehensive score... After the steps of the working mode corresponding to the score range that the value falls into, the control module is configured as follows:

[0147] When the comprehensive score When the value falls within the range corresponding to the battery inverter power supply mode, the first switching switch is controlled to enter the off state, and the second switching switch is controlled to enter the on state so that the intelligent power management device supplies power to the load through the battery module.

[0148] Specifically, when the comprehensive score When the value falls within the range corresponding to the battery inverter power supply mode, the mains input power supply may experience situations such as voltage sag depth exceeding a set threshold, severe voltage imbalance exceeding the standard, excessive voltage fluctuation amplitude, or harmonic voltage exceeding a certain limit in the future. At this time, the first switching switch is controlled to enter the off state, and the second switching switch is controlled to enter the on state so that the intelligent power management device enters the battery inverter power supply mode. The output voltage of the battery module is boosted sequentially through the boost circuit, and after being inverted by the inverter circuit, a stable AC power is provided to the load, thereby avoiding the unstable state of the mains input power supply from affecting the normal operation of the load or causing it to be damaged.

[0149] Furthermore, in determining the comprehensive score... After the steps of the working mode corresponding to the score range that the value falls into, the control module is configured as follows:

[0150] When the comprehensive score When the value falls within the range corresponding to the hybrid power supply mode, the first switching switch and the second switching switch are controlled to enter the on state, and the output voltage of the battery module is adjusted by the power conversion module to compensate for the supply voltage of the mains input power.

[0151] Specifically, in the hybrid power supply mode, for minor power anomalies, such as slight voltage fluctuations or low harmonic content, the intelligent power management device continues to supply power to the load via the mains input power. Simultaneously, based on predicted power state data for a future period, it controls the power conversion module to appropriately compensate and adjust the output voltage. For example, it may output a certain reverse harmonic current through the inverter circuit to cancel harmonic components in the mains input power, or adjust the output voltage of the boost circuit to compensate for voltage fluctuations in the mains input power.

[0152] like Figure 3 As shown, a second aspect of the present invention provides a control method for an intelligent power management device, comprising:

[0153] Obtain power status data of the AC power input;

[0154] The power state data is input into a pre-trained power state prediction model to predict power state data for a future period of time.

[0155] Use the predicted power state data to assess whether an abnormal power state is about to occur in the mains input power supply.

[0156] The operating mode decision is made based on the assessment of whether an abnormal power supply state is about to occur in the mains input power supply, so as to control the battery module, the power conversion module and the switching module to switch between mains power supply mode, battery inverter power supply mode and hybrid power supply mode according to the operating mode decision.

[0157] Preferably, the power monitoring module includes a high-precision voltage sensor for real-time acquisition of the voltage signal of the mains input power supply, and a high-precision current sensor for real-time acquisition of the current signal of the mains input power supply. The power status data acquired by the power monitoring module includes, but is not limited to, the amplitude data, phase data, and frequency data of the voltage and current of the mains input power supply, so that the control module can fully grasp the operating status of the mains input power supply.

[0158] The power state prediction model is obtained by using historical power state data of the mains input power at the input terminal of the intelligent power management device monitored by the power monitoring module as sample data, and training it with machine learning algorithms such as neural networks and support vector machines.

[0159] Preferably, the power state prediction model is a data sequence prediction model. This involves supervised training of the power state prediction model by dividing a continuous segment of historical power state data into input sample data and output sample data. After training, the model can be used as input data, consisting of a segment of power state data of equal length to the input sample data obtained from the power monitoring module, to predict the power state data of the mains input power source over a future period. The lengths of the input and output sample data can be configured according to actual implementation needs. Correspondingly, the acquisition time of the input data for the power state prediction model, and the duration of the predicted "future period," are determined based on the configured lengths of the input and output sample data.

[0160] The abnormal power supply state refers to a state in which the mains input power supply experiences abnormalities such as voltage dips, voltage imbalances, voltage fluctuations, and excessive harmonic voltages.

[0161] Voltage sag refers to the phenomenon where the effective value of the mains input voltage suddenly drops for a short period of time and then returns to the normal level. It is mainly caused by short circuit faults in the power system, the start-up of large-capacity equipment, lightning strikes, etc.

[0162] Voltage imbalance refers to a situation where the amplitudes of the three-phase voltages in the mains input power supply are not equal, which is usually caused by uneven distribution of the three-phase load.

[0163] Voltage fluctuation refers to the rapid, periodic or non-periodic change in the supply voltage of the mains input power source over a period of time, and its amplitude variation range usually exceeds ±10% of the rated voltage. It is usually caused by the instantaneous change of the grid load due to the start-up or shutdown of large loads in the grid, or by the large current fluctuation caused by the operation of impulsive loads.

[0164] When there is a nonlinear load in the power grid, the voltage waveform will be distorted, resulting in harmonic voltage. When the harmonic voltage exceeds the standard, it will increase the loss of electrical equipment, aggravate the heat generation of the equipment, and shorten the service life of the equipment.

[0165] Furthermore, the steps for assessing whether an abnormal power supply condition is imminent based on the predicted power status data specifically include:

[0166] Based on the predicted power state data for a future period, extract or calculate the set of input variables corresponding to each power anomaly state:

[0167] ,

[0168] in This refers to the voltage sag depth. The duration of the voltage sag. For voltage imbalance, This refers to the voltage fluctuation amplitude. The total harmonic voltage distortion rate;

[0169] The fuzzy variable set corresponding to the input variable set is determined based on the membership function of each input variable:

[0170] ,

[0171] The fuzzy set is used to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set:

[0172] ,

[0173] in The number of output variables in the output fuzzy set;

[0174] Obtain the basic score corresponding to each output element in the output fuzzy set. ;

[0175] Calculate the comprehensive prediction score for power supply anomalies occurring within a future period:

[0176] ,

[0177] in From 1 to Positive integers between;

[0178] Based on the comprehensive prediction score of power supply anomalies occurring in the near future Determine if the mains power input is about to experience a power abnormality.

[0179] In the technical solution of the above implementation, the set of input variables is constructed using abnormal power supply states such as voltage sag, voltage imbalance, voltage fluctuation, and excessive harmonic voltage as examples.

[0180] The voltage sag depth in the input variable set The value is the percentage of the difference between the real-time measured voltage value and the rated voltage relative to the rated voltage, and its range is between 0% and 100%. In the technical solution of the above embodiment, This represents the number of fuzzy states in the voltage sag depth. These represent the fuzzy variable values ​​for each fuzzy state corresponding to the voltage sag depth.

[0181] Voltage sag duration in the set of input variables The duration of the voltage sag is typically measured in seconds, and its value is configured based on empirical data; for example, it can be configured to be 0-10 seconds. In the technical solution of the above implementation, The number of fuzzy states for the duration of the voltage dip. These are the fuzzy variable values ​​for each fuzzy state corresponding to the duration of the voltage sag.

[0182] The voltage imbalance The negative-sequence voltage component can be represented by the percentage of the positive-sequence voltage component, ranging from 0% to 10%. The positive-sequence voltage component is a set of three-phase voltages of equal magnitude, with a phase difference of 120° and the same phase sequence as a normal three-phase system. When the three-phase system is perfectly symmetrical, only the positive-sequence voltage component exists. The negative-sequence voltage component is also a set of three-phase voltages of equal magnitude, with a phase difference of 120°, but its phase sequence is opposite to the positive sequence. Under normal circumstances, the three-phase voltages of a power system are symmetrical, and the negative-sequence voltage component is theoretically zero. However, when a fault occurs in the system (such as a single-phase ground fault, a two-phase short circuit, etc.) or the three-phase load is severely unbalanced, a negative-sequence voltage component will be generated. In the technical solution of the above implementation method, The number of fuzzy states representing the voltage imbalance. These represent the fuzzy variable values ​​for each fuzzy state corresponding to the voltage imbalance.

[0183] The voltage fluctuation amplitude It represents the magnitude of the monitored real-time voltage, with the unit being volts, and its value range is determined according to the power supply environment of the actual application. In the technical solution of the above implementation method, The number of fuzzy states representing the voltage fluctuation amplitude. These are the fuzzy variable values ​​for each fuzzy state corresponding to the voltage fluctuation amplitude.

[0184] The total distortion rate of harmonic voltage , which is the ratio of the square root of the sum of the squares of the effective values ​​of all harmonic voltages in a voltage signal to the effective value of the fundamental voltage, is used to measure the severity of harmonic content in a voltage signal. It is usually expressed as a percentage, and its value ranges from 0% to 20%. In the technical solution of the above implementation, The number of fuzzy states representing the voltage fluctuation amplitude. These are the fuzzy variable values ​​for each fuzzy state corresponding to the voltage fluctuation amplitude.

[0185] In the technical solutions of the above embodiments, From 1 to Positive integers between [a certain range].

[0186] Based on the comprehensive prediction score of power supply anomalies occurring in the near future. In the step of determining whether the mains input power supply is about to experience a power abnormality, the comprehensive prediction score is used... The score is compared with a pre-configured scoring threshold to determine whether an abnormal power supply condition is imminent at the mains input. When the comprehensive prediction score... If the score exceeds the threshold, it is determined that a power supply anomaly is likely to occur within the predicted future period.

[0187] Furthermore, the step of determining the fuzzy variable set corresponding to the input variable set based on the membership function of each input variable specifically includes:

[0188] Configure computational variables with values ​​ranging from 1 to 5 for iterating through the set of input variables. ;

[0189] The input variable values ​​are calculated by iterating through each input variable in the set of input variables as follows:

[0190] The currently iterated input variable is determined as the target input variable;

[0191] Configure the target input variable corresponding to Membership threshold ,in satisfy:

[0192] ;

[0193] use This represents the fuzzy state number in the fuzzy set of the target input variable. A fuzzy state, whose fuzzy variables satisfy:

[0194] when hour: ;

[0195] when hour: ;

[0196] when hour: .

[0197] Specifically, each input variable corresponds to a fuzzy set, which consists of several fuzzy states that describe the degree to which the abnormal power supply state corresponds to the input variable.

[0198] For example, the fuzzy set of voltage sag depth can be defined as: “Slight” (VS), “Moderate” (MS), and “Severe” (SS), in order to... The number of its fuzzy states is then ;

[0199] Similarly, the fuzzy set of voltage sag durations can be defined as: "Short" (ST), "Medium" (MT), and "Long" (LT), in order to... The number of its fuzzy states is then ;

[0200] The fuzzy set of voltage imbalance can be defined as: "low" (L), "medium" (M), and "high" (H), with... The number of its fuzzy states is then ;

[0201] The fuzzy set of voltage fluctuation amplitude can be defined as: “small” (VM), “small” (SM), “large” (LM), and “large” (GM). The number of its fuzzy states is then ;

[0202] The fuzzy set of harmonic voltage THD can be defined as: "Good" (G), "Average" (F), and "Poor" (P), in order to... The number of its fuzzy states is then .

[0203] It should be understood that the fuzzy sets listed above are merely examples, and the fuzzy states they contain and the number of fuzzy states do not constitute a limitation on the present invention. In actual implementation, they can be configured as fuzzy sets composed of other fuzzy states according to implementation needs.

[0204] Furthermore, the step of using the fuzzy variable set to aggregate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set specifically includes:

[0205] The fuzzy rules in the fuzzy rule base are divided into categories corresponding to each output variable. A subset of rules, where the first rule is... The rule subset contains A fuzzy rule;

[0206] Configure a counter variable to iterate through each fuzzy rule in each rule subset. For the first A subset of rules From 1 to Positive integers between;

[0207] The rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. ;

[0208] Rule strength based on fuzzy rules in each rule subset Calculate each output variable of the output fuzzy set:

[0209] .

[0210] Specifically, each rule subset corresponds to an output variable. For example, the output fuzzy set can be defined as "Slight" (SL), "Moderate" (MOD), and "Severe" (SEV), then the output variable set contains three output variables. In this implementation, the fuzzy rules in the fuzzy rule base can be divided into three rule subsets, using... This represents the number of fuzzy rules in the fuzzy rule base. The number of fuzzy rules in each rule subset satisfy:

[0211] .

[0212] Furthermore, the rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. The specific steps include:

[0213] Iterate through each fuzzy rule in each rule subset;

[0214] The traversed number The first rule subset The fuzzy rule was determined as the target rule;

[0215] Determine the associated input variables of the target rule Association membership degree And the associated fuzzy state, the associated membership degree For the associated input variables The fuzzy variable values ​​corresponding to the associated fuzzy state are in the fuzzy variable set, where From 1 to Positive integers between The number of associated input variables in the target rule;

[0216] Calculate the rule strength of the target rule:

[0217] .

[0218] Furthermore, the steps for making a working mode decision based on the assessment results of whether an abnormal power supply state is about to occur at the mains input power source specifically include:

[0219] Obtain the pre-configured comprehensive score The corresponding score ranges for each working mode;

[0220] Determine the comprehensive score The working mode corresponding to the score range that the score falls into;

[0221] When the comprehensive score When the value falls within the range corresponding to the mains power supply mode, the first switching switch is controlled to enter the on state, and the second switching switch is controlled to enter the off state so that the intelligent power management device supplies power to the load through the mains power input.

[0222] Specifically, in AC power supply mode, the intelligent power management device supplies power to the load via AC power input, and its power output terminal outputs AC power to the load. Furthermore, in AC power supply mode, the control module periodically acquires the remaining power of the battery pack, and when the remaining power is less than a threshold, it charges the battery pack via AC power input.

[0223] Furthermore, in determining the comprehensive score... Following the steps of the working mode corresponding to the score range that the score falls into, the method further includes:

[0224] When the comprehensive score When the value falls within the range corresponding to the battery inverter power supply mode, the first switching switch is controlled to enter the off state, and the second switching switch is controlled to enter the on state so that the intelligent power management device supplies power to the load through the battery module.

[0225] Specifically, when the comprehensive score When the value falls within the range corresponding to the battery inverter power supply mode, the mains input power supply may experience situations such as voltage sag depth exceeding a set threshold, severe voltage imbalance exceeding the standard, excessive voltage fluctuation amplitude, or harmonic voltage exceeding a certain limit in the future. At this time, the first switching switch is controlled to enter the off state, and the second switching switch is controlled to enter the on state so that the intelligent power management device enters the battery inverter power supply mode. The output voltage of the battery module is boosted sequentially through the boost circuit, and after being inverted by the inverter circuit, a stable AC power is provided to the load, thereby avoiding the unstable state of the mains input power supply from affecting the normal operation of the load or causing it to be damaged.

[0226] Furthermore, in determining the comprehensive score... Following the steps of the working mode corresponding to the score range that the score falls into, the method further includes:

[0227] When the comprehensive score When the value falls within the range corresponding to the hybrid power supply mode, the first switching switch and the second switching switch are controlled to enter the on state, and the output voltage of the battery module is adjusted by the power conversion module to compensate for the supply voltage of the mains input power.

[0228] Specifically, in the hybrid power supply mode, for minor power anomalies, such as slight voltage fluctuations or low harmonic content, the intelligent power management device continues to supply power to the load via the mains input power. Simultaneously, based on predicted power state data for a future period, it controls the power conversion module to appropriately compensate and adjust the output voltage. For example, it may output a certain reverse harmonic current through the inverter circuit to cancel harmonic components in the mains input power, or adjust the output voltage of the boost circuit to compensate for voltage fluctuations in the mains input power.

[0229] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0230] As described above, these embodiments of the present invention do not exhaustively cover all details, nor do they limit the invention to the specific embodiments described. Clearly, many modifications and variations can be made based on the above description. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to effectively utilize the invention and its modifications. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent power management device, characterized in that, The system includes a power monitoring module for monitoring the status of the mains input power, a battery module for providing backup power, a power conversion module for providing power conversion capabilities, a switch module for switching power supply modes, and a control module electrically connected to the power monitoring module, the battery module, the power conversion module, and the switch module. The control module monitors the status of the mains input power through the power monitoring module, predicts and assesses the probability of abnormal situations occurring in the mains input power status, and controls the battery module, the power conversion module, and the switch module to switch between mains power supply mode, battery inverter power supply mode, and hybrid power supply mode based on the assessment results. The input terminal of the power monitoring module is connected to the mains input power for monitoring the power status of the mains input power, and the output terminal of the power monitoring module is connected to the control module for providing monitoring data to the control module. The control module is configured to: Obtain power status data of the AC power input; The power state data is input into a pre-trained power state prediction model to predict power state data for a future period of time. Use the predicted power state data to assess whether an abnormal power state is about to occur in the mains input power supply. The operating mode decision is made based on the assessment result of whether the mains input power is about to experience a power abnormality, so as to control the battery module, the power conversion module and the switching module to switch between mains power supply mode, battery inverter power supply mode and hybrid power supply mode according to the operating mode decision; In the step of assessing whether an abnormal power supply condition is imminent based on the predicted power state data, the control module is configured to: Based on the predicted power state data for a future period, extract or calculate the set of input variables corresponding to each power anomaly state: ， in This refers to the voltage sag depth. This refers to the duration of the voltage sag. For voltage imbalance, This refers to the voltage fluctuation amplitude. The total harmonic voltage distortion rate; The fuzzy variable set corresponding to the input variable set is determined based on the membership function of each input variable: ， The fuzzy set is used to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set: ， in The number of output variables in the output fuzzy set; Obtain the basic score corresponding to each output element in the output fuzzy set. ; Calculate the comprehensive prediction score for power supply anomalies occurring within a future period: ， in From 1 to Positive integers between; Based on the comprehensive prediction score of power supply anomalies occurring in the near future Determine if the mains power input is about to experience a power abnormality.

2. The intelligent power management device according to claim 1, characterized in that, The battery module includes a battery pack and a charging circuit. The input terminal of the charging circuit is connected to the AC power input, and the output terminal is connected to the battery pack. It is used to charge the battery pack through the AC power input in AC power supply mode. The switching module includes a first switching switch with one end connected to the mains input power and the other end connected to the output terminal of the intelligent power management device, and a second switching switch with one end connected to the power conversion module and the other end connected to the output terminal of the intelligent power management device. The output terminal of the battery module is connected to the input terminal of the power conversion module, and is used to boost and invert the battery voltage through the power conversion module to supply power to the load. The power conversion module includes a boost circuit connected to the battery module and an inverter circuit connected to the second switching switch. When the second switching switch is turned on, the boost circuit boosts the battery voltage of the battery module and provides it to the subsequent inverter circuit. The inverter circuit converts the boosted voltage into a stable inverter voltage for output.

3. The intelligent power management device according to claim 1, characterized in that, In the step of determining the fuzzy variable set corresponding to the input variable set based on the membership function of each input variable, the control module is configured as follows: Configure computational variables with values ​​ranging from 1 to 5 for iterating through the set of input variables. ; The input variable values ​​are calculated by iterating through each input variable in the set of input variables as follows: The currently iterated input variable is determined as the target input variable; Configure the target input variable corresponding to Membership threshold ,in satisfy: ； use This represents the fuzzy state number in the fuzzy set of the target input variable. A fuzzy state, whose fuzzy variables satisfy: when hour: ; when hour: ; when hour: .

4. The intelligent power management device according to claim 1, characterized in that, In the step of using the fuzzy variable set to aggregate and calculate the fuzzy rules in the fuzzy rule base to obtain the output fuzzy set, the control module is configured as follows: The fuzzy rules in the fuzzy rule base are divided into categories corresponding to each output variable. A subset of rules, where the first rule is... The rule subset contains A fuzzy rule; Configure a counter variable to iterate through each fuzzy rule in each rule subset. For the first A subset of rules From 1 to Positive integers between; The rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. ; Rule strength based on fuzzy rules in each rule subset Calculate each output variable of the output fuzzy set: 。 5. The intelligent power management device according to claim 4, characterized in that, The rule strength of each fuzzy rule in each rule subset is calculated based on the fuzzy variable set. In the following steps, the control module is configured as follows: Iterate through each fuzzy rule in each rule subset; The traversed number The first rule subset The fuzzy rule was determined as the target rule; Determine the associated input variables of the target rule Association membership degree And the associated fuzzy state, the associated membership degree For the associated input variables The fuzzy variable values ​​corresponding to the associated fuzzy state are in the fuzzy variable set, where From 1 to Positive integers between The number of associated input variables in the target rule; Calculate the rule strength of the target rule: 。 6. The intelligent power management device according to claim 2, characterized in that, In the step of making a working mode decision based on the assessment result of whether an abnormal power supply state is about to occur in the mains input power, the control module is configured to: Obtain the pre-configured comprehensive prediction score The corresponding score ranges for each working mode; Determine the comprehensive prediction score The working mode corresponding to the score range that falls into; When the comprehensive prediction score When the value falls within the range corresponding to the mains power supply mode, the first switching switch is controlled to enter the on state, and the second switching switch is controlled to enter the off state so that the intelligent power management device supplies power to the load through the mains power input.

7. The intelligent power management device according to claim 6, characterized in that, In determining the comprehensive prediction score After the steps of the working mode corresponding to the score range that the value falls into, the control module is configured as follows: When the comprehensive prediction score When the value falls within the range corresponding to the battery inverter power supply mode, the first switching switch is controlled to enter the off state, and the second switching switch is controlled to enter the on state so that the intelligent power management device supplies power to the load through the battery module.

8. The intelligent power management device according to claim 7, characterized in that, In determining the comprehensive prediction score After the steps of the working mode corresponding to the score range that the value falls into, the control module is configured as follows: When the comprehensive prediction score When the value falls within the range corresponding to the hybrid power supply mode, the first switching switch and the second switching switch are controlled to enter the on state, and the output voltage of the battery module is adjusted by the power conversion module to compensate for the supply voltage of the mains input power.

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