Electric energy quality prediction method, device, equipment and medium

Abstract

The invention discloses an electric energy quality prediction method and device, equipment and a medium, and the method comprises the steps: automatically obtaining equipment operation parameters according to the type and specific number of electrical equipment selected by a user, and carrying out the matching of corresponding safety operation rules. And calculating the specific numerical value of each electric energy quality index in the safe operation rule. The potential electric energy quality problem can be effectively predicted by comparing and analyzing the index value obtained by real-time calculation and the standard reference interval. According to the prediction mechanism, all electric energy quality problems possibly occurring in the equipment operation process can be comprehensively pre-judged, so that early warning is given out in time before abnormity occurs, the operation reliability and stability of an electrical system are remarkably improved, and a powerful guarantee is provided for maintaining long-term stable operation of a power grid.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering technology, and in particular to a method, apparatus, equipment and medium for predicting power quality. Background Technology

[0002] The stable operation of smart city electrical systems faces severe challenges from power quality issues. Current solutions typically employ a reactive, ex-ante approach, passively detecting and remediating power quality anomalies after they occur. However, this reactive method fails to ensure the reliability of the power supply system, maintain long-term grid stability, and lacks sustainable management efficiency. Furthermore, this delayed response mechanism is particularly ineffective in addressing dynamic power quality issues such as harmonic pollution and voltage fluctuations, severely hindering the efficient operation and maintenance capabilities of smart grids. Summary of the Invention

[0003] This invention provides a power quality prediction method, device, electronic device, and medium to address the technical problems that passive detection and remediation after power quality anomalies occur cannot ensure the operational reliability of the power supply system, maintain the long-term stability of the power grid, or achieve sustainable development management efficiency.

[0004] Firstly, a power quality prediction method is provided, including:

[0005] Acquire equipment operation data for at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of each type of electrical device, the basic parameters of the equipment, and the real-time operation parameters of each electrical device;

[0006] Based on at least one type and quantity of equipment, generate target safe operation rules for at least one electrical device;

[0007] Based on the equipment's basic parameters and real-time operating parameters, calculate the power quality index values ​​in the target safe operation rules;

[0008] Based on the indicator values ​​and target safe operation rules, predict the power quality problems of at least one electrical device.

[0009] Secondly, a power quality prediction device is provided, comprising:

[0010] The acquisition module is used to acquire equipment operation data of at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of each type of electrical device, the basic parameters of the equipment, and the real-time operation parameters of each electrical device;

[0011] The generation module is used to generate target safe operation rules for at least one electrical device based on at least one device type and device quantity;

[0012] The calculation module is used to calculate the power quality index values ​​in the target safe operation rules based on the equipment's basic parameters and real-time operating parameters.

[0013] The prediction module is used to predict power quality problems of at least one electrical device based on indicator values ​​and target safe operation rules.

[0014] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the power quality prediction method described above.

[0015] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the power quality prediction method described above.

[0016] The aforementioned power quality prediction method, device, electronic equipment, and storage medium automatically acquire equipment operating parameters and match them with corresponding safe operation rules based on the type and quantity of electrical equipment selected by the user. It then calculates the specific values ​​of various power quality indicators within the safe operation rules. By comparing the real-time calculated indicator values ​​with standard reference ranges, potential power quality problems can be effectively predicted. This prediction mechanism can comprehensively anticipate all potential power quality problems that may occur during equipment operation, thereby issuing timely warnings before anomalies occur, significantly improving the reliability and stability of the electrical system and providing strong support for maintaining the long-term stable operation of the power grid. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a power quality prediction method in one embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram illustrating the deviation between the calculated and actual power factor values ​​of each electrical device in one embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram showing the relationship between the calculated and actual total harmonic distortion current of each electrical device in one embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram illustrating the deviation between the calculated and actual power factor values ​​under different device combination scenarios in one embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram illustrating the deviation between the calculated and actual total harmonic distortion current values ​​under different equipment combination scenarios in one embodiment of the present invention.

[0023] Figure 6 This is a schematic diagram of the current amplitude of a single electrical device in one embodiment of the present invention;

[0024] Figure 7 This is a schematic diagram of current amplitude under different device combination scenarios in one embodiment of the present invention;

[0025] Figure 8 This is a schematic diagram of the power quality prediction device in one embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in the present invention are only for illustrative and descriptive purposes and are not intended to limit the scope of protection of the present invention.

[0027] Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or performed simultaneously. Moreover, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0028] Furthermore, the embodiments described herein are merely some, not all, of the embodiments of the invention. The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0029] It should be noted that the term "comprising" will be used in the embodiments of the present invention to indicate the presence of a feature subsequently declared, but does not exclude the addition of other features. It should also be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0030] The following is a detailed description of this case, in conjunction with the relevant accompanying drawings in the instruction manual.

[0031] In the embodiments described in this specification, the sustainable operation of smart city electrical systems highly depends on three core characteristics: system reliability, fault recovery capability, and operational stability. However, power quality degradation caused by electromagnetic interference seriously threatens these key performance indicators. The main sources of interference include nonlinear loads, inductive loads, and fluctuating loads in the electrical system. These devices generate typical power quality problems such as harmonic pollution, voltage sags, power factor deterioration, and voltage flicker. Such problems will trigger a chain of negative impacts, including: insulation breakdown of electrical equipment, thermal overload of lines, abnormal temperature rise of motors, and signal interference in communication systems. The current industry-wide "remedial" approach to power quality management, which involves passive detection and handling after power quality anomalies occur, not only lacks preventative management mechanisms but also makes it difficult to achieve long-term reliable operation of the power grid.

[0032] To address the aforementioned issues, this application proposes a power quality prediction method. Based on the type and quantity of electrical equipment selected by the user, the method automatically acquires equipment operating parameters and matches them with corresponding safe operation rules. It then calculates the specific values ​​of various power quality indicators within the safe operation rules. By comparing the real-time calculated indicator values ​​with standard reference ranges, potential power quality problems can be effectively predicted. This prediction mechanism can comprehensively anticipate all potential power quality problems that may occur during equipment operation, thereby issuing timely warnings before anomalies occur. This significantly improves the reliability and stability of the electrical system, providing a strong guarantee for maintaining the long-term stable operation of the power grid.

[0033] Please see Figure 1 This description and embodiment provide a power quality prediction method, which specifically includes the following steps:

[0034] S10: Obtain equipment operation data for at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of devices of each type of electrical device, the basic parameters of the equipment, and the real-time operation parameters of each electrical device.

[0035] It is understood that the executing entity of this invention can be a power quality prediction device, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0036] In this step, based on the electrical system equipment list provided by the user, the type and specific quantity of each type of electrical equipment are identified. For example, a factory's electrical system includes three types of equipment: 5 transformers, 10 motors, and 50 lighting fixtures. Basic equipment parameters for each type of electrical equipment are obtained, such as the rated capacity, rated voltage, and turns ratio of transformers; and the rated power, rated speed, and rated current of motors. These parameters are determined during equipment design and manufacturing and are fundamental to understanding equipment performance and capabilities. Simultaneously, real-time operating parameters for each electrical device are dynamically collected to obtain dynamic data on the current operating status of the equipment, such as the real-time voltage, current, and temperature of transformers; and the real-time speed, power, and power factor of motors. By monitoring these parameters in real time, the operating status of the equipment can be understood promptly, providing real-time data support for power quality assessment.

[0037] In one embodiment of this application, a specific data acquisition scheme is provided. In S10, specifically acquiring the equipment operation data of at least one electrical device, the following steps S11-S15 are included:

[0038] S11: In response to a power quality prediction request, obtain at least one electrical device included in the power quality prediction request.

[0039] In this step, after receiving a power quality prediction request, the system obtains at least one electrical device selected by the user in the power quality prediction request.

[0040] In practical application scenarios, the system provides a standardized list of electrical equipment options and sends it to the user interface. The user selects one or more currently used and / or planned equipment and their quantities from the list on the interface and returns the selected list to the system. The system listens for and predicts request signals in real time and parses the equipment identification information in the signals.

[0041] S12: Determine at least one type of equipment based on the type to which each electrical device belongs.

[0042] In this step, the type of each electrical device is determined, and all types are summarized to identify one or more device types included in the electrical system.

[0043] S13: Obtain the number of electrical devices in each type of equipment.

[0044] This step involves counting the specific number of electrical devices included in each equipment type. The number of devices affects the overall power quality of the system. For example, the total power and impact on the power grid of multiple identical motors operating simultaneously in an electrical system will differ significantly from those of a single motor. Determining the quantity of each type of device is crucial for accurate subsequent analysis and prediction.

[0045] S14: Retrieve the basic parameters of each device type from the preset database.

[0046] In this step, a pre-set database stores basic parameter information for various types of equipment, such as the rated capacity, rated voltage, and turns ratio of transformers; and the rated power, rated speed, and rated current of motors. By accessing this basic parameter data, the system can accurately grasp the design specifications and performance limits of the equipment, providing reliable data support for subsequent performance analysis and operational evaluation.

[0047] S15: Obtain real-time operating data for each electrical device.

[0048] In this step, the current operating status of electrical equipment is collected in real time through various sensors and monitoring devices, such as the real-time voltage, current, and temperature of transformers; and the real-time speed, power, and power factor of motors. After acquiring the real-time operating data, combined with the previously obtained equipment type, quantity, and basic parameters, a comprehensive and accurate analysis and prediction of the power quality of the electrical equipment can be performed.

[0049] By using the methods described above, various types of information needed for power quality prediction are collected, so that the power quality of electrical equipment can be scientifically and reasonably predicted based on this information.

[0050] S20: Based on at least one type of equipment and the number of equipment, generate target safe operation rules for at least one electrical device.

[0051] In this step, different types of electrical equipment have different operating characteristics and safety requirements. For example, electric motors and transformers are two different types of electrical equipment. Electric motors mainly convert electrical energy into mechanical energy, and their safety operation rules may focus on preventing overload and overheating, ensuring good lubrication and heat dissipation, etc. Transformers, on the other hand, mainly transform voltage, and their safety operation rules focus more on winding temperature, insulation performance, oil level, etc. At the same time, the number of devices will also affect the overall system operation and safety rules. For example, starting multiple high-power motors at the same time may cause a momentary drop in grid voltage, affecting the normal operation of other equipment. Therefore, it is necessary to determine the matching safety operation rules according to the type and quantity of equipment to ensure the normal and safe operation of electrical equipment.

[0052] In one embodiment of this application, a specific scheme for determining operating rules is provided. In S20, based on at least one type and quantity of equipment, a target safe operating rule corresponding to at least one electrical device is generated, specifically including the following steps S21-S24:

[0053] S21: Retrieve the target execution rules corresponding to various types of devices and their quantities from the preset database.

[0054] In this step, a pre-set database stores execution rules that various electrical devices should follow under different operating scenarios. The system can automatically match and invoke the corresponding execution rules based on the current device type and quantity. These rules define detailed operating procedures and specifications for each type of device and its specific quantity, covering key control requirements such as device startup, shutdown, and operating parameter ranges. It is worth noting that the execution rules will vary depending on the number of devices. For example, the rules for a single transformer are significantly different from those for multiple transformers operating in parallel; similarly, the rules applicable to starting a small number of motors simultaneously will differ from those for starting a large group of motors.

[0055] In one embodiment of this application, a specific scheme for determining execution rules is provided. In S21, that is, in a preset database, the target execution rules corresponding to various types of devices and their quantities are obtained, specifically including the following steps S211-S212:

[0056] S211: In the preset database, obtain at least one execution rule corresponding to each type of device and its quantity.

[0057] S212: If there are multiple execution rules, obtain the priority of each execution rule and take the execution rule with the highest priority as the target execution rule corresponding to the type of device and the number of devices.

[0058] For steps S211-S212, for a certain type of device and its specific quantity, there may be multiple applicable execution rules. To determine which rule should be prioritized when multiple rules exist simultaneously, each execution rule is assigned a priority. Priority settings typically consider factors such as the rule's importance, urgency, and impact on device and system security. The system obtains the priority information for each execution rule for subsequent comparison and filtering. After obtaining multiple execution rules and their priorities, the system compares these priorities, identifies the execution rule with the highest priority, and determines it as the target execution rule corresponding to that type of device and its quantity.

[0059] For example, the database contains the following three rules related to lighting equipment. When a luminaire's harmonic distortion (THD) exceeds 15%, its power factor is below 0.9, and the number of units exceeds 50, the conditions of all three rules may be met simultaneously:

[0060] Rule 3: If IFTHD > 15% THEN, trigger a harmonic mitigation alarm;

[0061] Rule 5: If the power factor is less than 0.9, start reactive power compensation.

[0062] Rule 6: If the number of lamps > 50, perform cluster harmonic superposition calculation;

[0063] At this point, even though all three rules meet the execution conditions, the priority of the three rules will be determined according to the physical storage order of the rules in the database. If rule 3 is defined at the beginning of the rule base, then rule 3 has the highest priority and will be executed first. That is, rule 3 will be the target execution rule for the 50 lighting fixtures, instead of processing all rules in parallel.

[0064] S22: Obtain at least one device combination method between device types from the preset database.

[0065] S23: Retrieve the interaction rules corresponding to the device combination method from the preset database.

[0066] For steps S22-S23, in the electrical system, various electrical devices are combined in specific ways to form a coordinated overall architecture. These combinations have a significant impact on power quality assessment, fault diagnosis, and operational status prediction. By accessing device combination information in a preset database, the system can accurately grasp the interrelationships between devices.

[0067] Furthermore, when different devices are combined and run in specific ways, complex interactions can occur. In such cases, specific interaction rules must be followed to ensure the safe and stable operation of the system. The pre-set database stores detailed interaction rules corresponding to various device combinations, and the system can automatically match the appropriate interaction rules based on the combination methods between device types.

[0068] S24: Based on the target execution rules and interaction rules, generate at least one target safe operation rule for electrical equipment.

[0069] In this step, the execution rules for each type of equipment and the interaction rules after equipment combination are comprehensively considered to generate a complete set of safe operation rules applicable to at least one electrical device in the entire electrical system. These rules must ensure the safe operation of each individual device while also considering the mutual influence between devices, ensuring the safe, stable, and efficient operation of the entire electrical system. For example, in a system containing transformers, motors, and capacitors, the target safe operation rules would specify the transformer's output range, the motor's starting sequence and operating parameters, and the timing of capacitor switching, to ensure the power quality and equipment safety of the entire system.

[0070] By using the above method, relevant rules are obtained from a preset database and combined with the combination of equipment to generate a comprehensive and reasonable set of safety operation rules for electrical equipment.

[0071] S30: Based on the equipment's basic parameters and real-time operating parameters, calculate the power quality index values ​​in the target safe operation rules.

[0072] In this step, the basic equipment parameters are inherent and relatively stable characteristic data of the equipment, while the real-time operating parameters reflect the current operating status of the equipment. Using these two types of parameters, the specific values ​​of the power quality indicators involved in the target safe operation rules can be calculated, thereby determining whether the equipment is in a safe and stable operating state.

[0073] In one embodiment of this application, a specific scheme for calculating power quality index values ​​is provided. In S30, that is, based on the equipment's basic parameters and real-time operating parameters, the index values ​​of power quality indicators in the target safe operation rules are calculated, specifically including the following steps S31-S33:

[0074] S31: Obtain at least one power quality indicator contained in the target safe operation rules, wherein the power quality indicator includes at least one of the following: total harmonic distortion factor of voltage, total harmonic distortion factor of current, harmonic spectrum of total current, total effective current, power factor and three-phase voltage / current imbalance.

[0075] In this step, the target safe operation rules specify the various conditions and standards that the electrical system must meet during operation. One or more power quality indicators are identified from the target safe operation rules; these indicators are used to measure the quality of the electrical system's operation. Specifically, these include: total harmonic distortion factor (THD) of voltage, total harmonic distortion factor (THD) of current, harmonic spectrum of total current, total effective current, power factor, and three-phase voltage / current imbalance.

[0076] S32: Based on the calculation formula of each power quality index, determine the target real-time operating parameters and target equipment basic parameters corresponding to each power quality index.

[0077] In this step, each power quality indicator has a specific calculation formula based on electrical principles and relevant standards. Based on the calculation formula for each power quality indicator, the target real-time operating parameters and target equipment basic parameters required to calculate that indicator are clearly defined.

[0078] Optionally, the formulas for calculating the total harmonic distortion factor of current and voltage are as follows:

[0079]

[0080] Where THDx is the total harmonic distortion of voltage / current; X is the voltage (V) or current (I); X k X1 is the effective value of the k-th harmonic current or voltage; N is the total number of harmonics (usually an odd number, with a maximum of 49); X1 is the effective value of the fundamental current or voltage.

[0081] The harmonic spectrum is determined by calculating the complex number (phasor) of each harmonic k, where the sum of the harmonic currents of each device m is used as the total harmonic current.

[0082] The formula for calculating the harmonic spectrum of the total current is:

[0083]

[0084] Among them, I -T,k I is the total current on harmonic k; -m,k The k-th harmonic current injected into device m; D is the number of sampling points; k is the frequency index, k=0 corresponds to the DC component, k=1 corresponds to the fundamental frequency component, k=2 corresponds to the second harmonic component, and so on. The complex representation of the k-th frequency component covers the amplitude and phase information of that frequency component.

[0085] The formula for calculating the total effective current is:

[0086]

[0087] Among them, I T I is the root mean square value of the total current. T,k Let be the root mean square value of the k-harmonic current.

[0088] The formula for calculating the power factor (PF) is:

[0089]

[0090] Among them, P T P represents the total active power. m Q is the active power of device m; T Q represents the total reactive power. m Let m be the reactive power of the device.

[0091] The formula for calculating the three-phase voltage / current imbalance is:

[0092]

[0093]

[0094] Where X represents voltage or current; It is a negative imbalance factor; The imbalance factor is zero; X + X - X0 and X1 represent the positive-sequence, negative-sequence, and zero-sequence voltage / current components, respectively; a is the rotating operator, a = (-1 / 2 + j√3 / 2); A, B, and C are the three-phase components (i.e., phase A, phase B, and phase C) in the three-phase power system.

[0095] Understandably, the calculation of the PQ index is based on the assumption that the supply voltage is sinusoidal and balanced, and that PQ problems are caused only by the operation of user equipment.

[0096] S33: Input the target's real-time operating parameters and the target equipment's basic parameters into the calculation formula to calculate the value of each power quality indicator.

[0097] In this step, after determining the target real-time operating parameters and target equipment basic parameters corresponding to each power quality indicator, these parameters are substituted into the corresponding calculation formulas to calculate the specific values ​​of each power quality indicator in turn, so as to evaluate and analyze the operating quality of the electrical system.

[0098] S40: Based on the indicator values ​​and target safe operation rules, predict the power quality problems of at least one electrical device.

[0099] In this step, the calculated index values ​​are compared one by one with the standard values ​​in the target safe operation rules. Based on the comparison results, potential power quality problems of electrical equipment can be predicted in advance.

[0100] In one embodiment of this application, a specific power quality problem prediction scheme is provided. In S40, that is, based on the power quality index value and the target safe operation rules, the power quality problem of at least one electrical device is predicted, specifically including the following steps S41-S43:

[0101] S41: Obtain the standard range of each power quality indicator in the target safe operation rules.

[0102] In this step, the rules clearly specify the numerical range that each power quality indicator should fall within during normal operation, i.e., the standard range. For example, the standard range for voltage deviation might be set at ±5%; the standard range for frequency deviation might be ±0.2Hz; and the standard range for total harmonic distortion (THD) might require it to not exceed 5%. Obtaining these standard ranges is the basis for subsequent comparative analysis.

[0103] S42: Compare the value of each power quality indicator with the standard range.

[0104] S43: If the index value exceeds the standard range, predict power quality problems based on the comparison results.

[0105] For steps S42-S43, each calculated indicator value is compared with its corresponding standard range to determine whether the value falls within the standard range. When a power quality indicator value is outside the specified standard range, it indicates a potential power quality anomaly. For example, if the calculated total harmonic distortion (THD) rate is 6%, exceeding the standard range of no more than 5%, it indicates an anomaly in the harmonic content indicator. In this case, based on the specific circumstances of the indicator value exceeding the standard range, potential power quality problems are predicted.

[0106] For example, excessive voltage deviation can cause electrical equipment to overheat, have a shortened lifespan, or even malfunction. Excessive voltage may damage equipment insulation; insufficient voltage may prevent equipment from starting or reduce its operating efficiency. Abnormal frequency deviation can affect equipment that relies on stable frequency operation, such as motors, causing changes in motor speed and thus impacting production processes and equipment performance. Excessive harmonic content can cause increased vibration and noise in equipment, increase energy loss, and potentially interfere with the normal operation of electronic equipment. Excessive three-phase voltage imbalance can cause motors to generate additional torque, leading to overheating, reduced efficiency, and in severe cases, damage to the motor.

[0107] By using the above methods, potential power quality problems can be identified in advance, and corresponding measures can be taken in a timely manner to adjust and repair them, ensuring the safe and stable operation of the electrical system.

[0108] In one embodiment of this application, a specific graphical display scheme is provided. After S43, that is, if the index value exceeds the standard range, and the power quality problem is predicted based on the comparison results, the specific steps include the following:

[0109] Based on at least one electrical device, the device combination method, the index value of power quality indicators, and the predicted power quality problems, generate charts and visualize the charts.

[0110] In this embodiment, based on the collected data regarding electrical equipment, equipment combinations, power quality indicators, and power quality issues, an appropriate chart type (such as a bar chart) is selected for presentation. The generated charts are displayed in an intuitive and easy-to-understand manner so that users can quickly understand and analyze the data.

[0111] By generating charts and visualizing them in the above way, the power quality status and existing problems of electrical equipment can be presented more clearly, which helps relevant personnel to make quick and accurate judgments and decisions, and improve the operating efficiency and reliability of electrical systems.

[0112] As can be seen, in the above scheme, based on the type and quantity of electrical equipment selected by the user, the system automatically acquires equipment operating parameters and matches them with corresponding safe operation rules. It then calculates the specific values ​​of various power quality indicators within the safe operation rules. By comparing the real-time calculated indicator values ​​with standard reference ranges, potential power quality problems can be effectively predicted. This prediction mechanism can comprehensively anticipate all potential power quality problems that may occur during equipment operation, thereby issuing timely warnings before anomalies occur, significantly improving the reliability and stability of the electrical system and providing strong support for maintaining the long-term stable operation of the power grid.

[0113] In one embodiment, this application proposes a power quality prediction method based on a hybrid knowledge system, which specifically includes the following steps:

[0114] Step 1: Receive user input data and determine the type and quantity of electrical equipment selected by the user based on the input data.

[0115] Step 2: Using the received data, select the corresponding context data (i.e., device operating parameters) and execution rules from the preset database to build the inference engine.

[0116] Specifically, a pre-built database is first constructed to collect information on the sources of power quality for consumers, and this information is then transformed into knowledge expressed as rules. The primary source of this knowledge is measurements taken on various devices to determine their impact on power quality; these measurements are obtained from recognized literature and our own measurements.

[0117] Furthermore, an inference engine is constructed, which contains a control strategy based on forward reasoning. The inference engine's conflict resolution mechanism is the "first-execute rule." Forward reasoning requires finding executable rules based on the conditional parts of the rules. For example, if the input data relates to lighting fixture types, the inference engine will select all rules whose conditional parts contain data about the fixtures. When multiple executable rules exist, the first rule appearing in the rule base (i.e., the highest priority rule) will be executed. For example, if the executable rules are rules three, five, and six, then rule three will be executed.

[0118] Step 3: Calculate the relevant information of the Power Quality Index (PQI) to obtain the index value of the Power Quality Index;

[0119] Specifically, the math module receives relevant power quality index types and electrical characteristic parameters of the equipment. Based on this data, the math module estimates the index values ​​of power quality indices related to power quality issues.

[0120] Step 4: Predict power quality issues and visualize the predicted power quality problems and indicator values.

[0121] In one embodiment, this application proposes a power quality prediction system for predicting power quality. The power quality prediction system (PS) includes: a preset database (KB), a calculation model (MB), an information extractor (IE), and a graphical user interface (GUI). Taking a residential consumer with specific electrical equipment and the electrical equipment used by this type of consumer as an example, the power quality prediction system provided in this application will be described in detail:

[0122] First, the system receives input data, and the user can select the device to use from a provided list. For example, the user selects 17 electrical devices and their electrical characteristic parameters. If the angles of all harmonics are unavailable, the angle of the first harmonic is considered.

[0123] Table 1

[0124]

[0125] Taking the electrical characteristic parameters of an LED light (12W) as an example, " a"12" represents a model code or specific identifier for the LED light; "4" represents the power factor correction (PFC) level of the LED light; and "0.055" represents the total harmonic current distortion rate of the LED light. The three sets of data in parentheses collectively describe the harmonic characteristics of the LED light, as follows: The first set (1,3,5,7,9,11): indicates the harmonic orders considered, namely the 1st (fundamental), 3rd, 5th, 7th, 9th, and 11th harmonics, respectively. In harmonic analysis of power systems, usually only lower-order harmonics are considered because higher-order harmonics typically have smaller amplitudes and relatively less impact on the system. The second set (1,0... The values ​​(2, 0.15, 0.06, 0.036, 0.037) represent the ratio of the amplitude of each harmonic to the amplitude of the fundamental frequency. For example, the amplitude of the 3rd harmonic is 0.2 times that of the fundamental frequency, the amplitude of the 5th harmonic is 0.15 times that of the fundamental frequency, and so on. The third group (14, -151, -161, -176, 71, 19) represents the phase angle (usually in degrees) of each harmonic. The phase angle describes the relative position of each harmonic with respect to the fundamental frequency in time. For example, the phase angle of the fundamental frequency is 14°, and the phase angle of the 3rd harmonic is -151°. The negative sign indicates that the harmonic lags behind the fundamental frequency in time.

[0126] Secondly, the pre-defined database (KB) uses the received data to select the corresponding device operating parameters and execution rules through the information extractor (IE).

[0127] Next, the relevant information of PQI is passed to the calculation module (MB) for necessary calculations to obtain the power quality index values.

[0128] Finally, power quality issues are predicted using indicator values, and the predicted power quality issues and indicator values ​​are visualized using a graphical user interface (GUI).

[0129] This application embodiment performs a two-stage evaluation of the power quality prediction system, specifically including the following steps:

[0130] In the first phase of testing, the input data consisted of only one electrical device. Therefore, in this phase, the functionality of different modules of the Power Quality Prediction System (PS) was evaluated: the graphical user interface (GUI), the pre-defined database (KB), and the information extractor (IE), because the PS did not need to perform additional calculations at this stage, as the KB contained all the necessary information to describe the functionality of a single device. Figure 2 As shown, this diagram illustrates the deviation between the calculated and actual power factor values ​​for each electrical device. The power factor deviation represents the difference between the calculated value and the actual value.

[0131] The formula for calculating power factor deviation is:

[0132] ε X =X estim -X real ;

[0133] Where, ε X X is the deviation; X is the power factor; X estim X is the numerical value of the power factor. real This is the true value of the power factor.

[0134] pass Figure 3 The experimental results show that the values ​​are all between 0.05 and -0.01, with 16 deviations exceeding 0.01 and only one deviation value of 0.05. Therefore, it can be declared that the three evaluation components of the system, namely GUI, KB, and IE, are functioning correctly from a power factor perspective. Figure 3 The diagram illustrates the difference between the calculated and actual total harmonic distortion (THD) current for each electrical device. The THD deviation (current harmonic pollution deviation) is the difference between the THDI value and the actual value. The results show that 14 values ​​(82%) have a deviation exceeding 0.05. The other three values ​​can be explained by the fact that the data in KB does not include the complete harmonic spectrum of all devices, but only the first seven odd harmonics (3...15). By including the full spectrum of the devices in KB, the weakness of the prediction system can be overcome. Overcoming this inconvenience allows us to claim that the prediction system reasonably estimates the harmonic pollution of the devices and clearly demonstrates the PQI of a particular device in a user's electrical installation.

[0135] In the second step of the predictive system evaluation, seven scenarios were considered. Table 2 lists the equipment components for each of the seven scenarios. Scenarios A, C, and E involve the operation of LED lights with a television and a laptop, a television with an electric water heater, and a television with an air conditioner, respectively. Scenarios B, D, and F involve similar combinations to the above scenarios, except that CFLs are used for lighting. The final scenario involves the operation of all equipment. Figures 4 to 7 The test results are displayed when all components of PS are working. Specifically, such as... Figure 4 The diagram illustrates the discrepancy between the calculated and actual power factor values ​​under different device combinations. Figure 5 The diagram illustrates the discrepancy between the calculated and actual total harmonic distortion current (THD) values ​​under different equipment combinations. Figure 6 The diagram shown illustrates the current amplitude of a single electrical device; as shown... Figure 7 The diagram shows the current amplitude under different device combinations. Figure 4 and Figure 5 A bias can be observed between -0.07 and 0.05, and the error also increases in the case of THDI compared to the estimation of a single device (see [reference]). Figure 6 This is because it does not take all harmonics into account. Figure 6 and Figure 7 The RMS values ​​of the total current and fundamental current are displayed. The difference between the current of a single device and the current of the scenario can be seen. When multiple devices are running, the current increases significantly in all scenarios, with the exception of the last scenario. This can be explained by the opposite phase shift of the current, thus increasing the total current. The results of the second phase of the prediction system evaluation show that the proposed method operates correctly, and the resulting error is due to incomplete data implemented in KB.

[0136] Table 2

[0137]

[0138] In one embodiment, a power quality prediction device is provided, which corresponds one-to-one with the power quality prediction method described in the above embodiments. For example... Figure 8 As shown, the power quality prediction device 100 includes: an acquisition module 101, a generation module 102, a calculation module 103, and a prediction module 104. Detailed descriptions of each functional module are as follows:

[0139] The acquisition module 101 is used to acquire equipment operation data of at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of each type of electrical device, the basic parameters of the equipment, and the real-time operation parameters of each electrical device;

[0140] The determination module is used to determine the target safe operation rules for at least one electrical device based on at least one device type and the number of devices.

[0141] The calculation module 103 is used to calculate the index values ​​of power quality indicators in the target safe operation rules based on the basic parameters of the equipment and real-time operating parameters.

[0142] The prediction module 104 is used to predict power quality problems of at least one electrical device based on indicator values ​​and target safe operation rules.

[0143] In one embodiment, the acquisition module 101 is specifically used for:

[0144] In response to a power quality prediction request, acquire at least one electrical device included in the power quality prediction request;

[0145] Based on the type of each electrical device, at least one device type must be determined;

[0146] Obtain the quantity of electrical equipment among various types of equipment;

[0147] Retrieve the basic parameters of each device type from the preset database.

[0148] Obtain real-time operating data for each electrical device.

[0149] In one embodiment, the generation module 102 is specifically used for:

[0150] Retrieve the target execution rules corresponding to various types of devices and their quantities from the pre-defined database;

[0151] Retrieve at least one device combination method between device types from the preset database;

[0152] Retrieve the interaction rules corresponding to the device combination methods from the preset database;

[0153] Based on the target execution rules and interaction rules, generate at least one target safe operation rule for electrical equipment.

[0154] In one embodiment, the generation module 102 is further configured to:

[0155] In the preset database, retrieve at least one execution rule corresponding to each type of device and its quantity;

[0156] If there are multiple execution rules, obtain the priority of each execution rule, and take the execution rule with the highest priority as the target execution rule corresponding to the type of device and the number of devices.

[0157] In one embodiment, the calculation module 103 is specifically used for:

[0158] Obtain at least one power quality indicator contained in the target safe operation rules, wherein the power quality indicator includes at least one of the following: voltage deviation, frequency deviation, harmonic content, and three-phase voltage imbalance.

[0159] Based on the calculation formula for each power quality index, the target real-time operating parameters and target equipment basic parameters corresponding to each power quality index are determined.

[0160] By inputting the target's real-time operating parameters and the target equipment's basic parameters into the calculation formula, the value of each power quality indicator is calculated.

[0161] In one embodiment, the prediction module 104 is specifically used for:

[0162] Obtain the standard range for each power quality indicator in the target safe operation rules;

[0163] Compare the value of each power quality indicator with the standard range;

[0164] If the index value exceeds the standard range, power quality problems are predicted based on the comparison results.

[0165] In one embodiment, the device further includes:

[0166] The generation module 102 is used to generate charts based on at least one electrical device, the device combination method, the index value of power quality indicators, and the predicted power quality problems;

[0167] The display module is used to visualize charts.

[0168] This invention provides a power quality prediction device that automatically acquires equipment operating parameters and matches them with corresponding safe operation rules based on the type and quantity of electrical equipment selected by the user. It calculates the specific values ​​of various power quality indicators within the safe operation rules. By comparing the real-time calculated indicator values ​​with standard reference ranges, it can effectively predict potential power quality problems. This prediction mechanism can comprehensively anticipate all potential power quality problems that may occur during equipment operation, thereby issuing timely warnings before anomalies occur, significantly improving the reliability and stability of electrical system operation, and providing strong support for maintaining the long-term stable operation of the power grid.

[0169] Specific limitations regarding the power quality prediction device can be found in the limitations of the power quality prediction method described above, and will not be repeated here. Each module in the aforementioned power quality prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the electronic device, or stored in software in the memory of the electronic device, so that the processor can call and execute the corresponding operations of each module.

[0170] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0171] Acquire equipment operation data for at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of each type of electrical device, the basic parameters of the equipment, and the real-time operation parameters of each electrical device;

[0172] Based on at least one type and quantity of equipment, determine the target safe operation rules for at least one electrical device;

[0173] Based on the equipment's basic parameters and real-time operating parameters, calculate the power quality index values ​​in the target safe operation rules;

[0174] Based on the indicator values ​​and target safe operation rules, predict the power quality problems of at least one electrical device.

[0175] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0176] Acquire equipment operation data for at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of each type of electrical device, the basic parameters of the equipment, and the real-time operation parameters of each electrical device;

[0177] Based on at least one type and quantity of equipment, determine the target safe operation rules for at least one electrical device;

[0178] Based on the equipment's basic parameters and real-time operating parameters, calculate the power quality index values ​​in the target safe operation rules;

[0179] Based on the indicator values ​​and target safe operation rules, predict the power quality problems of at least one electrical device.

[0180] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or electronic device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0182] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0183] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting power quality, characterized in that, include: Acquire equipment operation data for at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the quantity of each type of electrical device, basic equipment parameters, and real-time operation parameters of each electrical device; Based on the at least one type of equipment and the number of equipment, generate target safe operation rules corresponding to the at least one electrical equipment; Based on the equipment's basic parameters and the real-time operating parameters, calculate the power quality index values ​​in the target safe operation rules; Based on the index values ​​and the target safe operation rules, predict the power quality problems of the at least one electrical device.

2. The method according to claim 1, characterized in that, The step of acquiring equipment operation data of at least one electrical device specifically includes: In response to a power quality prediction request, acquire the at least one electrical device included in the power quality prediction request; Based on the type of each electrical device, at least one device type must be determined; Obtain the quantity of electrical equipment among various types of equipment; Retrieve the basic parameters of each device type from the preset database. Obtain real-time operating data for each electrical device.

3. The method according to claim 1, characterized in that, The step of generating target safe operation rules corresponding to the at least one electrical device based on the at least one device type and the number of devices specifically includes: Retrieve the target execution rules corresponding to various types of devices and their quantities from the pre-defined database; Obtain device combination methods among the at least one device type from the preset database; Retrieve the interaction rules corresponding to the device combination method from the preset database; Based on the target execution rule and the interaction rule, the target safe operation rule for the at least one electrical device is generated.

4. The method according to claim 3, characterized in that, The step of obtaining the target execution rules corresponding to various types of devices and their quantities from the preset database specifically includes: In the preset database, at least one execution rule corresponding to each type of device and its quantity is obtained; If there are multiple execution rules, the priority of each execution rule is obtained, and the execution rule with the highest priority is taken as the target execution rule corresponding to the type of device and the number of devices.

5. The method according to claim 1, characterized in that, The step of calculating the power quality index values ​​in the target safe operation rules based on the equipment's basic parameters and the real-time operating parameters specifically includes: Obtain at least one power quality indicator contained in the target safe operation rules, wherein the power quality indicator includes at least one of the following: total harmonic distortion factor of voltage, total harmonic distortion factor of current, harmonic spectrum of total current, total effective current, power factor and three-phase voltage / current imbalance. Based on the calculation formula for each power quality index, the target real-time operating parameters and target equipment basic parameters corresponding to each power quality index are determined. By substituting the target's real-time operating parameters and the target equipment's basic parameters into the calculation formula, the index value of each power quality indicator is calculated.

6. The method according to claim 1, characterized in that, The step of predicting the power quality problem of at least one electrical device based on the indicator value and the target safe operation rule specifically includes: Obtain the standard range for each power quality indicator in the target safe operation rules; Compare the value of each power quality indicator with the standard range; If the value of the indicator exceeds the standard range, power quality problems are predicted based on the comparison results.

7. The method according to claim 1, characterized in that, If the value of the indicator exceeds the standard range, after predicting power quality problems based on the comparison results, the method further includes: Based on at least one electrical device, the device combination method, the index value of power quality indicators, and the predicted power quality problems, a chart is generated and the chart is visualized.

8. A power quality prediction device, characterized in that, include: The acquisition module is used to acquire equipment operation data of at least one electrical device, wherein the equipment operation data includes at least one type of equipment, the number of each type of electrical device, basic equipment parameters, and real-time operation parameters of each electrical device; A generation module is used to generate target safe operation rules corresponding to the at least one electrical device based on the at least one device type and the number of devices; The calculation module is used to calculate the index values ​​of the power quality indicators in the target safe operation rules based on the basic parameters of the equipment and the real-time operating parameters. The prediction module is used to predict the power quality problems of the at least one electrical device based on the index value and the target safe operation rules.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power quality prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power quality prediction method as described in any one of claims 1 to 7.

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