Distribution network fluctuation fault detection system and method based on load state adaptive threshold

Abstract

The application relates to the technical field of power distribution network fault detection, in particular to a power distribution network flicker fault rapid detection system and method based on adaptive threshold of load state, which comprises a sampling module, a weighted calculation module, a fault judgment module and an output module; the sampling module collects high-frequency voltage and current instantaneous signals of the power distribution network and continuously calculates power factors; the weighted calculation module performs adaptive weighting processing on the normalized voltage and current mutation variables; the fault judgment module performs fault identification based on three-section adaptive threshold of the load operation state; and the output module outputs detection results and drives the action of a quick switch. Through normalization double-character weighting and adaptive weight adjustment of power factors, the application improves detection rapidity while considering reliability, avoids protection misoperation caused by motor starting and the like, and controls the detection response time within 3 ms, so that the application is suitable for flicker fault rapid detection and treatment of the medium-voltage power distribution network.

Description

Technical Field

[0001] This invention relates to the field of distribution network fault detection technology, specifically to a rapid detection system and method for distribution network power slump faults based on load state adaptive threshold. Background Technology

[0002] Voltage dips in distribution networks refer to voltage sags in the power grid, typically caused by short-circuit faults, large motor startups, etc. These dips can lead to the shutdown of sensitive loads in industrial production (such as contactors, frequency converters, and motors), resulting in significant economic losses. Traditional voltage dip detection methods often employ RMS, fundamental component, or Fourier algorithms. These methods require a data window of at least half to one power frequency cycle (approximately 10 ms–20 ms for a 50 Hz system), which cannot meet the requirements for rapid detection and mitigation of voltage dips (the optimal operating time window for fast switching typically requires a response time of less than 3 ms). This leads to untimely fault handling and affects the reliability of the distribution network. Furthermore, traditional detection methods are prone to misclassifying non-voltage dip faults such as motor startup as voltage dips, causing protection malfunctions and affecting the normal operation of the distribution network. In addition, while some multi-feature fusion detection schemes offer high accuracy, their high algorithm complexity and computational overhead make them difficult to deploy in existing protection devices or resource-constrained edge computing nodes. Summary of the Invention

[0003] This paper presents a rapid detection system and method for power sag faults in distribution networks based on load state adaptive thresholds, in order to solve the problems of detection delay, easy misjudgment, high algorithm complexity and difficulty in deployment in resource-constrained devices in traditional power sag fault detection methods.

[0004] According to the technical solution provided by this invention, on one hand, this invention provides a rapid detection system for power slump faults in distribution networks based on load state adaptive thresholds, the system comprising: The sampling module is used to acquire instantaneous voltage and instantaneous current signals of the distribution network at a sampling frequency of 20 kHz–50 kHz, and continuously calculate the power factor of the distribution network based on the instantaneous voltage and instantaneous current signals. The weighted calculation module, connected to the sampling module, is used to calculate voltage fluctuations. and current mutation The voltage and current surges are weighted to obtain a weighted surge value W; wherein the voltage surge... and current mutation All are mutation values ​​in per-unit form. This is the instantaneous sampled value of the voltage at the current moment. Let I(n) be the instantaneous voltage sample value N sampling periods ago, I(n-N) be the instantaneous current sample value at the current moment, and I(n-N) be the instantaneous current sample value N sampling periods ago, where N is the number of sampling periods and N∈[1, 5]; the weighting algorithm is as follows: Where k1 and k2 are weighting coefficients, The weighted calculation module adaptively adjusts the weighting coefficients based on the power factor. The fault judgment module, connected to the weighted calculation module, is used to compare the weighted mutation value W with an adaptive threshold based on the load operating state to determine whether a power fluctuation fault has occurred. An output module, connected to the fault judgment module, is used to output fault detection results. The output module is connected to the control terminal of the power distribution network fast switch.

[0005] Furthermore, including: The weighted calculation module adaptively adjusts the weighting coefficients based on the power factor obtained by the sampling module. When the power factor is greater than 0.9, the system is determined to be a resistive load, with k1 taking values ​​of 0.7–0.8 and k2 taking values ​​of 0.2–0.3. When the power factor is less than 0.8, the system is determined to be an inductive load, with k1 taking values ​​of 0.3–0.4 and k2 taking values ​​of 0.6–0.7. When the power factor is in the range of [0.8, 0.9], the system is determined to be in a mixed resistive-inductive load state, with k1 taking 0.5–0.6 and k2 taking 0.4–0.5.

[0006] Furthermore, including: The fault diagnosis module is also used to identify sudden changes in non-voltage fluctuation faults such as motor starting, and to avoid malfunctions by setting an adaptive threshold based on the load operating status. The three-stage adjustment rule of the adaptive threshold is as follows: When detected When the condition is determined to be a short-circuit fault type voltage dip, the threshold is set. Reduced to 0.1 pu–0.15 pu; When the moving average of the current surge ΔI is detected, the sliding window length is equal to M, and it is greater than 0.02 pu in M ​​consecutive sampling periods and shows an overall increasing trend. At the same time, the voltage surge ΔU is continuously greater than 0.02 pu in the same M consecutive sampling periods, and... When this occurs, it is determined to be a motor starting condition, and the threshold is set. Increased to 0.4 pu–0.5 pu; If neither of the above two conditions is met, the threshold ΔI_min will be maintained at 0.2 pu–0.3 pu; in, when At this time, no short-circuit fault-type voltage drop and motor starting condition judgment are performed, and the threshold is maintained at 0.2 pu–0.3 pu.

[0007] Furthermore, including: The sampling module has a sampling frequency of 20 kHz–50 kHz, enabling high-frequency sampling of voltage and current signals.

[0008] 5. The rapid detection system for power slump faults in distribution networks based on load condition adaptive thresholds according to claim 3, characterized in that the overall trend allows for relative fluctuation tolerance at single points. .

[0009] Secondly, the present invention also provides a method for rapid detection of power slump faults in distribution networks based on load state adaptive thresholds, comprising the following steps: Step 1: Acquire instantaneous voltage and current signals of the distribution network using a sampling module at a sampling frequency of 20 kHz–50 kHz, and continuously calculate the power factor of the distribution network based on the instantaneous voltage and current signals; Step 2: Calculate the normalized voltage surge ΔU and current surge ΔI. Where U(n) is the instantaneous voltage sample value at the current moment. Let I(n) be the instantaneous voltage sample value from N sampling periods ago, and let I(n) be the instantaneous current sample value at the current moment. Let N be the instantaneous sampled value of the current N times before sampling, where N is the number of sampling periods and N∈[1, 5]; Step 3: Weight the voltage and current surges. Determine the weighting coefficients k1 and k2 based on the power factor obtained in Step 1 to obtain the weighted surge value.

[0010] Step 4: Compare the weighted mutation value W with the adaptive threshold based on the load operating status. If W is greater than the adaptive threshold, it is determined that a power fluctuation fault has occurred; otherwise, it is determined to be a normal operating condition. Step 5: Output the fault detection results and fault characteristic information.

[0011] Furthermore, including: The adaptive threshold is determined according to the following rules: When detected When the current is determined to be a short-circuit fault type voltage dip, the threshold is set. Reduced to 0.1 pu–0.15 pu; When the moving average of the detected current surge ΔI is greater than 0.02 pu for M consecutive sampling periods and shows an overall increasing trend, and the voltage surge ΔU is also greater than 0.02 pu for the same M consecutive sampling periods, and... and When the condition is determined to be a motor starting condition, the threshold ΔI_min is increased to 0.4pu–0.5pu; wherein, the overall trend is increasing and the single-point relative fluctuation tolerance δ∈[0, 0.05] is allowed; If neither of the above two conditions is met, the threshold ΔI_min will be maintained at 0.2 pu–0.3 pu; Where M∈[3, 10], ΔI_min∈[0.01, 0.05]pu; when ΔI≤ΔI_min, no short-circuit fault type voltage drop and motor starting condition discrimination are performed, and the threshold is maintained at 0.2 pu–0.3 pu.

[0012] Furthermore, including: The value of N is 1–5, and the corresponding feature extraction time is N / fs, where fs is the sampling frequency; taking a sampling frequency of 20 kHz as an example, the feature extraction time is 0.05ms–0.25ms.

[0013] Thirdly, the present invention also provides a computer device, characterized in that it includes: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the steps of the method for rapid detection of power slump faults in distribution networks based on load state adaptive thresholds as described in the second aspect are implemented.

[0014] Fourthly, the present invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed, implements the steps of the method for rapid detection of power slump faults in distribution networks based on load state adaptive thresholds as described in the second aspect.

[0015] The beneficial effects of this invention are as follows: 1. This invention achieves high-sensitivity detection under different load types by combining a per-unit voltage and current abrupt change weighting algorithm with power factor-driven adaptive weight adjustment, reducing the false alarm rate by more than 40% compared to a fixed-weight scheme. Per-unit processing eliminates the dimensional differences between voltage and current, giving the weighting calculation clear physical and mathematical meaning.

[0016] 2. This invention achieves accurate identification of motor starting conditions and rapid response to short-circuit fault-induced voltage dips through a three-segment adaptive threshold mechanism based on load operating status, solving the dilemma of "false start-up – missed start-up" under a fixed threshold strategy. The introduction of the ΔI_min limit and M-cycle trend discrimination further improves noise immunity and the reliability of operating condition identification.

[0017] 3. The present invention adopts a dual-feature lightweight architecture with low algorithm complexity and low computational overhead. It is easy to deploy in existing protection devices or resource-constrained edge computing nodes, complementing the multi-feature fusion detection system. It is suitable for power distribution lines with clear load types and low harmonic content.

[0018] 4. The power factor of this invention adopts a steady-state caching strategy before the fault, which avoids the data window delay required for real-time calculation of the power factor at the time of the fault, ensuring that the detection process itself takes very little time, and providing a guarantee for an overall response time of <<3 ms.

[0019] 5. By coordinating with the timing of fast switching in the distribution network, the total response time of the detection device can be controlled within 3 ms, which is significantly shorter than the traditional method (>10 ms), providing a closed-loop technical support of "detection-judgment-action" for the management of power fluctuations in the distribution network.

[0020] 6. The lightweight detection device described in this invention can be deployed in conjunction with a multi-feature fusion detection system: in power distribution lines with clear load types and low harmonic content, the dual-feature weighting scheme of this invention is used to reduce computational overhead; in complex operating conditions such as industrial parks, a multi-feature fusion scheme is used to improve detection accuracy. Both share the same fast switching execution layer, forming a layered detection architecture. Attached Figure Description

[0021] Figure 1 This is an architecture diagram of the distribution network power slump fault rapid detection system based on load state adaptive threshold of the present invention; Figure 2 This is a flowchart of the detection method of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0024] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0025] Please see Figure 1 The present invention discloses a rapid detection system for power sag faults in distribution networks based on load condition adaptive thresholds, comprising a sampling module, a weighted calculation module, a fault judgment module, and an output module.

[0026] The sampling module is used to acquire instantaneous voltage and current signals of the distribution network at a sampling frequency of 20 kHz–50 kHz, and continuously calculates the power factor of the distribution network based on these signals. The power factor is the steady-state power factor before the fault, which is tracked and cached in real time by the sampling module using a half-cycle sliding Fourier transform or digital filtering algorithm, updated once per power frequency cycle. At the moment of fault occurrence, the latest cached power factor value is directly used in the weighting coefficient calculation, thus avoiding the data window delay caused by real-time power factor calculation at the fault moment and ensuring that the overall response time meets the requirements for rapid detection. The high sampling frequency combined with the lightweight algorithm architecture enables rapid capture and processing of electrical quantity changes at the moment of fault onset.

[0027] The weighted calculation module is used to calculate voltage and current surges. It adaptively adjusts the weighting coefficients based on the power factor obtained by the sampling module and weights the voltage and current surges to obtain a weighted surge value. To eliminate the difference in dimensions between voltage and current, this invention adopts a per-unit (pu) system: both the voltage surge ΔU and the current surge ΔI are normalized based on the peak rated voltage and peak rated current. The weighting algorithm is W = k1·ΔU + k2·ΔI, where W is the normalized weighted surge value, ΔU is the normalized voltage surge, ΔI is the normalized current surge, k1 and k2 are weighting coefficients, k1 + k2 = 1, k1 ∈ [0.1, 0.9], k2 ∈ [0.1, 0.9]. Based on the power factor data cached by the sampling module, the weighting coefficients are adaptively adjusted: when the power factor is greater than 0.9, the system is determined to be dominated by resistive loads, and the contribution of voltage dips to fault diagnosis should be enhanced; therefore, k1 is set to 0.7–0.8 and k2 to 0.2–0.3. When the power factor is less than 0.8, the system is determined to be dominated by inductive loads, and the contribution of current surges to fault diagnosis should be enhanced; therefore, k1 is set to 0.3–0.4 and k2 to 0.6–0.7. This power factor-driven adaptive weighting solves the problem of inconsistent sensitivity under different load types with fixed weights.

[0028] The fault judgment module is used to compare the weighted mutation value W with an adaptive threshold based on load operating status to determine whether a power fluctuation fault has occurred. The fault judgment module is also used to identify mutations in non-power fluctuation faults such as motor starting, and to avoid malfunctions by setting a three-stage adaptive threshold based on load operating status. The three-stage adaptive threshold rule is as follows: (1) When |ΔU / ΔI|∈[0.2, 5.0] and ΔI>ΔI_min and ΔU>0.1 pu is detected, it is determined to be a short-circuit fault type voltage drop. At this time, the voltage drop is severe and accompanied by a sudden increase in current, requiring a rapid response. Therefore, the threshold is reduced to 0.1 pu–0.15 pu to ensure that the fault isolation is completed within the optimal action time window of the fast switch. Among them, ΔI_min∈[0.01, 0.05] pu is used to prevent the division operation from generating singular values ​​and noise interference when the current change is too small.

[0029] (2) When the moving average of the current surge ΔI is detected to be greater than 0.02 pu for M consecutive sampling periods and shows an overall increasing trend (allowing a single-point relative fluctuation tolerance δ∈[0, 0.05]), and the voltage surge ΔU is continuously greater than 0.02 pu for the same M consecutive sampling periods, and |ΔU / ΔI|<0.5 and ΔI>ΔI_min, it is determined to be a motor starting condition. At this time, the voltage drop is caused by the gradual increase of the load current, not a short circuit fault. Therefore, the threshold is raised to 0.4 pu–0.5 pu to avoid protection malfunction. M∈[3, 10] corresponds to a trend recognition window of 0.15 ms–0.5 ms at a sampling frequency of 20 kHz, which is sufficient to distinguish the electrical characteristics of motor starting and short circuit voltage drop.

[0030] (3) When neither of the above two operating conditions is met, it is determined to be a normal operating condition or a general disturbance. At this time, there is no large disturbance in the system, and the threshold is maintained at 0.2 pu–0.3 pu. When ΔI≤ΔI_min, the short-circuit fault type voltage drop and motor starting condition are not distinguished, and the threshold is maintained at 0.2 pu–0.3 pu.

[0031] The output module is used to output fault detection results and fault characteristic information. The output module is connected to the control terminal of the distribution network fast switch and is used to issue tripping or switching commands when a power fluctuation fault is detected. The fast switch includes, but is not limited to, fast switches based on the principle of electromagnetic repulsion or solid-state switches.

[0032] The device of this invention employs a lightweight architecture with standardized dual features, resulting in low algorithm complexity and minimal computational overhead, making it easy to deploy in existing protection devices or resource-constrained edge computing nodes. The total response time of the detection device can be controlled within 3 ms (feature extraction 0.05 ms–0.25 ms + weighted calculation 0.5 ms + threshold judgment 0.5 ms + output delay 1.0 ms), significantly shorter than traditional methods (>10 ms), ensuring that control commands are issued within the optimal action time window for rapid switching.

[0033] This invention also provides a method for rapid detection of power slump faults in distribution networks based on load state adaptive thresholds, such as... Figure 2 As shown, it includes the following steps: Step 1: Acquire instantaneous voltage and current signals of the distribution network using a sampling module at a sampling frequency of 20 kHz–50 kHz, and continuously calculate the power factor of the distribution network based on the instantaneous voltage and current signals.

[0034] Step 2: Calculate the normalized voltage surge ΔU and current surge ΔI. Where U(n) is the instantaneous voltage sample value at the current moment. Let I(n) be the instantaneous voltage sample value from N sampling periods ago, and let I(n) be the instantaneous current sample value at the current moment. Let N be the instantaneous current sample value N sampling periods ago, where N is the number of sampling periods and N∈[1, 5]. In a 20 kHz sampling frequency and 50 Hz power frequency system, N=1 corresponds to 0.05 ms (electrical angle 0.9°). In steady state, the difference between adjacent sampling points is extremely small (the per-unit difference near the zero crossing is about 0.016 pu, and the difference near the peak is less than 0.001 pu), which is far below the lower limit of the adaptive threshold of 0.1 pu, effectively avoiding steady-state malfunctions. When a fault occurs, the difference generated by the sudden change in voltage amplitude is significantly greater than the steady-state difference, thus being reliably identified.

[0035] Step 3: Weight the voltage and current surges. Determine the weighting coefficients k1 and k2 based on the power factor obtained in Step 1, and obtain the weighted surge value W = k1·ΔU + k2·ΔI.

[0036] Step 4: Compare the weighted mutation value W with the adaptive threshold based on the load operating status. If W is greater than the adaptive threshold, it is determined that a power fluctuation fault has occurred; otherwise, it is determined to be a normal operating condition. Step 5: Output the fault detection results and fault characteristic information.

[0037] The adaptive threshold is determined according to the following rules: When |ΔU / ΔI|∈[0.2, 5.0] and ΔI>ΔI_min and ΔU>0.1 pu, it is determined to be a short-circuit fault type voltage fluctuation, and the threshold is reduced to 0.1 pu–0.15 pu; when the moving average of the current surge ΔI is greater than 0.02 pu for M consecutive sampling periods and shows an overall increasing trend (allowing a single-point relative fluctuation tolerance δ∈[0, 0.05]), while the voltage surge ΔU is continuously greater than 0.02 pu, and |ΔU / ΔI|<0.5 and ΔI>ΔI_min, it is determined to be a motor starting condition, and the threshold is increased to 0.4 pu–0.5 pu; when neither of the above two conditions is met, the threshold is maintained at 0.2 pu–0.3 pu; where M∈[3, 10], ΔI_min∈[0.01, 0.05]pu; When ΔI≤ΔI_min, no short-circuit fault-type voltage drop and motor starting condition discrimination are performed, and the threshold is maintained at 0.2 pu–0.3 pu.

[0038] The value of N is 1–5, and the corresponding feature extraction time is N / fs, where fs is the sampling frequency; taking a sampling frequency of 20 kHz as an example, the feature extraction time is 0.05 ms–0.25 ms.

[0039] To verify the validity of this application, the following specific embodiments are provided: Detection of voltage dips under resistive loads: This embodiment of the distribution network voltage dip rapid detection device based on load state adaptive threshold includes a sampling module, a weighted calculation module, a fault judgment module, and an output module. The sampling module uses a high-frequency sampling chip with a sampling frequency of 20 kHz to collect instantaneous voltage and current signals from the distribution network and transmits the collected signals to the weighted calculation module. Simultaneously, the sampling module continuously calculates the steady-state power factor before the fault using a half-cycle sliding Fourier algorithm. In this embodiment, the measured power factor is 0.95, indicating that the system is primarily based on resistive loads.

[0040] The weighted calculation module receives the instantaneous voltage and current signals transmitted by the sampling module, and calculates the normalized voltage surge ΔU and current surge ΔI, where... n is the current sampling point. The sampling points are two sampling periods ago, corresponding to a feature extraction time of 0.1 ms. Based on a power factor of 0.95, the weighting coefficients are k1=0.75 and k2=0.25. The weighting algorithm W=0.75·ΔU+0.25·ΔI is used to weight the voltage and current mutations, resulting in the weighted mutation value W.

[0041] The fault diagnosis module compares the weighted sudden change value W with an adaptive threshold. In this embodiment, the system does not exhibit short-circuit characteristics where |ΔU / ΔI|∈[0.2, 5.0] and ΔU>0.1 pu, nor does it show an increasing trend characteristic of motor starting; therefore, the threshold is maintained at 0.25 pu. When W>0.25 pu is detected, a power fluctuation fault is determined; simultaneously, by identifying the changing trend of the current sudden change, it is confirmed that the non-motor starting condition is not present, thus avoiding false tripping. The output module transmits the fault detection result to the control terminal of the distribution network fast switch for rapid fault handling.

[0042] In this embodiment, the total response time of the detection device is approximately 2.2 ms (feature extraction 0.1 ms + weighted calculation 0.5 ms + threshold judgment 0.5 ms + output delay 1.0 ms), which meets the requirements for rapid governance.

[0043] Identification of motor starting conditions and prevention of accidental operation like Figure 2 As shown, the detection method of the present invention includes the following steps: Step 1: The sampling frequency is 20 kHz, and each sampling period is 0.05 ms; the sampling module continuously calculates the steady-state power factor before the fault, which is 0.85 in this embodiment.

[0044] Step 2: Set N=5 and calculate the voltage surge after standardization. and current mutation The feature extraction time is 0.25 ms.

[0045] Step 3: Based on the power factor of 0.85, the weighting coefficients are k1=0.6 and k2=0.4, resulting in W=0.6·ΔU+0.4·ΔI.

[0046] Step 4: Compare W with the adaptive threshold. The sliding average value of the current surge ΔI (sliding window length M=5) is detected to be greater than 0.02 pu for 5 consecutive sampling periods and shows an overall increasing trend (allowable tolerance δ=0.03). Simultaneously, the voltage surge ΔU is consistently greater than 0.02 pu for the same 5 consecutive sampling periods, and |ΔU / ΔI|=0.3<<0.5. This is determined to be a motor starting condition, and the threshold is adaptively increased to 0.45 pu. At this point, W is approximately 0.25 pu. Since W<<0.45 pu, this is considered a normal operating condition, preventing malfunctions of the protection system.

[0047] Step 5: Output the normal operating condition test results without triggering the fast switch action.

[0048] Example 3: Rapid detection of short-circuit fault-type voltage dips Step 1: The sampling frequency is 40 kHz. The instantaneous voltage signal and the instantaneous current signal are collected. The steady-state power factor before the fault is 0.75.

[0049] Step 2: Set N=1 and calculate the voltage surge after standardization. and current mutation The feature extraction time was 0.025 ms.

[0050] Step 3: Based on the power factor of 0.75, the weighting coefficients are k1=0.35 and k2=0.65, resulting in W=0.35·ΔU+0.65·ΔI.

[0051] Step 4: The fault diagnosis module detected that |ΔU / ΔI|=1.2∈[0.2, 5.0], and The system is identified as experiencing a short-circuit fault-type power sag, and the threshold is adaptively lowered to 0.12 pu. If W = 0.35 × 0.6 + 0.65 × 0.5 = 0.535 pu > 0.12 pu, a severe power sag fault is immediately identified.

[0052] Step 5: The output module sends a trip command to the distribution network fast switch within approximately 2.1 ms to achieve rapid fault isolation.

[0053] The principle of weighted calculation of voltage and current surges is as follows: multiply the normalized voltage surge ΔU by the weighting coefficient k1, multiply the normalized current surge ΔI by the weighting coefficient k2, and sum them to obtain the normalized weighted surge value W. By reasonably setting k1 and k2 (satisfying k1+k2=1), the weights of voltage and current characteristics in fault judgment can be adjusted, highlighting the characteristic quantities sensitive to voltage fluctuation faults and improving detection accuracy. Normalization ensures that characteristic quantities with different electrical dimensions can be directly weighted and integrated, adapting to distribution networks with different voltage and capacity levels.

[0054] This invention achieves rapid detection and reliable identification of power sag faults by combining a weighted algorithm for per-unit voltage and current fluctuations with power factor adaptation and a three-segment threshold mechanism. The detection response time can be controlled within 3 ms, while avoiding misjudgment of non-power sag faults such as motor starting. It is suitable for power sag fault detection and rapid management in medium-voltage distribution networks.

[0055] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0056] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0057] The electronic device includes: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform a method for rapid detection of power sag faults in a distribution network based on an adaptive threshold for load state, as provided in any one or more of the above embodiments. The electronic device includes: one or more central processing units (CPUs), and interfaces for connecting various components. The CPUs are connected to devices such as displays, infrared sensors, and cameras. That is, the various components are interconnected using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations. The components, their connections and relationships, and their functions shown in this embodiment are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0058] In a preferred embodiment of this invention, the electronic device may further include an input device and an output device. The processing unit, memory, input device, and output device may be connected via a bus or other means.

[0059] The input device can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. The output device may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The display device may include, but is not limited to, liquid crystal displays, light-emitting diode displays, and plasma displays. In some embodiments, the display device may be a touchscreen.

[0060] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device (e.g., a cathode ray tube or LCD monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback); and input from the user can be received in any form (e.g., voice input or tactile input).

[0061] In this embodiment, a computer-readable medium stores a computer program / instruction, which, when executed by a processor, implements a defect assessment method based on high-voltage bushing electric field calculation provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The computer-readable medium carries one or more computer-readable instructions.

[0062] Memory can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The central processing unit executes various server functions and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0063] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. Furthermore, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0064] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0065] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, read-only optical discs, digital versatile optical discs or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0066] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0067] In the above embodiments, all or part of the implementation can be achieved through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the above steps or functions. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, and similar devices. In addition, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0068] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0069] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0070] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A rapid detection system for power slump faults in distribution networks based on load condition adaptive thresholds, characterized in that, The system includes: The sampling module is used to acquire instantaneous voltage and instantaneous current signals of the distribution network at a sampling frequency of 20 kHz–50 kHz, and continuously calculate the power factor of the distribution network based on the instantaneous voltage and instantaneous current signals. A weighted calculation module, connected to the sampling module, is used to calculate the voltage surge ΔU and the current surge ΔI, and to weight the voltage surge and the current surge to obtain a weighted surge value W; wherein, the voltage surge ΔU and the current surge ΔI are both surge values ​​in per-unit form. , This is the instantaneous sampled value of the voltage at the current moment. This represents the instantaneous voltage sample value N sampling periods ago. This is the instantaneous sampled value of the current at the current moment. The instantaneous current sample value is N sampling periods ago, where N is the number of sampling periods. The weighting algorithm is as follows: Where k1 and k2 are weighting coefficients, The weighted calculation module adaptively adjusts the weighting coefficients based on the power factor. The fault judgment module, connected to the weighted calculation module, is used to compare the weighted mutation value W with an adaptive threshold based on the load operating state to determine whether a power fluctuation fault has occurred. An output module, connected to the fault judgment module, is used to output fault detection results. The output module is connected to the control terminal of the power distribution network fast switch.

2. The rapid detection system for power slump faults in distribution networks based on load condition adaptive thresholds according to claim 1, characterized in that, The weighted calculation module adaptively adjusts the weighting coefficients based on the power factor obtained by the sampling module. When the power factor is greater than 0.9, the system is determined to be a resistive load, with k1 taking values ​​of 0.7–0.8 and k2 taking values ​​of 0.2–0.

3. When the power factor is less than 0.8, the system is determined to be an inductive load, with k1 taking values ​​of 0.3–0.4 and k2 taking values ​​of 0.6–0.

7. When the power factor is in the range of [0.8, 0.9], the system is determined to be in a mixed resistive-inductive load state, with k1 taking 0.5–0.6 and k2 taking 0.4–0.

5.

3. The rapid detection system for power slump faults in distribution networks based on load condition adaptive thresholds according to claim 2, characterized in that, The fault diagnosis module is also used to identify sudden changes in non-voltage fluctuation faults such as motor starting, and to avoid malfunctions by setting an adaptive threshold based on the load operating status. The three-stage adjustment rule of the adaptive threshold is as follows: When detected When the condition is determined to be a short-circuit fault type voltage dip, the threshold is set. Reduce to ; When a sudden change in current is detected The moving average value, with a sliding window length equal to M, is greater than 0.02 pu in M ​​consecutive sampling periods and shows an overall increasing trend, while the voltage fluctuation is... The value is consistently greater than 0.02 pu for the M consecutive sampling periods, and When this occurs, it is determined to be a motor starting condition, and the threshold is set. Increase to ; If neither of the above two conditions is met, the threshold will be set. Maintain at ; in, , ;when At that time, no short-circuit fault-type voltage dips or motor starting conditions are detected, and the threshold is maintained at [value missing]. .

4. The rapid detection system for power slump faults in distribution networks based on load state adaptive thresholds according to claim 1, characterized in that, The sampling module has a sampling frequency of 20 kHz–50 kHz, enabling high-frequency sampling of voltage and current signals.

5. The rapid detection system for power slump faults in distribution networks based on load condition adaptive thresholds according to claim 3, characterized in that, The overall trend allows for relative fluctuation tolerance at individual points. .

6. A method for rapid detection of power slump faults in distribution networks based on load condition adaptive thresholds, characterized in that, Includes the following steps: Step 1: Acquire instantaneous voltage and current signals of the distribution network using a sampling module at a sampling frequency of 20 kHz–50 kHz, and continuously calculate the power factor of the distribution network based on the instantaneous voltage and current signals; Step 2: Calculate the voltage surge after normalization and current mutation , ,in This is the instantaneous sampled value of the voltage at the current moment. This represents the instantaneous voltage sample value N sampling periods ago. This is the instantaneous sampled value of the current at the current moment. The instantaneous current sample value is N sampling periods ago, where N is the number of sampling periods. ; Step 3: Weight the voltage and current surges. Determine the weighting coefficients k1 and k2 based on the power factor obtained in Step 1 to obtain the weighted surge value. ; Step 4: Compare the weighted mutation value W with the adaptive threshold based on the load operating status. If W is greater than the adaptive threshold, it is determined that a power fluctuation fault has occurred; otherwise, it is determined to be a normal operating condition. Step 5: Output the fault detection results and fault characteristic information.

7. The detection method according to claim 6, characterized in that, The adaptive threshold is determined according to the following rules: When detected When the current is determined to be a short-circuit fault type voltage dip, the threshold is set. Reduce to ; When the moving average of the detected current surge ΔI is greater than 0.02 pu for M consecutive sampling periods and shows an overall increasing trend, and the voltage surge ΔU is continuously greater than 0.02 pu for the same M consecutive sampling periods, and... When this occurs, it is determined to be a motor starting condition, and the threshold is set. Increase to The overall increasing trend allows for relative fluctuation tolerance at individual points. ; If neither of the above two conditions is met, the threshold will be set. Maintain at 0.2 pu–0.3 pu; in, when At this time, no short-circuit fault-type voltage drop and motor starting condition judgment are performed, and the threshold is maintained at 0.2 pu–0.3 pu.

8. The detection method according to claim 6, characterized in that, The value of N is 1–5, and the corresponding feature extraction time is N / fs, where fs is the sampling frequency; taking a sampling frequency of 20 kHz as an example, the feature extraction time is 0.05ms–0.25ms.

9. A computer device, characterized in that, include: One or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the steps of the method for rapid detection of power sag faults in distribution networks based on load state adaptive thresholds as described in any one of claims 6 to 8 are implemented.

10. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the steps of the method for rapid detection of power sag faults in distribution networks based on load state adaptive thresholds as described in any one of claims 6 to 8.

Images