Photovoltaic system dc series arc fault detection method and apparatus

Abstract

The application discloses a photovoltaic system direct-current series arc fault detection method and device, and relates to the technical field of fault detection. The method comprises the following steps: monitoring the input voltage of a photovoltaic power optimizer in a photovoltaic system, identifying whether the input voltage abnormally drops based on the change characteristics of the input voltage; when it is identified that the input voltage abnormally drops, adjusting the working parameters of the photovoltaic power optimizer to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer; judging the fault based on the change of the corresponding input voltage in the current adjustment process, and outputting the judgment result of the direct-current series arc fault of the photovoltaic system. The application does not need to add special hardware, can reuse the existing resources of the photovoltaic power optimizer, and can effectively distinguish the arc fault from the normal working condition disturbances such as light and load fluctuation.

Description

Technical Field

[0001] This application relates to the field of fault detection technology, and in particular to a method and device for detecting DC series arc faults in photovoltaic systems. Background Technology

[0002] DC series arcing faults are severe electrical faults that occur in series power supply circuits composed of photovoltaic modules, DC transmission cables, photovoltaic power optimizers, and other devices. They are often caused by problems such as loose cable joints, conductor breaks, damaged insulation, or poor contact. After the fault occurs, a continuous, self-sustaining high-temperature arc discharge is formed, which can easily lead to equipment burnout and fire accidents. Moreover, when the fault occurs, the overall current in the circuit does not show significant distortion, but is accompanied only by implicit changes in local voltage and equivalent impedance, making detection very difficult.

[0003] Current detection technologies primarily rely on intelligent models trained from historical data to analyze and judge electrical signals at a single moment to arrive at detection results. This approach can only monitor and compare the static electrical state of the system at a specific instant, failing to reflect the continuous trend of electrical parameters changing with operating conditions. This reliance on single-point static information cannot distinguish whether abnormal electrical behavior is caused by a permanent, non-healable DC series arc fault or by temporary, self-healing fluctuations in operating conditions such as changes in lighting, load switching, or routine power regulation. The electrical characteristics of these two scenarios are highly similar at a single moment and can be simultaneously identified by the same static feature, resulting in a lack of discriminative basis for judgment and hindering accurate determination. Ultimately, this makes it difficult to guarantee the accuracy and stability of fault detection. Summary of the Invention

[0004] In view of the above problems, this application provides a method and device for detecting DC series arc faults in photovoltaic systems.

[0005] To solve the above-mentioned technical problems, this application proposes the following solution: In a first aspect, this application provides a method for detecting DC series arc faults in a photovoltaic system. The method includes: monitoring the input voltage of a photovoltaic power optimizer in the photovoltaic system; identifying whether an abnormal drop in the input voltage has occurred based on the change characteristics of the input voltage, wherein the change characteristics are used to characterize the degree of deviation of the input voltage from the input voltage under normal operating conditions of the photovoltaic power optimizer; when an abnormal drop in the input voltage is detected, adjusting the operating parameters of the photovoltaic power optimizer to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer, wherein the minimum duty cycle is the lowest limit that the duty cycle of the power switch in the photovoltaic power optimizer is allowed to reach; and judging the fault based on the corresponding input voltage change during the current adjustment process, and outputting the judgment result of the DC series arc fault in the photovoltaic system.

[0006] This application, by actively changing the current and observing how the voltage follows the change, can directly distinguish between two fundamentally different responses. A real fault will cause the voltage to continuously decrease as the current decreases, while normal fluctuations will cause the voltage to gradually rise as the current decreases. This dynamic trend has a unique correspondence and will not result in the ambiguity of the same feature corresponding to multiple situations. Therefore, it can stably and accurately identify DC series arc faults.

[0007] In conjunction with the first aspect, in one possible implementation, the instantaneous value of the input voltage is collected at a preset sampling frequency, and the average value of all instantaneous values ​​within a preset sliding time window is calculated; when the drop in the instantaneous value of the input voltage relative to the average value reaches a preset relative threshold and the instantaneous value is lower than a preset low voltage threshold, it is determined that an abnormal drop in the input voltage has occurred.

[0008] This application adopts a dual judgment method combining average value and instantaneous value, which can effectively filter out random voltage fluctuations and short-term interference, making the identification results of abnormal drops more consistent with the actual operating state of the system, improving the stability and accuracy of the initial fault trigger judgment, and avoiding the false triggering problem caused by a single threshold judgment.

[0009] In conjunction with the first aspect, in one possible implementation, the electrical time constant, switching frequency, and sampling delay of the photovoltaic power optimizer are obtained; the current operating value of the photovoltaic power optimizer's duty cycle to the preset minimum duty cycle is determined as the duty cycle adjustment range, and the duty cycle adjustment range is divided into multiple adjustment stages according to the preset down-adjustment step size and the preset stabilization waiting time; based on the electrical time constant, switching frequency, and sampling delay, the adjustment slope corresponding to different adjustment stages of the photovoltaic power optimizer's duty cycle during the down-adjustment process is determined; starting from the current operating value, the duty cycle of the photovoltaic power optimizer is linearly adjusted sequentially to the preset minimum duty cycle according to the adjustment slope corresponding to each adjustment stage.

[0010] This application achieves a continuous, gradual, and controllable reduction of the input current by adjusting the duty cycle to a preset minimum value through multi-level linear smooth adjustment. This avoids electrical oscillations in the circuit caused by sudden parameter changes, ensures stable system operation during the adjustment process, and provides a stable and clearly defined current condition basis for fault diagnosis.

[0011] In conjunction with the first aspect, in one possible implementation, the input voltage under different input current conditions is obtained; based on the input voltage corresponding to each input current condition, the voltage change characteristics of the photovoltaic power optimizer and the resistance change characteristics of the DC series arc are determined; based on the voltage change characteristics and resistance change characteristics, it is determined whether the photovoltaic system has a DC series arc fault. The voltage change characteristics are used to characterize the time-series change law of the input voltage as the input current decreases, and the resistance change characteristics are used to characterize the time-series change law of the equivalent resistance of the DC series arc as the input current decreases. The DC series arc refers to the DC arc fault that is connected in series with the load in the DC-side series power supply circuit of the photovoltaic system.

[0012] This application combines two types of characteristics, voltage change and arc resistance, to make a judgment. It can cross-verify the fault state from two dimensions: external electrical performance and internal fault characteristics, which greatly improves the accuracy and anti-interference ability of fault judgment. It can effectively distinguish between normal operating condition disturbances and real arc faults, making the judgment results more reliable and consistent.

[0013] In conjunction with the first aspect, in one possible implementation, the continuous change of the input voltage and the amount of voltage change within a preset time period are calculated based on the input voltage corresponding to each input current condition, forming voltage change characteristics; the DC series arc resistance is calculated based on the preset line impedance parameters of the photovoltaic system, the output voltage of the photovoltaic modules in the photovoltaic system, and the input current; the change of the DC series arc resistance is determined based on the DC series arc resistance at different times, forming resistance change characteristics.

[0014] This application transforms abstract electrical responses into comparable and analyzable specific features by quantitatively calculating the voltage time-series changes and arc resistance variation trends. This provides clear numerical basis for fault diagnosis, objectively reflects the internal impedance changes and voltage response patterns of the circuit, and improves the standardization of feature extraction and the reproducibility of the diagnosis process.

[0015] In conjunction with the first aspect, in one possible implementation, when the voltage change characteristics show a continuous decrease in input voltage, the voltage change amount meets a preset threshold, and the resistance change characteristics show an increasing resistance value, a suspected DC series arc fault is identified. When a suspected DC series arc fault exists, the voltage change characteristics are evaluated based on the degree of matching between the voltage change amount and the preset threshold to obtain a voltage fault assessment value. The resistance change characteristics are evaluated based on the rate and stability of the resistance value increase to obtain a resistance fault assessment value. The voltage fault assessment value and the resistance fault assessment value are weighted according to preset weights to obtain a fault confidence level. The fault confidence level is compared with a preset confidence threshold. When the fault confidence level is greater than or equal to the preset confidence threshold, it is finally determined that a DC series arc fault has occurred in the photovoltaic system.

[0016] This application adopts a two-level discrimination method that combines preliminary qualitative judgment with weighted confidence verification. While quickly locking in the fault trend, it can further improve the judgment accuracy under complex working conditions through numerical calculation, reduce the impact of external interference on the final result, and achieve a highly reliable and accurate final judgment of DC series arc faults.

[0017] In conjunction with the first aspect, in one possible implementation, if a DC series arc fault is determined to have occurred in the photovoltaic system, the photovoltaic power optimizer is controlled to maintain a preset minimum duty cycle to extinguish the arc; if a DC series arc fault is determined not to have occurred in the photovoltaic system, the photovoltaic power optimizer is controlled to return to normal operating mode.

[0018] This application implements differentiated closed-loop control based on the fault identification results, which can quickly suppress and extinguish the electric arc under fault conditions, eliminating safety hazards from the source.

[0019] Secondly, this application provides a photovoltaic system DC series arc fault detection device, which includes: The monitoring module is used to monitor the input voltage of the photovoltaic power optimizer in the photovoltaic system. Based on the change characteristics of the input voltage, it identifies whether the input voltage has dropped abnormally. The change characteristics are used to characterize the degree of deviation of the input voltage from the input voltage under normal operating conditions of the photovoltaic power optimizer. The adjustment module is used to adjust the operating parameters of the photovoltaic power optimizer when an abnormal drop in input voltage is detected, so as to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer. The minimum duty cycle is the lowest limit that the duty cycle of the power switch in the photovoltaic power optimizer is allowed to reach. The judgment module is used to determine faults based on the corresponding changes in input voltage during current regulation and output the judgment result of DC series arc fault in the photovoltaic system.

[0020] To achieve the above objectives, according to a third aspect of this application, a storage medium is provided, the storage medium including a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the photovoltaic system DC series arc fault detection method of the first aspect.

[0021] To achieve the above objectives, according to a fourth aspect of this application, an electronic device is provided, the device including at least one processor, and at least one memory and bus connected to the processor; wherein the processor and memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the photovoltaic system DC series arc fault detection method of the first aspect described above.

[0022] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for detecting DC series arc faults in a photovoltaic system according to an embodiment of this application is shown. Figure 2 A flowchart illustrating a method for detecting and extinguishing DC series arc faults in a photovoltaic system according to an embodiment of this application is shown. Figure 3 This paper presents a schematic diagram of the time-domain response waveforms of the input voltage and duty cycle of a photovoltaic power optimizer under normal operating conditions and arc fault conditions, according to an embodiment of this application. Figure 4 This paper shows a schematic diagram of the structure of a photovoltaic system DC series arc fault detection device provided in an embodiment of this application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0024] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0025] In the embodiments of this application, the terms "first," "second," etc., do not have a logical or temporal dependency, nor do they limit the quantity or execution order. It should also be understood that although the following description uses the terms "first," "second," etc., to describe various elements, these elements should not be limited by the terms. These terms are merely used to distinguish one element from another.

[0026] In this application, the term "at least one" means one or more, and the term "multiple" means two or more.

[0027] It should also be understood that the term “if” can be interpreted as “when” or “upon”, or “in response to determination” or “in response to detection”. Similarly, depending on the context, the phrase “if determination…” or “if detection [the stated condition or event]” can be interpreted as “when determination…” or “in response to determination…” or “when detection [the stated condition or event]” or “in response to detection [the stated condition or event]”.

[0028] Photovoltaic (PV) systems are the mainstream method of clean energy power generation and have achieved large-scale widespread application. Their stable and safe operation directly affects power generation efficiency, equipment assets, and personnel safety. DC arc faults are among the most dangerous and difficult-to-control serious electrical faults in the current operation of PV systems. Essentially, a DC arc fault occurs in the PV DC transmission circuit when various inducing factors, such as loose cable terminals, conductor breaks, aging and damage to insulation layers, and poor device contact, cause the insulation between electrodes to break down under the system's DC voltage. This results in a continuously self-sustaining plasma conductive channel, accompanied by a violent, high-temperature arc discharge. Once formed, this fault continuously releases a large amount of heat energy; the temperature in the center of the arc channel can reach thousands of degrees Celsius, easily igniting PV modules, cable sheaths, metal supports, and surrounding flammable materials, ultimately causing a PV power plant fire, damaging power generation equipment, shutting down the system, and even threatening the lives of on-site personnel.

[0029] Among various DC arc faults in photovoltaic systems, based on their form of existence in the electrical circuit, electrical characteristics, and detection difficulty, they can be divided into two types: DC series arc faults and DC parallel arc faults. DC parallel arc faults mainly occur between the positive and negative busbars of the photovoltaic system. When a fault occurs, the circuit current will experience extremely obvious sudden changes and fluctuations. The fault characteristics can be quickly captured using conventional current monitoring methods, making detection and identification relatively easy. DC series arc faults, on the other hand, occur in the power supply circuit formed by the series connection of key components such as photovoltaic modules, DC transmission cables, and photovoltaic power optimizers. When this type of fault occurs, the overall current amplitude of the entire series circuit will not show significant changes or distortions; it will only cause local voltage parameter anomalies and implicit changes in equivalent impedance. The fault characteristics are highly concealed and deceptive, making it the type of fault with the highest actual occurrence rate, the greatest difficulty in detection and identification, and the most prominent safety hazard in photovoltaic systems.

[0030] To meet the needs of detecting and preventing DC series arc faults in photovoltaic systems, existing technologies have proposed various fault detection schemes. Currently, commonly used detection methods in the industry mainly fall into two categories: time-domain and frequency-domain signal analysis methods, and artificial intelligence algorithm detection methods. Detection methods based on time-domain and frequency-domain signal analysis primarily involve continuously and in real-time acquisition of electrical signals such as voltage and current during the operation of the photovoltaic system. Then, using professional signal processing techniques such as Fourier transform and wavelet transform, the time-domain disturbance characteristics and frequency-domain harmonic characteristics generated when an arc fault occurs are deeply extracted. These extracted feature parameters serve as the main basis for fault identification, thereby enabling the determination of DC series arc faults. Artificial intelligence-based detection methods require the pre-collection of massive amounts of electrical signal sample data under normal photovoltaic system operation and arc fault conditions. Intelligent algorithms such as machine learning and pattern recognition are used to train, verify, and optimize the fault identification model. The trained intelligent model then classifies and judges the real-time acquired electrical signals, thereby achieving the detection and location of DC series arc faults.

[0031] While the aforementioned existing detection technologies can achieve preliminary identification of DC series arc faults under ideal experimental conditions, they all suffer from inherent and insurmountable defects when applied to modular photovoltaic systems equipped with photovoltaic power optimizers. Existing detection technologies generally require the addition of dedicated arc fault signal acquisition devices to the existing photovoltaic system hardware. This significantly increases the hardware procurement and deployment costs of the photovoltaic system and makes the system's electrical topology and control logic more complex and cumbersome. Furthermore, most existing detection technologies only possess the single function of passively detecting faults, capable only of identifying faults and outputting alarms, but unable to actively extinguish DC series arc faults that have already occurred. After fault detection, subsequent processing relies entirely on manual intervention, resulting in a significant delay in fault handling and an inability to eliminate safety hazards in the early stages of arc fault occurrence, thus failing to achieve true proactive safety protection. In addition, existing detection technologies are highly susceptible to disturbances from real-time fluctuations in light intensity, load switching operations, and normal power regulation by photovoltaic power optimizers, leading to insufficient accuracy and stability in fault detection, and frequent instances of missed or false diagnoses.

[0032] To address the shortcomings of existing DC series arc fault detection technologies, such as high hardware dependence, large system complexity, insufficient detection accuracy, and lack of active arc extinguishing capability, this application provides a method for detecting DC series arc faults in photovoltaic systems. This method eliminates the need for any dedicated fault detection hardware in the photovoltaic system, directly relying on the monitoring and control units of the existing photovoltaic power optimizer to achieve accurate detection and active extinguishing of DC series arc faults.

[0033] The following section provides a detailed explanation of the method for detecting DC series arc faults in photovoltaic systems, with reference to the accompanying drawings. Figure 1 This application provides a flowchart illustrating a method for detecting DC series arc faults in a photovoltaic system. The method specifically includes the following steps: Step 110: Monitor the input voltage of the photovoltaic power optimizer in the photovoltaic system, and identify whether the input voltage has dropped abnormally based on the characteristics of the input voltage change.

[0034] DC series arcing faults occur within the series circuit consisting of photovoltaic modules, DC transmission cables, and the photovoltaic power optimizer. When a fault occurs, the overall current amplitude of the circuit does not fluctuate significantly, making it impossible to effectively distinguish between normal operating disturbances and true fault conditions based solely on the current signal. However, DC series arcing faults directly alter the equivalent impedance characteristics of the series circuit, causing a rapid and significant abnormal drop in the input voltage of the photovoltaic power optimizer. This abnormal drop refers to a rapid decrease in the input voltage of the photovoltaic power optimizer that far exceeds the fluctuations under normal operating conditions, unlike the small, gradual voltage fluctuations caused by changes in light intensity, load switching, and power regulation during normal operation. This voltage change characteristic is the most intuitive and easily detectable external manifestation of DC series arcing faults, providing a reliable basis for initial fault identification. Furthermore, the photovoltaic power optimizer integrates a voltage monitoring module, enabling real-time acquisition and data reading of the input voltage without the need for additional dedicated acquisition equipment. This allows for full utilization of existing system hardware resources, reducing hardware costs and system complexity, and adapting to the operational characteristics of distributed deployments in modular photovoltaic systems. Based on the above characteristics, this application achieves a preliminary determination of DC series arc faults by monitoring the input voltage of the photovoltaic power optimizer and determining whether it has experienced an abnormal drop.

[0035] In one implementation, the instantaneous value of the input voltage is collected at a preset sampling frequency, and the average value of all instantaneous values ​​within a preset sliding time window is calculated. When the drop in the instantaneous value of the input voltage relative to the average value reaches a preset relative threshold and the instantaneous value is lower than a preset low voltage threshold, it is determined that an abnormal drop in the input voltage has occurred.

[0036] Specifically, firstly, relying on the voltage monitoring unit built into the photovoltaic power optimizer, instantaneous input voltage data is continuously collected at a fixed sampling frequency. Simultaneously, the arithmetic mean of all instantaneous voltage values ​​within a preset time window is calculated to obtain the average input voltage. For example, setting the time window to 0.5 seconds and collecting data at a sampling frequency of 1kHz, 500 sets of instantaneous voltage values ​​can be obtained within 0.5 seconds. Summing all instantaneous values ​​and dividing by the number of sampling points yields the average voltage for that time period. This step uses the average value over a time period as the judgment benchmark, rather than a single instantaneous sample value, which eliminates random sampling errors in the voltage signal and avoids interference from instantaneous fluctuations in the judgment results, making the benchmark value more closely reflect the actual level of normal system operation. After obtaining a stable average voltage value, the difference between the latest collected instantaneous voltage value and this average value is calculated in real time to obtain the actual voltage drop.

[0037] In actual judgment, the voltage drop magnitude and instantaneous voltage value are combined for comprehensive evaluation. Only when the voltage drop magnitude meets the standard and the instantaneous voltage value itself is below the safety limit can it be confirmed as an abnormal voltage drop caused by a fault. For example, if the relative threshold is set to 12V and the low-voltage threshold is 0.8 times the current average voltage, and the current average voltage is 60V, then the low-voltage threshold is 48V. When the instantaneous voltage drops to 45V, the drop magnitude is 15V and the instantaneous value is below 48V, thus meeting all judgment conditions. Under normal operating conditions such as gradual changes in light intensity, regular load switching, and optimizer power adjustment, the voltage changes gradually and slightly, without large instantaneous drops or easily falling below the reasonable voltage range for the corresponding operating conditions. If only a single voltage drop magnitude is used for judgment, it is easy to misjudge small voltage fluctuations during normal operation as faults. If only a single voltage threshold value is used for judgment, the component output is already low under low light conditions, and the steady-state voltage is likely to fall below the threshold value, which will also lead to misjudgment. The method of determining voltage fluctuations by using a combination of drop amplitude and voltage threshold value is adopted. Only when both of these conditions occur simultaneously, such as a sudden and significant drop in voltage and a voltage drop below a reasonable range, is the condition considered abnormal. This method can accurately screen out voltage disturbances caused by various normal operating conditions, avoid the loopholes in a single determination rule, effectively reduce the occurrence of misjudgments, and make the initial fault diagnosis results more reliable.

[0038] Step 120: When an abnormal drop in input voltage is detected, adjust the operating parameters of the photovoltaic power optimizer to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer.

[0039] From the perspective of electrical response characteristics, DC series arc faults possess inherently different operating characteristics from conventional operating condition disturbances. For DC series arc faults, after the arc breaks down within the series circuit, it generates a highly stable equivalent fault impedance in the line. This fault impedance differs from the inherent physical impedance of the cables and components themselves. The plasma conductive structure generated by the arc breakdown can maintain a continuous conductive state and will not dissipate spontaneously with slight fluctuations in circuit conditions; it is an irreversible additional impedance. Analyzing the voltage division principle of series circuits, the electrical components in a photovoltaic series circuit are connected in series, and the circuit voltage is distributed according to the impedance ratio of each component. Under the premise of a persistent arc fault, the arc equivalent impedance can maintain a stable value over a long period and will not decrease with a reduction in circuit current. When the system issues a control command to actively reduce the circuit operating current, the voltage division ratio of the arc impedance in the series circuit remains high, continuously compressing the voltage space available at the input of the photovoltaic power optimizer. Ultimately, the system exhibits unique fault characteristics, namely, the input voltage will continue to decrease as the loop current decreases, and the voltage distortion caused by the fault will only become more prominent, without recovering on its own, and it cannot be self-repaired by the photovoltaic system's own adjustment capabilities. It is a hard electrical fault that is difficult to recover from.

[0040] In contrast, voltage fluctuations during normal operation of a photovoltaic system caused by gradual changes in sunlight intensity, switching of downstream loads, and the power regulation of the equipment itself are all temporary and reversible electrical changes. In a fault-free, normal series circuit, the line only has the factory-inherent basic impedance; there is no additional fault-equivalent impedance, and the overall circuit impedance ratio remains stable. According to the voltage division rules of conventional circuits, the voltage drop generated by the inherent impedance of a normal line will change positively correlated with the circuit current. When the system issues a control command to slightly reduce the circuit input current, the voltage drop corresponding to the basic line impedance will decrease synchronously. The input port voltage of the photovoltaic power optimizer, which initially showed a slight deviation, will gradually rise along with the decreasing current, naturally returning to the normal steady-state range.

[0041] As can be seen from the above, there are significant differences in the electrical response patterns between the two scenarios. Under normal operating conditions, the voltage gradually rises and recovers as the current decreases, while under arc fault conditions, the input voltage continues to drop as the current decreases. If fault identification is based solely on a single static voltage drop characteristic, it is impossible to fully distinguish between irreversible arc faults and self-healing normal operating disturbances, which can easily lead to misjudgments. Based on the above differences in characteristics, this application, after determining that an abnormal drop in input voltage has occurred through dual threshold logic, actively intervenes in the operating state of the photovoltaic power optimizer and performs targeted adjustments to the internal operating parameters of the device to reduce the input current of the photovoltaic power optimizer. The purpose of this operation is to actively change the existing electrical operating conditions of the series circuit and observe the electrical feedback state of the system under different scenarios.

[0042] Among various controllable operating parameters, this application preferentially selects pulse width modulation (PWM) duty cycle as the adjustment variable. The on / off duration of the switching devices inside the photovoltaic power optimizer is directly determined by the duty cycle parameter. This parameter can directly change the equivalent load impedance of the optimizer itself, providing continuous, monotonic, and stable control over the input current. Compared to other controllable parameters such as voltage threshold and switching frequency, the duty cycle does not require modification of the hardware wiring and can be adjusted solely by software control commands issued by the system. Furthermore, duty cycle adjustment does not disrupt the optimizer's original hardware topology, has a wide adjustment range, and exhibits high device tolerance. It can continuously change the circuit's electrical conditions within a safe range, facilitating the capture of electrical feedback differences under different operating conditions. Therefore, the duty cycle is selected as the preferred adjustment parameter for this current regulation. In one embodiment, the electrical time constant, switching frequency, and sampling delay of the photovoltaic power optimizer are obtained; the current operating value of the photovoltaic power optimizer's duty cycle to a preset minimum duty cycle is determined as the duty cycle adjustment range, and the duty cycle adjustment range is divided into multiple adjustment stages according to a preset down-adjustment step size and a preset stabilization waiting time; based on the electrical time constant, switching frequency, and sampling delay, the adjustment slope corresponding to different adjustment stages of the photovoltaic power optimizer's duty cycle during the down-adjustment process is determined; starting from the current operating value, the duty cycle of the photovoltaic power optimizer is linearly adjusted sequentially to the preset minimum duty cycle according to the adjustment slope corresponding to each adjustment stage.

[0043] Specifically, this application achieves linear and smooth adjustment of the duty cycle according to a preset adjustment slope, where the adjustment slope corresponds to the variation range of the duty cycle within a unit time. This preset adjustment slope is determined in the initial offline phase using the following method: First, the inductance L, capacitance C, and equivalent series resistance R of the input-side filter circuit of the photovoltaic power optimizer are measured on an offline test platform. The circuit's electrical time constant τ is calculated using the formula τ=L / R. Then, based on the switching frequency f of the power device... s With current sampling delay T delay The maximum permissible duty cycle change rate V of the calculation system without oscillation or distortion. max =1 / (2·τ·f s +T delay Simultaneously, on the offline testing platform, a constant impedance load was used to simulate the equivalent characteristics of an electric arc. The duty cycle descent rate was gradually changed, and the input voltage waveform was acquired. The minimum necessary duty cycle change rate V, which reliably reflects the fault characteristics, was measured using the condition that the input voltage could remain continuous, stable, and without abrupt drops. min Finally, using the formula K= The optimal adjustment slope K is calculated and written into the system's fixed storage area as the final preset adjustment slope. It is then directly called during operation to constrain the duty cycle so that it does not adjust too quickly or too slowly.

[0044] In actual control, the real-time duty cycle of the photovoltaic power optimizer under load is used as the starting point for adjustment. Within the range between this starting duty cycle and the preset minimum duty cycle, several intermediate duty cycle values ​​with transitional characteristics are evenly divided, with the variation amplitude between adjacent values ​​remaining uniform, thus forming an ordered and progressive parameter gradient sequence. Then, parameter adjustment commands are issued to the photovoltaic power optimizer one by one according to the sequence of gradients. After setting the parameter for each intermediate duty cycle, the current operating state is maintained for a fixed duration, waiting for the loop voltage and current to stabilize before executing the switching operation for the next intermediate duty cycle. During the process of successively changing the duty cycle value, the equivalent impedance of the photovoltaic power optimizer itself changes synchronously with the parameters. Utilizing the gradual change in impedance, the input current supplied to the photovoltaic module's side loop is adjusted step by step.

[0045] By employing a gradual and smooth adjustment method with a fixed slope and multiple intermediate levels, the duty cycle change remains continuously controllable throughout. Electrical parameters such as circuit voltage and current change smoothly in sync, avoiding electrical oscillations caused by large parameter jumps. This reduces the operational impact of sudden changes in electrical parameters on photovoltaic modules, DC transmission cables, and downstream power devices, ensuring the overall operational stability of the entire photovoltaic series circuit. From a fault suppression perspective, the self-sustaining combustion state of a DC series arc requires a continuously flowing current of a corresponding magnitude. The higher the current flowing through the circuit, the higher the heat power released by the arc, and the greater the risk of high-temperature burns to the terminals, cable insulation, and surrounding electrical components. Appropriately and gradually reducing the input current of the power optimizer can gradually weaken the energy supply conditions for the arc to maintain self-sustaining combustion, reduce the actual discharge intensity of the arc, constrain the further spread of the fault's impact, and prevent the fault's operational state from continuously deteriorating and spreading.

[0046] Furthermore, when the photovoltaic power optimizer is in normal operating condition, it can continuously adjust its maximum power point tracking (MPPT) according to changes in sunlight conditions, ambient temperature fluctuations, and downstream load conditions, relying on its own mechanism to modify operating parameters and maintain a stable power generation state. However, when an abnormal drop in input voltage occurs in the circuit, indicating a suspected fault, retaining the original autonomous adjustment function would cause the parameter changes generated by the photovoltaic power optimizer itself to interfere with the parameter adjustments made by the system's control commands. This interference is detrimental to the stable control of the circuit current and fails to maintain consistent circuit operating conditions, thus failing to meet the operating conditions required for fault characteristic comparison. The device's spontaneous parameter fluctuations can also mask the true electrical changes in the circuit itself. Therefore, this application restricts the autonomous adjustment action of MPPT, stopping the device's normal MPPT operating mode, so that the photovoltaic power optimizer no longer adjusts its parameters and operates entirely according to the fixed control commands issued by the system. This setting avoids the influence of the device's built-in adaptive adjustment logic on command control, avoids the problem of autonomous parameter fluctuations masking the electrical changes in the circuit, and ensures a stable and consistent current adjustment process. The circuit will exhibit different electrical behaviors during parameter adjustment. By distinguishing the electrical changes corresponding to arc faults and normal operating condition disturbances, the actual cause of the voltage drop can be identified. This changes the way of judging based on a single voltage drop feature and reduces the misjudgment that is prone to occur in this type of judgment.

[0047] To further ensure the reliability of fault diagnosis data, this application introduces a voltage stability determination mechanism. After each current adjustment stage, voltage data is not immediately collected; instead, variance calculation is performed on multiple consecutive sampling points. The number of consecutive sampling points is based on a preset configuration of the current sampling frequency and the loop steady-state establishment time, ensuring that the sampled data fully reflects the fluctuation characteristics of the current electrical state. The variance of the voltage sampling values ​​is used to quantify the instantaneous fluctuation amplitude and dispersion of the input voltage. The smaller the variance, the more stable the input voltage operation. Only when the variance of the voltage sampling values ​​is less than a preset stability threshold is the current electrical state determined to have entered a steady state, and valid voltage data is collected. If the variance is greater than the preset stability threshold, it indicates that the current electrical state is still in a transient adjustment state or has disturbance fluctuations; therefore, continued waiting and sampling continue until the stability condition is met. This mechanism can effectively filter out electrical oscillations, sampling circuit noise, and signal interference caused by instantaneous fluctuations in light and load during duty cycle adjustment. It ensures that all voltage data used for fault diagnosis are real, stable, and interference-free effective steady-state signals, avoiding deviations in the extraction of voltage and resistance change characteristics caused by transient distorted data, and improving the accuracy and anti-interference capability of fault diagnosis from the data source.

[0048] Step 130: Based on the corresponding input voltage changes during the current regulation process, perform fault identification and output the identification result of DC series arc fault of the photovoltaic system.

[0049] After completing the graded control of the input current, this application performs fault diagnosis based on the input voltage corresponding to different current conditions. Static detection can only capture the voltage drop state at a certain moment and cannot distinguish whether the state is caused by the addition of fault impedance in the circuit or by conventional factors such as light and load. The static voltage performance in the two scenarios is similar, which can easily lead to judgment errors. However, the dynamic response of the input voltage with the change of current is directly related to the impedance composition inside the series circuit. The quantity, properties and changing trend of the circuit impedance will be directly reflected by the voltage change with the current. Under normal operating conditions, the circuit only has inherent line impedance and no additional impedance. When the current changes, the voltage will change according to the inherent impedance characteristics. Under arc faults, the circuit will have a new stable fault equivalent impedance, which will cause the voltage change with the current to deviate significantly. By observing the voltage dynamic response under multiple current conditions, it is possible to directly identify whether there is abnormal additional impedance in the circuit, avoid the shortcomings of static detection that only relies on instantaneous signals, accurately distinguish fault states from conventional disturbances, and improve the reliability of fault diagnosis results.

[0050] In one implementation, the DC series arc fault is determined directly based on the changing trend of the photovoltaic power optimizer's input voltage. During the step-by-step reduction of the input current, the actual input voltage values ​​at the beginning and end of each preset fixed measurement interval are collected. The actual change in input voltage within the measurement interval is calculated by subtracting the voltage value at the beginning from the voltage value at the end. The preset measurement interval is preferably set to 0.5 seconds, and the fault determination threshold for the voltage change value is preferably set between -20V and -12V. The calculated voltage change value is compared with the threshold range. If the voltage change value is within the -20V to -12V range, a DC series arc fault is determined to have occurred in the photovoltaic system. If the voltage change value is not within this range, the photovoltaic system is determined to be operating normally, and the previously detected abnormal input voltage drop is caused by fluctuations in normal operating conditions such as changes in light intensity, switching of downstream loads, and normal power regulation of the optimizer.

[0051] Under normal operating conditions with fluctuations, the photovoltaic series circuit only contains the inherent impedance of the transmission cables and power devices themselves, without any additional impedance corresponding to arc faults. As the input current gradually decreases, the voltage loss caused by the inherent impedance of the line decreases synchronously. The input voltage of the photovoltaic power optimizer, which initially shows a slight deviation, gradually recovers to the normal stable operating range, resulting in a positive calculated voltage change. Under DC series arc fault conditions, a stable arc equivalent impedance forms within the circuit. This impedance is an additional impedance caused by the fault and increases continuously as the input current decreases. The voltage distribution ratio occupied by the arc impedance in the circuit continuously increases, and a large amount of voltage is consumed by the arc impedance. The input voltage of the photovoltaic power optimizer shows a continuous downward trend, resulting in a negative calculated voltage change, which stabilizes within the preferred threshold range of -20V to -12V. The direction and magnitude of the input voltage change with decreasing current under normal and arc fault conditions are significantly different, allowing for direct differentiation between fault and normal disturbance states.

[0052] While this method of fault diagnosis relying solely on input voltage changes is simple to implement and has a fast response time, it still has limitations under complex real-world conditions. When a photovoltaic system encounters rapid changes in sunlight intensity, frequent switching of downstream loads, or minor electrical interference on-site, external factors can have a cumulative effect on the input voltage change trend, potentially causing small abnormal fluctuations in the voltage value, leading to false or missed fault diagnoses. Furthermore, this method analyzes only the voltage dimension without cross-validating it based on the actual changes in the arc impedance within the circuit, leaving room for improvement in the stability and reliability of the diagnosis under extreme conditions.

[0053] To address this, this application provides another implementation method with higher accuracy and stronger anti-interference capability. By combining voltage change characteristics and resistance change characteristics for dual joint discrimination, the accuracy and reliability of fault diagnosis are further improved. In another implementation method, the input voltage under different input current conditions is obtained; based on the input voltage corresponding to each input current condition, the voltage change characteristics of the photovoltaic power optimizer and the resistance change characteristics of the DC series arc are determined. The voltage change characteristics are used to characterize the time-series change law of the input voltage as the input current decreases, and the resistance change characteristics are used to characterize the time-series change law of the equivalent resistance of the DC series arc as the input current decreases. The DC series arc refers to a DC arc fault connected in series with the load in the DC-side series power supply circuit of the photovoltaic system; based on the voltage change characteristics and resistance change characteristics, it is determined whether a DC series arc fault has occurred in the photovoltaic system.

[0054] Specifically, the data acquisition method for input voltage under different input current conditions is consistent with the data acquisition rules for the current graded control process described above. In each adjustment stage, the duty cycle is linearly reduced by a preset reduction step size according to the corresponding adjustment slope, and the current duty cycle is maintained unchanged. The aforementioned voltage stability determination mechanism is then invoked, which calculates whether the circuit has entered a steady state by measuring the variance of continuous sampling points. When the variance of the input voltage sampling value is less than a preset stability threshold, it is confirmed that the circuit electrical parameters are stable and without fluctuation under the current operating condition. After steady state confirmation, a preset number of instantaneous input voltage values ​​are continuously collected at a preset sampling frequency under the current duty cycle condition, and the arithmetic mean is taken as the input voltage value under that input current condition. Simultaneously, instantaneous input current values ​​within the same time period are collected and their arithmetic mean is taken, and paired with the aforementioned input voltage value to store a set of current-voltage corresponding data. After completing this set of data acquisition, the next adjustment stage begins, and the above duty cycle reduction, steady state determination, and data acquisition process is repeated until the duty cycle is adjusted to a preset minimum duty cycle, thereby obtaining multiple sets of complete and continuous current-voltage corresponding data. For example, the preset sampling frequency is 1kHz. After each steady-state confirmation, 100 sets of instantaneous values ​​are continuously collected, and the arithmetic mean of voltage and current is calculated as a set of corresponding data under this operating condition.

[0055] When determining the voltage variation characteristics of the photovoltaic power optimizer, the input voltages corresponding to each stable operating condition are collected and arranged sequentially according to the order of current regulation. Taking the first stable voltage at the beginning of current regulation as a benchmark, the numerical difference between the subsequent stable voltage and the previous stable voltage is calculated. By observing the positive and negative states of each set of differences, the continuous direction of input voltage change during the gradual decrease of current is determined. Simultaneously, the total change in input voltage is statistically analyzed throughout the entire process from the start to the end of current regulation. Combining the continuous direction of change with the total change value forms a complete and quantifiable voltage variation characteristic. The purpose of calculating the differences group by group is to avoid misjudgment caused by voltage fluctuations at a single moment; by using multiple sets of differences, the true trend of voltage change can be identified. Statistical analysis of the total change value is to transform the change trend into a comparable numerical value, ensuring that the voltage variation characteristics have a clear basis for judgment. This section uses a specific example for illustration. During the current-level regulation process, input voltage data for three stable operating conditions are acquired. The initial stable voltage for the first level is 45V, the stable voltage after current reduction for the second level is 40V, and the stable voltage after further current reduction for the third level is 33V. Calculating the differences sequentially yields 40V-45V=-5V and 33V-40V=-7V, with a total change of 33V-45V=-12V. Since each set of differences is negative and the total change is negative, it indicates that the input voltage continuously decreases as the current decreases, a typical characteristic of voltage changes during an arc fault. If the voltages for the three operating conditions are 45V, 48V, and 52V respectively, the calculated differences are 48V-45V=+3V and 52V-48V=+4V, with a total change of +7V. Both the individual differences and the total change are positive, indicating that the input voltage continuously recovers as the current decreases, a characteristic of voltage changes during normal operating conditions.

[0056] When determining the resistance variation characteristics of a DC series arc, the voltage divider principle of a series circuit is used as the calculation basis. This is combined with the pre-calibrated inherent resistance of the photovoltaic system before delivery, the output voltage of the photovoltaic modules collected under various operating conditions, the input voltage of the photovoltaic power optimizer, and the real-time input current. First, the voltage drop generated by the inherent impedance of the line under the current current is calculated. Then, the voltage drop across the inherent impedance of the line and the input voltage of the photovoltaic power optimizer are successively subtracted from the output voltage of the photovoltaic modules to obtain the voltage drop across the DC series arc under the current operating condition. Finally, the voltage drop across the arc is divided by the real-time input current under the current operating condition to obtain the equivalent resistance value of the DC series arc under that condition. This calculation method is applied to each stable operating condition, obtaining the arc resistance values ​​corresponding to multiple operating conditions. By comparing the changing trends of the resistance values ​​at each level, the resistance variation characteristics of the DC series arc are formed. This calculation method can isolate the influence of the inherent impedance of the line and extract the additional impedance parameters generated by the arc fault. A DC series arc possesses a unique negative resistance characteristic. As long as the arc persists, the equivalent resistance gradually increases as the input current decreases. Under fault-free conditions, since there is no additional impedance from the arc, the calculated arc resistance value is always zero or infinitely close to zero. For example, if the inherent resistance of a photovoltaic system line is calibrated to 0.5Ω, and under a certain operating condition, the photovoltaic module output voltage is 60V, the photovoltaic power optimizer input voltage is 45V, and the real-time input current is 5A, the line voltage drop is first calculated as 0.5Ω × 5A = 2.5V. The arc voltage drop is 60V - 2.5V - 45V = 12.5V, and the arc resistance is 12.5V ÷ 5A = 2.5Ω. When the current drops to 3A, the photovoltaic module output voltage is 60V, the optimizer input voltage is 40V, the line voltage drop is 0.5Ω × 3A = 1.5V, the arc voltage drop is 60V - 1.5V - 40V = 18.5V, and the arc resistance is 18.5V ÷ 3A ≈ 6.17Ω. When the current further decreases to 2A, the arc resistance rises to approximately 9.8Ω. The resistance value continuously increases with decreasing current, which is consistent with the resistance change characteristics of an arc fault. Under normal operating conditions without faults, calculated using the same formula, the arc voltage drop is always 0, and the arc resistance value is always 0.

[0057] After extracting the voltage and resistance change characteristics, a dual joint judgment is performed based on these two characteristics to determine whether a DC series arc fault has occurred in the photovoltaic system. The entire judgment process is divided into two stages: preliminary qualitative judgment and quantitative confidence verification, which are executed sequentially. This judgment logic, through cross-verification and weighted quantification of multi-dimensional electrical characteristics, can effectively overcome the problems of single voltage judgment being susceptible to operating condition interference and insufficient reliability.

[0058] The initial judgment process begins with a preliminary assessment, which requires the simultaneous fulfillment of two interdependent conditions. These conditions are mutually restrictive and indispensable, thus enabling a preliminary determination of the fault state. The first condition is a voltage change characteristic, requiring the input voltage to exhibit a continuous decreasing trend as the input current gradually decreases, with the overall voltage change meeting a preset judgment threshold. The second condition is a resistance change characteristic, requiring the equivalent resistance of the DC series arc to gradually increase as the input current gradually decreases. The reason for setting these two conditions is that a continuous decrease in input voltage is the external electrical manifestation of an arc fault in the circuit, while the increasing arc resistance with decreasing current is a unique inherent negative resistance characteristic of a DC series arc. Only when both conditions are met simultaneously can voltage anomalies caused by normal operating conditions such as fluctuations in light intensity and load switching be effectively eliminated, ensuring the reliability of the preliminary judgment. To illustrate with the specific example above, during the graded adjustment of the input current, the input voltage of the photovoltaic power optimizer decreases sequentially from an initial 45V to 40V and then to 33V, exhibiting a continuous downward trend with a total change of -12V, falling within the preset threshold range of -20V to -12V, thus meeting the fault identification requirements for voltage timing changes. Simultaneously, the DC series arc resistance increases sequentially from 2.5Ω to 6.17Ω and then to 9.8Ω, continuously increasing as the current decreases, meeting the fault identification requirements for arc resistance changes. If the input voltage does not show a continuous downward trend, or the arc resistance does not show an increasing change, the possibility of a DC series arc fault can be directly ruled out. This is because if the input voltage does not continuously decrease with decreasing current, it indicates the absence of an arc-related additional impedance that continuously consumes voltage in the circuit. Furthermore, if the arc resistance does not show an increasing trend, it violates the inherent negative resistance characteristic of a DC series arc. Both of these scenarios are inconsistent with the core electrical characteristics of a DC series arc fault, thus allowing for direct exclusion of the possibility of a fault.

[0059] Furthermore, a weighted calculation and final verification of fault confidence are performed. The degree of feature matching is transformed into a quantifiable and comparable judgment index through numerical calculation, further improving the accuracy of judgment under extremely complex operating conditions and avoiding misjudgments due to fluctuations in a single feature. First, the voltage change feature is quantitatively evaluated based on the degree of matching between the voltage change and a preset threshold. Then, the resistance change feature is quantitatively evaluated based on the rate and stability of the resistance value increase. Both features output continuous quantitative scores within the range of 0 to 100. The more stable the feature change trend and the higher the degree of conformity with fault characteristics, the higher the corresponding score, which can truly reflect the feature differences under fluctuating operating conditions.

[0060] For voltage change characteristic assessment, a pre-defined voltage change threshold range is set as [V]. th,min V th,max The value range is set to -20V to -12V. The measured total change in input voltage ΔV during the current-level adjustment process is obtained. real When ΔVreal If the value falls within the preset threshold range, a base score of 80 points is assigned. Calculate the measured voltage change and the midpoint V of the threshold range. th,mid Deviation ΔV dev For every 1V increase in deviation, 5 points are deducted, with a minimum score of 0. A stability bonus mechanism is also introduced: if the input voltage shows a progressively decreasing trend without any reverse rebound during multi-level adjustment, an additional 20 points are awarded, with the total score for voltage characteristic assessment capped at 100 points.

[0061] For resistance variation characteristic assessment, the arc resistance sequence R1, R2, ..., R corresponding to multi-level current conditions is obtained. n Based on the negative resistance characteristic of a DC series arc, the resistance should remain monotonically increasing as the current decreases. First, a monotonicity check is performed. If the resistance sequence strictly increases throughout, a base score of 70 points is awarded. For each instance of the value remaining flat or decreasing in the sequence, 15 points are deducted, until all points are deducted. Next, the rate of increase is evaluated, and the average slope k of the resistance sequence is calculated. R and compared with the preset fault reference slope k ref A comparison is performed: 20 points are added if the deviation is no more than 20%; 10 points are added if the deviation is between 20% and 50%; and no points are added if the deviation exceeds 50%. Finally, a stability assessment is conducted, and the standard deviation σ of the resistance sequence is calculated. R 10 points are added when the standard deviation is no greater than 0.5Ω, 5 points are added when the standard deviation is greater than 0.5Ω but no greater than 1Ω, and no points are added when the standard deviation is greater than 1Ω. The total score for resistance characteristic evaluation is capped at 100 points, and the lowest score is 0 points.

[0062] After completing the quantitative evaluation of the two features, corresponding weighting coefficients are assigned according to the different contributions of voltage change features and resistance change features in fault identification. Resistance change features, as the primary criterion for determining DC series arc faults, are given higher weight. In this example, the weighting coefficient for voltage change features can be set to 0.4, and the weighting coefficient for resistance change features can be set to 0.6. The scores of the two features are substituted into a preset formula for calculation: Fault Confidence = Voltage Change Feature Score × 0.4 + Resistance Change Feature Score × 0.6, resulting in a continuous value between 0 and 100 points, which serves as the quantitative basis for fault judgment. Taking the previous example again, during the current grading adjustment process, the input voltage continuously decreases and the change remains stable within the preset threshold range without significant fluctuations or deviations; the voltage change feature score is 95 points. The arc resistance shows a steady increasing trend as the current decreases, which highly matches the inherent negative resistance characteristic of DC series arcs; the resistance change feature score is 100 points. Substituting the above scores into the formula, the fault confidence score is 95 × 0.4 + 100 × 0.6 = 98 points. In this application, the confidence threshold is preferably set to 80 points. The calculated fault confidence score is compared with this threshold. If 98 points is higher than 80 points, the final fault discrimination condition is met, and it is determined that a DC series arc fault has occurred in the photovoltaic system. After the above-mentioned preliminary judgment and quantitative confidence verification dual discrimination process, it can be finally determined that a DC series arc fault has occurred in the photovoltaic system. When the calculated fault confidence score is lower than the preset confidence threshold, it indicates that the overall matching degree between the voltage change characteristics and the resistance change characteristics does not meet the fault discrimination standard. Even if there is an abnormal drop in input voltage in the early stage, it is not caused by a DC series arc fault, but by normal operating condition disturbances such as changes in light intensity, switching of downstream loads, and normal power regulation of the system. Therefore, it can be determined that a DC series arc fault has not occurred in the photovoltaic system.

[0063] After completing the final determination of the DC series arc fault, in order to promptly address the safety risks under the fault state and ensure the stable power generation operation of the system in non-fault scenarios, this application will execute corresponding closed-loop control actions on the photovoltaic power optimizer based on the determination results. Through differentiated parameter control strategies, the active suppression of arc faults and the restoration of the operating state under normal conditions will be achieved. Figure 2 This application provides a schematic flowchart of a method for detecting and extinguishing DC series arc faults in a photovoltaic system, as illustrated in an embodiment of this application. Figure 2 As shown.

[0064] Step 210: If it is determined that a DC series arc fault has occurred in the photovoltaic system, control the photovoltaic power optimizer to maintain a preset minimum duty cycle in order to extinguish the arc.

[0065] Because the photovoltaic power optimizers in a modular photovoltaic system are connected in series, the overall output current remains consistent and relatively stable. The duty cycle directly determines the input current level of the photovoltaic power optimizer. Therefore, fixing the duty cycle at a preset minimum value reliably and directionally reduces the input current, shifting the electrical state of the entire circuit in a direction conducive to arc extinguishing. As the input current is stably reduced, the electrical characteristics of the DC series arc are further activated. The equivalent resistance of the arc increases continuously as the current decreases, and the voltage across the arc also rises synchronously. During this process, the input voltage of the photovoltaic power optimizer is continuously compressed and gradually reduced, eventually approaching zero. When the arc voltage rises to approximately equal to the output voltage of the photovoltaic module, the entire circuit no longer possesses the voltage difference and energy supply conditions necessary to maintain continuous arc discharge. The arc can no longer sustain its combustion and thus extinguishes itself without external intervention, fundamentally eliminating the fault.

[0066] Throughout the period of maintaining the minimum duty cycle, the operating parameters such as the input voltage and loop current of the photovoltaic power optimizer are continuously collected to determine the arc extinction status in real time. After confirming that the arc is completely extinguished, the minimum duty cycle is maintained for a set period of time to prevent the arc from reigniting due to loop voltage fluctuations, load changes or line disturbances.

[0067] The entire arc extinguishing process relies solely on the control logic of the photovoltaic power optimizer itself. It does not require additional detection devices or actuators, nor does it require modification of the original circuit structure of the system. It does not increase hardware costs or system complexity, and can quickly and safely complete the handling of DC series arc faults, fundamentally avoiding safety risks such as fires caused by high-temperature arcs.

[0068] Step 220: If it is determined that no DC series arc fault has occurred in the photovoltaic system, control the photovoltaic power optimizer to return to normal working mode.

[0069] If it is determined that no DC series arc fault has occurred in the photovoltaic system, it indicates that the previously detected abnormal input voltage drop was caused only by non-faulty operating conditions such as fluctuations in irradiance, switching of downstream loads, or routine power regulation. In this case, the fault detection and handling process should be exited promptly, and the system should be restored to normal power generation operation. At this point, the mandatory limit on the duty cycle of the photovoltaic power optimizer should be lifted, the control logic maintaining the minimum duty cycle should be stopped, and the system should be restarted and switched to the maximum power point tracking (MPPT) control mode, which is the conventional MPPT operating mode in this field.

[0070] During the restoration of normal operating mode, the photovoltaic power optimizer will gradually increase the duty cycle to the optimal operating point under the current illumination and load conditions according to the preset smooth adjustment strategy. This avoids drastic fluctuations in loop voltage and current caused by sudden changes in duty cycle, ensuring smooth and shock-free system operation. As the duty cycle steadily recovers, the input current of the photovoltaic power optimizer will synchronously return to the normal operating level, and the input voltage will also return to the stable range with the normal distribution of loop impedance, maintaining consistency with the electrical characteristics under non-fault conditions.

[0071] Throughout the recovery process, operating parameters such as input voltage, output current, and module operating temperature of the photovoltaic power optimizer are continuously collected to verify the smoothness of the recovery process in real time, ensuring no overshoot, no drops, and no abnormal disturbances. Once the duty cycle is adjusted to the optimal operating point and all operating parameters are stable, the fault detection process is exited, and the system returns to the initial voltage monitoring state to continue monitoring changes in the input voltage in real time, preparing for the next possible abnormal drop detection.

[0072] Figure 3 This paper illustrates the time-domain response waveforms of the input voltage and duty cycle of a photovoltaic power optimizer according to an embodiment of this application under normal operating conditions and arc fault conditions. The following is a related illustration. Figure 3 This paper describes the entire process of fault detection, identification, and arc extinguishing in this application. The figure shows the time-domain response curves of the photovoltaic power optimizer's input voltage and duty cycle under normal and arc fault conditions. Before time t0, the photovoltaic system is in normal power generation operation, and the input voltage V of the photovoltaic power optimizer is... in Maintaining stability, the duty cycle is adjusted in real time by the maximum power point tracking mode, ensuring smooth overall operation of the photovoltaic system. At time t0, a DC series arc forms in the circuit and begins to discharge continuously, subsequently generating a corresponding arc voltage V. arcThe presence of an electric arc directly causes a significant abnormal drop in the input voltage of the photovoltaic power optimizer, triggering the fault detection process. During the time intervals t1 and t2, voltage data is continuously collected, and the abnormal drop is confirmed according to preset conditions, locking the fault trigger state. From time t2, the duty cycle of the photovoltaic power optimizer is linearly reduced to a preset minimum value and maintained until time t3. During this process, the input current gradually decreases, and the circuit electrical conditions are directionally adjusted. As the input current continues to decrease, the negative resistance characteristic of the electric arc is further activated, the arc resistance continues to increase, and the arc voltage rises accordingly, continuously occupying a share of the circuit voltage, thus simultaneously squeezing down the input voltage of the photovoltaic power optimizer. Under normal operating conditions without faults, a decrease in current causes the input voltage to show a recovery trend; the voltage change trends of the two operating conditions are clearly distinguishable at this stage. The formal fault diagnosis stage takes place from time t3 to t4, where the operating condition is determined based on the voltage change data collected from time t2 to t3. Under normal operating conditions, a decrease in input current corresponds to a recovery in input voltage. Under arc fault conditions, the input voltage continuously decreases due to the continuous rise in arc voltage. Upon reaching time t4, the system enters the fault handling phase based on the judgment results from times t3 to t4, executing corresponding control actions. If the system is determined to be in normal operation, it immediately exits the fault detection process, resumes maximum power point tracking, and smoothly adjusts the duty cycle to the optimal operating point, allowing the input voltage to quickly return to the normal operating range. If the system is determined to be in arc fault condition, it continuously maintains the preset minimum duty cycle, further raising the arc voltage to near the photovoltaic module output voltage while simultaneously reducing the photovoltaic power optimizer input voltage to zero, thus depriving the arc of its self-sustaining combustion energy supply and achieving reliable extinction of the DC series arc.

[0073] In summary, this application, through real-time monitoring and targeted adjustment of the photovoltaic power optimizer's operating parameters, can quickly initiate the detection process when abnormal voltage fluctuations occur. It achieves stable control of the loop current through linear duty cycle adjustment, and further utilizes the inherent electrical characteristics of the electric arc to differentiate operating conditions and diagnose faults. The overall solution achieves full automation of fault identification and handling on the existing system architecture without requiring additional detection units or actuators. During fault diagnosis, the voltage change difference caused by current regulation clearly distinguishes between normal operating condition disturbances and genuine arc faults, effectively improving the accuracy and anti-interference capability of the judgment results and avoiding false triggering and malfunctions caused by environmental fluctuations or load changes. In the fault handling stage, by maintaining a minimum duty cycle to suppress current and raise the arc voltage, reliable arc extinguishing can be achieved by addressing the energy conditions for self-sustaining arc combustion, fundamentally preventing the continued development of the fault and eliminating the safety hazards caused by high-temperature arcs.

[0074] It is understood that, in order to achieve the functions in the above embodiments, the computer device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0075] Furthermore, as a response to the above Figure 1 The implementation of the method embodiment shown in this application provides a DC series arc fault detection device for a photovoltaic system. The embodiment of this device corresponds to the foregoing method embodiments. For ease of reading, this embodiment will not repeat the details of the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can correspondingly implement all the contents of the foregoing method embodiments. Specifically, as shown... Figure 4 As shown, the photovoltaic system DC series arc fault detection device 400 includes: The monitoring module 410 is used to monitor the input voltage of the photovoltaic power optimizer in the photovoltaic system. Based on the change characteristics of the input voltage, it identifies whether the input voltage has dropped abnormally. The change characteristics are used to characterize the degree of deviation of the input voltage from the input voltage under normal operating conditions of the photovoltaic power optimizer. The adjustment module 420 is used to adjust the operating parameters of the photovoltaic power optimizer when an abnormal drop in input voltage is detected, so as to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer. The minimum duty cycle is the lowest limit that the duty cycle of the power switch in the photovoltaic power optimizer is allowed to reach. The judgment module 430 is used to judge the fault based on the corresponding input voltage change during the current regulation process, and output the judgment result of DC series arc fault of photovoltaic system.

[0076] Furthermore, such as Figure 4 As shown, the monitoring module 410 is specifically used to collect the instantaneous value of the input voltage according to the preset sampling frequency, calculate the average value of all instantaneous values ​​within a preset sliding time window; when the drop in the instantaneous value of the input voltage relative to the average value reaches a preset relative threshold and the instantaneous value is lower than the preset low voltage threshold, it is determined that the input voltage has abnormally dropped.

[0077] Furthermore, such as Figure 4As shown, the adjustment module 420 is specifically used to acquire the electrical time constant, switching frequency, and sampling delay of the photovoltaic power optimizer; to determine the duty cycle adjustment range from the current operating value of the photovoltaic power optimizer to the preset minimum duty cycle; to divide the duty cycle adjustment range into multiple adjustment stages according to the preset down-adjustment step size and the preset stabilization waiting time; to determine the adjustment slope corresponding to different adjustment stages of the photovoltaic power optimizer's duty cycle during the down-adjustment process based on the electrical time constant, switching frequency, and sampling delay; and to linearly adjust the duty cycle of the photovoltaic power optimizer from the current operating value to the preset minimum duty cycle according to the adjustment slope corresponding to each adjustment stage.

[0078] Furthermore, such as Figure 4 As shown, the judgment module 430 is specifically used to obtain the input voltage under different input current conditions; based on the input voltage corresponding to each input current condition, it determines the voltage change characteristics of the photovoltaic power optimizer and the resistance change characteristics of the DC series arc. The voltage change characteristics are used to characterize the time-series change law of the input voltage as the input current decreases, and the resistance change characteristics are used to characterize the time-series change law of the equivalent resistance of the DC series arc as the input current decreases. The DC series arc refers to the DC arc fault connected in series with the load in the DC-side series power supply circuit of the photovoltaic system; based on the voltage change characteristics and resistance change characteristics, it determines whether the photovoltaic system has a DC series arc fault.

[0079] Furthermore, such as Figure 4 As shown, the judgment module 430 is specifically used to calculate the continuous change of the input voltage and the voltage change within a preset time period based on the input voltage corresponding to each input current condition, forming voltage change characteristics; calculate the DC series arc resistance based on the preset line impedance parameters of the photovoltaic system, the output voltage of the photovoltaic module in the photovoltaic system, and the input current; and determine the change of the DC series arc resistance based on the DC series arc resistance at different times, forming resistance change characteristics.

[0080] Furthermore, such as Figure 4 As shown, the judgment module 430 is specifically used to determine the existence of a suspected DC series arc fault when the voltage change characteristics show that the input voltage continuously decreases, the voltage change amount meets the preset threshold, and the resistance change characteristics show that the resistance value increases; when a suspected DC series arc fault exists, the voltage change characteristics are evaluated based on the degree of matching between the voltage change amount and the preset threshold to obtain a voltage fault evaluation value; the resistance change characteristics are evaluated based on the rate and stability of the resistance value increase to obtain a resistance fault evaluation value; the voltage fault evaluation value and the resistance fault evaluation value are weighted according to preset weights to obtain a fault confidence level; the fault confidence level is compared with a preset confidence threshold, and when the fault confidence level is greater than or equal to the preset confidence threshold, the photovoltaic system is finally determined to have experienced a DC series arc fault.

[0081] Furthermore, such as Figure 4 As shown, the photovoltaic system DC series arc fault detection device 400 also includes a fault processing module 440, which is used to control the photovoltaic power optimizer to maintain a preset minimum duty cycle to extinguish the arc if it is determined that a DC series arc fault has occurred in the photovoltaic system; and to control the photovoltaic power optimizer to return to normal working mode if it is determined that no DC series arc fault has occurred in the photovoltaic system.

[0082] Optionally, the DC series arc fault detection device for the photovoltaic system can be an electronic device with data processing capabilities, or a functional module within the electronic device, without limitation.

[0083] For example, the electronic device can be a server, which can be a single server or a server cluster consisting of multiple servers. As another example, the electronic device can be a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, as well as cellular phones, personal digital assistants (PDAs), augmented reality (AR) devices, virtual reality (VR) devices, and other terminal devices. As yet another example, the electronic device can also be a recording device, video surveillance equipment, etc. This application does not impose any special limitations on the specific form of the electronic device.

[0084] The following example uses a photovoltaic system DC series arc fault detection device as an electronic device. Figure 5 As shown, Figure 5 The hardware structure of an electronic device 500 provided in this application.

[0085] like Figure 5 As shown, the electronic device 500 includes a processor 510, a communication line 520, and a communication interface 530.

[0086] Optionally, the electronic device 500 may also include a memory 540. The processor 510, memory 540, and communication interface 530 can be connected via a communication line 520.

[0087] The processor 510 can be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 510 can also be any other device with processing capabilities, such as a circuit, device, or software module, without limitation.

[0088] In one example, processor 510 may include one or more CPUs, for example Figure 5 CPU0 and CPU1 in the CPU.

[0089] As an optional implementation, the electronic device 500 may include multiple processors; for example, in addition to processor 510, it may also include processor 570. A communication line 520 is used to transmit information between the components included in the electronic device 500.

[0090] Communication interface 530 is used for communication with other devices or other communication networks. These other communication networks can be Ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), etc. Communication interface 530 can be a module, circuit, transceiver, or any device capable of enabling communication.

[0091] Memory 540 is used to store instructions. These instructions can be computer programs.

[0092] The memory 540 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and / or instructions; it may also be a random access memory (RAM) or other type of dynamic storage device capable of storing information and / or instructions; it may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, etc., without limitation.

[0093] It should be noted that the memory 540 can exist independently of the processor 510, or it can be integrated with the processor 510. The memory 540 can be used to store instructions, program code, or some data, etc. The memory 540 can be located inside or outside the electronic device 500, without restriction.

[0094] The processor 510 is configured to execute instructions stored in the memory 540 to implement the communication method provided in the following embodiments of this application. For example, when the electronic device 500 is a terminal or a chip in a terminal, the processor 510 can execute instructions stored in the memory 540 to implement the steps performed by the sending end in the following embodiments of this application.

[0095] As an optional implementation, the electronic device 500 also includes an output device 550 and an input device 560. The output device 550 can be a display screen, speaker, or other device capable of outputting data from the electronic device 500 to the user. The input device 560 can be a keyboard, mouse, microphone, joystick, or other device capable of inputting data into the electronic device 500.

[0096] It should be pointed out that, Figure 5 The structure shown does not constitute a limitation on the electronic device, except... Figure 5 In addition to the components shown, the electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0097] The photovoltaic system DC series arc fault detection device and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of photovoltaic system DC series arc fault detection devices and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0098] This application provides a storage medium storing a program that, when executed by a processor, implements the method for detecting DC series arc faults in a photovoltaic system.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting DC series arc faults in a photovoltaic system, characterized in that, The method includes: The input voltage of the photovoltaic power optimizer in the photovoltaic system is monitored, and the abnormal drop of the input voltage is identified based on the change characteristics of the input voltage. The change characteristics are used to characterize the degree of deviation of the input voltage from the input voltage under normal operating conditions of the photovoltaic power optimizer. When an abnormal drop in input voltage is detected, the operating parameters of the photovoltaic power optimizer are adjusted to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer. The minimum duty cycle is the lowest limit that the duty cycle of the power switch in the photovoltaic power optimizer is allowed to reach. Fault identification is performed based on the corresponding input voltage changes during current regulation, and the identification result of DC series arc fault of the photovoltaic system is output.

2. The method according to claim 1, characterized in that, Identifying whether the input voltage has experienced an abnormal drop based on the characteristics of the input voltage change includes: The instantaneous value of the input voltage is collected according to the preset sampling frequency, and the average value of all instantaneous values ​​within the preset sliding time window is calculated. When the instantaneous value of the input voltage drops by a certain amount relative to the average value, reaching a preset relative threshold, and the instantaneous value is lower than a preset low voltage threshold, it is determined that the input voltage has experienced an abnormal drop.

3. The method according to claim 1, characterized in that, Adjusting the operating parameters of the photovoltaic power optimizer includes: Obtain the electrical time constant, switching frequency, and sampling delay of the photovoltaic power optimizer; The current operating value of the duty cycle of the photovoltaic power optimizer to the preset minimum duty cycle is determined as the duty cycle adjustment range, and the duty cycle adjustment range is divided into multiple adjustment stages according to the preset downward adjustment step size and the preset stabilization waiting time. Based on the electrical time constant, the switching frequency, and the sampling delay, the adjustment slope corresponding to different adjustment stages of the duty cycle of the photovoltaic power optimizer during the down-adjustment process is determined; The duty cycle of the photovoltaic power optimizer is linearly adjusted from its current operating value to the preset minimum duty cycle according to the adjustment slope corresponding to each adjustment stage.

4. The method according to claim 1, characterized in that, Fault diagnosis is based on the corresponding input voltage changes during current regulation, including: Obtain the input voltage under different input current conditions; Based on the input voltage corresponding to each input current condition, the voltage variation characteristics of the photovoltaic power optimizer and the resistance variation characteristics of the DC series arc are determined. The voltage variation characteristics are used to characterize the time-series variation law of the input voltage as the input current decreases, and the resistance variation characteristics are used to characterize the time-series variation law of the equivalent resistance of the DC series arc as the input current decreases. The DC series arc refers to the DC arc fault connected in series with the load in the DC-side series power supply circuit of the photovoltaic system. Based on the voltage change characteristics and the resistance change characteristics, it is determined whether the photovoltaic system has a DC series arc fault.

5. The method according to claim 4, characterized in that, Based on the input voltage corresponding to each input current condition, the voltage variation characteristics of the photovoltaic power optimizer and the resistance variation characteristics of the DC series arc are determined, including: Based on the input voltage corresponding to each input current condition, calculate the continuous change of the input voltage and the amount of voltage change within a preset time period to form voltage change characteristics; Calculate the DC series arc resistance based on the preset line impedance parameters of the photovoltaic system, the output voltage of the photovoltaic modules in the photovoltaic system, and the input current; The variation of DC series arc resistance is determined by measuring the DC series arc resistance at different times, thus forming a resistance variation characteristic.

6. The method according to claim 4, characterized in that, Based on the voltage change characteristics and the resistance change characteristics, determining whether the photovoltaic system has experienced a DC series arc fault includes: When the voltage change characteristics are characterized by a continuous decrease in input voltage, the voltage change amount meets the preset threshold, and the resistance change characteristics are characterized by an increasing resistance value, a suspected DC series arc fault is identified. When a suspected DC series arc fault is found, the voltage change characteristics are evaluated based on the degree of matching between the voltage change and a preset threshold to obtain a voltage fault evaluation value. The resistance change characteristics are evaluated based on the rate and stability of the resistance value increase to obtain a resistance fault assessment value. The voltage fault assessment value and the resistance fault assessment value are weighted according to preset weights to obtain the fault confidence level. The fault confidence level is compared with a preset confidence threshold. When the fault confidence level is greater than or equal to the preset confidence threshold, it is finally determined that the photovoltaic system has experienced a DC series arc fault.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: If a DC series arc fault is determined to occur in the photovoltaic system, the photovoltaic power optimizer is controlled to maintain the preset minimum duty cycle in order to extinguish the arc. If it is determined that no DC series arc fault has occurred in the photovoltaic system, the photovoltaic power optimizer is controlled to return to normal operating mode.

8. A DC series arc fault detection device for a photovoltaic system, characterized in that, The device includes: The monitoring module is used to monitor the input voltage of the photovoltaic power optimizer in the photovoltaic system, and to identify whether the input voltage has dropped abnormally based on the change characteristics of the input voltage. The change characteristics are used to characterize the degree of deviation of the input voltage from the input voltage under normal operating conditions of the photovoltaic power optimizer. The adjustment module is used to adjust the operating parameters of the photovoltaic power optimizer when an abnormal drop in input voltage is detected, so as to reduce the input current of the photovoltaic power optimizer to the current corresponding to the preset minimum duty cycle of the photovoltaic power optimizer. The minimum duty cycle is the lowest limit that the duty cycle of the power switch in the photovoltaic power optimizer is allowed to reach. The judgment module is used to determine the fault based on the corresponding input voltage change during the current regulation process, and output the judgment result of the DC series arc fault of the photovoltaic system.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the photovoltaic system DC series arc fault detection method as described in any one of claims 1-7.

10. An electronic device, characterized in that, The device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the photovoltaic system DC series arc fault detection method as described in any one of claims 1-7.

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