A distributed photovoltaic power station intelligent monitoring method and system

Abstract

The application relates to the field of new energy intelligent decentralized control technology and discloses a distributed photovoltaic power station intelligent monitoring method and system, wherein the method comprises the following steps: collecting electrical state parameters and performing normalization processing; performing abnormality detection and island detection based on a multi-dimensional deviation fusion mechanism; generating power support parameters through an adaptive reactive power injection mechanism; loading voltage frequency double droop joint control in an island mode; and realizing grid reconnection based on a phase progressive phase-locked loop mechanism after main grid recovery. Compared with the prior art which depends on centralized main station judgment and scheduling, especially under the condition that main grid communication is abnormal or frequency fluctuation occurs at a remote island edge node, the technical problem that fault isolation and independent operation cannot be realized in time is solved; thanks to the distributed state sensing and double droop control coupling mechanism driven by multi-source data, autonomous monitoring and island stable operation at the individual power station level are realized, and the stability of the system in a weak grid or island operation scene is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy intelligent distributed control technology, and particularly relates to a distributed photovoltaic power station intelligent monitoring method and system. BACKGROUND

[0002] At present, as an important part of new renewable energy, distributed photovoltaic power stations have been widely deployed in industrial parks, town roofs and remote areas and other types of scenes. The traditional monitoring architecture usually relies on a centralized master station for unified scheduling and state identification. The master station collects the operating parameters of all sub-stations and centrally processes them, and then issues operating control instructions to each power station. However, under the conditions of multi-station grid connection or large-scale deployment, the centralized master control has the following problems: on the one hand, the instability of the communication link and the bandwidth bottleneck can easily lead to state reporting delay, and thus cause fault identification lag. For example, when a power station has power fluctuations, frequency deviation or inverter abnormalities, the master station may not be able to receive and process the relevant abnormal information in time due to delay or network congestion, leading to problems such as fault diffusion and local grid voltage instability. On the other hand, under the conditions of inaccessible master control platform, abnormal master grid frequency or island state triggering, the existing system lacks on-site sensing and response capability, and cannot realize basic operating guarantee mechanisms such as local autonomous decision-making, fast reactive power injection and Droop regulation, which may eventually lead to power station disconnection, system reclosing failure, and affect photovoltaic output efficiency and power quality. In addition, although some existing solutions attempt to introduce edge computing and local strategy issuance, their control logic still relies on master station authorization or periodic synchronization, and cannot realize truly distributed autonomous response and control loop in emergency situations, especially in unattended or remote island deployment scenarios.

[0003] Therefore, there is an urgent need for a distributed photovoltaic power station intelligent monitoring method and system that can still automatically complete operating state identification, reactive power regulation, island switching and grid reconnection control based on distributed control mechanism under the conditions of abnormal master control system, communication link or serious grid fluctuation, so as to enhance dynamic autonomy and adapt to the future development needs of distributed new energy power grids. SUMMARY

[0004] In view of the above technical deficiencies, the purpose of the present application is to provide a distributed photovoltaic power station intelligent monitoring method, which aims to solve the technical problem that the existing technology relies too much on centralized master station for judgment and scheduling, and especially under the conditions of abnormal master grid communication or frequency fluctuation of remote island edge nodes, cannot realize fault isolation and independent operation in time.

[0005] To solve the above technical problems, the present application adopts the following technical solutions: the present application provides a distributed photovoltaic power station intelligent monitoring method,

[0006] The distributed photovoltaic power station intelligent monitoring method comprises:

[0007] Step S10: Collecting electrical state parameters at time t in real time at each photovoltaic power station i through local sensors, and performing normalization processing on the electrical state parameters by using an improved local fluctuation balanced normalization method to output an electrical state multi-source data set;

[0008] Step S20: Performing power prediction and residual analysis, voltage and frequency deviation detection, and island detection by using a multi-dimensional deviation fusion detection mechanism based on predicted residual driving according to the electrical state multi-source data set, and outputting a comprehensive abnormality detection score and an island determination index;

[0009] Step S30: When the comprehensive abnormality detection score is within a preset abnormality detection score range or the island determination index is within a preset island determination index range, determining a current target reactive power adjustment amount by using an impedance feedback driven adaptive reactive power injection regulation mechanism, and outputting an adaptive power support parameter set U;

[0010] Step S40: Obtaining a coupling factor representing a main grid state from the adaptive power support parameter set U, and starting an island operation mode of the photovoltaic power station i when the coupling factor is lower than a preset decoupling threshold, and loading a voltage frequency double droop joint control mechanism based on the current electrical state multi-source data set to realize local power self-balancing;

[0011] Step S50: Continuously monitoring a voltage frequency state of the photovoltaic power station i and a main grid frequency state of a distributed main grid, and updating an output state of a grid-connected flag based on the voltage frequency state and the main grid frequency state.

[0012] Preferably, in step S10, the electrical state parameters include port voltage , current , frequency , reactive power , active power , ambient temperature , illuminance , and energy storage state ; the improved local fluctuation balanced normalization method introduces an adaptive window method based on local fluctuation to dynamically adjust the mean and standard deviation calculation interval in the normalization processing process based on local data fluctuation in the power station; wherein the local data fluctuation in the power station includes power fluctuation and illumination change.

[0013] Preferably, in step S20, the steps of power prediction and residual analysis and voltage and frequency deviation detection specifically include:

[0014] Power prediction and residual analysis: obtain historical power data from the electrical state multi-source data set, predict the power output of the next time period through the time series prediction network LSTM to obtain the predicted power, compare the difference between the actual power and the predicted power, and calculate the power residual based on the prediction error adaptive reconstruction method, wherein, is the power residual of the ith power station at time t, which is used to represent the difference between the actual power and the predicted power; is the actual power of the ith power station at time t; is the predicted power of the ith power station at time t; is an adaptive adjustment coefficient, which is used to adjust the influence degree of the change rate of the prediction error on the power residual; when the power residual is greater than the preset power residual threshold, it is determined that the power output is abnormal;

[0015] Voltage and frequency deviation detection: calculate the voltage deviation based on the symmetric relative deviation rate determination method, wherein, is the voltage deviation of the ith photovoltaic power station at time t, is the preset microgrid reference voltage; is the port voltage value measured by the ith photovoltaic power station at time t; β is the voltage change weight adjustment coefficient;

[0016] Calculate the frequency deviation based on the perturbation stability domain deviation method, wherein, is the frequency deviation of the ith photovoltaic power station at time t; is the frequency value measured by the ith photovoltaic power station at time t; is the nominal frequency of the power grid; is the frequency fluctuation sensitivity adjustment coefficient, which is used to adjust the response degree of the frequency rapid fluctuation.

[0017] Preferably, in step S20, the step of island detection specifically comprises: identifying whether the power grid and the photovoltaic power station are off-grid through phase disturbance, and defining the island determination index of the ith photovoltaic power station at time t based on the weighted sum of voltage change, frequency change and voltage phase angle change , wherein, , and are weight coefficients of each parameter, respectively, which are used to adjust the contribution degree of voltage change, frequency change and phase change to the island determination index according to the normalization of the corresponding parameter dimension; and are the voltage phase angles of the ith power station at time t and the previous time t−1, respectively.

[0018] Preferably, in step S30, the adaptive power support parameter set U includes target reactive power set value, voltage limiting parameter, reactive power regulation response rate, maximum power support capability and coupling factor representing the main grid state.

[0019] Preferably, in step S40, the step of realizing local power self-balancing by loading the voltage-frequency double droop joint control mechanism based on the current electrical state multi-source data set specifically includes:

[0020] According to the current electrical state multi-source data set and the adaptive power support parameter set U generated in step S30, corresponding target reference frequency value and target reference voltage value are generated and used as dynamic set points of the frequency control chain and the voltage control chain.

[0021] A current real-time frequency value is obtained, a frequency control deviation is constructed based on the difference between the current real-time frequency value and the target reference frequency value, a frequency control coefficient is dynamically set according to the frequency control deviation, the active power output of the photovoltaic inverter is adjusted, and a first droop control chain is constructed.

[0022] A current real-time voltage value is obtained, a voltage control deviation is constructed based on the difference between the current real-time voltage value and the target reference voltage value, a voltage control coefficient is dynamically set according to the voltage control deviation, the reactive power output of the photovoltaic inverter is adjusted, and a second droop control chain is constructed.

[0023] A power error index is constructed based on the current reactive power output, the active power output and the load power demand, and the control coefficients in the first droop control chain and the second droop control chain are corrected in a dynamic correction mode based on error feedback, closed-loop adjustment of the double droop joint control mechanism is completed, and local power self-balancing is realized.

[0024] Preferably, in step S50, the step of continuously monitoring the voltage frequency state of the photovoltaic power station i and the main grid frequency state of the distributed main grid and updating the output state of the grid-connected flag based on the voltage frequency state and the main grid frequency state specifically includes: continuously monitoring the voltage frequency state of the photovoltaic power station i and the main grid frequency state of the distributed main grid, when the main grid frequency is detected to recover to the reference frequency range and the phase difference between the voltage frequency state and the main grid frequency state meets the preset gradual synchronization condition, a phase gradual phase-locked reconnection mechanism is used to update the output state of the grid-connected flag.

[0025] The application also provides a distributed photovoltaic power station intelligent monitoring system, which comprises:

[0026] The state normalization processing module is configured to collect electrical state parameters of each photovoltaic power station i at a time point t in real time through a local sensor, normalize the electrical state parameters by using an improved local fluctuation balancing normalization method, and output an electrical state multi-source data set.

[0027] The abnormality detection and island determination module is configured to perform power prediction and residual analysis, voltage and frequency deviation detection, and island detection by using a multi-dimensional deviation fusion detection mechanism based on a predicted residual driving mechanism according to the electrical state multi-source data set, and output a comprehensive abnormality detection score and an island determination index.

[0028] The reactive power regulation parameter generation module is configured to determine a current target reactive power adjustment amount by using an impedance feedback driven adaptive reactive power injection regulation mechanism when the comprehensive abnormality detection score is within a preset abnormality detection score range or the island determination index is within a preset island determination index range, and output an adaptive power support parameter set U.

[0029] The island control activation module is configured to obtain a coupling factor representing a main grid state from the adaptive power support parameter set U, and start an island operation mode of the photovoltaic power station i when the coupling factor is lower than a preset decoupling threshold, so as to load a voltage-frequency double droop joint control mechanism based on the current electrical state multi-source data set to realize local power self-balancing.

[0030] The grid synchronization monitoring module is configured to continuously monitor a voltage-frequency state of the photovoltaic power station i and a main grid frequency state of a distributed main grid, and update an output state of a grid synchronization flag bit based on the voltage-frequency state and the main grid frequency state.

[0031] The application further provides a distributed photovoltaic power station intelligent monitoring device, which comprises a memory, a processor, and a distributed photovoltaic power station intelligent monitoring program stored in the memory and executable on the processor.

[0032] The application further provides a computer program product comprising a distributed photovoltaic power station intelligent monitoring program, which, when executed by a processor, implements the distributed photovoltaic power station intelligent monitoring method.

[0033] The application has the advantages that the distributed control mechanism is adopted to enable each photovoltaic power station to independently decide and adjust an operation strategy, ensure the stability of the power grid and the efficient operation of the power station without relying on central control, and avoid the single-point failure risk in the traditional centralized control mode.

[0034] The application combines distributed control and multi-source data-driven real-time state perception, can timely switch to island mode and realize self-balancing when the main network abnormity occurs, guarantees the reliability and stability of the power station under complex environmental conditions, and significantly improves the self-healing ability of power under local disturbance. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0036] Figure 1 The figure is a flowchart of the first embodiment of the intelligent monitoring method of the distributed photovoltaic power station.

[0037] Figure 2 The figure is a voltage fluctuation comparison diagram under different control conditions of the first embodiment of the intelligent monitoring method of the distributed photovoltaic power station.

[0038] Figure 3 The figure is a device diagram of the intelligent monitoring method of the distributed photovoltaic power station. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0040] Embodiment one: as shown in the figure, the figure is a flowchart of the first embodiment of the intelligent monitoring method of the distributed photovoltaic power station, and the first embodiment of the intelligent monitoring method of the distributed photovoltaic power station is proposed. Figure 1

[0041] In the first embodiment, the intelligent monitoring method of the distributed photovoltaic power station comprises:

[0042] Step S10: collecting the electrical state parameters of each photovoltaic power station i at time t in real time through a local sensor, normalizing the electrical state parameters by using an improved local fluctuation balancing normalization method, and outputting an electrical state multi-source data set;

[0043] ​It should be noted that the "improved local fluctuation normalization method" is not simply a proportional scaling process of multi-dimensional physical quantities, but a "disturbance perception driven normalization strategy" combined with the disturbance mode and frequency of electrical quantities in the actual operation environment of the photovoltaic power station. Specifically, in the traditional method, all parameters are usually linearly mapped with reference to the full cycle extreme value, but this approach ignores the dynamic nonlinear characteristics in the photovoltaic power generation scenario, such as high-frequency disturbances caused by sudden changes in light intensity, cloud cover, or rapid load switching. The above method captures the current parameter change trend by introducing a short-time sliding window, and uses the local fluctuation as a parameter adjustment factor to dynamically expand or compress the normalization interval, so that various electrical parameters better retain their relative change characteristics in the short term. The purpose of normalization is expanded from "stretching to standard scale" to "maximizing fluctuation trend without distorting the original energy characteristics".

[0044] It can be understood that through this normalization method, the internal correlation trend between parameters can still be extracted when facing sharp fluctuations in electrical state parameters (such as voltage surge, frequency deviation), avoiding the fact that the key signal changes are masked by the normalization operation itself. That is, the normalization result not only serves to unify the scale, but also carries characteristic information such as change rate and fluctuation frequency, so that the subsequent abnormal detection mechanism inputs "real perception expression" rather than "compressed and harmonized" parameters, thereby improving the accuracy and timeliness of the overall perception decision of the intelligent monitoring system.

[0045] It should be understood that compared with the traditional global extreme normalization method, the present application emphasizes the "adaptability" and "fluctuation sensitivity" of normalization. The traditional method will fail under extreme operating conditions, for example, the sampled power value is close to zero under extremely low light conditions at night. If the maximum power of the whole day is still used as the normalization benchmark, the processing result will make the local fluctuation be submerged and unable to identify the actual small fault precursor. In the present method, the normalization interval is adjusted in real time based on the local window, which can automatically adapt to different data distribution characteristics in different operating stages, so that feature patterns can be stably extracted even in low-illumination operation or energy storage access fluctuation stages, thereby greatly improving the expression ability and applicability of normalization processing in abnormal operating scenarios.

[0046] Step S20: According to the electrical state multi-source data set, a multi-dimensional deviation fusion detection mechanism based on predicted residual driving is used for power prediction and residual analysis, voltage and frequency deviation detection, and island detection, to output a comprehensive abnormal detection score and an island determination index;

[0047] It should be noted that the "multi-dimensional deviation fusion detection mechanism based on predicted residual error driving" refers to: in a time series modeling manner, active power is short-term predicted, dynamic residual error between actual value and predicted value is extracted, real-time voltage and frequency change trend are continuously tracked, and time sequence characteristics of these deviation signals are fused to construct a unified abnormal score model and island determination rule. Specifically, "power residual error" is used to identify significant deviation between power generation capacity and model expectation; "voltage deviation" reflects power quality fluctuation amplitude; and "frequency deviation" is sensitive to power imbalance and decoupling trend on the network side. Through a multi-channel input structure, it is uniformly input into a fusion layer, and it can be judged whether the current power station is in an unstable state or a decoupling risk rising state. This mechanism not only focuses on the change of a single feature, but also emphasizes the interactive consistency and deviation trend coordination between multiple source data, so as to realize more accurate abnormal identification.

[0048] It can be understood that the technical effect of the fusion detection mechanism is that it can use "power prediction error" as a guide signal to guide the subsequent detection process. When a certain type of abnormality occurs (such as cloud shadow blocking or energy storage interference), the prediction model error will first increase, and then the stability indicators in the voltage and frequency channels will deteriorate. Therefore, through the residual error driven mode, the response can be awakened in advance, and the time foresight warning ability of abnormal development can be formed.

[0049] It should be understood that, compared with the traditional single-threshold triggered voltage / frequency detection mechanism, the fusion model of the present application determines abnormal score based on the consistency of trends among multiple channels. The traditional method is prone to miss reports or misjudgments in voltage sudden change or transient frequency jitter scenes, especially in non-fault fluctuation scenes (such as load disturbance). The method introduces "power prediction residual error" as a dynamic indicator, which can be used as an abnormal trigger factor to guide the scoring mechanism. The fusion score is not a static logic judgment, but a dynamic identification mode for trend, structure and residual error coordination mode, which adapts to the self-organizing abnormal judgment logic under the islanding trend, and significantly enhances the local autonomy of distributed control.

[0050] Step S30: When the comprehensive abnormality detection score is within a preset abnormality detection score range or the island determination index is within a preset island determination index range, determine the current target reactive power adjustment amount through the impedance feedback driven adaptive reactive power injection regulation mechanism, and output the adaptive power support parameter set U;

[0051] It should be noted that the "impedance feedback driven adaptive reactive power injection regulation mechanism" refers to that when the running state is detected to be abnormal or the decoupling trend of the power grid is obvious, the photovoltaic power station i does not depend on the master control center, but dynamically estimates the required reactive power support capability according to the equivalent impedance change trend of the local power grid, and combines the current voltage margin and frequency stability to autonomously calculate the optimal reactive power adjustment amount. This mechanism takes "local perception-adaptive response" as the core logic, measures the changes of the equivalent resistance and the equivalent reactance through continuous feedback, and adjusts the reactive power support strategy in real time, thereby forming a "power support parameter set U" including target reactive power, injection direction, voltage control sensitivity and other parameters.

[0052] It can be understood that the introduction of the impedance feedback mechanism enables the photovoltaic power station to infer the risk level of the main grid strength, load mutation or local decoupling by inversely deducing the equivalent impedance from the measured voltage disturbance response. This local inference mechanism without global communication support has the ability of "fault area self-identification and local flexible support", effectively supports power quality restoration during voltage sag and dynamic voltage pullback control during main grid voltage undershoot. The adaptive injection strategy can also dynamically adjust the injection rate and target value to avoid oscillation or reactive power backflow caused by overcompensation.

[0053] It should be understood that the traditional centralized reactive power control scheme needs to rely on the SCADA system or remote master control strategy to push control instructions, which cannot respond in time in the case of communication link interruption or islanding, and is prone to problems such as expansion of reactive power shortage and rapid deterioration of voltage. The impedance feedback logic used in the present application establishes a local adaptive loop, which is completely executed by the distributed photovoltaic power station in a closed loop, avoiding the delay and failure risk caused by relying on central control. Compared with the reactive power control based on fixed voltage margin, the present application can realize more fine-grained and more scene-matched dynamic adjustment, effectively improving the voltage stability and grid-side support capability.

[0054] Step S40: Obtain the coupling factor representing the state of the main grid from the adaptive power support parameter set U, and when the coupling factor is lower than the preset decoupling threshold, start the island operation mode of the photovoltaic power station i, and load the voltage and frequency double droop joint control mechanism based on the current electrical state multi-source data set to realize local power self-balancing;

[0055] It should be noted that the "coupling factor" is a quantitative indicator used to characterize the dynamic electrical connection strength between the photovoltaic power station i and the upstream main grid. It is calculated based on the phase correlation between the output power change of the photovoltaic power station and the bus voltage frequency disturbance. Its core reflects whether "local power regulation can effectively affect voltage and frequency response." A low coupling factor indicates weak grid responsiveness, which may lead to physical disconnection, impedance distortion, severe grid voltage droop, and other phenomena, triggering the necessity of islanding. The "dual droop joint control mechanism" refers to the use of P-V droop control law in the voltage regulation channel and Q-f droop law in the frequency channel, respectively. Through joint regulation, it maintains local power balance and stable operation, and is suitable for islanding control scenarios under grid decoupling conditions.

[0056] Understandably, the coupling factor is equivalent to a "signal of electrical connectivity," possessing dynamic and real-time characteristics. It is estimated by tracking the sensitivity of reactive and active response to voltage frequency, serving as an adaptive connection quality perception indicator. This mechanism does not rely on external communication channels; it can construct the coupling estimate solely through local sampling, greatly improving the real-time performance and robustness of decoupling judgment. The introduction of dual droop control means that after a photovoltaic power station is disconnected from the main grid, it can independently control its output voltage and frequency and quickly compensate for changes in local load, achieving dynamic and stable operation in a "quasi-microgrid state."

[0057] It should be understood that, compared to traditional solutions that directly disconnect after islanding detection or passively maintain operation using a single droop mode, this invention introduces a main grid coupling factor to construct a priori judgment mechanism. This allows for early intervention in control preparation before signs of coupling degradation appear, improving the predictability and flexibility of switching islanding modes. Simultaneously, the dual droop joint control, while actively supporting the system, balances voltage stability and frequency consistency, overcoming the problems of weak regulation capability and slow dynamic tracking in single droop mode. This achieves steady-state control and dynamic equilibrium under islanding conditions with multiple load types and concurrent disturbances at multiple time scales.

[0058] For example, such as Figure 2As shown, for ten groups of typical test conditions numbered T1 to T10, the voltage fluctuation amplitude performance of the traditional single droop control mechanism and the dual droop joint control mechanism described in the application in island mode is tested respectively. In each test, the voltage stability response of each photovoltaic power station after the occurrence of external disturbance (such as load mutation, main network disconnection) is recorded. As can be seen from the figure, the voltage fluctuation amplitude under the traditional single droop mechanism is generally between 5.2% and 8.1%, the fluctuation is large and the response is slow, which is easy to cause control false triggering and energy storage system false judgment. After adopting the voltage frequency dual droop joint control mechanism proposed in the application, the voltage fluctuation amplitude is significantly reduced, and the overall control is between 3.1% and 4.6%, the fluctuation amplitude is reduced by more than 30%. This shows that the application scheme can effectively improve the island operation stability and voltage regulation robustness, enhance the disturbance suppression ability, and is especially suitable for scenes with autonomous response demand in distributed photovoltaic systems, and realizes high-performance decentralized control without relying on central coordination.

[0059] Step S50: Continuously monitor the voltage frequency state of the photovoltaic power station i and the main network frequency state of the distributed main network, and update the output state of the grid connection flag bit based on the voltage frequency state and the main network frequency state.

[0060] It should be noted that the "voltage frequency state" refers to the effective value of the alternating output end voltage, the frequency value and its change rate collected at the photovoltaic power station i through the local sensor, and the "main network frequency state" refers to the reference frequency data from the adjacent public access node through the communication module. Updating the "grid connection flag bit" means setting the state register bit indicating "whether the grid connection condition is met" according to the judgment result of the current frequency difference and phase difference, which is used to drive the subsequent grid connection instruction execution.

[0061] It can be understood that this step realizes the flexible transition from island mode to grid-connected mode by continuously comparing the local output state with the main network synchronization state, effectively avoiding the grid connection impact caused by frequency jump and phase inconsistency, and ensuring the disturbance-free reconnection capability of the distributed photovoltaic power station. This mechanism has adaptive reconnection judgment capability, rather than passive waiting for upper-level scheduling instructions, thereby improving the autonomy in decentralized control scenarios.

[0062] It should be understood that compared with the reconnection mechanism of "fixed time detection + manual reset + fixed threshold judgment" in the traditional scheme, the application introduces a logic based on master-slave phase gradual locking and frequency adaptive return judgment. When the frequency difference and the phase difference simultaneously satisfy the preset synchronization condition, the grid connection flag bit is automatically set, supporting slow recombination at a low synchronization rate, so that the reconnection process has gradualness and high fault tolerance, and is suitable for actual deployment environments with large voltage disturbance, unstable network communication or delayed scheduling response.

[0063] Embodiment two: In addition, the application provides a distributed photovoltaic power station intelligent monitoring system, which adopts the distributed photovoltaic power station intelligent monitoring method in the above embodiment, and can solve the technical problem of the distributed photovoltaic power station intelligent monitoring. Compared with the prior art, the beneficial effects of the distributed photovoltaic power station intelligent monitoring system provided by the application are the same as those of the distributed photovoltaic power station intelligent monitoring method provided by the above embodiment, and other technical features in the distributed photovoltaic power station intelligent monitoring system are the same as those disclosed in the above embodiment method, which will not be repeated here.

[0064] Embodiment three: The application provides a distributed photovoltaic power station intelligent monitoring device, please refer to Figure 3A distributed photovoltaic power station intelligent monitoring device includes at least one processor, and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the distributed photovoltaic power station intelligent monitoring method in the above-mentioned embodiment one. The distributed photovoltaic power station intelligent monitoring device in the embodiment of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. A distributed photovoltaic power station intelligent monitoring device is only an example, and should not bring any limitation to the function and use range of the embodiment of the present application. A distributed photovoltaic power station intelligent monitoring device can include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in a read-only memory 1002 or loaded from a storage device 1003 to a random access memory 1004. In the random access memory 1004, various programs and data required for the operation of a distributed photovoltaic power station intelligent monitoring device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. An I / O interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, and the like; storage devices 1003 including, for example, magnetic tapes, hard disks, and the like; and communication devices 1009. The communication device 1009 can allow a distributed photovoltaic power station intelligent monitoring device to communicate with other devices wirelessly or by wire to exchange data. Although a distributed photovoltaic power station intelligent monitoring device with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0065] Embodiment four: the application further provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a distributed photovoltaic power station intelligent monitoring method as described above. The computer program product provided by the application can solve the technical problem of a distributed photovoltaic power station intelligent monitoring. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the distributed photovoltaic power station intelligent monitoring method provided by the above-described embodiments, and are not described here in detail.

[0066] In particular, according to the embodiments disclosed by the application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments disclosed by the application include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed by the application are executed.

[0067] It should be understood that various parts of the application disclosed can be realized in hardware, software, firmware or a combination thereof. In the description of the above-described embodiments, specific features, structures, materials or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0068] Obviously, those skilled in the art can make various modifications and variations to the application without departing from the spirit and scope of the application. Thus, if these modifications and variations of the application fall within the scope of the claims of the application and their equivalent technologies, the application also intends to include these modifications and variations.

Claims

1. A distributed photovoltaic power station intelligent monitoring method, characterized in that, The method comprises: Step S10: collecting electrical state parameters at time t in real time at each photovoltaic power station i through local sensors, normalizing the electrical state parameters by using an improved local fluctuation equalization normalization method, and outputting an electrical state multi-source data set; Step S20: performing power prediction and residual analysis, voltage and frequency deviation detection, and island detection by using a multi-dimensional deviation fusion detection mechanism based on a prediction residual driving mechanism according to the electrical state multi-source data set, and outputting a comprehensive abnormality detection score and an island determination index; wherein the steps of power prediction and residual analysis and voltage and frequency deviation detection specifically comprise: Power prediction and residual analysis: obtain historical power data from the electrical state multi-source data set, predict the power output of the next time period through the time series prediction network LSTM to obtain the predicted power, compare the difference between the actual power and the predicted power, and calculate the power residual based on the prediction error adaptive reconstruction method, wherein, is the power residual of the ith power station at time t, which is used to represent the difference between the actual power and the predicted power; is the actual power of the ith power station at time t; is the predicted power of the ith power station at time t; is an adaptive adjustment coefficient, which is used to adjust the influence degree of the change rate of the prediction error on the power residual; when the power residual is greater than the preset power residual threshold, it is determined that the power output is abnormal; Voltage and frequency deviation detection: voltage deviation is calculated based on the symmetrical relative shift rate determination method, wherein, is the voltage deviation of the ith photovoltaic power station at time t, is the preset microgrid reference voltage; is the measured port voltage value of the ith photovoltaic power station at time t; and β is a voltage change weight adjustment coefficient. calculating the frequency deviation based on the perturbation stability domain deviation method, wherein, is the frequency deviation of the ith photovoltaic power station at time t; is the frequency value measured at time t by the ith photovoltaic power station; is the nominal frequency of the electrical grid; is the frequency fluctuation sensitivity adjustment coefficient, for the degree of response to frequency rapid fluctuations; The island detection step specifically comprises: identifying whether the power grid and the photovoltaic power station are off-grid through phase disturbance, and defining an island determination index of the ith photovoltaic power station at time t based on a weighted sum of voltage variation, frequency variation and voltage phase angle variation , wherein, , and are weight coefficients of respective parameters, and are used to adjust the contribution degree of the voltage variation, the frequency variation and the phase variation to the island determination index according to the normalization of the corresponding parameter item dimension; and are voltage phase angles of the ith power station at time t and the previous time t 1, respectively. Step S30: when the comprehensive abnormality detection score is within a preset abnormality detection score range or the island determination index is within a preset island determination index range, determining a current target reactive power adjustment amount by using an impedance feedback driven adaptive reactive power injection regulation mechanism, and outputting an adaptive power support parameter set U; Step S40: obtaining a coupling factor representing a main grid state from the adaptive power support parameter set U, and starting an island operation mode of the photovoltaic power station i when the coupling factor is lower than a preset decoupling threshold, and loading a voltage-frequency dual droop joint control mechanism based on the current electrical state multi-source data set to realize local power self-balancing; Step S50: continuously monitoring a voltage-frequency state of the photovoltaic power station i and a main grid frequency state of a distributed main grid, and updating an output state of a grid-connected flag based on the voltage-frequency state and the main grid frequency state.

2. The method of claim 1, wherein the method further comprises: In step S10, the electrical state parameters include port voltage , current , frequency , reactive power , active power , ambient temperature , illumination and energy storage state ; the improved local fluctuation equalization normalization method introduces a local fluctuation-based adaptive window method, which dynamically adjusts the mean and standard deviation calculation interval in the normalization process based on local data fluctuations in the power station; wherein the local data fluctuations in the power station include power fluctuations and illumination changes.

3. The method of claim 1, wherein the method further comprises: In step S30, the adaptive power support parameter set U includes a target reactive power set value, a voltage limiting parameter, a reactive power regulation response rate, a maximum power support capability, and a coupling factor representing a main grid state.

4. The method of claim 1, wherein the method further comprises: In step S40, the step of loading a voltage-frequency dual droop joint control mechanism based on the current electrical state multi-source data set to realize local power self-balancing specifically comprises: generating corresponding target reference frequency and target reference voltage values based on the current electrical state multi-source data set and the adaptive power support parameter set U generated in step S30, which are used as dynamic set points of a frequency control chain and a voltage control chain; obtaining a current real-time frequency value, constructing a frequency control deviation based on the difference between the current real-time frequency value and the target reference frequency value, dynamically setting a frequency control coefficient according to the frequency control deviation, adjusting the active power output of the photovoltaic inverter, and constructing a first droop control chain; obtaining a current real-time voltage value, constructing a voltage control deviation based on the difference between the current real-time voltage value and the target reference voltage value, dynamically setting a voltage control coefficient according to the voltage control deviation, adjusting the reactive power output of the photovoltaic inverter, and constructing a second droop control chain; constructing a power error index based on the current reactive power output, the active power output, and the load power demand, and correcting the control coefficients in the first droop control chain and the second droop control chain by using an error feedback dynamic correction method, completing closed-loop adjustment of the dual droop joint control mechanism, and realizing local power self-balancing.

5. The method of claim 1, wherein the method further comprises: In step S50, the voltage frequency state of the photovoltaic power station i and the main grid frequency state of the distributed main grid are continuously monitored, and the output state of the grid connection flag bit is updated based on the voltage frequency state and the main grid frequency state. The step specifically comprises: continuously monitoring the voltage frequency state of the photovoltaic power station i and the main grid frequency state of the distributed main grid, and updating the output state of the grid connection flag bit using a phase gradual phase-locked reconnection mechanism when the main grid frequency is detected to return to the reference frequency range and the phase difference between the voltage frequency state and the main grid frequency state meets the preset gradual synchronization condition.

6. A distributed photovoltaic power station intelligent monitoring system applied to the distributed photovoltaic power station intelligent monitoring method in any one of claims 1 to 5, characterized in that, The distributed photovoltaic power station intelligent monitoring system comprises: A state normalization processing module is configured to collect electrical state parameters of each photovoltaic power station i at a time t in real time through a local sensor, normalize the electrical state parameters using an improved local fluctuation balancing normalization method, and output an electrical state multi-source data set. An abnormality detection and island determination module is configured to perform power prediction and residual analysis, voltage and frequency deviation detection, and island detection using a multi-dimensional deviation fusion detection mechanism based on a prediction residual driving mechanism based on the electrical state multi-source data set, and output a comprehensive abnormality detection score and an island determination index. The power prediction and residual analysis and the voltage and frequency deviation detection specifically comprise: Power prediction and residual analysis: Obtain historical power data from the electrical state multi-source data set, predict the power output of the next time period through the time series prediction network LSTM to obtain the predicted power, compare the difference between the actual power and the predicted power, and calculate the power residual based on the prediction error adaptive reconstruction method, wherein, is the power residual of the ith power station at time t, which is used to represent the difference between the actual power and the predicted power; is the actual power of the ith power station at time t; is the predicted power of the ith power station at time t; is an adaptive adjustment coefficient, which is used to adjust the influence degree of the change rate of the prediction error on the power residual; when the power residual is greater than the preset power residual threshold, it is determined that the power output is abnormal; voltage and frequency deviation detection: voltage deviation is calculated based on the symmetrical relative shift rate determination method, wherein, is the voltage deviation of the ith photovoltaic power station at time t, is a preset micro-grid reference voltage; is the measured port voltage value of the ith photovoltaic power station at time t; and β is a voltage change weight adjustment coefficient. calculating the frequency deviation based on the perturbation stability domain deviation method, wherein, is the frequency deviation of the ith photovoltaic power station at time t; is the frequency value measured by the ith photovoltaic power station at time t; is the nominal frequency of the power grid; is the frequency fluctuation sensitivity adjustment coefficient, used for the response degree to the frequency rapid fluctuation; The island detection step specifically comprises: identifying whether the power grid and the photovoltaic power station are off-grid through phase disturbance, and defining an island determination index of the ith photovoltaic power station at time t based on a weighted sum of voltage variation, frequency variation and voltage phase angle variation , , , , are weight coefficients of respective parameters, and are used to adjust the contribution degree of the voltage variation, the frequency variation and the phase variation to the island determination index according to the normalization of the corresponding parameter dimension; and are voltage phase angles of the ith power station at time t and the previous time t 1, respectively. A reactive power regulation parameter generation module is configured to determine a current target reactive power adjustment amount by using an impedance feedback driven adaptive reactive power injection regulation mechanism when the comprehensive abnormality detection score is within a preset abnormality detection score range or the island determination index is within a preset island determination index range, and output an adaptive power support parameter set U. An island control activation module is configured to obtain a coupling factor representing a main grid state from the adaptive power support parameter set U, and start an island operation mode of the photovoltaic power station i when the coupling factor is lower than a preset decoupling threshold, and load a voltage frequency double droop joint control mechanism based on the current electrical state multi-source data set to realize local power self-balancing. A grid synchronization monitoring module is configured to continuously monitor the voltage frequency state of the photovoltaic power station i and the main grid frequency state of the distributed main grid, and update the output state of the grid connection flag bit based on the voltage frequency state and the main grid frequency state.

7. A distributed photovoltaic power station intelligent monitoring device, characterized in that, The distributed photovoltaic power station intelligent monitoring device comprises a memory, a processor, and a distributed photovoltaic power station intelligent monitoring program stored in the memory and executable on the processor. The distributed photovoltaic power station intelligent monitoring program is executed by the processor to implement the distributed photovoltaic power station intelligent monitoring method of any one of claims 1 to 5.

8. A computer program product, characterised in that, The computer program product comprises a distributed photovoltaic power station intelligent monitoring program, which is executed by a processor to implement the distributed photovoltaic power station intelligent monitoring method of any one of claims 1 to 5.

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