Photovoltaic power station loss decomposition monitoring method and system

Abstract

The invention relates to the technical field of photovoltaic power stations, in particular to a photovoltaic power station loss decomposition monitoring method and system. Comprising the following steps: establishing a plurality of monitoring points according to equipment parameters of a photovoltaic power station, and setting a plurality of loss indexes according to historical loss parameters; obtaining a feedback data packet of each monitoring point according to a preset feedback time node, and generating an electric quantity loss table of each monitoring point according to the feedback data packet; judging whether a maintenance instruction is generated or not according to the all-electric-quantity loss table; by setting a plurality of loss indexes and establishing an evaluation sub-model of each loss index, the power loss parameters of each monitoring point are comprehensively analyzed, the power loss proportion caused by different factors is generated, the power loss of each monitoring point is visually monitored, a corresponding maintenance strategy is formulated according to the power loss table, and the maintenance efficiency is improved. The abnormal operation risk of the monitoring points is eliminated in time, the maintenance efficiency of each monitoring point is improved, the comprehensive electric quantity loss is reduced, and the operation efficiency of the photovoltaic station is improved.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic power stations, and particularly to a method and system for monitoring the decomposition of losses in a photovoltaic power station. Background Art

[0002] The power curtailment of a photovoltaic power station is inseparable from the active power control system (AGC) of the photovoltaic. Therefore, large-scale photovoltaic power stations need to be equipped with an AGC system to receive the active power control instructions from the dispatching center and achieve load distribution according to predetermined rules and strategies. Due to different power curtailment ratios, the resulting power generation losses will also be different. Currently, the power curtailment losses of photovoltaic power stations are mainly estimated by the power station operation and maintenance personnel based on experience, which is time-consuming and laborious, and the calculation standards are not consistent, and the calculation accuracy is relatively low.

[0003] In summary, how to automatically calculate the power curtailment losses of a photovoltaic power station and improve the calculation accuracy is a technical problem that needs to be solved urgently by those skilled in the art at present. Summary of the Invention

[0004] The purpose of the present application is: to solve the above technical problems, the present application provides a method and system for monitoring the decomposition of losses in a photovoltaic power station, aiming to improve the monitoring efficiency of the power losses of the photovoltaic power station and improve the operation efficiency of the photovoltaic power station.

[0005] In some embodiments of the present application, a plurality of loss indicators are set and evaluation sub-models for each loss indicator are established, so as to comprehensively analyze the power loss parameters of each monitoring point, generate the proportion of power losses caused by different factors, and thus realize the visual monitoring of the power losses of each monitoring point and improve the operation efficiency of the photovoltaic power station.

[0006] In some embodiments of the present application, by periodically analyzing the power losses of each monitoring point, timely warning is given to the monitoring points with abnormal power losses, and corresponding maintenance strategies are formulated according to their power loss tables, timely eliminating the abnormal operation risks of the monitoring points, improving the maintenance efficiency of each monitoring point, reducing the comprehensive power losses, and improving the operation efficiency of the photovoltaic power station.

[0007] In some embodiments of the present application, a method for monitoring the decomposition of losses in a photovoltaic power station is provided, including:

[0008] Establishing a plurality of monitoring points according to the equipment parameters of the photovoltaic power station, and setting a plurality of loss indicators according to the historical loss parameters;

[0009] Obtaining the feedback data packets of each monitoring point according to the preset feedback time node, and generating the power loss tables of each monitoring point according to the feedback data packets;

[0010] Judging whether to generate a maintenance instruction according to all the power loss tables;

[0011] Among them, it further includes:

[0012] Establish a sequence of monitoring points A, A = (a1, a2... a i …a n ), where a i is the i-th monitoring point; n is the number of monitoring points;

[0013] Establish a sequence of loss indicators P, P = (p1, p2... p i …p m ), where p i is the i-th loss indicator; m is the number of loss indicators.

[0014] In some embodiments of the present application, according to the feedback data packet, an electricity loss table for each monitoring point is generated, including:

[0015] Set p i as the target loss indicator in sequence according to the loss indicator sequence P;

[0016] Generate an evaluation sub-model for the target loss indicator;

[0017] Establish evaluation sub-models for each loss indicator in sequence, and establish a sequence of evaluation sub-models S, S = (s1, s2... s i …s m ), where s i is the evaluation sub-model for the i-th loss indicator; m is the number of loss indicators;

[0018] Establish an evaluation model according to the sequence of evaluation sub-models S;

[0019] Obtain the feedback data packets of each monitoring point at the current feedback time node;

[0020] Generate an electricity loss table for each monitoring point according to the evaluation model and all feedback data packets.

[0021] In some embodiments of the present application, when generating an electricity loss table for each monitoring point, it includes:

[0022] Set a i as the target monitoring point in sequence according to the monitoring point sequence A;

[0023] Obtain the feedback data packet of the target monitoring point at the current feedback time node;

[0024] Extract all characteristic parameters in the feedback data packet;

[0025] Generate the first-level loss values corresponding to each loss indicator of the monitoring point according to the characteristic parameters and the evaluation model;

[0026] Establish a first-level loss value sequence B of the target monitoring point at the current feedback time node, B = (b1, b2... b i …b m ), where bi is the first-level loss value of the i-th loss index of the target monitoring point at the current feedback time node, and m is the number of loss indices;

[0027] Obtain the total power loss value of the target monitoring point;

[0028] Generate a correction parameter for the first-level loss value sequence according to the total power loss value;

[0029] Generate the corresponding second-level loss value for each loss index according to the correction result, and establish a power loss table of the target monitoring point at the current feedback time node based on all the second-level loss values;

[0030] Generate power loss tables for each monitoring point at the current feedback time node in sequence.

[0031] In some embodiments of the present application, determining whether to generate a maintenance instruction according to all the power loss tables includes:

[0032] Obtain the power loss table of the target monitoring point;

[0033] Generate an abnormal loss value f of the target monitoring point according to the power loss table;

[0034] Generate abnormal loss values for each monitoring point in sequence;

[0035] Establish an abnormal loss value sequence F, F = (f1, f2... f i …f n ), where f i is the abnormal loss value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points;

[0036] Determine whether to generate a correction instruction according to the abnormal loss value sequence F.

[0037] In some embodiments of the present application, generating the abnormal loss value f of the target monitoring point includes:

[0038]

[0039] Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; c i is the second-level loss value of the i-th loss index of the target monitoring point at the current feedback time node; m is the number of loss indices; μ i is the influence factor of the i-th loss index; c' i is the loss value threshold of the i-th loss index; Y(i) is a selection coefficient; if (c i-c' i ) > 0, Y(i) = 1; If (c i -c' i ) < 0, Y(i) = 0.

[0040] In some embodiments of the present application, determining whether to generate a correction instruction according to the abnormal loss value sequence F includes:

[0041] Presetting an abnormal loss value threshold F1;

[0042] If f i > F1, a first-level correction instruction is generated at the i-th monitoring point of the current feedback time node, and a maintenance sub-strategy is generated according to the first-level correction instruction and the power loss table of the i-th monitoring point;

[0043] Generating a maintenance evaluation value h according to the abnormal loss value sequence F;

[0044] Presetting a maintenance evaluation value threshold H1;

[0045] If h > H1, a second-level maintenance instruction is generated at the current feedback time node.

[0046] In some embodiments of the present application, generating the maintenance evaluation value h includes:

[0047]

[0048] Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; n is the number of monitoring points; β i is the influence factor of the i-th monitoring point; T(i) is a selection coefficient; if (f i -F1) > 0, T(i) = 1 / (f i -F1); if (f i -F1) < 0, T(i) = 0.

[0049] In some embodiments of the present application, a photovoltaic power station loss decomposition monitoring system is provided, including:

[0050] A central control unit for establishing a plurality of monitoring points according to the equipment parameters of the photovoltaic power station and setting a plurality of loss indicators according to the historical loss parameters;

[0051] A monitoring unit includes a plurality of monitoring sub-modules, the monitoring sub-modules are arranged at each monitoring point, and the monitoring unit is used to collect the operation data of each monitoring point and generate a feedback data packet for each monitoring point;

[0052] The central control unit includes:

[0053] A first processing module for establishing a monitoring point sequence A, A = (a1, a2... ai …a n )), where a i is the i-th monitoring point; n is the number of monitoring points;

[0054] The second processing module is used to establish a loss index sequence P, P = (p1, p2... p i …p m ), where p i is the i-th loss index; m is the number of loss indices;

[0055] The third processing module is used to obtain the feedback data packets of each monitoring point according to the preset feedback time node, and generate the power loss tables of each monitoring point based on the feedback data packets;

[0056] The warning module is used to determine whether to generate a maintenance instruction according to all the power loss tables.

[0057] In some embodiments of the present application, the third processing module is further used for:

[0058] Set p i as the target loss index in sequence according to the loss index sequence P;

[0059] Generate an evaluation sub-model of the target loss index;

[0060] Establish the evaluation sub-models of each loss index in sequence, and establish an evaluation sub-model sequence S, S = (s1, s2... s i …s m ), where s i is the evaluation sub-model of the i-th loss index; m is the number of loss indices;

[0061] Establish an evaluation model according to the evaluation sub-model sequence S;

[0062] Set a i as the target monitoring point in sequence according to the monitoring point sequence A;

[0063] Obtain the feedback data packet of the target monitoring point at the current feedback time node;

[0064] Extract all the characteristic parameters in the feedback data packet;

[0065] Generate the first-level loss values corresponding to each loss index of the monitoring point according to the characteristic parameters and the evaluation model;

[0066] Establish a first-level loss value sequence B of the target monitoring point at the current feedback time node, B = (b1, b2... b i …b m ), where bi is the first-level loss value of the i-th loss index of the target monitoring point at the current feedback time node, and m is the number of loss indices;

[0067] Obtain the total power loss value of the target monitoring point;

[0068] Generate a correction parameter for the first-level loss value sequence according to the total power loss value;

[0069] Generate secondary loss values corresponding to each loss index according to the correction result, and establish a power loss table of the target monitoring point at the current feedback time node according to all the secondary loss values;

[0070] Generate power loss tables of each monitoring point at the current feedback time node in sequence.

[0071] In some embodiments of the present application, the warning module is further configured to:

[0072] Obtain the power loss table of the target monitoring point;

[0073] Generate an abnormal loss value f of the target monitoring point according to the power loss table;

[0074]

[0075] Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; c i is the secondary loss value of the i-th loss index of the target monitoring point at the current feedback time node; m is the number of loss indexes; μ i is the influence factor of the i-th loss index; c' i is the loss value threshold of the i-th loss index; Y(i) is a selection coefficient; if (c i -c' i )>0, Y(i)=1; if (c i -c' i )<0, Y(i)=0;

[0076] Generate abnormal loss values of each monitoring point in sequence;

[0077] Establish an abnormal loss value sequence F, F=(f1, f2...f i ...f n )), where f i is the abnormal loss value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points;

[0078] Judge whether to generate a correction instruction according to the abnormal loss value sequence F.

[0079] Compared with the prior art, the beneficial effects of a photovoltaic power station loss decomposition monitoring method and system in an embodiment of the present application are as follows:

[0080] Multiple loss indicators are set, and evaluation sub-models for each loss indicator are established, so as to comprehensively analyze the power loss parameters of each monitoring point, generate the proportion of power loss caused by different factors, and thus achieve visual monitoring of the power loss of each monitoring point, improving the operation efficiency of the photovoltaic power station.

[0081] By periodically analyzing the power loss of each monitoring point, timely warning is given to the monitoring points with abnormal power loss, and corresponding maintenance strategies are formulated according to their power loss tables, so as to timely eliminate the abnormal operation risks of the monitoring points, improve the maintenance efficiency of each monitoring point, reduce the comprehensive power loss, and improve the operation efficiency of the photovoltaic power station. Description of the Drawings

[0082] Figure 1 It is a schematic flow chart of a method for monitoring the loss decomposition of a photovoltaic power station in a preferred embodiment of the embodiment of the present application. Detailed Embodiments

[0083] The following will further describe in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0084] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present application.

[0085] The terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0086] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0087] Such asFigure 1 As shown in Figure 1 , a method for monitoring the loss decomposition of a photovoltaic power station according to a preferred embodiment of the present application includes:

[0088] S101: Establish a plurality of monitoring points according to the equipment parameters of the photovoltaic power station, and set a plurality of loss indicators according to the historical loss parameters;

[0089] S102: Obtain the feedback data packets of each monitoring point according to the preset feedback time node, and generate the power loss tables of each monitoring point according to the feedback data packets;

[0090] S103: Determine whether to generate a maintenance instruction according to all the power loss tables;

[0091] Among them, it also includes:

[0092] Establish a monitoring point sequence A, A=(a1, a2...a i …a n ), where a i is the i-th monitoring point; n is the number of monitoring points;

[0093] Establish a loss indicator sequence P, P=(p1, p2...p i …p m ), where p i is the i-th loss indicator; m is the number of loss indicators.

[0094] Specifically, establish a monitoring point sequence according to the number of photovoltaic modules in the photovoltaic power station, where a single monitoring point represents a photovoltaic module.

[0095] Specifically, by analyzing the historical parameters, a plurality of loss indicators are generated, and the loss indicators include, but are not limited to, component efficiency loss indicators, occlusion loss indicators, system conversion loss indicators, system matching loss indicators, system maintenance loss indicators, and environmental loss indicators and other parameters.

[0096] Specifically, generating the power loss tables of each monitoring point according to the feedback data packets includes:

[0097] Set p i as the target loss indicator in sequence according to the loss indicator sequence P;

[0098] Generate an evaluation sub-model for the target loss indicator;

[0099] Establish the evaluation sub-models of each loss indicator in sequence, and establish an evaluation sub-model sequence S, S=(s1, s2...s i …s m ), where s i is the evaluation sub-model of the i-th loss indicator; m is the number of loss indicators;

[0100] Establish an evaluation model based on the evaluation sub-model sequence S;

[0101] Obtain the feedback data packets of each monitoring point at the current feedback time node;

[0102] Generate the power loss tables of each monitoring point according to the evaluation model and all the feedback data packets.

[0103] Specifically, according to the categories of different loss indicators, corresponding monitoring characteristic indicators are generated, and corresponding evaluation sub-models are constructed. For example, for the component efficiency loss indicator, multiple monitoring characteristic indicators such as temperature loss, light-induced attenuation, annual attenuation, and spectral response parameters can be set, and the mapping relationship between each characteristic monitoring indicator and the power loss is generated, so as to construct the corresponding evaluation sub-model.

[0104] It can be understood that in the above embodiments, by setting multiple loss indicators and establishing evaluation sub-models for each loss indicator, the power loss parameters of each monitoring point are comprehensively analyzed, and the proportion of power loss caused by different factors is generated, so as to realize the visual monitoring of the power loss of each monitoring point and improve the operation efficiency of the photovoltaic power station.

[0105] In the preferred embodiment of the present application, when generating the power loss tables of each monitoring point, it includes:

[0106] Set a i as the target monitoring point according to the monitoring point sequence A;

[0107] Obtain the feedback data packet of the target monitoring point at the current feedback time node;

[0108] Extract all the characteristic parameters in the feedback data packet;

[0109] Generate the first-level loss values corresponding to each loss indicator of the monitoring point according to the characteristic parameters and the evaluation model;

[0110] Establish the first-level loss value sequence B of the target monitoring point at the current feedback time node, B = (b1, b2... b i ... b m ), where bi is the first-level loss value of the i-th loss indicator of the target monitoring point at the current feedback time node, and m is the number of loss indicators;

[0111] Obtain the total power loss value of the target monitoring point;

[0112] Generate the correction parameter of the first-level loss value sequence according to the total power loss value;

[0113] Generate the second-level loss values corresponding to each loss indicator according to the correction result, and establish the power loss table of the target monitoring point at the current feedback time node according to all the second-level loss values;

[0114] Generate the power loss tables for each monitoring point at the current feedback time node in sequence.

[0115] Specifically, the primary loss value refers to the power loss value calculated based on the feedback data packet and the corresponding evaluation sub-model.

[0116] Specifically, by performing an overall calculation on the photovoltaic power station, the total power loss values corresponding to each monitoring point are generated. The difference between the total power loss value and the sum of all primary loss values is allocated according to the proportion of each primary loss value, thereby generating the secondary loss values of each loss indicator.

[0117] Specifically, the power loss table includes the secondary loss values caused by each loss indicator of the target monitoring point at the current feedback time node.

[0118] It can be understood that in the above embodiments, through multi-level correction, the power loss of a single monitoring point is accurately analyzed, realizing the visual monitoring of the power loss of the monitoring point, providing data support for subsequent risk analysis and maintenance of the monitoring point, and improving the management efficiency of the photovoltaic power station.

[0119] In the preferred embodiment of the present application, determining whether to generate a maintenance instruction according to all the power loss tables includes:

[0120] Obtain the power loss table of the target monitoring point;

[0121] Generate the abnormal loss value f of the target monitoring point according to the power loss table;

[0122] Generate the abnormal loss values of each monitoring point in sequence;

[0123] Establish an abnormal loss value sequence F, F = (f1, f2... f i …f n ), where f i is the abnormal loss value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points;

[0124] Determine whether to generate a correction instruction according to the abnormal loss value sequence F.

[0125] Specifically, generating the abnormal loss value f of the target monitoring point includes:

[0126]

[0127] Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; c i is the secondary loss value of the i-th loss indicator of the target monitoring point at the current feedback time node; m is the number of loss indicators; μi is the influence factor of the i-th loss index; c' i is the loss value threshold of the i-th loss index; Y(i) is the selection coefficient; if (c i - c' i ) > 0, Y(i) = 1; if (c i - c' i ) < 0, Y(i) = 0.

[0128] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter is within the same value range.

[0129] Specifically, the larger the abnormal loss value, the greater the possibility that the current monitoring point has an abnormal operation.

[0130] Specifically, judging whether to generate a correction instruction according to the abnormal loss value sequence F includes:

[0131] Presetting the abnormal loss value threshold F1;

[0132] If f i > F1, the i-th monitoring point at the current feedback time node generates a first-level correction instruction, and a maintenance sub-strategy is generated according to the first-level correction instruction and the power loss table of the i-th monitoring point;

[0133] Generating a maintenance evaluation value h according to the abnormal loss value sequence F;

[0134] Presetting the maintenance evaluation value threshold H1;

[0135] If h > H1, a second-level maintenance instruction is generated at the current feedback time node.

[0136] Specifically, the abnormal loss value threshold and the maintenance evaluation value threshold are set according to historical parameters. The first-level correction instruction means that there is a risk of abnormal operation at the current monitoring point. By analyzing the secondary loss values corresponding to each loss index, the abnormal operation category of the monitoring point is analyzed, and a corresponding maintenance sub-strategy is generated according to the analysis result.

[0137] Specifically, generating the maintenance evaluation value h includes:

[0138]

[0139] Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; n is the number of monitoring points; β i is the influence factor of the i-th monitoring point; T(i) is the selection coefficient; if (f i - F1) > 0, T(i) = 1 / (f i-(F1); if (f i -(F1) < 0, T(i) = 0.

[0140] Specifically, the larger the overhaul evaluation value is, the greater the possibility of systemic risk in the current PV power station. When the overhaul evaluation value is greater than the preset overhaul evaluation value threshold, it indicates that there is a systemic operation risk in the PV power station, and it is necessary to generate a secondary overhaul instruction in time to conduct an overall overhaul of the PV power station, reduce the overall power loss, and improve the operation efficiency of the PV power station.

[0141] Specifically, all parameters in the model are normalized by presetting a third fixed coefficient and a fourth fixed coefficient, so that each parameter is within the same value range.

[0142] It can be understood that in the above embodiments, by periodically analyzing the power loss of each monitoring point, warning the monitoring points with abnormal power loss in time, formulating corresponding overhaul strategies according to their power loss tables, eliminating the abnormal operation risks of the monitoring points in time, improving the overhaul efficiency of each monitoring point, reducing the comprehensive power loss, and improving the operation efficiency of the PV power station.

[0143] Based on another preferred embodiment of a PV power station loss decomposition monitoring method in any of the above preferred embodiments, this preferred embodiment provides a PV power station loss decomposition monitoring system, including:

[0144] A central control unit, configured to establish a plurality of monitoring points according to the equipment parameters of the PV power station, and set a plurality of loss indicators according to historical loss parameters;

[0145] A monitoring unit, including a plurality of monitoring sub-modules, the monitoring sub-modules are arranged at each monitoring point, and the monitoring unit is configured to collect the operation data of each monitoring point and generate a feedback data packet for each monitoring point;

[0146] The central control unit includes:

[0147] A first processing module, configured to establish a monitoring point sequence A, A = (a1, a2... a i ... a n ), where a i is the i-th monitoring point; n is the number of monitoring points;

[0148] A second processing module, configured to establish a loss indicator sequence P, P = (p1, p2... p i ... p m ), where p i is the i-th loss indicator; m is the number of loss indicators;

[0149] A third processing module, configured to obtain feedback data packets of each monitoring point according to a preset feedback time node, and generate a power loss table for each monitoring point based on the feedback data packets;

[0150] An early warning module, configured to determine whether to generate a maintenance instruction according to the total power loss table.

[0151] In a preferred embodiment of the present application, the third processing module is further configured to:

[0152] Set p in sequence according to the loss index sequence P i as the target loss index;

[0153] Generate an evaluation sub-model for the target loss index;

[0154] Establish evaluation sub-models for each loss index in sequence, and establish an evaluation sub-model sequence S, S=(s1, s2…s i …s m ), where s i is the evaluation sub-model of the i-th loss index; m is the number of loss indexes;

[0155] Establish an evaluation model according to the evaluation sub-model sequence S;

[0156] Set a in sequence according to the monitoring point sequence A i as the target monitoring point;

[0157] Obtain the feedback data packet of the target monitoring point at the current feedback time node;

[0158] Extract all feature parameters in the feedback data packet;

[0159] Generate the first-level loss values corresponding to each loss index of the monitoring point according to the feature parameters and the evaluation model;

[0160] Establish a first-level loss value sequence B of the target monitoring point at the current feedback time node, B=(b1, b2…b i …b m ), where bi is the first-level loss value of the i-th loss index of the target monitoring point at the current feedback time node, and m is the number of loss indexes;

[0161] Obtain the total power loss value of the target monitoring point;

[0162] Generate a correction parameter for the first-level loss value sequence according to the total power loss value;

[0163] Generate the second-level loss values corresponding to each loss index according to the correction result, and establish a power loss table of the target monitoring point at the current feedback time node according to all the second-level loss values;

[0164] Generate the power loss tables of each monitoring point at the current feedback time node in sequence.

[0165] In a preferred embodiment of the present application, the early warning module is further configured to:

[0166] Obtain the power loss table of the target monitoring point;

[0167] Generate an abnormal loss value f of the target monitoring point according to the power loss table;

[0168]

[0169] Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; c i is the secondary loss value of the i-th loss index of the target monitoring point at the current feedback time node; m is the number of loss indices; μ i is the influence factor of the i-th loss index; c' i is the loss value threshold of the i-th loss index; Y(i) is a selection coefficient; if (c i -c' i )>0, Y(i)=1; if (c i -c' i )<0, Y(i)=0;

[0170] Generate the abnormal loss values of each monitoring point in sequence;

[0171] Establish an abnormal loss value sequence F, F=(f1, f2...f i ...f n ), where f i is the abnormal loss value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points;

[0172] Judge whether to generate a correction instruction according to the abnormal loss value sequence F.

[0173] According to the first concept of the present application, a plurality of loss indices are set and evaluation sub-models of each loss index are established, so as to comprehensively analyze the power loss parameters of each monitoring point, generate the proportion of power loss caused by different factors, and thus realize the visual monitoring of the power loss of each monitoring point, improving the operation efficiency of the photovoltaic power station.

[0174] According to the second concept of the present application, by periodically analyzing the power loss of each monitoring point, timely warning is given to the monitoring points with abnormal power loss, and corresponding maintenance strategies are formulated according to their power loss tables, timely eliminating the abnormal operation risks of the monitoring points, improving the maintenance efficiency of each monitoring point, reducing the comprehensive power loss, and improving the operation efficiency of the photovoltaic power station.

[0175] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.

Claims

1. A photovoltaic power station loss decomposition monitoring method, characterized in that: include: Establish multiple monitoring points based on the equipment parameters of the photovoltaic power station, and set multiple loss indicators based on historical loss parameters; Obtain feedback data packets from each monitoring point according to a preset feedback time node, and generate a power loss table for each monitoring point according to the feedback data packets; Determine whether to generate a maintenance instruction based on the total power loss table; Among them, it also includes: Establish a monitoring point sequence A, A = (a1, a2…a i …a n ), where a i is the i-th monitoring point; n is the number of monitoring points; Establish the loss index sequence P, P = (p1, p2...p i …p m ), where p i is the i-th loss indicator; m is the number of loss indicators.

2. The photovoltaic power station loss decomposition monitoring method according to claim 1, characterized in that: Generate the power loss table of each monitoring point according to the feedback data packet, including: According to the loss index sequence P, set p i is the target loss indicator; Generate an evaluation sub-model for the target loss metric; The evaluation sub-models of each loss index are established in turn, and the evaluation sub-model sequence S is established, S = (s1, s2…s i …s m ), where s i is the evaluation sub-model of the i-th loss index; m is the number of loss indicators; Establish an evaluation model according to the evaluation sub-model sequence S; Obtain the feedback data packet of each monitoring point at the current feedback time node; Generate a power loss table for each monitoring point based on the evaluation model and all feedback data packets.

3. The photovoltaic power station loss decomposition monitoring method according to claim 2, characterized in that: When generating the power loss table for each monitoring point, include: According to the monitoring point sequence A, set a i It is the target monitoring point; Obtain the feedback data packet of the target monitoring point at the current feedback time node; Extract all characteristic parameters in the feedback data packet; Generate the first-level loss value corresponding to each loss indicator of the monitoring point according to the characteristic parameters and the evaluation model; Establish the first-level loss value sequence B of the target monitoring point at the current feedback time node, B = (b1, b2…b i …b m ), where bi is the primary loss value of the current feedback time node of the i-th loss indicator in the target monitoring point, and m is the number of loss indicators; Obtain the total power loss value of the target monitoring point; Generate correction parameters for the first-level loss value series according to the total power loss value; Generate the secondary loss value corresponding to each loss indicator according to the correction result, and establish the power loss table of the target monitoring point at the current feedback time node according to all the secondary loss values; Generate the power loss table of each monitoring point at the current feedback time node in turn.

4. The photovoltaic power station loss decomposition monitoring method according to claim 3, characterized in that: Determine whether to generate a maintenance instruction based on the total power loss table, including: Obtain the power loss table of the target monitoring point; Generate an abnormal loss value f of the target monitoring point according to the power loss table; Generate abnormal loss values ​​for each monitoring point in turn; Establish abnormal loss value sequence F, F = (f1, f2…f i …f n ), where f i is the abnormal loss value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points; It is determined whether to generate a correction instruction based on the abnormal loss value sequence F.

5. The photovoltaic power station loss decomposition monitoring method according to claim 4, characterized in that: Generate the abnormal loss value f of the target monitoring point, including: Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; c i is the secondary loss value of the i-th loss indicator at the current feedback time node of the target monitoring point; m is the number of loss indicators; μ i is the influencing factor of the i-th loss index; c' i is the loss value threshold of the i-th loss indicator; Y(i) is the selection coefficient; if (c i -c' i )>0, Y(i)=1; if (c i -c' i )<0, Y(i)=0.

6. The photovoltaic power station loss decomposition monitoring method according to claim 5, characterized in that: Determining whether to generate a correction instruction according to the abnormal loss value sequence F includes: Preset abnormal loss value threshold F1; If f i >F1, the i-th monitoring point generates a first-level correction instruction at the current feedback time node, and generates a maintenance sub-strategy based on the first-level correction instruction and the power loss table of the i-th monitoring point; Generate maintenance evaluation value h according to abnormal loss value series F; Preset maintenance evaluation value threshold H1; If h>H1, the current feedback time node generates a secondary maintenance instruction.

7. The photovoltaic power station loss decomposition monitoring method according to claim 6, characterized in that: Generate maintenance evaluation value h, including: Wherein, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; n is the number of monitoring points; β i is the influencing factor of the i-th monitoring point; T(i) is the selection coefficient; if (f i -F1)>0,T(i)=1 / (f i -F1); if (f i -F1)<0,T(i)=0.

8. A photovoltaic power station loss decomposition monitoring system, using the photovoltaic power station loss decomposition monitoring method according to any one of claims 1 to 7, characterized in that: include: The central control unit is used to establish multiple monitoring points according to the equipment parameters of the photovoltaic power station and set multiple loss indicators according to historical loss parameters; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at each monitoring point, and the monitoring unit is used to collect operation data of each monitoring point and generate feedback data packets of each monitoring point; The central control unit comprises: The first processing module is used to establish a monitoring point sequence A, A = (a1, a2...a i …a n ), where a i is the i-th monitoring point; n is the number of monitoring points; The second processing module is used to establish a loss index sequence P, P = (p1, p2...p i …p m ), where p i is the i-th loss index; m is the number of loss indicators; The third processing module is used to obtain the feedback data packets of each monitoring point according to the preset feedback time node, and generate the power loss table of each monitoring point according to the feedback data packets; The early warning module is used to determine whether to generate a maintenance instruction based on the total power loss table.

9. The photovoltaic power station loss decomposition monitoring system according to claim 8, characterized in that: The third processing module is also used for: According to the loss index sequence P, set p i is the target loss indicator; Generate an evaluation sub-model for the target loss metric; The evaluation sub-models of each loss index are established in turn, and the evaluation sub-model sequence S is established, S = (s1, s2…s i …s m ), where s i is the evaluation sub-model of the i-th loss index; m is the number of loss indicators; Establish an evaluation model according to the evaluation sub-model sequence S; According to the monitoring point sequence A, set a i It is the target monitoring point; Obtain the feedback data packet of the target monitoring point at the current feedback time node; Extract all characteristic parameters in the feedback data packet; Generate the first-level loss value corresponding to each loss indicator of the monitoring point according to the characteristic parameters and the evaluation model; Establish the first-level loss value sequence B of the target monitoring point at the current feedback time node, B = (b1, b2…b i …b m ), where bi is the primary loss value of the current feedback time node of the i-th loss indicator in the target monitoring point, and m is the number of loss indicators; Obtain the total power loss value of the target monitoring point; Generate correction parameters for the first-level loss value series according to the total power loss value; Generate the secondary loss value corresponding to each loss indicator according to the correction result, and establish the power loss table of the target monitoring point at the current feedback time node according to all the secondary loss values; Generate the power loss table of each monitoring point at the current feedback time node in turn.

10. The photovoltaic power station loss decomposition monitoring system according to claim 9, characterized in that: The early warning module is also used for: Obtain the power loss table of the target monitoring point; Generate an abnormal loss value f of the target monitoring point according to the power loss table; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; c i is the secondary loss value of the i-th loss indicator at the current feedback time node of the target monitoring point; m is the number of loss indicators; μ i is the influencing factor of the i-th loss index; c' i is the loss value threshold of the i-th loss indicator; Y(i) is the selection coefficient; if (c i -c' i )>0, Y(i)=1; if (c i -c' i )<0, Y(i)=0; Generate abnormal loss values ​​for each monitoring point in turn; Establish abnormal loss value sequence F, F = (f1, f2…f i …f n ), where f i is the abnormal loss value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points; It is determined whether to generate a correction instruction based on the abnormal loss value sequence F.

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