Big data-based electric power data supervision system and method

Abstract

The invention discloses an electric power data supervision system and method based on big data, and relates to the technical field of electric power data supervision. The system comprises a data processing module which is configured to obtain power data and meteorological data, clean the power data and the meteorological data and then send the cleaned power data and meteorological data to a data storage management module; the prediction analysis module is configured to predict the estimated generating capacity of the new energy according to the called meteorological data and send the estimated generating capacity to the data storage management module; the data storage management module is configured to receive the electric power data, the meteorological data and the estimated generating capacity, integrate the received data and store the data, so that the data can be extracted by other modules; and the electric energy quality analysis module is configured to evaluate the power supply quality of the power system based on the power grid data called from the data storage management module. The problem that an existing electric power data supervision system is low in supervision efficiency is solved. The supervision efficiency of the power data supervision system and the exception handling efficiency of the power system are improved, and the power supply quality is ensured.

Description

Technical Field

[0001] This application relates to the technical field of power system supervision, and in particular, to a power data supervision system and method based on big data. Background Art

[0002] The power system is the core link of energy conversion, transmission and distribution, converting primary energy (such as coal, oil, natural gas, nuclear energy, water energy, wind energy, solar energy, etc.) into electric energy, and safely, economically and reliably transporting it to the user side through the power grid. The power system is a basic industry for the development of the national economy, which can meet the electricity demands of various aspects such as industry, commerce and residents. Therefore, its operating state directly affects the normal progress of industrial production and commercial activities. Stable power supply is one of the key factors to maintain economic growth and improve production efficiency.

[0003] Under the background of the global energy transformation (the process of the energy system changing from relying on fossil energy to renewable energy at present), the scale of the power system is getting larger and the structure is getting more complex. When the power department manages power data, most of the current links still supervise the power data in the power system manually, and the supervision efficiency is low. Especially when facing the more complex and large-scale power system today, the traditional way of relying on manual supervision of power data requires more manual participation and multi-faceted coordination, resulting in low supervision efficiency and being difficult to make timely adjustments to ensure power supply quality. Summary of the Invention

[0004] The embodiments of this application solve the problem of low supervision efficiency of the existing power system supervision system by providing a power data supervision system and method based on big data.

[0005] In a first aspect, the embodiments of this application provide a power data supervision system based on big data, including: a data processing module configured to obtain power data and meteorological data, and send the power data and the meteorological data to a data storage and management module after cleaning them; wherein, the power data includes grid data and equipment operation data of power equipment;

[0006] A prediction and analysis module configured to predict the estimated power generation of new energy according to the meteorological data retrieved from the data storage and management module, and send the estimated power generation to the data storage and management module;

[0007] A data storage and management module configured to: receive the power data, the meteorological data and the estimated power generation, and store the received data after integration for other modules to extract;

[0008] The power quality analysis module is configured to evaluate the power supply quality of the power system based on the grid data retrieved from the data storage and management module.

[0009] In a possible implementation manner in combination with the first aspect, the data processing module includes a grid data acquisition unit, a device monitoring unit, and a first preprocessing unit;

[0010] The grid data acquisition unit is configured to collect grid data at a set frequency and send the grid data to the first preprocessing unit; wherein, the grid data includes voltage, current, power, and frequency;

[0011] The grid data acquisition unit includes a voltage acquisition unit, a current acquisition unit, a power determination unit, and a frequency determination unit;

[0012] The voltage acquisition unit is configured to communicate with a voltage measuring device, receive the voltage measured by the voltage measuring device at a set frequency, and send the voltage to the first preprocessing unit;

[0013] The current acquisition unit is configured to communicate with a current measuring device, receive the current measured by the current measuring device at a set frequency, and send the current to the first preprocessing unit;

[0014] The power determination unit is configured to calculate power based on the received voltage and current and send the power to the first preprocessing unit;

[0015] The frequency determination unit is configured to calculate frequency based on the received voltage value and current value and send the frequency to the first preprocessing unit;

[0016] The device monitoring unit is configured to monitor the device operation data of the power devices in the power system in real time and send the device operation data to the first preprocessing unit;

[0017] The first preprocessing unit is configured to clean the received grid data and device operation data and send the cleaned grid data and device operation data to the data storage and management module.

[0018] In a second possible implementation manner in combination with the first aspect, the data processing module further includes a meteorological data acquisition unit and a second preprocessing unit;

[0019] The meteorological data acquisition unit is configured to collect meteorological data at a set frequency and send the meteorological data to the second preprocessing unit; wherein, the meteorological data includes photovoltaic data and wind power data;

[0020] The second preprocessing unit is configured to clean the received meteorological data and send the cleaned meteorological data to the data storage and management module.

[0021] Combined with the second possible implementation manner of the first aspect, in the third possible implementation manner, the meteorological data acquisition unit includes a photovoltaic data acquisition unit and a wind power data acquisition unit;

[0022] The photovoltaic data acquisition unit is configured to acquire photovoltaic data at a set frequency and send the photovoltaic data to the second preprocessing unit; wherein, the photovoltaic data includes solar radiation and temperature;

[0023] The wind power data acquisition unit is configured to acquire wind power data at a set frequency and send the wind power data to the second preprocessing unit; wherein, the wind power data includes wind speed, wind direction and temperature.

[0024] Combined with the first aspect, in the fourth possible implementation manner, the data storage and management module includes a data integration unit and a data sequence list unit;

[0025] The data integration unit is configured to construct key-value pairs with the acquired power data, meteorological data, estimated power generation amount and equipment operation data as values and their acquisition times as keys, and send the key-value pairs to the data sequence list unit;

[0026] The data sequence list unit is configured to: store the acquired power data, meteorological data, estimated power generation amount and equipment operation data as sequences in order of the time of each key-value pair, form a data sequence list for storage; update the data sequence list according to the received key-value pairs.

[0027] Combined with the first aspect, in the fifth possible implementation manner, the prediction and analysis module further includes an influence coefficient unit;

[0028] The influence coefficient unit is configured to acquire the local time, determine the photovoltaic influence coefficient and the wind power influence coefficient according to the local time, and send the photovoltaic influence coefficient and the wind power influence coefficient to the prediction and analysis module;

[0029] The prediction and analysis module is configured to extract the meteorological data stored in the data storage and management module, predict the estimated power generation amount of new energy according to the meteorological data and the received photovoltaic influence coefficient and / or wind power influence coefficient, and send the estimated power generation amount to the data storage and management module.

[0030] Combined with the first aspect, in the sixth possible implementation manner, it further includes an information analysis module, an alarm module, an emergency response module, a regulation module and a visualization module;

[0031] The information analysis module is configured to: compare the retrieved device operation data with preset operation parameters to determine whether the power equipment is abnormal and obtain a first judgment result, and send the first judgment result to the alarm module and the emergency response module; extract the power grid data from the data storage and management module, determine whether the power grid data is normal and obtain a second judgment result, and send the second judgment result to the alarm module and the emergency response module; obtain the power supply quality of the power quality analysis module, determine the influencing factors of the current power supply quality according to the retrieved power grid data and the power supply quality, and send the influencing factors to the alarm module and the emergency response module; perform fault location according to the first judgment result, the second judgment result and the influencing factors, and send the fault location result to the emergency response module; judge the influence of the access of new energy on the power supply quality of the power system according to the power grid data, the estimated power generation and the power supply quality, obtain a third judgment result, and send the third judgment result to the alarm module;

[0032] The alarm module is configured to perform corresponding-level alarms on the received first judgment result, second judgment result, influencing factors or third judgment result according to preset alarm levels;

[0033] The emergency response module is configured to: generate a recommended list of emergency plans according to the first judgment result and / or the second judgment result and / or the influencing factors and / or the third judgment result, determine a target emergency plan based on the priorities of the various recommended emergency plans in the recommended list of emergency plans, and send the determined target emergency plan to the control module; or, select a target emergency plan from the recommended list of emergency plans in response to a user's selection operation, and send the selected target emergency plan to the control module; respond to a user operation, and generate a control instruction and send it to the control module;

[0034] The control module is configured to regulate the power system according to the received target emergency plan or control instruction;

[0035] The visualization module is configured to convert the power data, meteorological data, power supply quality, first judgment result, second judgment result, influencing factors, fault location result or third judgment result into a visual form for screen display.

[0036] Combined with the sixth possible implementation manner of the first aspect, in the seventh possible implementation manner, an evaluation unit is further included;

[0037] The evaluation unit is configured to obtain the estimated power generation amount and the power supply quality, and evaluate the regulation effect of the target emergency plan or the control instruction generated by the emergency response module according to the estimated power generation amount and the power supply quality.

[0038] In a second aspect, an embodiment of the present application provides a method for supervising power data of big data. This method is applied to the first aspect and any possible implementation manner of the first aspect. The method includes: obtaining power data and meteorological data of a power system; where the power data includes grid data and equipment operation data of power equipment; predicting the estimated power generation amount of new energy according to the meteorological data, cleaning, integrating the power data, meteorological data and the estimated power generation amount, and storing them as a data sequence table; judging whether the grid data and the equipment operation data of the power equipment are abnormal to obtain an abnormal judgment result, and evaluating the power supply quality of the power system based on the grid data; performing fault location and alarming based on the abnormal judgment result, power supply quality and the data sequence table; generating a recommended list of emergency plans based on the result of the fault location and the power supply quality; determining a target emergency plan based on the priority of each emergency plan recommended in the recommended list of emergency plans; or, selecting a target emergency plan in the recommended list of emergency plans in response to a selection operation of a user; or, responding to a user operation and generating a control instruction by the user operation; regulating the power system based on the target emergency plan or the control instruction to eliminate the alarm.

[0039] Combined with the second aspect, in a possible implementation manner, the cleaning, integrating the power data, the meteorological data and the estimated power generation amount and storing them as a data sequence table includes:

[0040] Obtaining the acquisition time of the grid data, the equipment operation data of the power equipment, the meteorological data and the estimated power generation amount;

[0041] Removing the grid data, the equipment operation data of the power equipment, the meteorological data or the estimated power generation amount with anomalies or repeated acquisition times;

[0042] Taking the acquisition time as the primary key, and constructing key-value pairs with the grid data, the equipment operation data of the power equipment, the meteorological data and the estimated power generation amount corresponding to the acquisition time as the values;

[0043] Taking the acquisition time of each key-value pair as the order, storing the grid data, the equipment operation data of the power equipment, the meteorological data and the estimated power generation amount as multiple sequences respectively, and storing them as the data sequence table in this way.

[0044] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0045] In the embodiments of the present application, by adopting a power data supervision system and method based on big data to supervise power data, the problem of low supervision efficiency of the existing power data supervision system is effectively solved. Furthermore, the manual participation is reduced, the supervision efficiency of the power data supervision system is improved, and thus the abnormal handling efficiency of the power system can be improved, and the power supply quality can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application or the description of the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 FIG. is a schematic structural diagram of a power data supervision system based on big data provided by an embodiment of the present application;

[0048] Figure 2 FIG. is a schematic structural diagram of a data processing module provided by an embodiment of the present application;

[0049] Figure 3 FIG. is a flowchart of a method for supervising power data of a big data provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] The following explanations are made for some of the technologies involved in the embodiments of the present application to facilitate understanding. It should be considered that they are only exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described here without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of some well-known functions and structures is omitted below.

[0052] As Figure 1 shown, the present application also provides a schematic structural diagram of a power data supervision system based on big data. The system includes: a data processing module, a prediction and analysis module, a data storage and management module, and a power quality analysis module, which are specifically as follows.

[0053] In an embodiment of the present application, a data processing module is configured to obtain power data and meteorological data, and send the power data and meteorological data to a data storage and management module after cleaning them; wherein, the power data includes grid data and equipment operation data of power equipment.

[0054] Specifically, as Figure 2 shown, the data processing module includes a grid data acquisition unit, an equipment monitoring unit, a first preprocessing unit, a meteorological data acquisition unit, and a second preprocessing unit.

[0055] Among them, the grid data acquisition unit is configured to collect grid data at a set frequency and send the grid data to the first preprocessing unit. Among them, the grid data includes voltage, current, power, and frequency. The equipment monitoring unit is configured to monitor the equipment operation data of power equipment in the power system in real time and send the equipment operation data to the first preprocessing unit. The first preprocessing unit is configured to clean the received grid data and equipment operation data and send the cleaned grid data and equipment operation data to the data storage and management module.

[0056] Among them, the meteorological data acquisition unit is configured to collect meteorological data at a set frequency and send the meteorological data to the second preprocessing unit. Among them, the meteorological data includes photovoltaic data and wind power data. The second preprocessing unit is configured to clean the received meteorological data and send the cleaned meteorological data to the data storage and management module.

[0057] Further, the grid data acquisition unit includes a voltage acquisition unit, a current acquisition unit, a power determination unit, and a frequency determination unit. The voltage acquisition unit is configured to communicate with a voltage measurement device, receive the voltage measured by the voltage measurement device at a set frequency, and send the voltage to the first preprocessing unit. The current acquisition unit is configured to communicate with a current measurement device, receive the current measured by the current measurement device at a set frequency, and send the current to the first preprocessing unit. The power determination unit is configured to calculate power based on the received voltage and current and send the power to the first preprocessing unit. The frequency determination unit is configured to calculate frequency based on the received voltage value and current value and send the frequency to the first preprocessing unit.

[0058] Specifically, the measured voltage and current are sent to the first preprocessing unit for cleaning to remove the outliers.

[0059] Exemplarily, the voltage measurement device in the present application may be devices such as a voltmeter, a voltage detector, a digital multimeter, a voltage sensor, etc. The current measurement device may be devices such as an ammeter, a current transformer, a current sensor, etc. The power determination unit obtains the voltage and current collected by the voltage acquisition unit and the current acquisition unit, and then calculates the power by multiplying the voltage and the current. The frequency determination unit determines the frequency by measuring the period of the alternating current signal in the power distribution network and calculating the number of periodic changes in the waveforms of the voltage and current. Finally, the calculated power and frequency are sent to the first preprocessing unit for cleaning to remove the abnormal points therein.

[0060] The meteorological data acquisition unit includes a photovoltaic data acquisition unit and a wind power data acquisition unit. The photovoltaic data acquisition unit is configured to acquire photovoltaic data at a set frequency and send the photovoltaic data to the second preprocessing unit. Among them, the photovoltaic data includes solar radiation and temperature. The wind power data acquisition unit is configured to acquire wind power data at a set frequency and send the wind power data to the second preprocessing unit. Among them, the wind power data includes wind speed, wind direction and temperature.

[0061] Exemplarily, the photovoltaic data and the wind power data can obtain the meteorological data of the meteorological station by networking with the meteorological station. The data of solar radiation can also be obtained through a pyranometer, a radiometer, etc., the temperature can be obtained through a temperature sensor or an intelligent thermometer, the wind speed can be measured by an anemometer, and the wind direction can be measured by a weather vane, etc. Then the obtained photovoltaic data and wind power data are sent to the second preprocessing unit for data cleaning to remove the abnormal data therein.

[0062] The device monitoring unit is configured to monitor in real time the device operation data of the power equipment in the power system, send the device operation data to the first preprocessing unit for data cleaning, and then send the cleaned device operation data to the data storage management module.

[0063] The prediction and analysis module is configured to predict the estimated power generation of new energy according to the meteorological data retrieved from the data storage management module, and send the estimated power generation to the data storage management module.

[0064] In the embodiment of the present application, the prediction and analysis module further includes an influence coefficient unit. The influence coefficient unit is configured to obtain the local time, determine the photovoltaic influence coefficient and the wind power influence coefficient according to the local time, and send the photovoltaic influence coefficient and the wind power influence coefficient to the prediction and analysis module. The prediction and analysis module is configured to extract the meteorological data stored in the data storage management module, and predict the estimated power generation of new energy according to the meteorological data and the received photovoltaic influence coefficient and / or wind power influence coefficient, and send the estimated power generation to the data storage management module.

[0065] Specifically, the influence coefficient unit uses historical data for learning to determine the power generation efficiency of the photovoltaic module and the conversion efficiency of the wind turbine under the meteorological data conditions.

[0066] In the embodiment of the present application, the estimated power generation is calculated by the following formula:

[0067] E=(1-α)(E g +E f ),

[0068] E g =(f·m·v) / lnt,

[0069] E f =log 10 F×cos(p / b)×w·s×(lnt / 2).

[0070] In the formula, E represents the estimated power generation, E g Represents photovoltaic power generation, E f represents wind power generation, f represents solar radiation, m represents the area of PV modules, v represents the power generation efficiency of PV modules, t represents temperature, F represents wind force, wind speed can be used to represent wind force here, the unit is meter per second, p represents wind direction, indicating whether the wind turbine can effectively capture wind energy, b represents the direction of wind turbine blades, w represents the conversion efficiency of wind turbine, s represents the operating time of wind turbine, α represents system loss coefficient, which is exemplarily set to 0.05.

[0071] The data storage management module is configured to receive power data, meteorological data, equipment operation data and estimated power generation, and integrate the received data and store them for retrieval by other modules.

[0072] In an embodiment of the present application, the data storage management module includes a data integration unit and a data sequence table unit. The data integration unit is configured to construct a key-value pair with the acquired power data, meteorological data, estimated power generation and equipment operation data as values and the acquisition time as keys, and send the key-value pair to the data sequence table unit. The data sequence table unit is configured to store the acquired power data, meteorological data, estimated power generation and equipment operation data as sequences in order of the time of each key-value pair, and form a data sequence table for storage. The data sequence table is updated according to the received key-value pairs.

[0073] Specifically, by constructing a data sequence table with time as the key, when a fault occurs, various data at the fault moment and before and after the fault can be quickly retrieved, so that the fault cause can be quickly determined and the fault can be located. For example, when an abnormality occurs in the current power system, the key-value pair corresponding to the current time, as well as the key-value pairs for a period of time before and after the current time, can be retrieved from the data sequence table. By analyzing the power grid data, meteorological data, and equipment operation data in the key-value pairs, it can be quickly determined whether the abnormality is a voltage abnormality, a frequency abnormality, or other abnormalities through the power grid data. Whether the power system abnormality is caused by abnormal power equipment can be quickly judged through the equipment operation data. Whether it is caused by extreme weather can be judged through the meteorological data. Whether it is caused by insufficient power output of new energy power generation can be judged through the estimated power generation. In an embodiment of the present application, the above four types of data can be judged in parallel, and then the four judgment results can be integrated to more quickly and efficiently determine the fault cause, and the fault can also be located based on the above four types of data. For example, if the fault cause is a voltage abnormality caused by equipment failure and it is not extreme weather, the abnormal power equipment can be quickly determined according to the equipment operation data.

[0074] The power quality analysis module is configured to evaluate the power supply quality of the power system based on the power data retrieved from the data storage management module and send the power supply quality to the information analysis module.

[0075] Specifically, the power supply quality of the power system is judged by obtaining the fluctuations of the power data. If the voltage, current, power, or frequency fluctuates greatly, it indicates that the power supply quality is poor. If the fluctuations are small, it indicates that the power supply quality is good.

[0076] Continue to refer to Figure 1 , the present application further includes an information analysis module, an alarm module, an emergency response module, and a regulation module, which are specifically as follows.

[0077] The information analysis module is configured to: compare the retrieved equipment operation data with the preset operation parameters to judge whether the power equipment is abnormal to obtain a first judgment result, and send the first judgment result to the alarm module and the emergency response module; extract the power grid data from the data storage management module, judge whether the power grid data is normal to obtain a second judgment result, and send the second judgment result to the alarm module and the emergency response module; obtain the power supply quality of the power quality analysis module, and determine the influencing factors of the current power supply quality according to the retrieved power grid data and the power supply quality, and send the influencing factors to the alarm module and the emergency response module; perform fault location according to the first judgment result, the second judgment result, and the influencing factors, and send the fault location result to the emergency response module; judge the influence of the access of new energy on the power supply quality of the power system according to the power grid data, the estimated power generation, and the power supply quality to obtain a third judgment result, and send the third judgment result to the alarm module.

[0078] Specifically, by comparing the device operation data with preset operation parameters (i.e., the normal operation data range), it is determined whether the power equipment has an abnormality to obtain a first judgment result. It is judged whether the grid data (including voltage, current, power, and frequency) is normal to obtain a second judgment result (including whether the voltage is normal, whether the current is normal, whether the power is normal, and whether the frequency is normal). According to the grid data and power supply quality, it can be determined that the influencing factor of poor power supply quality is caused by voltage, current, power, or frequency fluctuations. According to the grid data, estimated power generation, and power supply quality, it is judged the impact of the access of new energy on the power supply quality of the power system, that is, according to the change curve of the grid data, power supply quality, and estimated power generation, it is judged whether the main reason for the current power supply quality fluctuation is caused by the access of new energy to obtain a third judgment result.

[0079] The alarm module is configured to perform alarms of corresponding levels on the received first judgment result, second judgment result, influencing factor, or third judgment result according to the preset alarm levels.

[0080] Specifically, after the alarm module receives the first judgment result, second judgment result, influencing factor, or third judgment result, it classifies and determines the first judgment result, second judgment result, influencing factor, or third judgment result according to the preset fault levels, and then performs alarms of corresponding levels. For example, if the first judgment result is abnormal, it indicates that there is a fault in the power equipment in the power system at this time, which belongs to a level-three fault, and the red light flashes and voice alarm corresponding to the level-three fault are performed.

[0081] The emergency response module is configured to: generate a recommended list of emergency plans according to the first judgment result and / or the second judgment result and / or the influencing factor and / or the third judgment result, determine the target emergency plan based on the priorities of the various emergency plans recommended in the recommended list of emergency plans, and send the determined target emergency plan to the control module. Or, in response to the user's selection operation, select the target emergency plan in the recommended list of emergency plans and send the selected target emergency plan to the control module. In response to the user operation, generate a control instruction and send it to the control module.

[0082] Specifically, determine the fault type according to the first judgment result and / or the second judgment result and / or the influencing factor and / or the third judgment result, and generate a recommended list of emergency plans composed of multiple emergency plans. Each emergency plan is displayed in descending order of priority. For example: the first judgment result is normal, the second judgment result is abnormal, the influencing factor is low voltage, and the third judgment result is caused by new energy access, indicating that all power equipment in the power system is operating normally at this time, and the access of new energy causes the power system to be abnormal. Further, it is found that the current estimated power generation is lower than the historical power generation in the same period, which is caused by insufficient new energy output. Then generate a recommended list of emergency plans for the planner to select the target emergency plan. If the technician does not select within the set time (5 minutes), the emergency plan with the highest priority will be automatically sent to the control module as the target emergency plan. In addition, the technician can also operate alone to generate a brand-new plan. The emergency response module generates a control instruction according to the user's operation and sends it to the dispatching module. Among them, the priority of the user's operation here is higher than that of the automatically generated emergency plan.

[0083] The control module is configured to regulate the power system according to the received target emergency plan or control instruction. Specifically, the dispatching module regulates the power system according to the received emergency plan or control instruction to solve the current abnormality / fault.

[0084] In the embodiment of the present application, an evaluation unit and a visualization module can also be set as follows.

[0085] The evaluation unit is configured to obtain the estimated power generation and power supply quality, and evaluate the regulation effect of the target emergency plan or control instruction generated by the emergency response module according to the estimated power generation and power supply quality.

[0086] Specifically, the evaluation module determines whether the target emergency plan or control instruction is effective by evaluating whether the power supply quality is high (stable). The evaluation module evaluates whether the emergency plan or control instruction makes efficient use of new energy power generation through the estimated power generation, so that the planner can further adjust the emergency plan according to the evaluation result or perform user operations to generate a new control instruction.

[0087] The visualization module is configured to convert power data, meteorological data, power supply quality, the first judgment result, the second judgment result, the influencing factor, the result of fault location or the third judgment result into a visual form for screen display.

[0088] Figure 3 It is a flowchart of a method for supervising power data of big data provided by an embodiment of the present application, including steps 101 to 106. Among them, Figure 3This is only an execution order shown in the embodiments of this application, and does not represent the only execution order of a power system supervision method for big data. Under the condition that the final result can be achieved, Figure 1 the steps shown can be executed in parallel or reversed.

[0089] Step 101: Obtain the power data and meteorological data of the power system. Among them, the power data includes grid data and the equipment operation data of power equipment. In the embodiments of this application, obtain the acquisition time of the grid data, power equipment operation data, meteorological data, and estimated power generation. Remove the abnormal grid data, power equipment operation data, meteorological data, or estimated power generation with duplicate acquisition times. Use the acquisition time as the key, and use the grid data, power equipment operation data, meteorological data, and estimated power generation corresponding to the acquisition time as the value to construct key-value pairs. In the order of the acquisition time of each key-value pair, store the grid data, power equipment operation data, meteorological data, and estimated power generation as multiple sequences respectively, and store them as a data sequence table in this way.

[0090] Specifically, by constructing a data sequence table, with time as the key, when a fault occurs, various data at the fault moment and before and after the fault can be quickly retrieved, and then the fault cause can be quickly determined for fault location. For example, when the current power system is abnormal, the key-value pair corresponding to the current time, as well as the key-value pairs for a period of time before and after the current time, can be retrieved from the data sequence table. By analyzing the grid data, meteorological data, and equipment operation data in the key-value pairs, it can be quickly determined whether it is voltage abnormality, frequency abnormality, or other abnormalities through the grid data, whether it is caused by abnormal power equipment leading to power system abnormality through the equipment operation data, whether it is caused by extreme weather through the meteorological data, and whether it is caused by insufficient new energy power generation through the estimated power generation. In an embodiment of this application, the above four types of data can be judged in parallel, and then the four judgment results can be integrated to more quickly and efficiently determine the fault cause, and fault location can also be performed based on the above four types of data. For example, if the fault cause is voltage abnormality caused by equipment failure and it is not extreme weather, the abnormal power equipment can be quickly determined according to the equipment operation data.

[0091] Step 102: Predict the estimated power generation of new energy according to the meteorological data, and clean, integrate, and store the power data, meteorological data, and estimated power generation as a data sequence table. In the embodiments of this application, the grid data includes voltage, current, power, and frequency, and the meteorological data includes solar radiation, temperature, wind power, and wind direction.

[0092] Exemplarily, the estimated power generation of new energy is predicted by the following formula:

[0093] E = (1 - α)(E g + E f )

[0094] E g = (f·m·v) / lnt,

[0095] E f = log 10 F×cos(p / b)×w·s×(lnt / 2).

[0096] In the formula, E represents the estimated power generation, E g represents the photovoltaic power generation, E f represents the wind power generation, f represents the solar radiation, m represents the area of the photovoltaic module, v represents the power generation efficiency of the photovoltaic module, t represents the temperature, F represents the wind force, here the wind speed can be used to represent the wind force, with the unit of meters per second, p represents the wind direction, represents whether the wind turbine can effectively capture the wind energy, b represents the direction of the wind turbine blade, w represents the conversion efficiency of the wind turbine, s represents the operating time of the wind turbine, and α represents the system loss coefficient, which is exemplarily set to 0.05.

[0097] Step 103: Determine whether the grid data and the operation data of the power equipment are abnormal, obtain the abnormal judgment result, and evaluate the power supply quality of the power system based on the grid data. In the embodiment of the present application, by comparing the equipment operation data with the preset operation parameters (i.e., the normal operation data range), it is determined whether the power equipment is abnormal to obtain the abnormal judgment result.

[0098] In the embodiment of the present application, the power supply quality of the power system is determined by obtaining the fluctuation of the grid data. If the voltage, current, power, or frequency fluctuates greatly, it indicates that the power supply quality is poor. If the fluctuation is small, it indicates that the power supply quality is good.

[0099] Step 104: Locate the fault and give an alarm based on the abnormal judgment result, power supply quality, and data sequence table. In the embodiment of the present application, the fault type is determined according to the key-value pairs in the abnormal judgment result, power supply quality, and data sequence table. Specifically. According to the abnormal judgment result, it can be determined whether the power supply quality affects the abnormality of the power system. If the influence is small and the abnormal trend of the power system is consistent with the abnormal trend of the power supply quality, it indicates that the poor power supply quality is caused by the abnormality of the power system. If the abnormality of the power system is not closely related to the power supply quality or the change trends are not synchronized, then according to the time of the abnormality, the key-value pairs corresponding to the time in the data sequence table are obtained, and it is respectively determined whether the voltage, current power, frequency, power equipment operation data, meteorological data, and estimated power generation are abnormal in them, and further determine whether the abnormality of the power system is caused by the abnormality of the power equipment. If so, the abnormal power equipment is located according to the equipment operation data. If not, it is determined whether it is caused by insufficient or excessive new energy power generation according to the estimated power generation. If not, it is determined whether it is caused by extreme weather according to the meteorological data.

[0100] Those skilled in the art should be aware that the above judgments can all be learned based on historical data to clarify the normal change ranges of voltage, current power, frequency, power equipment operation data, meteorological data and the estimated power generation, as well as the reasons for abnormalities, and then fault location can be achieved based on the above data.

[0101] Step 105: Generate a recommended list of emergency plans based on the results of fault location and power supply quality. In the embodiment of the present application, a recommended list of emergency plans composed of multiple emergency plans is generated according to the results of fault location and power supply quality, and each emergency plan is displayed in the order of priority from high to low.

[0102] Step 106: Determine the target emergency plan based on the priorities of the various emergency plans recommended in the recommended list of emergency plans. Alternatively, in response to the user's selection operation, select the target emergency plan in the recommended list of emergency plans. Alternatively, in response to the user's operation, generate a control instruction. In the embodiment of the present application, a recommended list of emergency plans is generated for the planner to select an emergency plan. If the technician does not select within the set time (5 minutes), the emergency plan with the highest priority is automatically locked as the target emergency plan. In addition, the technician can also operate alone to generate a control instruction as a brand-new plan, and here the priority of the user's operation is higher than that of the automatically generated emergency plan.

[0103] Step 107: Regulate the power system based on the target emergency plan or control instruction to eliminate the alarm. In the embodiment of the present application, the power system is regulated according to the target emergency plan or control instruction to solve the current abnormality / fault.

[0104] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on routine or non-creative labor. The order of steps listed in this embodiment is only one way among the execution orders of numerous steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in this embodiment or the drawings or executed in parallel (for example, in an environment of parallel processors or multi-threaded processing).

[0105] Some modules in the device described in the present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0106] The device or module described in the above application embodiments can be specifically implemented by a computer chip or an entity, or by a product with certain functions. For the convenience of description, when describing the above device, it is divided into various modules according to functions and described separately. When implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware. Of course, the module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0107] The method, device or module described in the present application can be implemented in the form of computer-readable program code. The controller can be implemented in any appropriate manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium that stores computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, application specific integrated circuit (ASIC), programmable logic controller, and embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as a part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers to achieve the same function. Therefore, such a controller can be regarded as a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and the structures within the hardware component.

[0108] In addition, in each embodiment of the present invention, the functional modules can be integrated into one processing module, or each module can exist independently, or two or more modules can be integrated into one module.

[0109] The above storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions.

[0110] As can be seen from the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on such an understanding, the technical solution of this application, in essence, or the part that makes contributions to the prior art, can be embodied in the form of a software product, or can also be embodied in the implementation process of data migration. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0111] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. All or part of this application can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0112] The above embodiments are only used to illustrate the technical solutions of this application, rather than limiting this application; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A power data supervision system based on big data, characterized in that Including: A data processing module, configured to obtain power data and meteorological data, and send the power data and the meteorological data to a data storage and management module after cleaning the same; wherein, the power data includes grid data and equipment operation data of power equipment; A prediction and analysis module, configured to predict the estimated power generation of new energy according to the meteorological data retrieved from the data storage and management module, and send the estimated power generation to the data storage and management module; A data storage and management module, configured to: receive the power data, the meteorological data and the estimated power generation, and store the received data after integration for extraction by other modules; A power quality analysis module, configured to evaluate the power supply quality of a power system based on the grid data retrieved from the data storage and management module.

2. The system according to claim 1, wherein The data processing module includes a grid data acquisition unit, an equipment monitoring unit and a first preprocessing unit; The grid data acquisition unit is configured to collect grid data at a set frequency and send the grid data to the first preprocessing unit; wherein, the grid data includes voltage, current, power and frequency; The grid data acquisition unit includes a voltage acquisition unit, a current acquisition unit, a power determination unit and a frequency determination unit; The voltage acquisition unit is configured to communicate with a voltage measuring device, receive the voltage measured by the voltage measuring device at a set frequency, and send the voltage to the first preprocessing unit; The current acquisition unit is configured to communicate with a current measuring device, receive the current measured by the current measuring device at a set frequency, and send the current to the first preprocessing unit; The power determination unit is configured to calculate power according to the received voltage and current, and send the power to the first preprocessing unit; The frequency determination unit is configured to calculate frequency according to the received voltage value and current value, and send the frequency to the first preprocessing unit; The equipment monitoring unit is configured to monitor the equipment operation data of power equipment in a power system in real time and send the equipment operation data to the first preprocessing unit; The first preprocessing unit is configured to clean the received grid data and equipment operation data, and send the cleaned grid data and equipment operation data to the data storage and management module.

3. The system according to claim 1, wherein The data processing module further includes a meteorological data acquisition unit and a second preprocessing unit; The meteorological data acquisition unit is configured to collect meteorological data at a set frequency and send the meteorological data to the second preprocessing unit; wherein, the meteorological data includes photovoltaic data and wind power data; The second preprocessing unit is configured to clean the received meteorological data and send the cleaned meteorological data to the data storage and management module.

4. The system according to claim 3, characterized in that, The meteorological data acquisition unit includes a photovoltaic data acquisition unit and a wind power data acquisition unit; The photovoltaic data acquisition unit is configured to obtain photovoltaic data at a set frequency and send the photovoltaic data to the second preprocessing unit; wherein, the photovoltaic data includes solar radiation and temperature; The wind power data acquisition unit is configured to acquire wind power data at a set frequency and send the wind power data to the second preprocessing unit; wherein, the wind power data includes wind speed, wind direction, and temperature.

5. The system according to claim 1, characterized in that, The data storage and management module includes a data integration unit and a data sequence list unit; The data integration unit is configured to construct key-value pairs with the acquired power data, meteorological data, estimated power generation, and equipment operation data as values and their acquisition times as keys, and send the key-value pairs to the data sequence list unit; The data sequence list unit is configured to: store the acquired power data, meteorological data, estimated power generation, and equipment operation data as sequences in order of the time of each key-value pair to form a data sequence list for storage; update the data sequence list according to the received key-value pairs.

6. The system according to claim 1, characterized in that The prediction and analysis module further includes an influence coefficient unit; The influence coefficient unit is configured to obtain the local time, determine the photovoltaic influence coefficient and the wind power influence coefficient according to the local time, and send the photovoltaic influence coefficient and the wind power influence coefficient to the prediction and analysis module; The prediction and analysis module is configured to extract the meteorological data stored in the data storage and management module, predict the estimated power generation of new energy according to the meteorological data and the received photovoltaic influence coefficient and / or wind power influence coefficient, and send the estimated power generation to the data storage and management module.

7. The system according to claim 1, wherein It further includes an information analysis module, an alarm module, an emergency response module, a regulation module, and a visualization module; The information analysis module is configured to: compare the retrieved equipment operation data with preset operation parameters to determine whether the power equipment is abnormal to obtain a first judgment result, and send the first judgment result to the alarm module and the emergency response module; Extract the grid data from the data storage and management module, determine whether the grid data is normal to obtain a second judgment result, and send the second judgment result to the alarm module and the emergency response module; Obtain the power supply quality of the power quality analysis module, determine the influencing factors of the current power supply quality according to the retrieved grid data and the power supply quality, and send the influencing factors to the alarm module and the emergency response module; perform fault location according to the first judgment result, the second judgment result, and the influencing factors, and send the fault location result to the emergency response module; Judge the influence of the access of new energy on the power supply quality of the power system according to the grid data, the estimated power generation, and the power supply quality to obtain a third judgment result, and send the third judgment result to the alarm module; The alarm module is configured to perform corresponding-level alarms on the received first judgment result, second judgment result, influencing factors, or third judgment result according to preset alarm levels; The emergency response module is configured to: generate a recommended list of emergency plans based on the first judgment result and / or the second judgment result and / or the influencing factor and / or the third judgment result, determine a target emergency plan based on the priorities of the various emergency plans recommended in the recommended list of emergency plans, and send the determined target emergency plan to the regulation module; or, in response to a user's selection operation, select a target emergency plan from the recommended list of emergency plans and send the selected target emergency plan to the regulation module; In response to a user operation, generate a control instruction and send it to the regulation module; The regulation module is configured to regulate the power system according to the received target emergency plan or the control instruction; The visualization module is configured to convert the power data, the meteorological data, the power supply quality, the first judgment result, the second judgment result, the influencing factor, the result of fault location or the third judgment result into a visual form for screen display.

8. The system according to claim 7, wherein It further includes an evaluation unit; The evaluation unit is configured to obtain the estimated power generation and the power supply quality, and evaluate the regulation effect of the target emergency plan or the control instruction generated by the emergency response module according to the estimated power generation and the power supply quality.

9. A method for supervising power data of big data, characterized in that, Applied to the system according to any one of claims 1 to 8, the method includes: Obtain the power data and meteorological data of the power system; wherein, the power data includes grid data and equipment operation data of power equipment; predict the estimated power generation of new energy according to the meteorological data, clean, integrate the power data, meteorological data and estimated power generation, and store them as a data sequence table; judge whether the grid data and the equipment operation data of the power equipment are abnormal to obtain an abnormality judgment result, and evaluate the power supply quality of the power system based on the grid data; perform fault location and alarm based on the abnormality judgment result, the power supply quality and the data sequence table; generate a recommended list of emergency plans based on the result of the fault location and the power supply quality; determine a target emergency plan based on the priorities of the various emergency plans recommended in the recommended list of emergency plans; or, in response to a user's selection operation, select a target emergency plan from the recommended list of emergency plans; or, in response to a user operation, generate a control instruction; regulate the power system according to the target emergency plan or the control instruction to eliminate the alarm.

10. The method according to claim 9, wherein The step of cleaning, integrating the power data, the meteorological data and the estimated power generation and storing them as a data sequence table includes: Obtain the acquisition time of the grid data, the equipment operation data of the power equipment, the meteorological data and the estimated power generation; Remove the abnormal grid data, the equipment operation data of the power equipment, the meteorological data or the estimated power generation or the data with duplicate acquisition time; Construct key-value pairs with the acquisition time as the key and the grid data, the equipment operation data of the power equipment, the meteorological data and the estimated power generation corresponding to the acquisition time as the values; Taking the acquisition time of each key-value pair as the order, storing the grid data, the operation data of the power equipment, the meteorological data, and the estimated power generation as multiple sequences respectively, and storing them as the data sequence table in this way.

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