BMS-EMS intelligent control system of optical storage charging station

Abstract

The invention provides an optical storage charging station BMS-EMS intelligent control system, which comprises a fault positioning analysis unit and an energy management unit, and is characterized in that the execution steps of the fault positioning analysis unit are as follows: step S10, collecting basic data of an AC cable through a collector; s11, voltage, current and frequency waveforms are drawn based on the basic data, and whether the waveforms are normal or not is judged; step S12, if the waveform is abnormal, drawing an abnormal waveform; step S13, filtering the abnormal waveform by using a Fourier function algorithm, decomposing a plurality of oscillograms, and judging and obtaining a fault position through internal data of a database; after the fault position is obtained, the energy management unit can carry out distribution management on the electric energy and isolate the fault position, energy management and fault positioning are combined together, the fault position can be quickly responded and processed, and normal use of the optical storage charging station is prevented from being influenced.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a BMS-EMS intelligent control system for a photovoltaic charging station. Background Art

[0002] With the widespread application of renewable energy and the popularity of electric vehicles, photovoltaic charging stations, as a comprehensive energy service facility integrating photovoltaic power generation, energy storage systems and electric vehicle charging functions, have become an important part of modern urban energy infrastructure. By converting solar energy into electrical energy and storing it, photovoltaic charging stations can not only effectively utilize renewable energy, but also balance the grid load and improve the flexibility and reliability of the power system. In terms of power energy management, photovoltaic charging stations need to achieve a dynamic balance between photovoltaic systems, energy storage systems and power grids. Since the amount of electricity generated by photovoltaic systems is intermittent and uncertain, the energy storage system is responsible for providing power support when photovoltaic power generation is insufficient or the power supply of the power grid is unstable. Through BMS (electric vehicle battery management system)-EMS (energy management system), intelligent control of each module between photovoltaic charging stations can be achieved. However, the existing BMS-EMS intelligent control system only has energy management functions and is not organically combined with fault location analysis. When an AC cable fails, it is often necessary for staff to operate the BMS-EMS intelligent control system for processing. The response is relatively slow and the fault location cannot be quickly isolated, which easily affects the normal use of the entire photovoltaic charging station. Summary of the invention

[0003] In view of this, the present invention proposes a BMS-EMS intelligent control system for a photovoltaic charging station, which combines energy management and fault location analysis, can quickly determine and respond to the fault location, thereby isolating the fault location and avoiding affecting the normal operation of the photovoltaic charging station.

[0004] The technical solution of the present invention is achieved in this way:

[0005] A BMS-EMS intelligent control system for a photovoltaic charging station, comprising:

[0006] A fault location analysis unit is used to perform fault location analysis based on the collected basic data of the AC cable and obtain the fault location;

[0007] Energy management unit, used to manage the distribution of electric energy and isolate fault locations;

[0008] The execution steps of the fault location analysis unit are:

[0009] Step S10, collecting basic data of the AC cable through a collector;

[0010] Step S11, drawing voltage, current, and frequency waveforms based on basic data, and determining whether the waveforms are normal;

[0011] Step S12: If the waveform is abnormal, draw the abnormal waveform;

[0012] Step S13: The abnormal waveform is filtered using a Fourier function algorithm, and multiple waveform diagrams are decomposed to obtain the fault location through judgment through the internal data of the database.

[0013] Preferably, the specific steps of step S10 are: collecting the starting node voltage value V k , and record a node voltage value V every millisecond k+n , the frequency is calculated based on the voltage of each two nodes, and the calculation formula is:

[0014] V k +V k+n =cos(2πft).

[0015] Preferably, when it is determined that the waveform is normal, step S11 returns to step S10 to perform frequency calculation in a loop.

[0016] Preferably, the specific steps of step S13 are:

[0017] Step S131, by assigning the cosine and sine components of the mth harmonic in different time periods, filter calculation is performed using the Fourier function, and the calculation formula is:

[0018] Step S132, decomposing and drawing each harmonic image, and displaying the normal waveform, first to Nth harmonic graphs;

[0019] Step S133, comparing with common waveform data, determining that it is a device fault or a line fault, and displaying a fault waveform diagram;

[0020] Step S134: If there is a line fault, display the collector model and specific location;

[0021] Step S135: If there is a device failure, the device failure location is displayed.

[0022] Preferably, the specific steps of step S135 are: using the traveling wave moment obtained by the collector as an element, establishing an interval matrix to calculate the fault position, calculating and comparing multiple matrices, and obtaining the matrix where the abnormal data is located, and determining the fault interval, and displaying the equipment fault position according to the fault interval.

[0023] Preferably, the energy management unit includes an energy management center, an energy storage battery and a main control box of a power distribution cabinet, and the execution steps of the energy distribution management are as follows:

[0024] Step S20: Collecting photovoltaic module power generation data and grid power access data, and transmitting them to the energy management center;

[0025] Step S21: When the energy management center determines that photovoltaic power generation meets the load, photovoltaic power is input into the main control box of the power distribution cabinet and the energy storage battery;

[0026] Step S22: When the energy management center determines that photovoltaic power generation does not meet the load, it switches to the grid power supply and invests in the distribution cabinet master control box and energy storage battery;

[0027] Step S23: The energy management center distributes electric energy according to the load;

[0028] Step S24: The energy management center sets the grid power supply to charge the energy storage battery according to the time period, and controls the energy storage battery to discharge to the main control box of the power distribution cabinet.

[0029] Preferably, the specific steps of step S23 are: when the load power is less than the power of the energy storage battery, the energy storage battery transmits electric energy to the main control box of the distribution cabinet; when the load power is less than the photovoltaic power generation, the photovoltaic power generation transmits electric energy to the main control box of the distribution cabinet; when the power of the photovoltaic power generation and the energy storage battery is insufficient, the grid power supply transmits electric energy to the main control box of the distribution cabinet.

[0030] Preferably, the specific steps in step S24 are:

[0031] Step S241, obtaining historical charge and discharge data of the energy storage battery, and evaluating the health of the energy storage battery according to the historical charge and discharge data;

[0032] Step S242, calculating the charging loss coefficient and the discharging loss coefficient of different energy storage batteries;

[0033] Step S243: assigning a weight to the charging loss coefficient or the discharging loss coefficient to the health degree, sorting the weighted health degrees from high to low, and forming a charging path or a discharging path;

[0034] Step S244: The energy management center sets the grid power supply according to the time period and selects the energy storage battery for charging according to the charging path, and controls the energy storage battery to discharge to the main control box of the distribution cabinet according to the discharging path.

[0035] Preferably, the specific steps of step S242 are:

[0036] Step S2421, obtaining the charging period and discharging period set by the energy management center;

[0037] Step S2422, querying weather data, grid voltage and frequency, and load status at the location of the energy storage battery during the charging period and the discharging period;

[0038] Step S2423, inputting weather data, grid voltage and frequency, load status, and health of the energy storage battery into the trained neural network;

[0039] Step S2424: obtain the charging loss coefficient and the discharging loss coefficient by neural network processing.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] A BMS-EMS intelligent control system for a photovoltaic charging station of the present invention is divided into two major parts: a fault location analysis unit and an energy management unit. The fault location analysis unit can determine whether the waveform is normal based on the basic data of the AC cable. When the waveform is abnormal, filtering calculation is performed through the Fourier function, and the fault location is obtained after judgment with the internal data of the database. After the fault location is obtained, the fault location can be transmitted to the energy management unit, and the energy management unit can isolate the fault location, combine energy management and fault location, and can quickly respond to the fault location and process it to avoid affecting the normal use of the photovoltaic charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 This is a diagram of the execution steps of a fault location analysis unit of a BMS-EMS intelligent control system for a photovoltaic charging station of the present invention;

[0044] Figure 2 A diagram showing the execution steps of the energy management unit of a BMS-EMS intelligent control system for a photovoltaic charging station of the present invention for distributing and managing electric energy;

[0045] Figure 3 This is a flow chart of step S24 of an energy management unit of a BMS-EMS intelligent control system for a photovoltaic charging station of the present invention;

[0046] Figure 4 This is a flow chart of step S242 of the energy management unit of the BMS-EMS intelligent control system of a photovoltaic charging station of the present invention. DETAILED DESCRIPTION

[0047] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0048] See also Figures 1 to 4 The present invention provides a BMS-EMS intelligent control system for a photovoltaic charging station, comprising:

[0049] A fault location analysis unit is used to perform fault location analysis based on the collected basic data of the AC cable and obtain the fault location;

[0050] Energy management unit, used to manage the distribution of electric energy and isolate fault locations;

[0051] The execution steps of the fault location analysis unit are:

[0052] Step S10, collecting basic data of the AC cable through a collector;

[0053] Step S11, drawing voltage, current, and frequency waveforms based on basic data, and judging whether the waveforms are normal. When judging that the waveforms are normal, returning to step S10 to perform frequency calculation in a loop;

[0054] Step S12: If the waveform is abnormal, draw the abnormal waveform;

[0055] Step S13: The abnormal waveform is filtered using a Fourier function algorithm, and multiple waveform diagrams are decomposed to obtain the fault location through judgment through the internal data of the database.

[0056] A BMS-EMS intelligent control system for a photovoltaic charging station of the present invention comprises two major parts, a fault location analysis unit and an energy management unit, wherein the energy management unit integrates the BMS and the EMS, and can manage the charging and discharging of the photovoltaic charging station. The photovoltaic charging station integrates three parts, namely, a grid power supply, photovoltaic power generation and an energy storage battery. By reasonably managing the discharge of different power supplies and the charging and discharging of energy storage batteries, it can ensure that stable charging power is provided for new energy vehicles.

[0057] In addition to the power distribution management function, the energy management unit can also isolate the fault location, which is obtained by the fault location analysis unit after data collection on the AC cable used to transmit electric energy and obtaining basic data. The voltage, current and frequency waveforms can be drawn based on the basic data, and it can be judged whether the drawn waveform is normal. When the AC cable is transmitting electricity normally, its waveform is regular, and occasionally has a little fluctuation. Therefore, it can be clearly checked by the drawn waveform whether there is an abnormality. When the waveform is normal, it returns to recalculate the frequency. When there is an abnormality in the waveform, the abnormal waveform is drawn, and then the Fourier function algorithm is used to filter and calculate the abnormal waveform. After decomposing multiple waveform graphs, the fault location can be judged by the internal data of the database. After obtaining the fault location, the energy management unit can isolate the fault location, combining energy management and fault location analysis, and can quickly respond to the fault location and isolate it to avoid affecting the normal use of the entire photovoltaic charging station.

[0058] Preferably, the specific steps of step S10 are: collecting the starting node voltage value V k , and record a node voltage value V every millisecond k+n , the frequency is calculated based on the voltage of each two nodes, and the calculation formula is:

[0059] V k +V k+n =cos(2πft).

[0060] The collector sends the data of multiple collectors in the area to the computer center, which records and calculates the data through analysis software. The data of each collector is recorded every millisecond in the form of a data table, and converted into a spectrum diagram through the angular frequency calculation formula;

[0061] Preferably, the specific steps of step S13 are:

[0062] Step S131, by assigning the cosine and sine components of the mth harmonic in different time periods, filter calculation is performed using the Fourier function, and the calculation formula is:

[0063] Step S132, decomposing and drawing each harmonic image, and displaying the normal waveform, first to Nth harmonic graphs;

[0064] Step S133, comparing with common waveform data, determining that it is a device fault or a line fault, and displaying a fault waveform diagram;

[0065] Step S134: If there is a line fault, display the collector model and specific location;

[0066] Step S135: If it is a device failure, the device failure location is displayed. The specific steps are: using the traveling wave moment obtained by the collector as an element, establishing an interval matrix to calculate the fault location, and comparing multiple matrices to obtain the matrix where the abnormal data is located, and determine the fault interval, and display the device failure location according to the fault interval.

[0067] When the waveform is abnormal, it means that a fault has occurred. Different faults on the line will generate fault current inside the line to form harmonics, which are filtered and calculated using the Fourier function algorithm, where m is the harmonic order, m = 0, 1, 2...n, and ω is the angular frequency of the fundamental wave; X cos and X sin is the amplitude of the cosine and positive components of the mth harmonic. After decomposing multiple waveform graphs, the common waveform data in the database is obtained and judged to determine the type of fault. There are two types of faults, namely line faults and equipment faults. If it is a line fault, the designated collector can be directly located to determine the fault location. If it is an equipment fault, the interval matrix algorithm is established through the traveling wave moment obtained by the collector to calculate the fault interval, which can display the collector installation area to quickly determine the fault point.

[0068] Preferably, the energy management unit includes an energy management center, an energy storage battery and a main control box of a power distribution cabinet, and the execution steps of the energy distribution management are as follows:

[0069] Step S20: Collecting photovoltaic module power generation data and grid power access data, and transmitting them to the energy management center;

[0070] Step S21: When the energy management center determines that photovoltaic power generation meets the load, photovoltaic power is input into the main control box of the power distribution cabinet and the energy storage battery;

[0071] Step S22: When the energy management center determines that photovoltaic power generation does not meet the load, it switches to the grid power supply and invests in the distribution cabinet master control box and energy storage battery;

[0072] Step S23: The energy management center distributes electric energy according to the load;

[0073] Step S24: The energy management center sets the grid power supply to charge the energy storage battery according to the time period, and controls the energy storage battery to discharge to the main control box of the power distribution cabinet.

[0074] The energy management center in the energy management unit is the core component. In addition, it also includes the distribution cabinet control box and energy storage batteries. The distribution cabinet control box is used to distribute electric energy to the load side. The load side includes new energy charging pile load, office building load and factory load, etc. The energy storage battery can be charged and discharged for peak load and valley filling of the power grid, frequency regulation and backup power supply, etc. The collected photovoltaic module power generation data and grid access side data will be transmitted to the corresponding database. The energy management center can extract the photovoltaic module power generation data and grid access side data from the database. When photovoltaic power generation can meet the load usage, photovoltaic power can be put into the distribution cabinet control box for power distribution and energy storage battery charging. When photovoltaic power generation does not meet the load, it is switched to the grid power supply to achieve it. In addition, the energy management center can also receive feedback data from the load to distribute electric energy, and charge and discharge the energy storage battery according to time periods.

[0075] Preferably, the specific steps of step S23 are: when the load power is less than the power of the energy storage battery, the energy storage battery transmits electric energy to the main control box of the distribution cabinet; when the load power is less than the photovoltaic power generation, the photovoltaic power generation transmits electric energy to the main control box of the distribution cabinet; when the power of the photovoltaic power generation and the energy storage battery is insufficient, the grid power supply transmits electric energy to the main control box of the distribution cabinet.

[0076] The load data of new energy charging pile loads, office building loads, factory loads, etc. can be collected and transmitted to the load center database, where the energy management center will determine the power distribution based on the load, and compare the load with the power of the energy storage battery and photovoltaic power generation. If the energy storage battery and photovoltaic power generation can provide the rated load, the distribution cabinet master control box can allocate the power of the energy storage battery or photovoltaic power generation to the load side. If the power of the photovoltaic and energy storage batteries is insufficient, the distribution cabinet master control box will control the grid power supply to provide the load.

[0077] Preferably, the specific steps in step S24 are:

[0078] Step S241, obtaining historical charge and discharge data of the energy storage battery, and evaluating the health of the energy storage battery according to the historical charge and discharge data;

[0079] Step S242, calculating the charging loss coefficient and the discharging loss coefficient of different energy storage batteries;

[0080] Step S243: assigning a weight to the charging loss coefficient or the discharging loss coefficient to the health degree, sorting the weighted health degrees from high to low, and forming a charging path or a discharging path;

[0081] Step S244: The energy management center sets the grid power supply according to the time period and selects the energy storage battery for charging according to the charging path, and controls the energy storage battery to discharge to the main control box of the distribution cabinet according to the discharging path.

[0082] There will be several energy storage batteries in the photovoltaic charging station of the present invention. The energy storage batteries will have different losses during long-term use, and the use of the energy storage batteries has not been well planned, resulting in different degrees of loss of the energy storage batteries during long-term use. The health of the energy storage batteries can be evaluated based on their historical charging and discharging data. After charging and discharging in a preset time period, the health of the energy storage batteries will further decline. In order to ensure that the health of the energy storage batteries can be maintained at a similar level after charging and discharging, so that maintenance can be carried out at the same time to reduce maintenance costs, the charging loss coefficient and the discharging loss coefficient of each energy storage battery are calculated, and the charging loss coefficient and the discharging loss coefficient are multiplied by the current health of the energy storage battery to obtain the health of the energy storage battery after charging and discharging in the preset time period. After sorting the weighted health from high to low, a charging path or a discharging path can be formed. The energy management center can control the charging and discharging of the energy storage batteries in the preset time period according to the charging path or the discharging path to ensure the uniformity of the health of the energy storage batteries.

[0083] Preferably, the specific steps of step S242 are:

[0084] Step S2421, obtaining the charging period and discharging period set by the energy management center;

[0085] Step S2422, querying weather data, grid voltage and frequency, and load status at the location of the energy storage battery during the charging period and the discharging period;

[0086] Step S2423, inputting weather data, grid voltage and frequency, load status, and health of the energy storage battery into the trained neural network;

[0087] Step S2424: obtain the charging loss coefficient and the discharging loss coefficient by neural network processing.

[0088] For the calculation of the charging loss coefficient and the discharging loss coefficient, the present invention adopts a deep learning algorithm to train a neural network through historical data. The trained neural network can automatically evaluate the charging loss coefficient and the discharging loss coefficient. The charging loss coefficient and the discharging loss coefficient of the energy storage battery are not only related to the health of the battery itself, but also related to the weather data of the geographical location of the energy storage battery, the local grid voltage and frequency, and the load status. These parameters are used as the input of the neural network. After the neural network is processed, the charging loss coefficient and the discharging loss coefficient can be obtained.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A BMS-EMS intelligent control system for a photovoltaic charging station, characterized in that: include: A fault location analysis unit is used to perform fault location analysis based on the collected basic data of the AC cable and obtain the fault location; Energy management unit, used to manage the distribution of electric energy and isolate fault locations; The execution steps of the fault location analysis unit are: Step S10, collecting basic data of the AC cable through a collector; Step S11, drawing voltage, current, and frequency waveforms based on basic data, and determining whether the waveforms are normal; Step S12: If the waveform is abnormal, draw the abnormal waveform; Step S13: The abnormal waveform is filtered using a Fourier function algorithm, and multiple waveform diagrams are decomposed to obtain the fault location through internal data of the database.

2. A BMS-EMS intelligent control system for a photovoltaic charging station according to claim 1, characterized in that: The specific steps of step S10 are: collecting the starting node voltage value V k , and record a node voltage value V every millisecond k+n , the frequency is calculated based on the voltage of each two nodes, and the calculation formula is: V k +V k+n =coS(2πft)。 3. A BMS-EMS intelligent control system for a photovoltaic charging station according to claim 2, characterized in that: When the waveform is judged to be normal in step S11, the process returns to step S10 to perform frequency calculation in a loop.

4. According to claim 1, a BMS-EMS intelligent control system for a photovoltaic charging station is characterized in that: The specific steps of step S13 are: Step S131, by assigning the cosine and sine components of the mth harmonic in different time periods, filter calculation is performed using the Fourier function, and the calculation formula is: Step S132, decomposing and drawing each harmonic image, and displaying the normal waveform, and the first to Nth harmonic graphs; Step S133, comparing with common waveform data, determining whether it is a device fault or a line fault, and displaying a fault waveform diagram; Step S134: If there is a line fault, display the collector model and specific location; Step S135: If there is a device failure, the device failure location is displayed.

5. A BMS-EMS intelligent control system for a photovoltaic charging station according to claim 4, characterized in that: The specific steps of step S135 are: using the traveling wave moment obtained by the collector as an element, establishing an interval matrix to calculate the fault position, calculating and comparing multiple matrices, and obtaining the matrix where the abnormal data is located, and determining the fault interval, and displaying the equipment fault position according to the fault interval.

6. A BMS-EMS intelligent control system for a photovoltaic charging station according to claim 1, characterized in that: The energy management unit includes an energy management center, an energy storage battery, and a main control box of a power distribution cabinet. The execution steps of the energy distribution management are as follows: Step S20: Collecting photovoltaic module power generation data and grid power access data, and transmitting them to the energy management center; Step S21: When the energy management center determines that photovoltaic power generation meets the load, photovoltaic power is input into the main control box of the power distribution cabinet and the energy storage battery; Step S22: When the energy management center determines that photovoltaic power generation does not meet the load, it switches to the grid power supply and invests in the distribution cabinet master control box and energy storage battery; Step S23: The energy management center distributes electric energy according to the load; Step S24: The energy management center sets the grid power supply to charge the energy storage battery according to the time period, and controls the energy storage battery to discharge to the main control box of the power distribution cabinet.

7. A BMS-EMS intelligent control system for a photovoltaic charging station according to claim 6, characterized in that: The specific steps of step S23 are: when the load power is less than the power of the energy storage battery, the energy storage battery transmits electric energy to the main control box of the distribution cabinet; when the load power is less than the photovoltaic power generation, the photovoltaic power generation transmits electric energy to the main control box of the distribution cabinet; when the power of the photovoltaic power generation and the energy storage battery is insufficient, the grid power supply transmits electric energy to the main control box of the distribution cabinet.

8. The BMS-EMS intelligent control system for a photovoltaic charging station according to claim 6 is characterized in that: The specific steps in step S24 are: Step S241, obtaining historical charge and discharge data of the energy storage battery, and evaluating the health of the energy storage battery according to the historical charge and discharge data; Step S242, calculating the charging loss coefficient and the discharging loss coefficient of different energy storage batteries; Step S243: assigning a weight to the charging loss coefficient or the discharging loss coefficient to the health degree, sorting the weighted health degrees from high to low, and forming a charging path or a discharging path; Step S244: The energy management center sets the grid power supply according to the time period and selects the energy storage battery for charging according to the charging path, and controls the energy storage battery to discharge to the main control box of the distribution cabinet according to the discharging path.

9. A BMS-EMS intelligent control system for a photovoltaic charging station according to claim 8, characterized in that: The specific steps of step S242 are: Step S2421, obtaining the charging period and discharging period set by the energy management center; Step S2422, querying weather data, grid voltage and frequency, and load status at the location of the energy storage battery during the charging period and the discharging period; Step S2423, inputting weather data, grid voltage and frequency, load status, and health of the energy storage battery into the trained neural network; Step S2424: obtain the charging loss coefficient and the discharging loss coefficient by neural network processing.

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