CN105024390A - Micro-grid cell energy storage system frequency modulation control method based on BP nerve network - Google Patents
Micro-grid cell energy storage system frequency modulation control method based on BP nerve network Download PDFInfo
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Abstract
The invention provides a micro-grid cell energy storage system frequency modulation control method based on a BP nerve network. The method comprises the steps of: measuring a real-time frequency value of a micro-grid cell energy storage system; obtaining the frequency difference between the real-time frequency value and a rated frequency value of the micro-grid cell energy storage system, inputting the frequency difference into the BP nerve network, and carrying out intelligent inference so as to obtain current optimal parameters of a PI controller; according to the current optimal parameters of the PI controller, carrying out parameter setting on the PI controller, and utilizing the PI controller after parameter setting to obtain an inverter-controlled active power difference reference value of the micro-grid cell energy storage system according to the frequency difference; and obtaining an active power reference value according to the active power difference reference value, and carrying out system frequency modulation on the micro-grid cell energy storage system by means of PQ inversion controlling. By adopting the method, the precision of active power control of the micro-grid cell energy storage system and the stability of the micro-grid frequency are effectively improved.
Description
Technical field
The present invention relates to electric power system microgrid field, particularly a kind of micro-grid battery energy storage system frequency modulation control method based on BP neural net.
Background technology
In recent years, micro-capacitance sensor is because it is to perfectly compatible in distributed power source access, and becoming the important component part of intelligent grid, is one of direction of national grid future development.Micro-capacitance sensor is primarily of compositions such as distributed power source, energy storage device, power electronic equipment, loads, it is a region controllable in bulk power grid, it is again the micro electric Force system of an independent completion, therefore, micro-grid operation mode has grid-connected with isolated power grid two kinds of operational modes, and in being incorporated into the power networks, microgrid accesses bulk power grid by power distribution network, with bulk power grid cooperation, realize two-way circulating of electric energy.When the grid collapses or the electrical network quality of power supply can not meet user when requiring, microgrid will disconnect PCC point, proceed to isolated power grid pattern.
After micro-capacitance sensor proceeds to isolated power grid, as miniature electric power system, its inertia is very little, power supply exert oneself or the fluctuation of load all by the oscillation on large scale because of its system frequency, even cause micro-grid system to collapse.And micro-capacitance sensor is as the effective grid-connected form of distributed power generation, its power supply of exerting oneself is to containing the renewable energy power generation devices such as a large amount of blower fan, photovoltaic, small power station natively, these distributed power sources will inevitably cause intermittence and fluctuation that power supply exerts oneself along with the change of external environment, this proposes serious threat to the operating power-balance of micro-grid system, therefore, for renewable energy power generation device a large amount of in microgrid possible go out fluctuation, need energy storage device to supplement balance fast and accurately.Therefore, must solve micro-grid system relies on energy-storage system to carry out frequency modulation control.
Micro-grid energy storage system is under the frequency modulation control device of himself controls, quick tracing compensation system power deviation, the control algolithm that current controller adopts adjusts two kinds containing droop control and urgent Direct Power substantially, but nearly all containing PI controlling unit in all types of controller, the input of control frequency deviation and control power need to be processed by PI parameter between adjusting and exporting, for realizing frequency modulation response fast and accurately, the PI parameter tuning of frequency modulation control device is very important.Meanwhile, micro-capacitance sensor is as strongly non-linear system, in frequency-modulating process, system parameters has time variation and non-linear, strengthen parameter tuning difficulty, and the parameter of to adjust for concrete micro-grid system, concrete operating mode is after there is significantly change in system performance, will not have adaptability, and cannot carry out adaptation of overall importance.Therefore, how accurately realizing controling parameters to the self-adaptive sites in system frequency modulation working conditions change process, is problem demanding prompt solution.
Summary of the invention
Object of the present invention is intended at least solve one of above-mentioned technological deficiency.
For this reason, the object of the invention is to propose a kind of micro-grid battery energy storage system frequency modulation control method based on BP neural net.The method effectively can improve precision in micro-grid battery energy storage system active power controller and microgrid frequency stability.
To achieve these goals, embodiments of the invention disclose a kind of micro-grid battery energy storage system frequency modulation control method based on BP neural net, comprise the following steps: the real-time frequency value measuring described micro-grid battery energy storage system; Obtain the frequency-splitting between described real-time frequency value and the rated frequency value of described micro-grid battery energy storage system, and carry out intelligent inference, to obtain the parameter of the PI controller of current optimum in the BP neural net input of described frequency-splitting trained; Parameter according to the PI controller of described current optimum carries out parameter tuning to described PI controller, and utilizes the PI controller after parameter tuning according to described frequency-splitting, obtains the inverter control active power difference reference value of described micro-grid battery energy storage system; Obtain active power reference value according to described active power difference reference value, take PQ inversion control to carry out system frequency modulation to make described micro-grid battery energy storage system.
In addition, the micro-grid battery energy storage system frequency modulation control method based on BP neural net according to the above embodiment of the present invention can also have following additional technical characteristic:
In some instances, the parameter of described PI controller comprises Proportional coefficient K p and integral coefficient Ki.
In some instances, intelligent inference is carried out in described BP neural net frequency-splitting input trained, before the parameter of PI controller obtaining current optimum, also comprise: the BP neural net trained described in training to obtain to described BP neural net.
In some instances, described BP neural net is trained, specifically comprise: described BP neural net carries out the backpropagation of forward-propagating and error to predetermined information, obtain the neuronic weights of optimum of every one deck in described BP neural net, wherein, in described BP neural net every one deck the neuronic weights of optimum be biased consecutive variations for the frequency error according to described micro-grid battery energy storage system, real-time assessment current working, provides the parameter of the PI controller of current optimum.
In some instances, the training sample of described BP neural net is arranged according to following setting principle: (absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is greater than the first preset difference value) be greater than (absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value), wherein, described first preset difference value is greater than described second preset difference value; (absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is greater than the first preset difference value) be less than (absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value); (absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value) be greater than (absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described second preset difference value); (absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value) be greater than (absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than described second preset difference value).
According to the micro-grid battery energy storage system frequency modulation control method based on BP neural net of the embodiment of the present invention, effectively can adapt to the non-linear and time variation of the operational factor under microgrid different frequency error operating mode, realize the controling parameters Self-tuning System under different frequency operating mode, obtain optimum system dynamic response, realize the good dynamic response characteristic of micro-grid battery energy storage system frequency modulation and robustness, effectively improve the precision in micro-grid battery energy storage system active power controller and microgrid frequency stability.
The aspect that the present invention adds and advantage will part provide in the following description, and part will become obvious from the following description, or be recognized by practice of the present invention.
Accompanying drawing explanation
The present invention above-mentioned and/or additional aspect and advantage will become obvious and easy understand from the following description of the accompanying drawings of embodiments, wherein,
Fig. 1 is the flow chart of the micro-grid battery energy storage system frequency modulation control method based on BP neural net of one embodiment of the invention;
Fig. 2 is the schematic diagram of three layers of BP neural net of one embodiment of the invention;
Fig. 3 is the schematic diagram of the BP neural net trained for the controling parameters response output of the absolute value of system frequency difference of one embodiment of the invention;
Fig. 4 is the schematic diagram of the BP neural net trained for the controling parameters response output of the absolute value of system frequency difference of another embodiment of the present invention;
Fig. 5 is the main electrical scheme schematic diagram of the micro-grid battery energy storage system based on BP neural net of one embodiment of the invention;
Fig. 6 is the control block diagram of the micro-grid battery energy storage system frequency modulation control based on BP neural net of one embodiment of the invention;
Fig. 7 is the frequency modulation design sketch adopting fixing PI in correlation technique; And
Fig. 8 is the frequency modulation design sketch of the micro-grid battery energy storage system frequency modulation control based on BP neural net of one embodiment of the invention.
Embodiment
Be described below in detail embodiments of the invention, the example of embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.
In describing the invention, it will be appreciated that, term " " center ", " longitudinal direction ", " transverse direction ", " on ", D score, " front ", " afterwards ", " left side ", " right side ", " vertically ", " level ", " top ", " end ", " interior ", orientation or the position relationship of the instruction such as " outward " are based on orientation shown in the drawings or position relationship, only the present invention for convenience of description and simplified characterization, instead of indicate or imply that the device of indication or element must have specific orientation, with specific azimuth configuration and operation, therefore limitation of the present invention can not be interpreted as.In addition, term " first ", " second " only for describing object, and can not be interpreted as instruction or hint relative importance.
In describing the invention, it should be noted that, unless otherwise clearly defined and limited, term " installation ", " being connected ", " connection " should be interpreted broadly, and such as, can be fixedly connected with, also can be removably connect, or connect integratedly; Can be mechanical connection, also can be electrical connection; Can be directly be connected, also indirectly can be connected by intermediary, can be the connection of two element internals.For the ordinary skill in the art, concrete condition above-mentioned term concrete meaning in the present invention can be understood.
Below in conjunction with accompanying drawing, the micro-grid battery energy storage system frequency modulation control method based on BP neural net according to the embodiment of the present invention is described.
Fig. 1 is according to an embodiment of the invention based on the flow chart of the micro-grid battery energy storage system frequency modulation control method of BP neural net.As shown in Figure 1, according to an embodiment of the invention based on the micro-grid battery energy storage system frequency modulation control method of BP neural net, comprise the steps:
S101: the real-time frequency value measuring micro-grid battery energy storage system.Namely micro-grid system (micro-grid battery energy storage system) real-time frequency value f is measured.
S102: obtain the frequency-splitting between real-time frequency value and the rated frequency value of micro-grid battery energy storage system, and carry out intelligent inference, to obtain the parameter of the PI controller of current optimum in BP neural net frequency-splitting input trained.That is, the rated frequency (be such as 50Hz with standard frequency be example) of the frequency instantaneous value measured and micro-grid battery energy storage system is compared, its frequency-splitting is as the input variable of BP neural net, BP neural net will carry out intelligent inference according to current system frequency-splitting (i.e. frequency departure) input variable, the parameter of the PI controller of the current optimum of adaptive polo placement.Wherein, the parameter of PI controller comprises Proportional coefficient K p and integral coefficient Ki.
S103: the parameter according to the PI controller of current optimum carries out parameter tuning to PI controller, and utilize the PI controller after parameter tuning according to frequency-splitting, obtain the inverter control active power difference reference value of micro-grid battery energy storage system.In other words, real-time controller parameters setting, processes system frequency deviation, obtains battery energy storage system inverter control active power difference reference value.
S104: obtain active power reference value according to active power difference reference value, takes PQ inversion control to carry out system frequency modulation to make micro-grid battery energy storage system.Namely obtain active power reference value P_ref according to active power difference reference value, take PQ inversion control, battery energy storage system rapid adjustment is meritorious to be exported, and carries out system frequency modulation.
In one embodiment of the invention, intelligent inference is carried out in BP neural net frequency-splitting input trained, before the parameter of PI controller obtaining current optimum, also comprise: BP neural net is trained to the BP neural net trained.
Specifically, described BP neural net is trained, comprise: BP neural net carries out the backpropagation of forward-propagating and error to predetermined information, obtain the neuronic weights of optimum of every one deck in described BP neural net, wherein, in BP neural net every one deck the neuronic weights of optimum be biased consecutive variations for the frequency error according to micro-grid battery energy storage system, real-time assessment current working, provides the parameter of the PI controller of current optimum.
More specifically, the training of BP neural net is as follows:
As a concrete example, the structure of classical BP neural net as shown in Figure 2, by input layer, output layer and one or more layers hidden layer composition, appropriate connection weights between layers and the biased deterministic process that will BP neural net made to simulate biological nervous system well, the weights of BP neural net were formed through training with biased needs, in the training process, BP neural net is by the backpropagation of the forward-propagating and error of carrying out given information, finally obtain the optimum neuronic weights of each layer, realize the optimal processing to complicated nonlinear system, neural net provides its desired output to concrete input information.The BP neural net taked in the method is the neural net trained, and the weights of this network are with biased by the consecutive variations according to micro-grid system frequency error, and real-time assessment current working, provides current optimal control parameter.In one embodiment of the invention, take the BP neuronal structure of 1-3-1-2, i.e. input layer input variable, ground floor hidden layer contains 3 nodes, and second layer hidden layer contains 1 node, and output variable is Kp and Ki.
In one embodiment of the invention, the training sample of BP neural net is arranged according to following setting principle:
1, (absolute value of frequency-splitting | the Proportional coefficient K p adopted when deltaf| is greater than the first preset difference value) be greater than (absolute value of frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value), wherein, the first preset difference value is greater than the second preset difference value;
2, (absolute value of frequency-splitting | the integral coefficient Ki adopted when deltaf| is greater than the first preset difference value) be less than (absolute value of frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than the first preset difference value and is greater than the second preset difference value);
3, (absolute value of frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than the first preset difference value and is greater than the second preset difference value) be greater than (absolute value of frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than the second preset difference value);
4, (absolute value of frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than the first preset difference value and is greater than the second preset difference value) be greater than (absolute value of frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than the second preset difference value).
That is, the training sample setting principle of BP neural net is as follows, and the first, when frequency departure absolute value | when deltaf| is larger, takes larger Proportional coefficient K p to accelerate dynamic response, be aided with less Ki.The second, when frequency departure absolute value | when deltaf| is moderate, reduce control ratio parameter Kp, now Ki suitably increases, and accelerates system response time.3rd, when frequency departure absolute value | when deltaf| is less, system convergence stable state, reduces Proportional coefficient K p further, takes less integral coefficient Ki simultaneously, make system have good dynamic property.As shown in Figure 3 and Figure 4, the BP neural net that trains is provided respectively for system frequency error absolute value | the controling parameters response of deltaf| exports.
According to the micro-grid battery energy storage system frequency modulation control method based on BP neural net of the embodiment of the present invention, effectively can adapt to the non-linear and time variation of the operational factor under microgrid different frequency error operating mode, realize the controling parameters Self-tuning System under different frequency operating mode, obtain optimum system dynamic response, realize the good dynamic response characteristic of micro-grid battery energy storage system frequency modulation and robustness, effectively improve the precision in micro-grid battery energy storage system active power controller and microgrid frequency stability.
In order to more profoundly understand the method for the embodiment of the present invention, below in conjunction with object lesson, the present invention is described further.
Microgrid (micro-grid battery energy storage system) main wiring diagram as shown in Figure 5, take based on the micro-grid battery energy storage system frequency modulation control of BP neural net control block diagram as shown in Figure 6.Wherein, f
0be micro-capacitance sensor frequency rated value 50Hz, f is mains frequency measured value, T
flow pass filter time constant, Kp and K
iproportionality coefficient and the integral constant of energy-storage system frequency-modulation control system PI controller respectively.P
refit is the reference value that battery system active power exports.
Fig. 7 is the frequency modulation design sketch taking fixing PI in correlation technique.Fig. 8 is the micro-grid battery energy storage system frequency modulation design sketch based on BP neural net of the embodiment of the present invention.Proceed to isolated power grid when 0.05s in simulation example, when 0.1s, load increases by 20% suddenly., power shortage is all provided by battery energy storage system.
The frequency modulation design sketch that under contrast different modes, PI adjusts, can see the micro-grid battery energy storage system frequency modulation control based on BP neural net of the embodiment of the present invention can adapt to electrical network parameter time become with non-linear, when system frequency difference is larger, improve the response of Kp accelerating system, and when frequency is close to steady-state value, reduce Kp proportionality coefficient, reduce the transient state overshoot in frequency-modulating process, simultaneously integral coefficient effectively shows good response characteristic along with proportionality coefficient, effectively improves system frequency stability.
Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, those of ordinary skill in the art can change above-described embodiment within the scope of the invention when not departing from principle of the present invention and aim, revising, replacing and modification.
Claims (5)
1., based on a micro-grid battery energy storage system frequency modulation control method for BP neural net, it is characterized in that, comprise the following steps:
Measure the real-time frequency value of described micro-grid battery energy storage system;
Obtain the frequency-splitting between described real-time frequency value and the rated frequency value of described micro-grid battery energy storage system, and carry out intelligent inference, to obtain the parameter of the PI controller of current optimum in the BP neural net input of described frequency-splitting trained;
Parameter according to the PI controller of described current optimum carries out parameter tuning to described PI controller, and utilizes the PI controller after parameter tuning according to described frequency-splitting, obtains the inverter control active power difference reference value of described micro-grid battery energy storage system;
Obtain active power reference value according to described active power difference reference value, take PQ inversion control to carry out system frequency modulation to make described micro-grid battery energy storage system.
2. the micro-grid battery energy storage system frequency modulation control method based on BP neural net according to claim 1, is characterized in that, the parameter of described PI controller comprises Proportional coefficient K p and integral coefficient Ki.
3. the micro-grid battery energy storage system frequency modulation control method based on BP neural net according to claim 2, it is characterized in that, intelligent inference is carried out in described BP neural net frequency-splitting input trained, before the parameter of PI controller obtaining current optimum, also comprise: the BP neural net trained described in training to obtain to described BP neural net.
4. the micro-grid battery energy storage system frequency modulation control method based on BP neural net according to claim 3, is characterized in that, train, specifically comprise described BP neural net:
Described BP neural net carries out the backpropagation of forward-propagating and error to predetermined information, obtain the neuronic weights of optimum of every one deck in described BP neural net, wherein, in described BP neural net every one deck the neuronic weights of optimum be biased consecutive variations for the frequency error according to described micro-grid battery energy storage system, real-time assessment current working, provides the parameter of the PI controller of current optimum.
5. the micro-grid battery energy storage system frequency modulation control method based on BP neural net according to any one of claim 2-4, it is characterized in that, the training sample of described BP neural net is arranged according to following setting principle:
(absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is greater than the first preset difference value) be greater than (absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value), wherein, described first preset difference value is greater than described second preset difference value;
(absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is greater than the first preset difference value) be less than (absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value);
(absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value) be greater than (absolute value of described frequency-splitting | the Proportional coefficient K p adopted when deltaf| is less than described second preset difference value);
(absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than described first preset difference value and is greater than the second preset difference value) be greater than (absolute value of described frequency-splitting | the integral coefficient Ki adopted when deltaf| is less than described second preset difference value).
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