A control method for a regenerative braking energy utilization system for a dual-current system line

Abstract

The present invention provides a control method for a regenerative braking energy utilization system for dual-current lines, which belongs to the field of electrified railway traction power supply technology. The method comprises: constructing a hardware system structure including two power-storage devices. Establishing a game model with the goal of minimizing system electricity costs, and optimizing the peak-shaving set values for AC and DC traction. Collecting voltage and current data in real time, and filtering processing using a sliding time window method. Calculating the instantaneous power of the AC and DC loads based on the filtered data. Establishing a fuzzy neural network model, and determining the system operating mode, including energy storage charging, power transfer, and energy storage discharge, based on the load power and the peak-shaving set value. Establishing an adaptive power distribution equation group, and solving it to obtain the power reference value of each converter and energy storage system. Using power outer loop control, calculating the corresponding current reference value, and using an adaptive current tracker to generate a switching signal, to achieve the utilization control of regenerative braking energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrified railway traction power supply, and more specifically, relates to a control method for a regenerative braking energy utilization system for a dual-current system line. Background Art

[0002] The AC / DC dual-current urban rail transit traction power supply mode realizes seamless connection between urban general-speed rail transit lines and urban rail transit express lines, enabling "zero-distance" passenger transfer, and greatly improving the rail transit service level. It is the future development direction of urban rail transit. However, electric locomotives often use regenerative braking. In urban rail transit, due to short station intervals, trains start and brake frequently, and the braking energy is quite considerable. At the same time, in the AC traction power supply system, high-power regenerative braking energy is also generated. But when the locomotives on the same arm cannot absorb the regenerative braking energy, the excess regenerative braking energy will be injected into the power grid through the traction substation or dissipated by the braking resistor, resulting in energy waste. Moreover, due to the existence of neutral sections and electrical sections, the energy in the dual-current system cannot flow to each other, resulting in a low utilization rate of regenerative braking energy.

[0003] Energy storage technology can realize functions such as demand-side power management, peak shaving and valley filling, and load smoothing in the rail transit field; however, the regenerative braking energy utilization system for the dual-current traction power supply system is still blank, and due to the natural energy barrier between the DC system and the AC system lines, their energies cannot be directly transmitted, and the power flow cannot be uniformly managed.

[0004] Therefore, how to efficiently utilize the regenerative braking energy for the dual-current traction power supply system has become one of the key technologies to improve the energy efficiency of the rail transit system.

[0005] Currently, the utilization schemes of regenerative braking energy mainly include the following several types:

[0006] 1. DC bus type. In the DC traction power supply system, the feedback current of the regenerative motor can be directly connected to the DC bus to realize the utilization of regenerative energy on the DC side. However, this scheme can only utilize the regenerative electric energy on the DC side and cannot utilize the regenerative electric energy on the AC side.

[0007] 2. Bidirectional converter type. In the AC traction power supply system, a bidirectional converter is installed between the AC power supply arm and the traction load to realize the bidirectional transmission of regenerative electric energy between AC and DC. However, this scheme requires an additional large-capacity bidirectional converter, resulting in a high cost.

[0008] 3. Energy storage. In an AC / DC hybrid power supply system, an energy storage system is installed between the AC and DC power supply arms to absorb regenerative energy and release it when needed. This solution can utilize regenerative energy on both the AC and DC sides, but requires additional large-capacity energy storage devices, which increases system complexity.

[0009] Although the above solutions have achieved the utilization of regenerative braking energy to a certain extent, there are still the following major problems:

[0010] 1. Insufficient utilization of regenerative power on the AC and DC sides. Existing solutions either rely solely on DC-side regenerative power or require the addition of large-capacity bidirectional converters or energy storage devices, resulting in high system costs.

[0011] 2. Lack of intelligent power allocation and coordinated control. Existing solutions often rely on manually set power distribution strategies, which are unable to perceive and adaptively adjust system operating conditions in real time, making it difficult to optimize energy utilization.

[0012] 3. Relatively simple control strategies. Most existing solutions use relatively simple control strategies, such as current loop control and power loop control, which are difficult to meet the complex requirements of regenerative braking energy utilization systems.

[0013] Therefore, it is urgent to develop a new type of regenerative braking energy utilization system that can fully utilize the regenerative electric energy on the AC and DC sides, and adopt intelligent power distribution and coordinated control strategies to improve the energy utilization efficiency and economic performance of the system. Summary of the invention

[0014] In view of this, the present invention provides a control method for a regenerative braking energy utilization system for a dual-current circuit, which can solve the technical problem that the existing technology is difficult to achieve efficient utilization and intelligent management of regenerative electric energy on the AC and DC sides.

[0015] The present invention is achieved in that:

[0016] The present invention provides a control method for a regenerative braking energy utilization system for a dual-current circuit, comprising the following steps:

[0017] S10, constructing a hardware structure of a regenerative braking energy utilization system for a dual-current system, wherein the system includes a power financing device I and a power financing device II;

[0018] S20, using a particle swarm optimization algorithm to establish a game model with the lowest system electricity rate as the objective function, and obtaining the peak clipping set value of the AC traction station and the peak clipping set value of the DC traction station through iterative optimization calculation;

[0019] S30, collecting voltage and current data of the left and right power supply arms and the DC power supply arm of the AC traction station, and establishing a real-time database;

[0020] S40. Filter the voltage and current data in the real-time database using the sliding time window method to obtain the filtered voltage and current data;

[0021] S50. Calculate the instantaneous power of the load on the AC supply arms on both sides and the instantaneous power of the load on the DC supply arm based on the filtered voltage and current data;

[0022] S60. Establish a fuzzy neural network model, input the instantaneous AC load power, the instantaneous DC load power, and the peak shaving set value into the fuzzy neural network model, and output the determined system working mode;

[0023] S70. According to the system working mode, establish an adaptive power distribution equation set, and solve to obtain the power reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system;

[0024] S80. Adopt power outer loop control, establish a current reference value calculation model based on the power reference values, and calculate the current reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system;

[0025] S90. Compare the current reference values with the actual current values at the corresponding ports, obtain the modulation signals using an adaptive current tracker, and convert the modulation signals into the switching signals of each converter through space vector pulse width modulation technology to realize the utilization control of the regenerative braking energy.

[0026] On the basis of the above technical solutions, the control method of the regenerative braking energy utilization system for a dual-current system line of the present invention can also be improved as follows:

[0027] Among them, the working modes include an energy storage charging mode, a power transfer mode, and an energy storage discharging mode.

[0028] The objective function of the game model is specifically expressed as follows:

[0029]

[0030] In the formula, J is the total system cost; T is the optimization period, in hours; C grid，t is the grid power purchase cost at time t; C dem，t is the demand charge cost at time t; C loss，t is the system loss cost at time t.

[0031] Among them:

[0032] Cgrid，t = P buy，t P buy,t -p sell，t P sell，t ;

[0033] Wherein, p buy，t is the grid power purchase price at time t, with the unit of yuan / kWh; P buy,t is the power purchase power at time t, with the unit of kW; p sell，t is the on-grid electricity price at time t, with the unit of yuan / kWh; P sell，t is the power selling power at time t, with the unit of kW.

[0034] C dem，t = p dem max{P load，t};

[0035] Wherein, p dem is the demand price, with the unit of yuan / kW; P load，t is the load power at time t, with the unit of kW.

[0036] C loss,t = p buy，t (P loss,AC，t + P loss,DC,t + P loss，conv，t );

[0037] Wherein, P loss,AC，t is the loss power on the AC side at time t; P loss,DC，t is the loss power on the DC side at time t; P loss，conv，t is the loss power of the converter at time t.

[0038] Constraint conditions:

[0039] 1. Power balance constraint:

[0040] P buy,t - P sell，t + P ESS,t = P load，t + P loss,t ;

[0041] Wherein, P ESS，t is the charge and discharge power of the energy storage system at time t, positive for charging and negative for discharging.

[0042] 2. Energy storage system constraint:

[0043] E ESS,min ≤ E ESS，t ≤ E ESS,max ;

[0044] P ESS,min ≤ P ESS,t ≤ PESS,max ;

[0045] Where, E ESS，t is the remaining capacity of the energy storage system at time t; E ESS,min and E ESS,max are the lower and upper limits of the energy storage system capacity respectively; P ESS,min and P ESS,max are the lower and upper limits of the energy storage system power respectively.

[0046] The power financing device I is composed of an α-side step-down transformer, a β-side step-down transformer, an α-side AC-DC converter, and a β-side AC-DC converter.

[0047] Furthermore, the AC port of the α-side AC-DC converter is connected to the secondary side of the α-side step-down transformer, the primary side of the α-side step-down transformer is connected to the left power supply arm of the AC traction station, the DC port of the α-side AC-DC converter is connected to the DC port of the β-side AC-DC converter, the AC port of the β-side AC-DC converter is connected to the secondary side of the β-side step-down transformer, and the primary side of the β-side step-down transformer is connected to the right power supply arm of the AC traction station.

[0048] Furthermore, the power financing device II is composed of a step-down transformer on the left, an AC-DC converter on the left, a DC-DC converter on the right, and an energy storage system.

[0049] Furthermore, the AC port of the left AC-DC converter is connected to the secondary side of the left step-down transformer, the primary side of the left step-down transformer is connected to the left AC power supply arm of the neutral section, the DC port of the left AC-DC converter is connected to the capacitor port of the right DC-DC converter, and the inductor port of the right DC-DC converter is connected to the right DC power supply arm.

[0050] Furthermore, the energy storage system includes a DC-DC converter and a supercapacitor, the inductor end of the DC-DC converter is connected to the supercapacitor, and the capacitor end is connected to the DC port of the left AC-DC converter and the capacitor end of the right DC-DC converter.

[0051] Furthermore, the adaptive power distribution equation group includes an AC power distribution equation, a DC power distribution equation, an energy storage power distribution equation, a power balance equation, and a power regulation equation. The AC power distribution equation is used to calculate the power distribution of the AC side converter. The input is the AC load power and the peak clipping set value, and the output is the power reference value of the α-side and β-side converters. It is specifically expressed as follows:

[0052]

[0053] P β，ref =PAC，total -P α，ref ;

[0054] Wherein, P AC，total is the total AC load power; P AC，high is the AC peak shaving set value; k1 and k2 are adaptive coefficients obtained by online estimation using the least squares method; ε1 is an error compensation term.

[0055] The DC power distribution equation is used to calculate the power distribution of the DC-side converter. The inputs are the DC load power and the peak shaving set value, and the output is the power reference value of the DC converter, which is specifically expressed as follows:

[0056]

[0057] Wherein, P DC，total is the total DC load power; P DC,high is the DC peak shaving set value; k3 and k4 are adaptive coefficients; jωL is an inductive reactance compensation term; ε2 is an error compensation term.

[0058] The energy storage power distribution equation is used to calculate the power distribution of the energy storage system. The input is the total system power deviation, and the output is the power reference value of the energy storage system, which is specifically expressed as follows:

[0059] P ESS,ref = k5(P AC，total + P DC，total - P AC，high - P DC,high ) + k6∫(P AC，total + P DC，total )dt + ε3;

[0060] Wherein, k5 and k6 are adaptive coefficients; ε3 is an error compensation term.

[0061] The power balance equation is used to ensure system power balance, which is specifically expressed as follows:

[0062] P α，ref + P β，ref + P DC，ref + P ESS，ref = P AC，total + P DC，total .

[0063] The power regulation equation is used to achieve dynamic power regulation, which is specifically expressed as follows:

[0064]

[0065] Wherein, P ref is the power reference value; P actual is the actual power; k7 and k8 are regulation coefficients; ε4 is an error compensation term.

[0066] Further, the AC power distribution equation is used to calculate the power distribution of the AC-side converter. The inputs are the AC load power and the peak shaving set value, and the outputs are the power reference values of the converters on the α side and the β side. The DC power distribution equation is used to calculate the power distribution of the DC-side converter. The inputs are the DC load power and the peak shaving set value, and the output is the power reference value of the DC converter. The energy storage power distribution equation is used to calculate the power distribution of the energy storage system. The input is the total system power deviation, and the output is the power reference value of the energy storage system. The power balance equation is used to ensure the system power balance. The power regulation equation is used to achieve dynamic regulation of power.

[0067] Further, the specific structure of the fuzzy neural network model is a five-layer feedforward network, including an input layer, a fuzzy layer, a rule layer, a defuzzification layer, and an output layer.

[0068] Optionally, it further includes a fuzzy adjustment equation set module and a fuzzy attention equation set module. The fuzzy adjustment equation set module includes a membership function adjustment equation, a rule weight adjustment equation, and an output scale factor adjustment equation.

[0069] The membership function adjustment equation is used to dynamically adjust the membership function. The inputs are the system error and the error change rate, and the output is the adjusted membership function parameter, which is specifically expressed as follows:

[0070]

[0071] In the formula, μ ij (x) is the jth membership function of the ith input variable; c ij is the center value; σ ij is the width parameter.

[0072] The rule weight adjustment equation is used to dynamically adjust the weights of the fuzzy rules. The input is the system performance index, and the output is the adjusted rule weight, which is specifically expressed as follows:

[0073]

[0074] In the formula, w k is the weight of the kth rule; η is the learning rate; ΔJ is the change in the performance index; is the sensitivity of the output to the weight.

[0075] The output scale factor adjustment equation is used to dynamically adjust the output scale factor. The input is the system output deviation, and the output is the adjusted scale factor, which is specifically expressed as follows:

[0076]

[0077] In the formula, Ko is the output scale factor; α and β are adjustment parameters; e is the output deviation of the system.

[0078] The fuzzy attention equation set module includes an attention weight calculation equation, a context vector calculation equation, and an output fusion equation;

[0079] The attention weight calculation equation is used to calculate the importance weights of different input features. The input is the system state vector, and the output is the attention weight, which is specifically expressed as follows:

[0080]

[0081] In the formula, α i is the attention weight of the i-th feature; s t-1 is the system state at the previous moment; h i is the i-th input feature; score() is the correlation scoring function.

[0082] The context vector calculation equation is used to generate a context vector considering the attention mechanism, which is specifically expressed as follows:

[0083]

[0084] In the formula, c t is the context vector at time t.

[0085] The output fusion equation is used to fuse the context vector with the current state, which is specifically expressed as follows:

[0086] o t = tanh(W c [c t ; s t );

[0087] In the formula, o t is the fused output; W c is the weight matrix; [c t ; s t represents the concatenation of the context vector and the current state.

[0088] The modulation signal is converted into the switching signals of each converter through space vector pulse width modulation technology, which specifically includes the following steps:

[0089] 1. Convert the three-phase modulation signal into a space vector:

[0090]

[0091] 2. Determine the sector where the space vector is located:

[0092]

[0093] In the formula, v α , v β are the components of the space vector in the α β coordinate system.

[0094] 3. Calculate the action time of the basic vector:

[0095]

[0096] T0=T s -T1-T2;

[0097] Where, T s is the switching period; θ is the angle between the space vector and the starting edge of the sector.

[0098] 4. Generate PWM waveform: Generate the conduction signal of each switch tube according to the action time sequence.

[0099] Compared with the prior art, the control method for a regenerative braking energy utilization system for a dual-current circuit provided by the present invention has the following beneficial effects:

[0100] 1. Efficient utilization of regenerative power on both the AC and DC sides. By building a hardware structure consisting of two sets of power integration equipment and an energy storage system, bidirectional conversion and storage of regenerative power on both the AC and DC sides can be achieved, maximizing the utilization rate of regenerative power.

[0101] 2. Adopting intelligent power distribution and coordinated control strategies. This invention uses a particle swarm optimization algorithm to determine the optimal AC and DC peak-shaving setpoints, establishes a fuzzy neural network model to adaptively determine the system operating mode, and designs an adaptive power distribution equation set, thus achieving intelligent distribution and coordinated control of regenerative power.

[0102] 3. More advanced and efficient control strategy. This invention adopts adaptive current tracking control at the current loop node and uses space vector pulse width modulation technology to generate switching signals. Compared with traditional current loop control and power loop control, it has stronger dynamic response capability and higher control accuracy.

[0103] 4. Improved system energy efficiency and economic performance: Through the above technical measures, the present invention can maximize the use of regenerative braking energy, reduce system energy loss, and thus significantly reduce the system's operating electricity costs and improve the system's overall energy efficiency and economic performance.

[0104] In summary, the regenerative braking energy utilization system control method proposed in the present invention has been innovatively designed in terms of hardware topology, power allocation strategy, control algorithm, etc., realizing efficient utilization and intelligent management of regenerative electric energy, and solving the technical problem that the existing technology is difficult to achieve efficient utilization and intelligent management of regenerative electric energy on the AC and DC sides. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 A flow chart of the method provided by the present invention;

[0106] Figure 2 Schematic diagram of the topology of the regenerative braking energy utilization system for the dual-flow circuit in the embodiment;

[0107] Figure 3 This is the topology of the power financing device 1 in the embodiment;

[0108] Figure 4 This is the topology of the power financing device II in the embodiment;

[0109] Figure 5 This is a timing diagram of system power distribution in the embodiment;

[0110] Figure 6 This is a stacked diagram of system power allocation in an embodiment;

[0111] Figure 7 A block diagram of a dual-loop control strategy for an AC-DC converter with an H-bridge structure, in which the voltage outer loop predicts the current inner loop, in an embodiment;

[0112] Figure 8 PI control strategy block diagram of a half-bridge DC-DC converter in an embodiment. DETAILED DESCRIPTION

[0113] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0114] like Figure 1 FIG. 1 is a flow chart of a control method for a regenerative braking energy utilization system for a dual-flow circuit provided by the present invention. The method includes the following steps:

[0115] S10. Constructing a hardware structure of a regenerative braking energy utilization system for a dual-current system, the system including a power financing device I and a power financing device II;

[0116] S20, using a particle swarm optimization algorithm to establish a game model with the lowest system electricity rate as the objective function, and obtaining the peak clipping set value of the AC traction station and the peak clipping set value of the DC traction station through iterative optimization calculation;

[0117] S30, collecting voltage and current data of the left and right power supply arms and the DC power supply arm of the AC traction station, and establishing a real-time database;

[0118] S40, filtering the voltage and current data in the real-time database using a sliding time window method to obtain filtered voltage and current data;

[0119] S50. Calculate the instantaneous power of the AC power supply arm load and the DC power supply arm load based on the filtered voltage and current data;

[0120] S60, establishing a fuzzy neural network model, inputting the AC load instantaneous power, the DC load instantaneous power and the peak clipping set value into the fuzzy neural network model, and outputting a determined system operating mode;

[0121] S70. Establish an adaptive power allocation equation group according to the system operating mode, and solve the equation group to obtain power reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system.

[0122] S80, using power outer loop control, establishing a current reference value calculation model based on the power reference value, and calculating current reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system;

[0123] S90. Compare the current reference value with the actual current value of the corresponding port, use an adaptive current tracker to obtain a modulation signal, and convert the modulation signal into a switching signal of each converter through space vector pulse width modulation technology to achieve utilization control of regenerative braking energy.

[0124] The specific implementation of the above steps is described in detail below:

[0125] Step S10: Build the system hardware structure. The purpose of this step is to establish the hardware environment of the regenerative braking energy utilization system.

[0126] First, construct the power integration device I. This device consists of an α-side step-down transformer, a β-side step-down transformer, an α-side AC-DC converter, and a β-side AC-DC converter. The AC port of the α-side AC-DC converter is connected to the secondary side of the α-side step-down transformer, and the primary side of the α-side step-down transformer is connected to the left power supply arm of the AC traction substation. The DC port of the α-side AC-DC converter is connected to the DC port of the β-side AC-DC converter. The AC port of the β-side AC-DC converter is connected to the secondary side of the β-side step-down transformer, and the primary side of the β-side step-down transformer is connected to the right power supply arm of the AC traction substation. This topology enables power integration between the left and right power supply arms of the AC traction substation.

[0127] Next, construct Power Integration Device II. Power Integration Device II consists of a step-down transformer on the left, an AC-DC converter on the left, a DC-DC converter on the right, and an energy storage system. The AC port of the left AC-DC converter is connected to the secondary side of the left step-down transformer, while the primary side of the left step-down transformer is connected to the left AC power supply arm of the neutral section. The DC port of the left AC-DC converter is connected to the capacitor port of the right DC-DC converter, while the inductor port of the right DC-DC converter is connected to the right DC power supply arm.

[0128] Finally, the energy storage system is constructed. This system consists of a DC-DC converter and a supercapacitor. The inductor side of the DC-DC converter is connected to the supercapacitor, while the capacitor side is connected to the DC port of the left AC-DC converter and the capacitor side of the right DC-DC converter. This energy storage system can store regenerative braking energy and release it when needed.

[0129] In general, by constructing the above hardware structure, a hardware environment including two sets of power integration equipment and an energy storage system is formed, laying the foundation for the subsequent regenerative braking energy utilization control.

[0130] Step S20: using a particle swarm optimization algorithm to establish a game model with the lowest system electricity fee as the objective function.

[0131] The purpose of this step is to use the particle swarm optimization algorithm to find the peak shaving setting value that minimizes the total electricity cost of the system. First, a game model with the total electricity cost of the system as the objective function is established. The total electricity cost of the system consists of three parts: the power purchase cost of the grid C grid,t , demand electricity cost C dem，t and system loss cost C loss,t . It can be expressed as:

[0132]

[0133] Where T is the optimization period in hours.

[0134] Grid electricity purchase cost C grid，t It can be expressed as:

[0135] C grid,t =p buy，t P buy,t -p sell，t P sell，t ;

[0136] Where p buy，t is the electricity purchase price from the power grid at time t, in yuan / kWh; P buy，t is the purchased power at time t, in kW; p sell，t is the on-grid electricity price at time t, in yuan / kWh; P sell，tThe sold electric power at time t, with the unit of kW.

[0137] The demand electricity cost C dem，t can be expressed as:

[0138] C dem，t = p dem max{P load，t};

[0139] In the formula, p dem is the demand electricity price, with the unit of yuan / kW; P load，t is the load power at time t, with the unit of kW.

[0140] The system loss cost C loss，t can be expressed as:

[0141] C loss,t = p buy，t (P loss,AC，t + P loss,DC，t + P loss，conv，t );

[0142] In the formula, P loss,AC，t is the loss power on the AC side at time t; P loss,DC，t is the loss power on the DC side at time t; P loss，conv，t is the loss power of the converter at time t.

[0143] Next, the particle swarm optimization algorithm is used to iteratively optimize this objective function to obtain the AC peak shaving setting value P AC，high and the DC peak shaving setting value P DC,high .

[0144] The particle swarm optimization algorithm is a swarm intelligence algorithm that finds the optimal solution by simulating the foraging behavior of a bird flock. In the algorithm, each particle represents a potential solution, and the particle continuously updates its position and velocity based on its own historical experience and the experience of the entire group, and finally converges to the global optimal solution. In this step, each particle represents a set of P AC，high and P DC,high , and the algorithm continuously iteratively updates the position and velocity of the particle to finally find the peak shaving setting value that minimizes the total electricity cost of the system.

[0145] Step S30: Collect the voltage and current data of the power supply arms on the left and right sides of the AC traction substation and the DC power supply arm, and establish a real-time database.

[0146] The purpose of this step is to obtain the real-time voltage and current data during the operation of the system, providing a basis for subsequent power calculation and control. The specific contents include the following:

[0147] First, collect the voltage and current data of the left and right supply arms of the AC traction substation. Use voltage and current sensors to collect the voltages v a (t), v b (t), v c (t) and the currents i a (t), i b (t), i c (t) of the left and right supply arms of the AC traction substation in real time.

[0148] Secondly, collect the voltage and current data of the DC supply arm. Use voltage and current sensors to collect the voltage v DC and the current i DC .

[0149] Finally, record the above-collected voltage and current data into the database in real time, providing a basis for subsequent data processing and power calculation.

[0150] Through this step, a database containing real-time voltage and current data of the AC and DC supply arms is established, laying a foundation for the real-time monitoring and control of the system.

[0151] Step S40: Use the sliding time window method to filter the voltage and current data in the real-time database to obtain the filtered voltage and current data.

[0152] The purpose of this step is to filter the original voltage and current data collected in step S30, remove high-frequency interference, and obtain smoother and more stable data. The specific method is as follows:

[0153] First, set the length N of the filtering time window. According to the dynamic characteristics of the system, select an appropriate length of the filtering time window. For example, N = 100, that is, 1 second.

[0154] Then, perform moving average filtering on the collected voltage and current data. For the data x i at a certain moment t, calculate the average value using the data within the time window centered on t as the filtered data at this moment The formula is as follows:

[0155]

[0156] In the formula, is the filtered data at moment t, x i is the original data at moment i, and N is the length of the time window.

[0157] Finally, store the filtered voltage and current data into the real-time database for use in subsequent steps.

[0158] Through this step, high-frequency interference in the original voltage and current data is removed, and smoother and more stable data is obtained, providing good input for subsequent power calculation and control.

[0159] Step S50: Calculate the instantaneous power of the load on the AC supply arms on both sides and the instantaneous power of the load on the DC supply arm based on the filtered voltage and current data.

[0160] The purpose of this step is to calculate the instantaneous power of the AC and DC supply arms, providing a basis for subsequent power distribution and control. The specific calculation method is as follows:

[0161] First, calculate the instantaneous power P AC，total of the AC supply arms on both sides. According to the filtered data of the AC supply arm voltages v a (t), v b (t), v c (t) and currents i a (t), i b (t), i C obtained in step S40, use the formula:

[0162]

[0163] Calculate the total instantaneous power P AC，total of the AC supply arms on both sides. In the formula, T is the power calculation time window.

[0164] Secondly, calculate the instantaneous power P DC，total of the DC supply arm. According to the filtered data of the DC supply arm voltage v DC and current i DC obtained in step S40, use the formula:

[0165] P DC，total = v DC i DC ;

[0166] Calculate the instantaneous power P DC，total of the DC supply arm.

[0167] Through this step, the instantaneous power data of the AC and DC supply arms is obtained, providing an important basis for subsequent power distribution and control.

[0168] Step S60: Establish a fuzzy neural network model, input the instantaneous power P AC，total of the AC load, the instantaneous power P DC，total of the DC load, as well as the peak shaving set values P AC，high and P DC,high into the fuzzy neural network model, and output the determined system working mode.

[0169] The purpose of this step is to establish a fuzzy neural network model to automatically determine the current working mode of the system based on the AC and DC load powers and the peak shaving set value. The specific method is as follows:

[0170] First, construct a five-layer feedforward fuzzy neural network model. This model includes an input layer, a fuzzy layer, a rule layer, a defuzzification layer, and an output layer. The input layer receives P AC，total 、P DC，total 、P AC，high and P DC,high . The output layer gives the current working mode of the system, including the energy storage charging mode, the power transfer mode, and the energy storage discharging mode.

[0171] Second, establish the fuzzy membership function. The Gaussian-type fuzzy membership function is adopted for the input variables:

[0172]

[0173] where μ ij (x) is the jth membership function of the ith input variable; c ij is the center value; σ ij is the width parameter. The adaptive adjustment of the membership function is realized by dynamically adjusting these two parameters.

[0174] Next, construct the fuzzy rule base. Design the fuzzy rules according to engineering experience and assign a certain weight W k to each rule. Optimize the fuzzy rule base by dynamically adjusting the rule weight:

[0175]

[0176] where η is the learning rate, ΔJ is the change in the performance index, is the sensitivity of the output to the weight.

[0177] Finally, adopt the fuzzy inference mechanism and the center-of-gravity method for defuzzification to obtain the final working mode of the system.

[0178] Through this step, an intelligent fuzzy neural network model is established, which can automatically determine the current working mode of the system according to the real-time AC and DC load powers and the peak shaving set value, providing a basis for subsequent power distribution and control.

[0179] Step S70: According to the working mode of the system, establish an adaptive power distribution equation set, and solve the equation set to obtain the power reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system.

[0180] The purpose of this step is to establish a set of adaptive power distribution equations based on the system operating mode determined in step S60, and calculate the power reference values of each converter and energy storage system. The specific method is as follows:

[0181] First, establish the AC power distribution equation. The AC power distribution equation is used to calculate the power distribution of the AC-DC converters on the α side and the β side. The inputs are the total AC load power P AC，total and the AC peak shaving set value P AC，high , and the outputs are the power reference values P α，ref and P β，ref . Specifically:

[0182]

[0183] P β，ref = P AC，total - P a，ref ;

[0184] In the formula, k1 and k2 are adaptive coefficients, and ε1 is an error compensation term.

[0185] Secondly, establish the DC power distribution equation. The DC power distribution equation is used to calculate the power distribution of the left AC-DC converter and the right DC-DC converter. The inputs are the total DC load power P DC，total and the DC peak shaving set value P DC,high , and the output is the power reference value P DC,ref . Specifically:

[0186]

[0187] In the formula, k3 and k4 are adaptive coefficients, jωL is an inductive reactance compensation term, and ε2 is an error compensation term.

[0188] Thirdly, establish the energy storage power distribution equation. The energy storage power distribution equation is used to calculate the power distribution of the energy storage system. The input is the deviation between the total AC and DC power, and the output is the power reference value P ESS，ref of the energy storage system. Specifically:

[0189] P ESS,ref = k5(P AC，total + P DC，total - P AC，high - P DC,high ) + k6∫(P AC，total + P DC，total )dt + ε3;

[0190] In the formula, k5 and k6 are adaptive coefficients, and ε3 is an error compensation term.

[0191] Next, establish a power balance equation. The power balance equation is used to ensure the power balance of the system:

[0192] P α，ref +P β，ref +P DC，ref +P ESS，ref =P AC，total +P DC，total ;

[0193] Finally, establish a power regulation equation. The power regulation equation is used to achieve dynamic regulation of power:

[0194]

[0195] In the formula, P ref is the power reference value, P actual is the actual power, k7 and k8 are regulation coefficients, and ε4 is the error compensation term.

[0196] By solving this set of adaptive power distribution equations, the power reference values of each converter and energy storage system can be calculated to meet the requirements of the system's power balance and dynamic characteristics.

[0197] Step S80: Adopt power outer loop control, establish a current reference value calculation model based on the power reference value, and calculate the current reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system.

[0198] The purpose of this step is to establish a current reference value calculation model according to the power reference values of each converter and energy storage system calculated in step S70, obtain the current reference values of each port, and provide a basis for subsequent current closed-loop control. The specific method is as follows:

[0199] First, establish a power outer loop control structure. Adopt power outer loop control, use the power reference value calculated in step S70 as the outer loop control target, and establish a corresponding control loop.

[0200] Secondly, establish a current reference value calculation model. Calculate the current reference value according to the power reference value of each port and in combination with the voltage data. Taking the α-side AC-DC converter as an example, its current reference value i α，ref can be calculated as:

[0201]

[0202] In the formula, P α，ref is the power reference value of the α-side converter calculated in step S70, and v α is the input voltage of the α-side converter.

[0203] Finally, the current reference values of the AC-DC converter on the α side, the AC-DC converter on the β side, the AC-DC converter on the left side, the DC-DC converter on the right side, and the energy storage system are calculated respectively.

[0204] Through this step, a calculation model of the current reference value based on the power outer loop is established, providing the required current reference value for the subsequent current tracking control.

[0205] Step S90: Compare the current reference value with the actual current value of the corresponding port, obtain the modulation signal by using an adaptive current tracker, and convert the modulation signal into the switching signal of each converter through space vector pulse width modulation technology to realize the utilization control of the regenerative braking energy.

[0206] The purpose of this step is to achieve precise current control of each converter and ensure that the regenerative braking energy can be effectively utilized. The specific method is as follows:

[0207] First, construct an adaptive current tracker. Compare the current reference value calculated in step S80 with the actual current value, and obtain the modulation signal by using an adaptive PI controller. The parameters of the adaptive PI controller can be dynamically adjusted according to the system operation condition to improve the current tracking performance.

[0208] Secondly, use space vector pulse width modulation technology to generate the switching signal. The specific steps are as follows:

[0209] 1. Convert the three-phase modulation signal into space vector representation:

[0210]

[0211] 2. Determine the sector where the space vector is located:

[0212]

[0213] In the formula, v α , v β are the components of the space vector in the αβ coordinate system.

[0214] 3. Calculate the action time of the basic vectors:

[0215]

[0216] T0 = T s - T1 - T2;

[0217] In the formula, T s is the switching period, and θ is the angle between the space vector and the starting side of the sector.

[0218] 4. Generate the PWM waveform: Generate the conduction signals of each switch tube according to the calculated action time sequence.

[0219] Finally, the output switching signal drives the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the switching tubes of the energy storage system to realize the utilization control of regenerative braking energy.

[0220] Through this step, adaptive current tracking control and space vector pulse width modulation technology are used to accurately control the switching state of each converter, ensuring that the regenerative braking energy can be effectively converted and stored between the AC and DC sides, thereby improving energy utilization efficiency.

[0221] Specifically, the principle of the present invention is:

[0222] 1. Principles of Hardware Structure Design. This invention constructs a hardware structure consisting of two power-switching devices and an energy storage system. Power-switching device I achieves power switching between the left and right power supply arms on the AC side through the α-side and β-side AC-DC converters. Power-switching device II achieves power conversion between the AC and DC sides through the left AC-DC converter and the right DC-DC converter. The energy storage system absorbs and releases regenerative braking energy. This hardware structure maximizes the conversion channels between the AC and DC sides of regenerative power, improving the utilization efficiency of regenerative power.

[0223] 2. Principle of the Power Allocation Strategy. This invention uses a particle swarm optimization algorithm to determine the optimal peak-shaving setpoints for the AC and DC sides and designs an adaptive set of power allocation equations. The AC power allocation equation calculates the power allocation for the α- and β-side converters based on the total AC load and the peak-shaving setpoint. The DC power allocation equation calculates the power allocation for the DC converter based on the total DC load and the peak-shaving setpoint. The energy storage power allocation equation calculates the power allocation for the energy storage system based on the total system power deviation. This adaptive power allocation strategy can fully utilize regenerative energy and reduce the total system electricity cost.

[0224] 3. Principle of the Control Algorithm. This invention uses a fuzzy neural network model to intelligently determine the system's operating mode. It also establishes a current reference value calculation model based on power outer loop control, and employs adaptive current tracking control and space vector pulse width modulation techniques to achieve precise control of each converter. The fuzzy neural network can adaptively determine the system's current operating mode based on the AC / DC load power and peak clipping setpoints; power outer loop control ensures dynamic power balance across all ports; and adaptive current tracking control and space vector pulse width modulation techniques enable precise control of the converter's switching state, improving the dynamic response speed and stability of regenerative power utilization.

[0225] To better understand and implement the present invention, the following describes a specific application scenario: a city's subway system uses a hybrid AC / DC power supply system. The line is 50 kilometers long and carries an average passenger capacity of 2,000 people per train. Subway trains utilize regenerative braking technology, which generates a large amount of regenerative electrical energy during braking. To fully utilize this regenerative energy, the city's subway company decided to adopt the regenerative braking energy utilization system control method proposed in this invention.

[0226] First, if Figures 2 - 4 As shown in FIG, the hardware structure of the regenerative braking energy utilization system is as follows:

[0227] Power Integration Equipment I consists of an α-side step-down transformer, a β-side step-down transformer, an α-side AC-DC converter, and a β-side AC-DC converter. The primary side of the α-side step-down transformer is connected to the left power supply arm of the AC traction station, while the primary side of the β-side step-down transformer is connected to the right power supply arm of the AC traction station. The DC ports of the two AC-DC converters are connected, enabling power integration between the left and right power supply arms on the AC side.

[0228] Power Integration Device II consists of a step-down transformer on the left, an AC-DC converter on the left, a DC-DC converter on the right, and an energy storage system. The primary side of the step-down transformer on the left is connected to the neutral section's left AC power supply arm, while the inductor end of the DC-DC converter on the right is connected to the right DC power supply arm. This enables bidirectional power conversion between the AC and DC sides.

[0229] The energy storage system consists of a 2MW, 500kWh supercapacitor bank connected to the left AC-DC converter and the right DC-DC converter via DC-DC converters. This energy storage system absorbs the energy generated by regenerative braking and releases it when needed.

[0230] The α-side AC-DC converter, the β-side AC-DC converter and the left-side AC-DC converter adopt an H-bridge structure, and the right-side DC-DC converter and the DC / DC converter of the energy storage system adopt a half-bridge bidirectional DC / DC converter topology.

[0231] The parameters of the system are set as follows:

[0232] The AC power supply system voltage level is AC25kV, frequency is 50Hz; the DC power supply system voltage level is DC750V. The rated power of each converter is 2MW, and the switching frequency is 1kHz. The resistance of the left and right power supply arms of the AC traction station is 0.2Ω and 0.25Ω respectively, and the resistance of the DC power supply arm is 0.15Ω. The power purchase price of the power grid is p buy =0.6 yuan / kWh, on-grid electricity price p sell = 0.4 yuan / kWh, demand price pdem = 100 yuan / kW.

[0233] Next, the control process of the system is as follows:

[0234] 1. The particle swarm optimization algorithm is used to determine the optimal AC and DC peak shaving set values. The objective function of this algorithm is to minimize the total electricity cost of the system, and the constraints include power balance and energy storage system capacity limit, etc. Through 50 iterations of optimization, the final AC peak shaving set value P AC，high = 1.5 MW, and the DC peak shaving set value P DC,high = 1 MW.

[0235] 2. Collect the voltage and current data of the left and right power supply arms of the AC traction substation and the DC power supply arm, and perform filtering processing through the sliding time window method. The time window length N = 100, that is, 1 second. After filtering, the smoothed data of the total AC load power P AC，total and the total DC load power P DC，total are obtained. Taking the data within 1 hour from 17:00 to 18:00 on a certain day as an example for analysis, as shown in Table 1: Table 1 Collected data within 1 hour

[0236] Time (h) <![CDATA[P AC，total (MW)]]> <![CDATA[P DC,total (MW)]]> 17:00 1.8 1.2 17:10 1.9 1.1 17:20 2.0 1.0 17:30 2.1 0.9 17:40 2.0 1.1 17:50 1.9 1.2 18:00 1.8 1.3

[0237] Figure 5 (System power distribution time series diagram) shows the change trends of the AC load power, DC load power, and energy storage system power during the period from 17:00 to 18:00. The red line represents the AC load power, the blue line represents the DC load power, and the green dashed line represents the energy storage system power. Through this diagram, the mutual relationship and change rules of the three powers can be clearly seen, especially the charge and discharge states of the energy storage system at different times. The green shaded area in the figure intuitively shows the working state of the energy storage system.

[0238] 3. Establish a fuzzy neural network model to judge the system working mode. This model includes a five-layer feedforward structure. The input layer receives P AC，total , P DC，total , P AC，high and P DC,high , and the output layer gives the current system working mode. For the data within 1 hour from 17:00 to 18:00 mentioned above, the model judges that:

[0239] From 17:00 to 17:20, the system is in the energy storage charging mode, charging the regenerative braking energy into the energy storage system;

[0240] From 17:30 to 17:50, the system is in the power transfer mode, transferring the regenerative electric energy on the AC side to the DC side;

[0241] At 18:00, the system is in the energy storage discharge mode, and the electrical energy in the energy storage system is released to the DC power supply arm.

[0242] 4. Establish an adaptive power distribution equation set according to the system working mode. The AC power distribution equation is:

[0243]

[0244] P β，ref = P AC，total - P α，ref ;

[0245] The DC power distribution equation is:

[0246]

[0247] The energy storage power distribution equation is:

[0248] P ESS,ref = 1.8(P AC，total + P DC，total - 1.5 - 1.0)+ 0.6∫(P AC，total + P DC，total )dt + 0.04;

[0249] Among them, k1 = 1.2, k2 = 0.5, k3 = 1.5, k4 = 0.4, k5 = 1.8, k6 = 0.6 are adaptive coefficients. These equations can dynamically calculate the power reference values of each port according to the real-time AC and DC load characteristics, and realize the optimal distribution of regenerative electric energy.

[0250] 5. Based on the power reference value, establish a current reference value calculation model. Taking the AC-DC converter on the α side as an example, its current reference value is:

[0251]

[0252] Among them, v α is the input voltage of the converter on the α side. The current reference values of other converters and the energy storage system can be calculated similarly.

[0253] 6. Adopt adaptive current tracking control and space vector pulse width modulation technology to achieve precise control. The proportional coefficient and integral coefficient of the adaptive PI controller are dynamically adjusted according to the real-time error, and the current tracking error can be quickly eliminated. The space vector pulse width modulation technology can accurately generate the switching signals of each converter, and realize the efficient conversion and storage of regenerative electric energy between the AC and DC sides.

[0254] In addition, it can also be as Figure 7 and Figure 8As shown, the AC-DC converter adopts a double-loop control of a power outer loop and a predictive current inner loop, and then obtains the switching signal for controlling the AC-DC converter through PWM modulation; the DC-DC converter adopts PI control, compares the reference current value with the actual current value through PI control, and then passes through a hysteresis comparator to obtain the switching signal for controlling the DC-DC converter.

[0255] Through the above control process, the operation of the system within the one-hour period from 17:00 to 18:00 is as follows:

[0256] From 17:00 to 17:20, the system is in the energy storage charging mode. The output power P of the converter on the α side αref = 0.6 MW, the output power P of the converter on the β side β，ref = 1.2 MW, and the power absorbed by the energy storage system P ESS,ref = -0.8 MW. During this stage, the electric energy generated by regenerative braking is mainly absorbed by the energy storage system.

[0257] From 17:30 to 17:50, the system is in the power transfer mode. P α，ref = 1.2 MW, P β，ref = 0.9 MW, P DC，ref = 0.8 MW, P ESS,ref = 0. During this stage, the regenerative electric energy on the AC side is converted to the DC side through the converters on the α side and the β side, and part of the DC load is satisfied.

[0258] At 18:00, the system is in the energy storage discharging mode. P α，ref = 1 MW, P β，ref = 0.8 MW, P DC，ref = 1 MW, P ESS,ref = 0.5 MW. During this stage, the energy storage system releases electric energy to the DC side to meet the demand of the DC load.

[0259] Figure 6 (System power distribution stack chart) In the form of a stacked area chart, it shows the power distribution of the converter on the α side, the converter on the β side, and the DC converter. The pink area represents the power of the converter on the α side, the blue area represents the power of the converter on the β side, and the light green area represents the power of the DC converter. This stacked display method can intuitively show the composition of the total power and the relative proportion changes of each part.

[0260] Through the above power distribution and precise control, the operation of the system within the one-hour period from 17:00 to 18:00 is as shown in Table 2 below:

[0261] Table 2 Operation situation table

[0262]

[0263] As can be seen from the above table, through the control method of the regenerative braking energy utilization system proposed by the present invention, within the one-hour period from 17:00 to 18:00, the total electricity cost of the system is only 6,750 yuan, which is reduced by 16.7% compared with 8,100 yuan when no measures are taken. Among them, the electricity purchase cost is reduced from 7,200 yuan to 6,600 yuan, the electricity selling income is increased from 1,100 yuan to 1,350 yuan, and the system loss cost remains unchanged at 800 yuan. It can be seen that this control method can not only improve the utilization rate of regenerative electric energy, but also significantly reduce the system operation cost, with good economic benefits.

[0264] The following gives Embodiment 2 of the present invention. The main difference between Embodiment 2 and Embodiment 1 is that the process in Embodiment 2 is a simpler process, and the working mode and working conditions are judged by calculation. The hardware structures of the regenerative braking energy utilization systems for both are the same for the dual-current system lines. For the convenience of describing Embodiment 2, the variable meanings in this Embodiment 2 are independent of the foregoing. The steps of this Embodiment 2 specifically include:

[0265] Step S10: Set the peak shaving set value of the AC traction substation and the peak shaving set value of the DC traction substation

[0266] Step S20: Respectively collect the voltages and currents of the AC and DC substations, and calculate the instantaneous power P of the load on both sides of the AC supply arm T-α and P T-β , as well as the instantaneous power P of the load on the DC supply arm T-DC . According to the AC load instantaneous power P T-α and P T-β , and the peak shaving set value range, determine the working mode of the system. The working modes include the energy storage charging mode, the power transfer mode, and the energy storage discharging mode;

[0267] Step S30: According to the working mode and specific working conditions, calculate the power reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system known

[0268] Step S40: Obtain the current reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system through the power outer loop and

[0269] Step S50: According to each current reference value and the actual current value of the corresponding port, use the current inner loop to obtain the corresponding modulation signal; then convert each modulation signal into the switching signals of the α-side AC-DC converter, β-side AC-DC converter, left-side AC-DC converter, right-side DC-DC converter, and energy storage system through PWM modulation

[0270] In the said step S10, the peak shaving set value of the AC traction substation and the peak shaving set value of the DC traction substation are calculated by using an intelligent algorithm to obtain the optimal values. The specific process is as follows:

[0271] A. Establish an objective function with the lowest system electricity cost

[0272] minf0 = C grid + C dem ;

[0273]

[0274] C dem = [max(P ACdem,t ) + max(P DCdem,t )]·c dem / N day ;

[0275] C grid is the sum of the electricity fees of the traction substations; C dem is the sum of the demand electricity fees of the traction substations; P ACgridbuy，t is the electric energy power obtained by the AC traction substation from the power grid; P DCgridbuy，t is the electric energy power obtained by the DC traction substation from the power grid; P ACgridfed，t is the electric energy power fed back by the AC traction substation to the power grid; P ACdem，t is the demand power of the AC traction substation; P DCdem，t is the demand power of the DC traction substation; C buy，t represents the unit electricity price of the power obtained from the three-phase power grid, c fed，t represents the unit electricity price of the power returned to the power grid through the AC traction substation; c dem represents the unit electricity price of the peak demand power, N day represents the number of days the system operates per month, for example, it can be 30 days.

[0276] B. With the constraints of the converter capacity, energy storage capacity, system power balance, and the peak shaving set values of the AC traction substation and the DC traction substation, use the intelligent algorithm for iterative optimization to obtain the peak shaving set values of the AC traction substation and the DC traction substation under the condition of the lowest system electricity cost.

[0277] In the step S20, the method for determining the working mode and working conditions of the energy router is as follows:

[0278] A: If P T-α +P T-β +P T-DC <0, it is determined that the system is in the energy storage charging mode, and it is determined as working condition 1. The power reference values of each converter are as follows:

[0279]

[0280] B: If it is determined that the energy router is in the power transfer mode. Since the power of the AC traction substation is significantly greater than that of the DC traction substation, P T-α +P T-β <0 will not occur in the power transfer mode;

[0281] b1) If P T-α +P T-β <0, it is determined as working condition 2. The power reference values of each converter are as follows:

[0282]

[0283] b2) If P T-α +P T-β <0, it is determined as working condition 3. The power reference values of each converter are as follows:

[0284]

[0285] b3) If P T-DC <0, it is determined as working condition 4. The power reference values of each converter are as follows:

[0286]

[0287] b4) If it is determined as working condition 5. The power reference values of each converter are as follows:

[0288]

[0289] b5) If it is determined as working condition 6. The power reference values of each converter are as follows:

[0290]

[0291] b6) If P T-DC <0, it is determined as working condition 7. The power reference values of each converter are as follows:

[0292]

[0293] b7) If it is determined to be operating condition 8, the power reference values of each converter are as follows:

[0294] C: If it is determined that the system is in the energy storage discharge mode, which is operating condition 9, and the power reference values of each converter are as follows:

[0295]

[0296] In the step S40, the calculation formulas for each current reference value are as follows:

[0297]

[0298] wherein, U α , U β , U left , U DC and U ESS are the voltages of the α-side AC-DC converter, β-side AC-DC converter, left-side AC-DC converter, right-side DC-DC converter, and energy storage system.

[0299] As Figure 7 and Figure 8 shown, in the step S50, the AC-DC converter adopts a double-loop control of a power outer loop and a predictive current inner loop, and then obtains the switching signal for controlling the AC-DC converter through PWM modulation; the DC-DC converter adopts PI control, and the reference current value is compared with the actual current value i x through PI control, and then through a hysteresis comparator, the switching signal for controlling the DC-DC converter is obtained.

[0300] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A control method for a regenerative braking energy utilization system for a dual-current system line, characterized in that, The following steps are involved: S10, constructing a hardware structure of a regenerative braking energy utilization system for a dual-current system, wherein the system includes a power financing device I and a power financing device II; S20, using a particle swarm optimization algorithm to establish a game model with the lowest system electricity rate as the objective function, and obtaining the peak clipping set value of the AC traction station and the peak clipping set value of the DC traction station through iterative optimization calculation; S30, collecting voltage and current data of the left and right power supply arms and the DC power supply arm of the AC traction station, and establishing a real-time database; S40, filtering the voltage and current data in the real-time database using a sliding time window method to obtain filtered voltage and current data; S50. Calculate the instantaneous power of the AC power supply arm load and the instantaneous power of the DC power supply arm load based on the filtered voltage and current data; S60, establishing a fuzzy neural network model, inputting the instantaneous power of the AC load, the instantaneous power of the DC load, and the peak clipping set value into the fuzzy neural network model, and outputting a determined system operating mode; the specific structure of the fuzzy neural network model is a five-layer feedforward network, including an input layer, a fuzzy layer, a rule layer, a defuzzification layer, and an output layer; further comprising a fuzzy adjustment equation group module and a fuzzy attention equation group module; the fuzzy adjustment equation group module comprises a membership function adjustment equation, a rule weight adjustment equation, and an output scale factor adjustment equation; the membership function adjustment equation is used to dynamically adjust the membership function, with the input being the system error and the error change rate, and the output being the adjusted membership function parameter; the rule weight adjustment equation is used to dynamically adjust the weight of the fuzzy rule, with the input being the system performance index, and the output being the adjusted rule weight; The output scale factor adjustment equation is used to dynamically adjust the output scale factor. The input is the system output deviation, and the output is the adjusted scale factor. S70: Establish an adaptive power allocation equation group according to the system operating mode, and solve the equation group to obtain power reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left-side AC-DC converter, the right-side DC-DC converter, and the energy storage system; S80: Using power outer loop control, establish a current reference value calculation model based on the power reference value, and calculate current reference values of the α-side AC-DC converter, the β-side AC-DC converter, the left AC-DC converter, the right DC-DC converter, and the energy storage system; S90. Compare the current reference value with the actual current value of the corresponding port, use an adaptive current tracker to obtain a modulation signal, and convert the modulation signal into a switching signal of each converter through space vector pulse width modulation technology to achieve utilization control of regenerative braking energy.

2. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 1, characterized in that The working modes include energy storage charging mode, power transfer mode, and energy storage discharging mode.

3. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 1, characterized in that The power integration device I is composed of an α-side step-down transformer, a β-side step-down transformer, an α-side AC-DC converter, and a β-side AC-DC converter.

4. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 3, characterized in that, The AC port of the α-side AC-DC converter is connected to the secondary side of the α-side step-down transformer, the primary side of the α-side step-down transformer is connected to the left power supply arm of the AC traction station, the DC port of the α-side AC-DC converter is connected to the DC port of the β-side AC-DC converter, the AC port of the β-side AC-DC converter is connected to the secondary side of the β-side step-down transformer, and the primary side of the β-side step-down transformer is connected to the right power supply arm of the AC traction station.

5. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 4, wherein The power integration device II is composed of a step-down transformer on the left, an AC-DC converter on the left, a DC-DC converter on the right, and an energy storage system.

6. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 5, characterized in that, The AC port of the left AC-DC converter is connected to the secondary side of the left step-down transformer, the primary side of the left step-down transformer is connected to the left AC power supply arm of the neutral section, the DC port of the left AC-DC converter is connected to the capacitor port of the right DC-DC converter, and the inductor port of the right DC-DC converter is connected to the right DC power supply arm.

7. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 6, characterized in that, The energy storage system includes a DC-DC converter and a supercapacitor, wherein the inductor end of the DC-DC converter is connected to the supercapacitor, and the capacitor end is connected to the DC port of the left AC-DC converter and the capacitor end of the right DC-DC converter.

8. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 7, characterized in that The adaptive power distribution equation group includes an AC power distribution equation, a DC power distribution equation, an energy storage power distribution equation, a power balance equation and a power regulation equation; wherein the AC power distribution equation is used to calculate the power distribution of the AC side converter, the input is the AC load power and the peak clipping setting value, and the output is the power reference value of the α-side and β-side converters; the DC power distribution equation is used to calculate the power distribution of the DC side converter, the input is the DC load power and the peak clipping setting value, and the output is the power reference value of the DC converter; the energy storage power distribution equation is used to calculate the power distribution of the energy storage system, the input is the total power deviation of the system, and the output is the power reference value of the energy storage system; the power balance equation is used to ensure the power balance of the system; and the power regulation equation is used to realize dynamic adjustment of power.

9. The control method of a regenerative braking energy utilization system for a dual-current system line according to claim 8, characterized in that, The AC power distribution equation is used to calculate the power distribution of the AC side converter, with the input being the AC load power and the peak clipping set value, and the output being the power reference value of the α-side and β-side converters; the DC power distribution equation is used to calculate the power distribution of the DC side converter, with the input being the DC load power and the peak clipping set value, and the output being the power reference value of the DC converter; the energy storage power distribution equation is used to calculate the power distribution of the energy storage system, with the input being the total system power deviation, and the output being the power reference value of the energy storage system; the power balance equation is used to ensure system power balance; and the power regulation equation is used to achieve dynamic power regulation.

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