Island micro-grid frequency control method based on unmanned intelligent power station

Abstract

The invention provides an island micro-grid frequency control method, and belongs to the technical field of power grid control. The method provided by the invention comprises the following steps: step 1, establishing a virtual rotor motion equation of a virtual synchronous generator (VSG) according to a transient motion equation of the synchronous generator; step 2, obtaining a transfer function of the system by using a virtual rotor motion equation of the VSG, researching the dynamic stability of the frequency, and analyzing the influence relationship of the virtual rotational inertia J and the damping coefficient D on the system; and 3, according to the current operation state of the power system, the virtual rotational inertia J and the damping coefficient D of the VSG system are updated in real time. The method has the advantages that the damping coefficient is adaptively adjusted, the BP neural network algorithm is used for regulating and controlling the virtual inertia, frequency oscillation can be effectively restrained, overshoot is reduced, and the stability and dynamic performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid control, and particularly to a frequency control method for an island microgrid based on an unmanned intelligent power station. Background Art

[0002] When a large number of distributed power sources are connected to an island microgrid, due to the characteristics of traditional synchronous generators such as low inertia, small capacity, and poor dynamic characteristics, it is difficult to ensure the stability of the power grid. A virtual synchronous generator is a grid-forming converter, which can greatly improve the inertia of the island microgrid when connected to the power grid.

[0003] However, in the VSG parallel system, under load disturbances, the frequency will experience large deviations and oscillations, and the transient frequency stability is poor, which greatly restricts the popularization and application of VSG. To suppress frequency oscillations, the control parameters of the virtual synchronous generator are generally set for adaptive adjustment, but the control parameters do not change linearly, and the regulation effect of ordinary adaptive functions is not ideal. Summary of the Invention

[0004] To solve the problems in the above background art, the present invention provides a frequency control method for an island microgrid, which can effectively suppress frequency oscillations, reduce overshoot, and improve the stability and dynamic performance of the system.

[0005] To achieve the above object, the present invention provides the following technical solutions: A frequency control method for an island microgrid is provided, and the specific steps of the method are as follows:

[0006] Step 1: According to the transient motion equation of the synchronous generator, establish the virtual rotor motion equation of the virtual synchronous generator;

[0007] Step 2: Use the virtual rotor motion equation of the virtual synchronous generator to obtain the transfer function of the system, study the frequency dynamic stability, and analyze the influence relationship between the virtual moment of inertia J and the damping coefficient D on the system;

[0008] Step 3: According to the current operating state of the power system, update the virtual moment of inertia J and the damping coefficient D of the virtual synchronous generator system in real time.

[0009] Further, in the above S3, the undetermined value D1 of the damping coefficient D will be adaptively updated with the operating state, and the relationship is as follows:

[0010]

[0011] Wherein, D0 is the initial value of the damping coefficient D, ω, ω n , Δω are the angular velocity, rated angular velocity, and angular velocity difference of the virtual synchronous generator respectively, k1 and k2 are two adjustment coefficients, k2 is a positive constant, and k1 has the following expression:

[0012]

[0013] Among them, a is the initial threshold for damping coefficient control;

[0014] Judge the magnitude of the value of D1. When its value is within the value range of the damping coefficient D, update the value of D, that is, let D = D1.

[0015] Furthermore, the value range of the damping coefficient is:

[0016]

[0017] Among them, P max , P min are respectively the upper and lower limit values of the active power allowed to be output by the virtual synchronous generator, and ω max , ω min are respectively the upper and lower limit values of the angular velocity of the virtual synchronous generator.

[0018] Furthermore, in the above S3, the virtual moment of inertia J is updated using the BP neural network algorithm. The BP neural network includes an input layer, a hidden layer, and an output layer. The input layer includes two input quantities, ω and dω / dt. The hidden layer includes m neurons, and the output layer outputs the updated value of the virtual moment of inertia J. The update steps are as follows:

[0019] S31 Determine the initial value J0 of the virtual moment of inertia, import the input variables, and denote the i-th neuron in the input layer as x i ;

[0020] S32 Calculate the input value i and output value of the i-th neuron h in the hidden layer. The calculation process is as follows:

[0021]

[0022] Among them, n is the number of neurons in the input layer, is the weight of the hidden layer, b1 is the bias of the hidden layer, and f in (·) is the activation function of the hidden layer;

[0023] S33 Calculate the input value y in and output value y out of the neuron y in the output layer. The calculation process is as follows:

[0024]

[0025] Among them, is the weight of the output layer, b2 is the bias of the output layer, and f out(·) is the activation function of the output layer, and J′ is the value of the virtual moment of inertia J to be updated;

[0026] S34 When J′ is within the value range of the virtual moment of inertia J, update the value of the virtual moment of inertia J of the virtual synchronous generator, that is, let J = J′;

[0027] S35 Repeat the above steps until the set maximum number of iterations is reached.

[0028] Furthermore, in step S33, the neural network is trained using the descending gradient method to iterate the weights and biases of the output layer. The iteration formula is as follows:

[0029]

[0030] where k is the number of neurons in the output layer, w out′ , b2′ are the values of the weights and biases of the output layer to be updated after iteration, η is the learning rate, E(k) is the calculation performance index function, O(k) is the output value of the neurons in the output layer, and O1(k) is the expected output value of the neurons in the output layer;

[0031] Judge the magnitude of E(k). When E(k) ≥ 1, update the weights and biases of the output layer, that is, let:

[0032]

[0033] Furthermore, in step S32, the activation function f in (·) of the hidden layer selects the hyperbolic tangent function, and its expression is:

[0034]

[0035] Furthermore, in step S33, the activation function f out (·) of the output layer selects the sigmoid function, and its expression is:

[0036]

[0037] Furthermore, in step S34, the value range of the virtual inertia J is:

[0038]

[0039] Furthermore, in step S1, the virtual rotor motion equation of the virtual synchronous generator is:

[0040]

[0041] where P m 、P eare the mechanical power and electromagnetic power of the virtual synchronous generator, and θ, θ n are the operating phase angle and rated phase angle of the virtual synchronous generator, respectively.

[0042] Furthermore, in the step S2, the system closed-loop transfer function G v is as follows:

[0043]

[0044] It can be seen from formula (2) that the virtual synchronous generator system is a second-order system. In formula (11), there are:

[0045]

[0046] where ω N is the undamped natural oscillation angular frequency, ξ is the damping ratio, U and U grid are the output voltage and the AC bus voltage respectively, and X is the line inductive reactance. Thus, the overshoot σ% and recovery time Ts, which are the indexes for evaluating the transient performance of the second-order system, can be obtained, and their expression forms are as follows:

[0047]

[0048] The beneficial effects of the present invention: The present invention provides a method for controlling the frequency of an islanded microgrid based on an unmanned intelligent power station, adaptively adjusts the damping coefficient, and uses the BP neural network algorithm to control the virtual inertia that is difficult to control, which can improve the control efficiency and effect, effectively suppress frequency oscillation, reduce overshoot, and improve the stability and dynamic performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described below in conjunction with the drawings and embodiments:

[0050] Figure 1 is the specific step flowchart of the method of the present invention;

[0051] Figure 2 is the schematic diagram of the virtual inertia update process of the present invention;

[0052] Figure 3 is the schematic diagram of the BP neural network algorithm structure of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] Embodiment 1

[0054] As Figure 1 shown in, a method for controlling the frequency of an islanded microgrid, the specific steps are as follows:

[0055] S1. According to the transient motion equation of the synchronous generator, establish the virtual rotor motion equation of the virtual synchronous generator as follows:

[0056]

[0057] Among them, P m and P e are the mechanical power and electromagnetic power of the virtual synchronous generator respectively, θ and θ n are the operating phase angle and rated phase angle of the virtual synchronous generator respectively, and J and D are the virtual moment of inertia and damping coefficient respectively.

[0058] In S2, the expression of P e in the inverter system is:

[0059]

[0060] In the formula, U and U grid are the output voltage and AC bus voltage respectively, X is the line inductive reactance, and δ is the phase angle of the virtual synchronous generator system;

[0061] Therefore, the closed-loop transfer function G v of the virtual synchronous generator system is:

[0062]

[0063] Therefore, there is:

[0064]

[0065] Among them, ω N is the undamped natural oscillation angular frequency, and ξ is the damping ratio; from this, it can be seen that the system frequency stability is related to the virtual moment of inertia J and the damping coefficient D. Write out the overshoot σ% and recovery time T s evaluating the transient performance of the second-order system, and their expression forms are as follows:

[0066]

[0067] According to the virtual synchronous generator control method designed by the University of Leuven, give the value ranges of J and D:

[0068]

[0069] In the formula, P max and P min are the upper and lower limit values of the active power that the virtual synchronous generator is allowed to output respectively, and ω max and ω min are the upper and lower limit values of the VSG angular velocity respectively.

[0070] Step 3: According to the current operating state of the power system, the virtual moment of inertia J and damping coefficient D of the virtual synchronous generator system are updated in real time. For the damping coefficient D, D will adaptively change with the operating state, and the relationship is as follows:

[0071]

[0072] where ω, ω n , and Δω are the angular velocity, rated angular velocity, and angular velocity difference of the virtual synchronous generator respectively, k1 and k2 are two adjustment coefficients, k2 is a positive constant, and k1 has the following expression:

[0073]

[0074] where a is the initial threshold for damping coefficient control;

[0075] Judge the magnitude of the value of D1. When its value is within the value range of the damping coefficient D, update the value of D, that is, let D = D1;

[0076] As Figure 2 and 3 shown, for the virtual moment of inertia J, the BP neural network algorithm is used for update. The BP neural network includes an input layer, a hidden layer, and an output layer. The input layer includes two input quantities ω and dω / dt. The hidden layer includes m neurons. The output layer outputs the updated value of the virtual moment of inertia J. The update steps are as follows:

[0077] S31 Determine the initial value J0 of the virtual moment of inertia, import the input variables, and denote the i-th neuron in the input layer as x i ;

[0078] S32 Calculate the input value i and output value of the i-th neuron h in the hidden layer. The calculation process is as follows:

[0079]

[0080] where n is the number of neurons in the input layer, is the weight of the hidden layer, b1 is the bias of the hidden layer, and f in (·) is the activation function of the hidden layer;

[0081] S33 Calculate the input value y in and output value y out of the neuron y in the output layer. The calculation process is as follows:

[0082]

[0083] where, is the weight of the output layer, b2 is the bias of the output layer, and f out (·) is the activation function of the output layer, and J′ is the value to be updated of the virtual moment of inertia J;

[0084] When J′ is within the value range of the virtual moment of inertia J, update the value of the virtual moment of inertia J, that is, let J = J′;

[0085] S35 Repeat the above steps until the set maximum number of iterations is reached;

[0086] Furthermore, use the gradient descent method to train the neural network, and iterate on the weights and biases of the output layer. The iteration formula is as follows:

[0087]

[0088] where k is the number of neurons in the output layer, w out′ 、b2′ are the values to be updated of the weights and biases of the output layer after iteration, η is the learning rate, E(k) is the calculation performance index function, O(k) is the output value of the neurons in the output layer, and O1(k) is the expected output value of the neurons in the output layer;

[0089] Judge the magnitude of E(k). When E(k) ≥ 1, update the weights and biases of the output layer, that is, let:

[0090]

[0091] In the preferred solution, the activation function f in (·) of the hidden layer is selected as the hyperbolic tangent function, and its expression is:

[0092]

[0093] The activation function f out (·) of the output layer is selected as the sigmoid function, and its expression is:

[0094]

[0095] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. An islanded microgrid frequency control method, characterized in that: It includes the following steps: S1. Establish the virtual rotor motion equation of the virtual synchronous generator according to the transient motion equation of the synchronous generator; S2. Obtain the transfer function of the system by using the virtual rotor motion equation of the virtual synchronous generator, study the frequency dynamic stability, and analyze the influence relationship between the virtual moment of inertia J and the damping coefficient D on the system; S3. Update the virtual moment of inertia J and the damping coefficient D of the virtual synchronous generator system in real time according to the current operating state of the power system.

2. The method for controlling the frequency of an islanded microgrid of an unmanned intelligent power station according to claim 1, wherein: In the above S3, the undetermined value D1 of the damping coefficient D will be adaptively updated with the operating state, and the relationship is as follows: where D0 is the initial value of the damping coefficient D, ω, ω n , and Δω are the angular velocity, rated angular velocity, and angular velocity difference of the virtual synchronous generator, respectively, k1 and k2 are two regulation coefficients, k2 is a positive constant, and k1 has the following expression: where a is the initial threshold of the damping coefficient control; Judge the magnitude of the value of D1. When its value is within the value range of the damping coefficient D, update the value of D, that is, let D = D1.

3. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 2, characterized in that: The value range of the damping coefficient is: Among them, P max and P min are respectively the upper and lower limit values of the active power allowed to be output by the virtual synchronous generator, and ω max and ω min are respectively the upper and lower limit values of the angular velocity of the virtual synchronous generator.

4. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 1, characterized in that: In the above S3, the virtual moment of inertia J is updated by using the BP neural network algorithm. The BP neural network includes an input layer, a hidden layer, and an output layer. The input layer includes two input quantities ω and dω / dt. The hidden layer includes m neurons. The output layer outputs the updated value of the virtual moment of inertia J. The update steps are as follows: S31 determines the initial value J0 of the virtual moment of inertia, imports the input variables, and denotes the i-th neuron in the input layer as x i ; S32 calculates the input value i of the i-th neuron h in the hidden layer and its output value The calculation process is as follows: where n is the number of neurons in the input layer, is the weight of the hidden layer, b1 is the bias of the hidden layer, and f in (·) is the activation function of the hidden layer; S33 calculates the input value y of neuron y in the output layer in and the output value y out , and the calculation process is as follows: Among them, is the weight of the output layer, b2 is the bias of the output layer, and f out (·) is the activation function of the output layer, and J′ is the value of the virtual moment of inertia J to be updated; S34. When J′ is within the value range of the virtual moment of inertia J, update the value of the virtual moment of inertia J of the virtual synchronous generator, that is, let J = J′; S35. Repeat the above steps until the set maximum number of iterations is reached.

5. A frequency control method for an island microgrid of an unmanned intelligent power station according to claim 4, characterized in that: In the above step S33, the descending gradient method is used to train the neural network, and the weights and biases of the output layer are iterated. The iteration formula is as follows: where k is the number of neurons in the output layer, w out′ , b2′ are the updated values of the weights and biases in the output layer to be updated, η is the learning rate, E(k) is the computational performance metric function, O(k) is the output value of the neurons in the output layer, and O1(k) is the expected output value of the neurons in the output layer; Judge the magnitude of E(k). When E(k) ≥ 1, update the weights and biases of the output layer, that is, let:

6. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 4, characterized in that: In the said step S32, the activation function f in (·) of the hidden layer selects the hyperbolic tangent function, and its expression is:

7. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 4, characterized in that: In the step S33 described above, the activation function f out (·) of the output layer selects the sigmoid function, and its expression is:

8. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 4, characterized in that: In the above step S34, the value range of the virtual inertia J is:

9. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 1, characterized in that: In the above step S1, the virtual rotor motion equation of the virtual synchronous generator is: Among them, P m and P e are the mechanical power and electromagnetic power of the virtual synchronous generator respectively, and θ, θ n are the operating phase angle and rated phase angle of the virtual synchronous generator respectively.

10. A frequency control method for an islanded microgrid of an unmanned intelligent power station according to claim 1, characterized in that: In the said step S2, the system closed-loop transfer function G v is as follows: It can be seen from formula (2) that the virtual synchronous generator system is a second-order system. In formula (11), there is: where ω N is the undamped natural oscillation angular frequency, ξ is the damping ratio, U and U grid are the output voltage and the AC bus voltage respectively, X is the line inductive reactance. Thus, the overshoot σ% and the recovery time Ts, which are the indexes for evaluating the transient performance of the second-order system, can be obtained, and their expression forms are as follows:

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