A method for estimating power system disturbance value based on dynamic admittance matrix

Abstract

The present invention relates to the technical field of power system safety and stability control, and specifically refers to a method for estimating a power system disturbance value based on a dynamic admittance matrix, comprising: obtaining voltage data and current data of each node at each sampling moment within a preset period after the disturbance occurs, calculating the power system dynamic admittance matrix, the synchronous power coefficient between each generator node and each disturbance node, and the initial value of the power system disturbance power; adding the frequency-based system load power deviation and the voltage-based system load power deviation at each sampling moment to obtain the power system load power change at each sampling moment; adding the power system disturbance power initial value and the power system load power change at each sampling moment to obtain the power system disturbance estimation value at each sampling moment, and then using a post-disturbance system frequency prediction model to predict the minimum value of the post-disturbance system frequency. The present invention improves the safety and stability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system safety and stability control, and in particular to a power system disturbance value estimation method based on a dynamic admittance matrix. Background Art

[0002] Traditional synchronous generators have a large moment of inertia, relying on their stored energy to maintain relative system frequency stability during load fluctuations or disturbances. However, renewable energy is intermittent and fluctuating, and unlike synchronous generators, it generally lacks direct inertia support. As a high proportion of renewable energy is integrated into the power system, the system's inertia level decreases significantly, making the system's dynamic characteristics more complex during disturbances. This weakens the frequency regulation capability, increases the amplitude of frequency fluctuations, and reduces frequency stability, making the system more susceptible to low-frequency oscillations, impacting the stability and safety of normal system operation. Furthermore, renewable energy is integrated into the power system through power electronic equipment, further complicating the system's dynamic characteristics during disturbances. This can lead to low voltage ride-through (LVRT), causing a rapid drop in system output power or even grid disconnection, resulting in system voltage sags. This can complicate and slow the system voltage recovery process, impacting the safe and stable operation of the power system. Therefore, it is necessary to estimate the power system disturbance value, understand the potential impact on the system in advance, and allow for flexibility and redundancy in system design. This helps optimize the economic dispatch of the power system.

[0003] Existing system disturbance estimation methods have significant defects. Most methods carry out power system disturbance estimation based on the steady-state power balance assumption, but fail to fully consider the complex dynamic characteristics of the power system. For example, the dependence of the load on frequency and voltage will change with the change of system state, and the dynamic behavior of the synchronous generator will also be different with the disturbance. Especially in the low-inertia grid environment, due to the high proportion of renewable energy access, the dynamic characteristics of the power system are more complex. The accuracy of the disturbance estimation method based on the steady-state power balance assumption is greatly reduced, and it cannot accurately reflect the actual disturbance situation, which seriously affects the judgment and control of the stable operation state of the power system.

[0004] In addition, the minimum frequency value is an important indicator to measure the stability of the power system after a disturbance. When the power system is disturbed, if the frequency continues to drop to an extremely low level, it may trigger a frequency collapse, leading to system disconnection or even large-scale power outages. Predicting the minimum system frequency value can determine in advance whether the system will face the risk of frequency collapse, so as to take measures such as load shedding and rapid start-up of backup units to maintain system frequency stability and avoid serious accidents. At the same time, it can evaluate the system's anti-interference and recovery capabilities under disturbances, and determine whether the system can maintain stable operation within an acceptable range, providing a key basis for system operation and control.

[0005] Existing methods for predicting the minimum frequency value after a system disturbance usually oversimplify system components and dynamic processes, have poor adaptability to complex scenarios, are constrained by issues such as data quality and dimension, and lack real-time performance, resulting in large deviations between the prediction results and actual results. They are difficult to adapt to complex power grid structures and cannot accurately predict when a high proportion of renewable energy is connected, affecting the safe and stable operation of the power system and emergency decision-making. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problem in the prior art that the system disturbance estimation value has low accuracy, resulting in low accuracy in the prediction of the minimum frequency value after the system disturbance occurs, causing poor stability in the operation of the power system.

[0007] To solve the above technical problems, the present invention provides a method for estimating a disturbance value of a power system based on a dynamic admittance matrix, comprising:

[0008] Obtain the voltage and current data of each node at each sampling time within a preset period after the power system disturbance occurs, construct the voltage matrix and current matrix, and calculate the power system dynamic admittance matrix;

[0009] Obtain the terminal potential of each generator node at each sampling moment, combine the voltage of each disturbance node at each sampling moment, the phase angle difference between each generator node and each disturbance node, and the dynamic admittance matrix of the power system, and calculate the synchronous power coefficient between each generator node and each disturbance node;

[0010] Calculate the initial value of the power system disturbance power based on the synchronous power coefficient between each generator node and each disturbance node, and the power change of each generator node within a preset period;

[0011] Obtain the frequency change at each sampling moment, combine it with the system gain coefficient at each sampling moment, and calculate the frequency-based system load power deviation at each sampling moment;

[0012] Obtain the total power generation power of the power system, the equivalent load voltage before the disturbance, and the equivalent load voltage at each sampling moment after the disturbance. Combined with the proportional parameters of different types of loads in the power system, calculate the voltage-based system load power deviation at each sampling moment.

[0013] The frequency-based system load power deviation and the voltage-based system load power deviation at each sampling moment are added together to obtain the power system load power change at each sampling moment;

[0014] The power system load power change at each sampling moment is added to the initial value of the power system disturbance power to obtain the power system total active power change at each sampling moment, which is used as the power system disturbance estimation value at each sampling moment.

[0015] Preferably, the expression of the power system dynamic admittance matrix is:

[0016] ;

[0017] in, represents the power system admittance matrix; represents the voltage matrix; represents the current matrix.

[0018] Preferably, The generator node and the Synchronous power coefficient between disturbance nodes The expression is:

[0019] ;

[0020] in, Indicates the The terminal potential of each generator node; Indicates the A disturbance node voltage; 、 The first Rank Column Value The real and imaginary parts of ; Indicates the The generator node and the The phase angle difference between the voltages of the two disturbance nodes.

[0021] Preferably, the initial value of the system disturbance power The expression is:

[0022] ;

[0023] in, Indicates the The generator node and the Synchronization power coefficient between disturbance nodes; Indicates the first The power variation of each generator node; Indicates the number of generator nodes; Indicates the number of perturbation nodes.

[0024] Preferably, Frequency-based system load power deviation at each sampling moment The expression is:

[0025] ;

[0026] in, Indicates the The system gain coefficient at each sampling moment; Indicates the The frequency change at each sampling moment.

[0027] Preferably, System load power deviation based on voltage at each sampling moment The expression is:

[0028] ;

[0029] in, Indicates the total power generation of the system; 、 and Respectively represent the proportional parameters of impedance type, constant current type and constant power type loads in the load; Indicates the The equivalent load voltage at each sampling moment; Indicates the equivalent load voltage before the disturbance occurs.

[0030] Preferably, The power load power change of the power system at each sampling moment The expression is:

[0031] ;

[0032] in, Indicates the Frequency-based system load power deviation at each sampling moment; Indicates the The system load power deviation based on voltage at each sampling moment.

[0033] Preferably, The change in total active power of the power system at each sampling moment The expression is:

[0034] ;

[0035] in, represents the initial value of the system disturbance power; Indicates the The power load change of the power system at each sampling moment.

[0036] Preferably, it also includes:

[0037] After normalizing the estimated value of the power system disturbance at each sampling moment, it is input into the trained post-disturbance system frequency prediction model, and the predicted minimum value of the post-disturbance system frequency is output.

[0038] Preferably, the post-disturbance system frequency prediction model is a neural network model comprising: an input layer, a hidden layer, and an output layer connected in sequence;

[0039] The Levenberg-Marquardt algorithm is used to train the system frequency prediction model after the initial disturbance until the minimum performance gradient or the maximum number of training rounds or the maximum number of verification failures is reached. The training is stopped to obtain the trained system frequency prediction model after the disturbance.

[0040] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0041] The present invention discloses a method for estimating a disturbance value of a power system based on a dynamic admittance matrix. When a disturbance occurs in the power grid, the voltage and current of each node will change instantaneously. By real-time acquisition of voltage and current data of the power system, a dynamic admittance matrix is ​​constructed to capture instantaneous changes in voltage and current in real time, providing more accurate power grid state information and more precise transient response characteristics of the power grid for disturbance estimation. In actual operation, when the system is disturbed and the frequency fluctuates, the power consumption of the load will also change accordingly. By accurately calculating the load power deviation corresponding to the frequency change, the change of the load power during the disturbance can be more accurately grasped. Since the load power also depends on the voltage, and different types of loads respond differently to voltage changes, when calculating the load power change caused by voltage changes, the proportional parameters of different types of loads in the load are introduced on the basis of considering the total power generation power of the system, the equivalent load voltage at each sampling moment after the disturbance, and the equivalent load voltage before the disturbance, so as to more carefully describe the power change of the load under the disturbance, so that the estimation of the system disturbance value is more in line with the actual situation and avoids estimation errors caused by ignoring the influence of frequency and voltage on load power. At the same time, by considering the frequency and voltage dependencies of the load, the load power variation is split into frequency-dependent and voltage-dependent components. Combined with the calculation of the synchronous power factor, this method enables more refined load power variation modeling, effectively improving the accuracy of disturbance estimation and providing highly accurate data for subsequent prediction of the system minimum frequency after the disturbance. In addition, a neural network is used to optimize frequency extreme value prediction. The power system estimate, which integrates multiple aspects of information such as the grid topology, load characteristics, and generator operating status, is first normalized to construct a post-disturbance system frequency prediction model. The model is then trained using the Levenberg-Marquardt algorithm and a reasonable stopping criterion is set. Compared with relying on only a single or a small number of parameters, this method improves the accuracy and adaptability of the prediction and can more accurately predict the minimum point of the system frequency after the disturbance. The predicted minimum frequency value and the corresponding disturbance estimate are fed back to the power grid dispatching system. Dispatchers can use this information to formulate more accurate control strategies, such as emergency frequency regulation, load reduction, or other control measures, to ensure the stable operation of the power system after the disturbance and improve the safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0043] Figure 1 This is a flow chart of a method for estimating a disturbance value of a power system based on a dynamic admittance matrix provided by the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0045] Reference Figure 1 As shown, Figure 1 This is a flow chart of a method for estimating a disturbance value of a power system based on a dynamic admittance matrix provided by the present invention; specifically comprising:

[0046] S1: Obtain the voltage and current data of each node at each sampling time within a preset period after the power system disturbance occurs, construct the voltage matrix and current matrix, and calculate the power system dynamic admittance matrix, which is expressed as:

[0047] ;

[0048] in, represents the power system admittance matrix; represents the voltage matrix; represents the current matrix;

[0049] Among them, the synchronous phasor measurement unit (PMU) in the power system is used to collect the first to the second sampling time at different sampling times after the system is disturbed. The voltage vector and current vector of the node, then The voltage vector and current vector at each sampling moment are expressed as:

[0050] ;

[0051] ;

[0052] in, Indicates the voltage data of the first node; Represents the current data of the first node; Represents the voltage data of the second node; Represents the current data of the second node; Indicates the Voltage data of each node; Indicates the Current data of each node; Indicates the number of all nodes;

[0053] The constructed voltage matrix and current matrix are expressed as:

[0054] ;

[0055] ;

[0056] in, Indicates the The voltage vector at each sampling moment; Indicates the The current vector at each sampling moment; represents the number of all sampling moments;

[0057] S2: Obtain the terminal potential of each generator node at each sampling moment, combine the voltage of each disturbance node at each sampling moment, the phase angle difference between each generator node and each disturbance node, and the dynamic admittance matrix of the power system, and calculate the synchronous power coefficient between each generator node and each disturbance node. The generator node and the Synchronous power coefficient between disturbance nodes The expression is:

[0058] ;

[0059] in, Indicates the The terminal potential of each generator node; Indicates the A disturbance node voltage; 、 The first Rank Column Value The real and imaginary parts of ; Indicates the The generator node and the The phase angle difference between the voltages of the disturbance nodes;

[0060] S3: Based on the synchronous power coefficient between each generator node and each disturbance node, and the power change of each generator node within the preset period, the initial value of the power system disturbance power is calculated. The expression is:

[0061] ;

[0062] in, Indicates the The generator node and the Synchronization power coefficient between disturbance nodes; Indicates the first The power variation of each generator node; Indicates the number of generator nodes; represents the number of perturbation nodes;

[0063] S4: Use the synchronized phasor measurement unit in the power system to obtain the frequency change at each sampling moment, and combine it with the system gain coefficient at each sampling moment to calculate the frequency-based system load power deviation at each sampling moment. Frequency-based system load power deviation at each sampling moment The expression is:

[0064] ;

[0065] in, Indicates the The system gain coefficient at each sampling moment; Indicates the Frequency change at each sampling moment; Characterize system load power deviation caused by frequency change;

[0066] In power systems, system gain usually does not remain constant but changes over time. When a disturbance occurs in the power system, such as a generator trip or a sudden load change, the frequency regulation process can be divided into multiple stages. Each stage has different system dynamic characteristics. Therefore, the system gain tends to change in stages, namely:

[0067] ;

[0068] in, Indicates the The time variable system gain that reflects the dynamic characteristics of the system in each time period; Indicates the The time variable system gain that reflects the dynamic characteristics of the system in each time period; Indicates the The time variable system gain that reflects the dynamic characteristics of the system in each time period; Indicates the The time variable system gain that reflects the dynamic characteristics of the system in each time period, in MW / Hz; Indicates the Time period dividing points, Indicates the Time period dividing points, Indicates the Time period dividing point; Indicates the Time period dividing point; Indicates the number of segments, generally ranging from 2 to 4;

[0069] In a specific embodiment of the present invention, based on the time demarcation point 、 and , and obtain the time variable system gain reflecting the dynamic characteristics of the system at each stage 、 and ,include:

[0070] exist Inertia response phase, ;in, Indicates the system inertia constant in seconds; Indicates the system base capacity in MW; Indicates the system rated frequency in Hz; The primary frequency modulation stage is determined by the speed regulator characteristics , usually better than More than twice as large; The secondary frequency regulation stage is determined by the regulation capability of the AGC unit. , usually better than further increase;

[0071] Assuming the system rated frequency The frequency is 50Hz, the system base capacity is 10000MW, and the system inertia constant is The frequency regulation reserve capacity is 1000MW and the regulation capacity of the AGC unit is 2000MW.

[0072] According to the theoretical formula and measurement data, the segmented system gain can be obtained as:

[0073] ;

[0074] in, MW / Hz reflects the inertial response (within 3s); MW / Hz reflects the primary frequency modulation effect (within 3s to 15s); MW / Hz reflects the secondary frequency modulation effect, that is, the AGC effect (within 15s to 60s); MW / Hz reflects the final steady state (not less than 60s);

[0075] Obtain the total power generation power of the power system, the equivalent load voltage before the disturbance occurs, and the equivalent load voltage at each sampling moment after the disturbance occurs. Combined with the proportional parameters of different types of loads in the power system, the system load power deviation based on voltage at each sampling moment is calculated. System load power deviation based on voltage at each sampling moment The expression is:

[0076] ;

[0077] in, Indicates the total power generation of the system; 、 and Respectively represent the proportional parameters of impedance type, constant current type and constant power type loads in the load; Indicates the The equivalent load voltage at each sampling moment; Indicates the equivalent load voltage before the disturbance occurs; Characterize system load power deviations caused by voltage variations. The total system power generation can be obtained directly from the SCADA (Supervisory Control and Data Acquisition) system or EMS (Energy Management System), or calculated by summing the outputs of all generators. The equivalent load voltage at each sampling moment can be directly obtained from the substation's voltage measurement devices (such as PMUs and SCADA). The equivalent load voltage before the disturbance is the system's rated voltage (e.g., 110kV, 220kV, 500kV, etc.), which is a known, fixed parameter.

[0078] The power load power variation of the power system at each sampling moment is obtained by adding the frequency-based system load power deviation and the voltage-based system load power deviation at each sampling moment. The power load power change of the power system at each sampling moment The expression is:

[0079] ;

[0080] in, Indicates the Frequency-based system load power deviation at each sampling moment; Indicates the The system load power deviation based on voltage at each sampling moment; Characterize the load power change based on the frequency-dependent part and the voltage-dependent part, that is, the additional power change caused by the voltage and frequency dependence of the load;

[0081] S5: Add the power system load power change at each sampling moment to the power system disturbance power initial value to obtain the power system total active power change at each sampling moment as the power system disturbance estimation value at each sampling moment. The change in total active power of the power system at each sampling moment The expression is:

[0082] ;

[0083] in, represents the initial value of the system disturbance power; Indicates the The power load power change of the power system at each sampling moment;

[0084] S6: Normalize the estimated power system disturbance value at each sampling moment to adapt to the neural network input requirements, input it into the trained post-disturbance system frequency prediction model, and output the predicted minimum post-disturbance system frequency. , whose expression is: ;in, Represents a neural network model; A sampling time sequence representing the estimated value of the power system disturbance;

[0085] The post-disturbance system frequency prediction model is a neural network model comprising: an input layer, a hidden layer, and an output layer connected in sequence; wherein the input layer receives a normalized power system disturbance estimation value sampling time sequence as an input parameter; the hidden layer comprises a number of neurons and uses a Sigmoid activation function to implement nonlinear mapping; the output layer uses a linear activation function to output the predicted minimum system frequency value;

[0086] The Levenberg-Marquardt algorithm is used to train the system frequency prediction model after the initial disturbance until the minimum performance gradient, the maximum number of training rounds, or the maximum number of verification failures are reached. The training is then stopped to obtain the trained system frequency prediction model after the disturbance.

[0087] Finally, the predicted minimum system frequency value and the corresponding disturbance estimate are fed back to the power grid dispatching system to assist in emergency frequency regulation, load reduction or other control measures. That is, after the training is completed, the trained post-disturbance system frequency prediction model is applied to real-time data, and the system frequency extreme value is predicted based on the online obtained power system disturbance estimate value, and the system frequency extreme value is fed back to the power grid dispatching control system in real time to support emergency frequency regulation and load management decisions.

[0088] In summary, the power system disturbance estimation method based on the dynamic admittance matrix designed in the present invention can adapt to changes in the power grid topology and improve the estimation accuracy; consider the frequency and voltage dependence of the load to achieve more refined load power change modeling; based on the neural network model, construct a post-disturbance system frequency prediction model to predict the system frequency extremes, thereby improving the prediction accuracy and adaptability.

[0089] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for estimating disturbance values ​​of a power system based on a dynamic admittance matrix, characterized in that: include: Obtain the voltage and current data of each node at each sampling time within a preset period after the power system disturbance occurs, construct the voltage matrix and current matrix, and calculate the power system dynamic admittance matrix; Obtain the terminal potential of each generator node at each sampling moment, combine the voltage of each disturbance node at each sampling moment, the phase angle difference between each generator node and each disturbance node, and the dynamic admittance matrix of the power system, and calculate the synchronous power coefficient between each generator node and each disturbance node; Calculate the initial value of the power system disturbance power based on the synchronous power coefficient between each generator node and each disturbance node, and the power change of each generator node within a preset period; Obtain the frequency change at each sampling moment, combine it with the system gain coefficient at each sampling moment, and calculate the frequency-based system load power deviation at each sampling moment; Obtain the total power generation power of the power system, the equivalent load voltage before the disturbance, and the equivalent load voltage at each sampling moment after the disturbance. Combined with the proportional parameters of different types of loads in the power system, calculate the voltage-based system load power deviation at each sampling moment. The frequency-based system load power deviation and the voltage-based system load power deviation at each sampling moment are added together to obtain the power system load power change at each sampling moment; The power system load power change at each sampling moment is added to the initial value of the power system disturbance power to obtain the power system total active power change at each sampling moment, which is used as the power system disturbance estimation value at each sampling moment.

2. A method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, characterized in that: The expression of the power system dynamic admittance matrix is: ； in, represents the power system admittance matrix; represents the voltage matrix; represents the current matrix.

3. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, wherein: No. The generator node and the Synchronous power coefficient between disturbance nodes The expression is: ； in, Indicates the The terminal potential of each generator node; Indicates the A disturbance node voltage; 、 The first Rank Column Value The real and imaginary parts of ; Indicates the The generator node and the The phase angle difference between the voltages of the two disturbance nodes.

4. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, wherein: The initial value of the system disturbance power The expression is: ； in, Indicates the The generator node and the Synchronization power coefficient between disturbance nodes; Indicates the first The power variation of each generator node; Indicates the number of generator nodes; Indicates the number of perturbation nodes.

5. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, wherein: No. Frequency-based system load power deviation at each sampling moment The expression is: ； in, Indicates the The system gain coefficient at each sampling moment; Indicates the The frequency change at each sampling moment.

6. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, characterized in that: No. System load power deviation based on voltage at each sampling moment The expression is: ； in, Indicates the total power generation of the system; 、 and Respectively represent the proportional parameters of impedance type, constant current type and constant power type loads in the load; Indicates the The equivalent load voltage at each sampling moment; Indicates the equivalent load voltage before the disturbance occurs.

7. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, characterized in that: No. The power load power change of the power system at each sampling moment The expression is: ； in, Indicates the Frequency-based system load power deviation at each sampling moment; Indicates the The system load power deviation based on voltage at each sampling moment.

8. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, characterized in that: No. The change in total active power of the power system at each sampling moment The expression is: ； in, represents the initial value of the system disturbance power; Indicates the The power load change of the power system at each sampling moment.

9. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 1, characterized in that: Also includes: After normalizing the estimated value of the power system disturbance at each sampling moment, it is input into the trained post-disturbance system frequency prediction model, and the predicted minimum value of the post-disturbance system frequency is output.

10. The method for estimating a disturbance value of a power system based on a dynamic admittance matrix according to claim 9, characterized in that: The post-disturbance system frequency prediction model is a neural network model comprising: an input layer, a hidden layer and an output layer connected in sequence; The Levenberg-Marquardt algorithm is used to train the system frequency prediction model after the initial disturbance until the minimum performance gradient or the maximum number of training rounds or the maximum number of verification failures is reached. The training is stopped to obtain the trained system frequency prediction model after the disturbance.

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