An intelligent distributed control method for on-load tap-changing transformers

Abstract

The large-scale adoption of renewable energy generation in the future will pose challenges to the proper voltage distribution in distribution networks. This paper proposes an intelligent distributed control method for on-load tap-changing transformers. By implementing a distributed optimization algorithm to calculate and adjust the tap positions of on-load tap-changing transformers in real time, this method effectively addresses voltage fluctuations caused by the random fluctuations in renewable energy output. This invention offers valuable insights into voltage regulation in distribution networks.

Description

Technical Field

[0001] The invention belongs to the field of power system voltage regulation. Background Art

[0002] With the national "3060" goal, large-scale renewable energy generation has become widespread. However, this has also brought various reliability issues to the current power system. As of 2021, my country's installed capacity of renewable energy power generation has reached 1.12 billion kilowatts, accounting for 47.10% of the total installed power generation capacity. Such a high penetration of renewable energy has brought considerable uncertainty to the stable operation of the power system, especially voltage stability.

[0003] The random fluctuations in renewable energy output can cause voltage fluctuations and voltage over-limits near access nodes. Existing distribution equipment no longer meets the distribution needs of current active distribution networks. New distribution equipment and control methods are needed to effectively manage distribution systems with large-scale renewable energy penetration, such as shunt capacitors, static VAR generators, and on-load tap-changing transformers.

[0004] Existing research on voltage optimization in renewable energy power systems can be divided into four main categories. The first is to optimize the operation of the power system, taking into account the load configuration throughout the day and achieving the optimal voltage distribution of the system through limited switching operations. The second is local control, in which the local voltage compensation device controls the voltage of the compensation node based on the real-time data collected by the voltage sensor. The essence of this method is to control the voltage of a single node. The control structure is simple and has strong reliability. However, from the perspective of the entire system, there is no communication and coordination between multiple control devices, which often leads to waste of control resources and cannot achieve the best control effect. The third category is centralized control, which can effectively solve the problem of waste of control resources caused by the lack of communication and coordination. It uses the optimal power flow algorithm to optimize the overall system scheduling in real time. However, this method is very unfeasible and requires huge investment. The last category is distributed control, which is a compromise between local control and centralized control. The entire system is divided into several clusters. The controlled devices within the cluster are controlled by the local controller, and the clusters communicate with each other, thereby ensuring that the control instructions of the local controller take into account not only the internal status information of the cluster, but also the external status information of the cluster. Therefore, this method can largely avoid the waste of control resources and is the most widely used method for voltage regulation in new energy distribution networks in recent years. Summary of the Invention

[0005] The present invention aims to overcome the shortcomings of existing technologies by proposing a new voltage control method for on-load tap-changing transformers in power distribution systems. This method, in turn, provides a novel distributed voltage control scheme for distribution networks. By using this distributed control method, the tap positions of on-load tap-changing transformers in distribution networks are adjusted, effectively addressing voltage fluctuations caused by the random fluctuations in photovoltaic power output and achieving optimal system voltage distribution.

[0006] The present invention solves the technical problem by adopting the following technical solutions:

[0007] An intelligent distributed control method for on-load tap-changing transformers comprises the following steps:

[0008] Step 1: Cluster status information collection. The local controller samples the node voltages within its control cluster and sorts the data in order of magnitude. It selects the maximum and minimum node voltages and adds them together to obtain the average value, which is used as the voltage of the cluster.

[0009] Step 2: Calculate the cluster voltage deviation. Take the average of the highest and lowest allowable voltages within the cluster as the internal voltage reference. Subtract the lowest allowable voltage from the highest allowable voltage, and subtract the difference between the maximum and minimum node voltages within the cluster from the result to obtain the corrected value for the cluster voltage deviation. Subtract the internal voltage reference from the cluster voltage, and subtract the corrected value from the result to obtain the cluster voltage deviation.

[0010] Step 3: Calculate the transformer action index within the cluster. Subtract the cluster internal voltage reference from the voltage of all transformer secondary winding nodes within the cluster, multiply by the normalized coefficient of the power flowing through the node, add all the results together, and multiply by the minimum transformer action minimum scale to obtain the transformer action index within the cluster.

[0011] Step 4: Update node status information. The local controller uploads the cluster internal status information to the cloud storage, including: cluster voltage deviation, the average value of the transformer action index for the last five times, and the transformer tap position, and sets the confidence level of all updated information to 1.

[0012] Step 5: The local controller reads the status information of the remaining nodes. The local controller reads the status information of the remaining nodes in the system from the cloud storage, and only reads the information with a confidence level of 1.

[0013] Step 6: Determine the standby mode. If the local cluster voltage deviation is greater than that of any other node, the controller enters the standby mode, the corresponding on-load tap-changing transformer prepares for tap change, and the timer starts or continues to count. If the local cluster voltage deviation is less than that of any other node, the controller switches to the normal mode or remains in the normal mode. The corresponding on-load tap-changing transformer does not operate. If the timer is in the timing state, the timer is reset and the process goes to step 1.

[0014] Step 7: Transformer tap action judgment. The local controller that enters the standby mode compares the sliding average of the cluster transformer action index with the set threshold. If it is greater than the threshold, the controller continues with the steps below. If it is less than the threshold, the controller returns to step 1. The controller also compares the time of the timer with the set time threshold. If it is greater than the set time threshold, the controller continues with the steps below. If it is less than the set time threshold, the controller returns to step 1.

[0015] Step 8: Transformer tap action. The local controller sends an on-load tap changer (OLTC) tap change command. If the cluster voltage is greater than the cluster's internal voltage reference, the transformer tap moves one step toward increasing the voltage. If the cluster voltage is less than the cluster's internal voltage reference, the transformer tap moves one step toward decreasing the voltage. After the OLTC tap position changes, the confidence level of all information in the cloud storage is set to 0, and the process proceeds to step 1.

[0016] If the system contains an automatically adjusting on-load tap-changing transformer, its automatic operation will be considered a disturbance in the proposed method. In this case, the proposed method will no longer consider the cluster containing the automatically adjusting on-load tap-changing transformer, and will perform voltage optimization on the system outside of this cluster. If communication between the local controller and the cloud storage fails, or if communication between the local controller and sensors or control devices in the cluster fails, the cluster will not participate in voltage optimization.

[0017] The advantages and positive effects of the present invention are:

[0018] This invention is rationally designed and proposes an intelligent distributed control method for on-load tap-changing transformers to address the voltage fluctuation issues faced by renewable energy distribution networks. This method maximizes the utilization of on-load tap-changing transformers in traditional distribution networks when addressing voltage fluctuations in renewable energy distribution networks, thereby reducing the investment required to address these issues. This control method also ensures optimal system voltage distribution even in the event of partial cluster communication failure. Compared to traditional distributed control methods, the use of cloud storage to store all cluster status information can largely avoid the redundant iterations found in traditional distributed algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a control schematic diagram of an intelligent distributed control method for on-load tap-changing transformers proposed in the present invention;

[0020] Figure 2 This is a control program block diagram of an intelligent on-load tap-changing transformer proposed in the present invention;

[0021] Figure 3 The load curves of P1 and P2 and the photovoltaic output curve in the simulation experiment of the present invention are shown;

[0022] Figure 4 The voltage change curves of node 1, node 2, and node 3 after applying the method proposed in the present invention; DETAILED DESCRIPTION

[0023] The implementation steps of the present invention are further described in detail below with reference to the accompanying drawings and specific examples.

[0024] An intelligent distributed control method for on-load tap-changing transformers comprises the following steps:

[0025] Step 1: Cluster division.

[0026] like Figure 1 As shown in the figure, the distribution system is divided into clusters according to the on-load tap-changing transformers, that is, starting from one transformer and ending at another transformer, all electrical lines in the area are regarded as a cluster, and named cluster 1, cluster 2, etc. in order of voltage level. k ,Each cluster is equipped with a local controller, which is responsible for monitoring the voltage status of each node in the cluster and sending ,tap action instructions of the on-load tap-changing transformer.

[0027] Step 2: Collect and process voltage status information within the cluster.

[0028] The local controller reads the voltage information of each bus node in the cluster, which are numbered in sequence. v k,i , first calculate the average value of the maximum node voltage and the minimum node voltage within the cluster v C,k , represents the voltage of the cluster, and its calculation formula is as follows:

[0029]

[0030] Step 3: Calculate cluster voltage deviation and transformer action index.

[0031] The local controller first calculates the cluster voltage based on the obtained node voltage information k Voltage deviation Δv k , the calculation formula is as follows:

[0032]

[0033] in, v CR,k and Δv M,k The clusters are represented by k The voltage size and cluster k The voltage offset correction value is calculated by the following formula:

[0034]

[0035]

[0036] in, v H,k and v L,k Represent the upper and lower limits of the node voltage respectively;

[0037] After that, start computing the cluster k The action index of the on-load tap-changing transformer S k , and its calculation formula is:

[0038]

[0039] in, r k Represents a cluster k The smallest unit of tap action of the on-load tap-changing transformer. m k,i Represents a cluster k In, i The weight of a node voltage is proportional to the power flow of the node, and the differential of the node voltage with respect to the tap of the on-load tap-changing transformer is shown as follows:

[0040]

[0041] Step 4: Calculate the sliding average of the last five transformer action indices.

[0042] In chronological order, the local controller calculates the sliding average of the last five transformer action indices. The calculation formula is as follows:

[0043]

[0044] Step 5: Upload the latest cluster status information to the cloud storage.

[0045] All local controllers upload the internal state information of the cluster to the cloud storage, including the cluster voltage deviation information, tap position information, and action index average, and set the confidence level of the updated information to 1.

[0046] Step 6: The local controller reads the remaining cluster status information.

[0047] The local controller reads other cluster status information from the cloud storage (only reads information with a confidence level of 1).

[0048] Step 7: Determine the transformer tap changer action criteria and take action.

[0049] The local controller compares the voltage deviation of its own cluster's transformers with that of the remaining clusters. If the voltage deviation of its own cluster is greater than that of any other node, the corresponding local controller enters standby mode and starts a timer. If the timer is already counting, it continues counting. It then determines whether the sliding average of the cluster's transformer action index exceeds a set threshold and whether the timer duration exceeds a specified waiting time. If all conditions are met, the local controller sends a tap adjustment instruction to the on-load tap-changer transformer, resets the confidence level of all information in the cloud storage to 0, and proceeds to step 1. If any of these conditions are not met, the process proceeds directly to step 1.

[0050] Figure 4 To verify the simulation experiment effect diagram of the present invention, it can be seen that the proposed intelligent on-load tap-changing transformer distributed control method can optimize the voltage distribution of each node to a great extent by adjusting the tap position of the on-load tap-changing transformer, further verifying the effectiveness of the proposed method.

[0051] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention is not limited to the embodiments described in the specific implementation methods. Any other implementation methods derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. An intelligent distributed control method for on-load tap-changing transformers divides the power distribution system into several clusters. Each cluster is equipped with a local controller, and information exchange between the clusters is achieved through cloud storage. The local controller monitors the cluster status information and controls the on-load tap-changing transformers in the cluster. It is characterized by the following steps: (1) The local controller arranges the voltage values ​​of all nodes in the cluster, selects the maximum and minimum values, and then takes the average of the maximum and minimum node voltages to represent the voltage of the cluster; (2) Take the average of the highest and lowest allowable voltages within the cluster as the voltage reference within the cluster. Then subtract the lowest allowable voltage from the highest allowable voltage of the cluster. Subtract the difference between the maximum and minimum node voltages within the cluster from the result to obtain the corrected value of the voltage deviation of the cluster. Then subtract the cluster reference voltage from the cluster representative voltage, and then subtract the corrected value of the cluster voltage deviation to obtain the voltage deviation of the cluster. (3) Subtract the cluster internal voltage reference from the voltage of all transformer secondary winding nodes in the cluster, multiply by the coefficient of the normalized power flowing through the node, add all the results, and multiply by the minimum transformer action scale to obtain the action index of the transformer in the cluster; (4) The local controller calculates the sliding average of the transformer action index of the last five times within the cluster and uploads the cluster status information to the cloud storage, including: voltage deviation, the sliding average of the transformer action index within the cluster, and the transformer tap position, and sets the confidence of the updated information to 1; (5) The local controller obtains the voltage deviation of the remaining clusters and compares it with the voltage deviation of the local cluster. If the voltage deviation of the local cluster is greater than that of the remaining nodes, the local controller enters the standby state and starts the timer. If the timer is already started, it continues to keep the timing state. Otherwise, it goes to step 1. (6) Compare the sliding average of the cluster transformer action index with the set threshold. If it is greater than the threshold, continue the step; if it is less than the threshold, return to step 1; (7) Compare the time of the timer with the set time threshold. If it is greater than the set time threshold, continue the step; if it is less than the set time threshold, return to step 1; (8) The local controller sends an on-load tap-changing transformer tap action instruction. If the voltage of the cluster is greater than the cluster internal voltage reference, the transformer tap moves one grid in the direction of increasing the voltage. If the voltage of the cluster is less than the cluster internal voltage reference, the transformer tap moves one grid in the direction of decreasing the voltage. After the on-load tap-changing transformer tap position changes, the confidence level of all information in the cloud storage is set to 0, and the process goes to step 1.

2. The intelligent distributed control method for on-load tap-changing transformer according to claim 1 is characterized in that If the communication module of the local controller fails, the cluster with failed communication will be ignored during the calculation process, and the optimization calculation of the tap position adjustment of the on-load tap changer of the remaining clusters will continue.

3. The intelligent distributed control method for on-load tap-changing transformer according to claim 1 is characterized in that If the on-load tap-changing transformers in the cluster are adaptively regulated, the operation of such transformers is regarded as a disturbance, and the cluster is no longer included in the optimization calculation during the optimization process.

4. The intelligent distributed control method for on-load tap-changing transformer according to claim 1 is characterized in that There is a central controller that communicates with the cloud storage to monitor the operating status of the entire distribution system and intervene in the original control instructions in an emergency.

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