# Online Monitoring of Power Grid Intelligent Voltage Stability considering Potential Factors

Authors: Shuyong Rao; Ye Yuan
Journal: Mobile Information Systems
 (2022)

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## Abstract

To improve the monitoring effect of voltage stability, an online monitoring method for intelligent voltage stability of the power grid is designed based on the consideration of potential factors. First, the converter is used as a voltage detecting device to collect the intelligent voltage data of the power grid, and then the characteristic quantity of power grid voltage is extracted by using the wavelet change method. Based on the practical criterion of voltage instability of smart grid, the quantitative evaluation factors that can reflect the severity of voltage instability are selected, and the voltage stability state is monitored online by wide-area measurement. The experimental results show that the proposed method has a high fitting degree between the voltage monitoring value and the actual voltage value, and the global maximum value of the transmission delay of the monitoring signal is only 90.57 ms, and the voltage identification rate can always remain above 95%, indicating that the proposed method has high effectiveness and timeliness in voltage stability monitoring. In addition, the Pearson coefficients of this method are in the range of 0.8∼1.0, indicating that the monitoring accuracy of voltage stability is higher, which proves that the design expectation is effectively realized.


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## Body

## 1. Introduction

The demand for electric energy in all sectors of society continues to increase, and the pressure on power supply construction and line facilities construction is also gradually increasing. In this process, the scope of the heavy load area is gradually expanded, and a new power flow mode is generated, resulting in the operation state of the power grid gradually approaching the limit. In this context, the contradiction among power load, line, and power supply is gradually intensifying, making the power grid expansion difficult [1, 2]. If the power grid is in a state of heavy load operation for a long time, it is very likely to have the problem of voltage drop, which will lead to voltage collapse in serious cases. At present, maintaining voltage stability has become the primary goal of power planning [3].Therefore, a data-driven static voltage stability situation assessment and monitoring method are designed in reference [4]. Using the simulation tool, the state data of the power system are counted from the point of view of time, and the state characteristics related to voltage stability are screened from them, and then the state data are classified and processed according to the power load margin. On this basis, a stochastic forest classifier model is constructed to convert the state data of the power system into behavior information, from which voltage stability behavior is detected, and voltage stability is analyzed according to the classification results of the stochastic forest. In reference [5], a voltage stability monitoring method based on a multiboundary voltage weak region is designed. First, the voltage is divided into several virtual units by the radial equivalent independence method, and then the equivalent parameters of voltage are obtained based on noise filtering. Based on this, the voltage stability state is comprehensively analyzed according to the voltage stability margin index. In reference [6], an online monitoring and identification method of voltage stability based on deep learning is designed. Firstly, the mapping relationship between voltage stability types and observed data is established, and a deep model is established. Then, based on the optimization model, the original cross-entropy objective function is modified, and the accuracy of the model is balanced by optimizing the learning rate. Finally, the dropout technology is used to monitor voltage stability online. Based on the above research, this study designs an online monitoring method of power grid intelligent voltage stability considering potential factors. The innovation of this method is that the converter is used as a voltage detection device to collect the intelligent voltage data of the power grid, and then the characteristic quantity of the power grid voltage is extracted by using the wavelet change method. Based on the practical criterion of voltage instability of smart grid, the quantitative evaluation factors that can reflect the severity of voltage instability are selected. Experimental results show that the method has good effectiveness, timeliness, and monitoring accuracy.


## 2. Online Monitoring Method Design

### 2.1. Power Grid Intelligent Voltage Data Collection

In the initial stage of analyzing the intelligent voltage stability of the power grid, the corresponding voltage data should be collected. Therefore, in this study, the converter is used as the voltage detection device, and the integrated intelligent column switch is used to collect the intelligent voltage data of the power grid.The intelligent voltage data collection process of the power grid is shown in Figure1.Figure 1
Power grid intelligent voltage data collection process.In Figure1, starting from the current operating point, the acquisition direction of the transformer is adjusted, the voltage transmission instruction is issued according to the specified position, and the voltage monitoring device is deployed to monitor the voltage passing frequency value in the voltage disorder state. In this process, the distance between the voltage transmission path and the central voltage passing through the frequency numerical storage system is shortened, the additional transmission voltage load value is taken into account, the external voltage load is reduced, the power automatic compensation operation of the power grid power generation system is realized, and the idle operation of the device is effectively reduced [7–9]. The voltage values of each node are compared to obtain the corresponding stable voltage data.Integrated intelligent column switch has a variety of sensors, through the signal line and substructure to connect different sensors, realize the switch body and distribution line voltage operating state measurement. The structure of the integrated intelligent column switch is shown in Figure2.Figure 2
Integrated intelligent column switch structure diagram.As can be seen from Figure2, in order to meet the transmission and diffusion of various voltage sensing information, a 37COregX12 butt socket was selected in this study, and an induction switch was installed on the main socket. A variety of lines at the same time to transmit voltage signals, ensure rapid signal transmission, and ensure the safety of staff. The 19-core GX12 socket is used to transmit signal storage and switch control, including load switch status signal and zero-sequence voltage signal, and the 6-core voltage socket is used to transmit all voltage signals.The specific collection steps are as follows:Step 1: The converter realizes voltage stability monitoring by controlling voltage frequency. Therefore, when selecting the converter, it is necessary to strengthen the analysis of the rotor AC excitation power supply of the power grid generation system [10–12].In order to effectively track and collect the power grid voltage, improve the internal storage capacity of the whole power generation system and reduce the detection loss of voltage stability evaluation, it is necessary to run the power generation system and converter generator synchronously to track voltage changes. In this study, a converter device with bi-directional energy flow is selected, and the inertia link is used to describe the data, and the process is as follows:(1)∂dP1dt=P1+P2.In formula (1), P1 represents the rotor power of the converter generator, P2 represents the mechanical power of the power grid generation system, and ∂ represents the operating parameters of the converter.Step 2: There is electromagnetic coupling between the rotor and stator in the power grid generation system, and the voltage will generate the corresponding harmonic potential during the transmission line of the power grid, and induce the conduction current existing in the stator [13–15]. Therefore, when selecting the converter, it is necessary to ensure that the output performance of the converter is in a good state, so as to ensure that the stator inside the power grid system can send high-quality electric energy.Step 3: The internal capacity of the converter will change as the power generation of the grid changes [16]. In order to avoid the interference of the external environment to the power grid generation process, it is necessary to adjust the external converter characteristic input system of the power grid to reduce the input amount of harmonic current, so as to realize the overall regulation of the converter and obtain high-quality voltage data.Step 4: Use of converter, by investigating the tangent vector voltage at zero point for matrix by numerical, and contrast feature vector and the gap between singular vectors, when the critical point of the tangent vector and get the minimum eigenvalue to coincide with right eigenvector value, are capable of measuring the voltage data obtained in equivalent condition, the result of the process is as follows:(2)v+Δn=∑k=1nnkvkcn−k.In formula (2), v represents voltage variable, Δ represents voltage equivalent parameter, n represents the number of parameters involved in the calculation, and n=1,2,⋯,k,⋯, thus obtaining grid voltage equivalent data.Step 5: After obtaining the result that the measured voltage data is in an equivalent state, complete the transmission of voltage data. Therefore, this study designed a ring network transmission network structure, which has high security and reliability, and network expansion is also very convenient.Ring network transmission network adopts Ethernet switching network with optical fiber communication framework, which is mainly used for remote monitoring of the communication of substations. The communication transfer station is installed in the substation, which can save investment and facilitate transmission maintenance [17, 18].In addition, several transmission servers are also set up in the smart grid. The design of the transmission server is as follows: the server adopts symmetrical cluster architecture, composed of a dynamic server cluster, static server cluster, and bus. Thus, the grid intelligent voltage data collection is completed, paving the way for the extraction of grid voltage characteristics.


### 2.2. Extract Grid Voltage Characteristic Quantity

Based on collecting power grid intelligent voltage data by converters, this study uses the wavelet change method to extract power grid voltage characteristic quantity.The wavelet transform method is the most novel, convenient and simple time-frequency resolution method, especially when dealing with unsteady signals. In the case of high-frequency signal, wavelet transform has high time resolution. In the case of low-frequency signal, wavelet transform has high-frequency resolution, that is, wavelet transform has strong superiority and time-frequency localization, making wavelet transform to become a powerful tool for processing power signal [19–21].Assume that the finite signal in the smart grid isvt∈C2L. Where C represents the inductance value of the smart grid, f represents the power supply frequency, l represents the distance between two conductors, and t represents the time variable. Then, the wavelet transform process of this signal is as follows:(3)δl,tτ=∫1Lvtψt−tτ/ldtl.In formula (3), d represents the distance between phase conductors, tτ represents the center value of the time axis, δ represents the wavelet transform coefficient, ψ represents the electric flux of the smart grid, and l∈L.According to formula (3), the time function of the voltage signal processed will be mapped to the time plane of two-dimensional space. This processing method is convenient for extracting the characteristic quantity of grid voltage.


### 2.3. Analysis of Potential Factors of Power Grid Voltage Instability

Smart grids can create dedicated corridors for electricity transmission. The power consumption characteristics of the terminals connected to the power grid are periodic, so the load of the terminals fluctuates. This kind of fluctuation has no law to follow, for example, environmental interference and other factors will affect the voltage and form clutter in the line [22–24]. These influences may affect the quality of power supply at a light level, or cause voltage collapse at a serious level, affecting the voltage stability of the smart grid.Therefore, based on the above research, this study analyzes the potential factors of power grid voltage instability. Based on the practical criterion of voltage instability of smart grid, the quantitative evaluation factors that can reflect the severity of voltage instability are selected, including voltage instability severity factorf1, voltage qualification index f2 and voltage qualification rate f3.(a)
Severity factor of voltage instability, which can reflect the transient voltage stability state in a smart grid. The smaller the value of this indicator, the more stable the transient voltage. The calculation process is as follows:(4)f1=∑i=1nωiλ+Δt∑vit.In formula (4), ωi represents the weight of node i, n represents the number of nodes in the smart grid, Δt represents the step of time domain simulation calculation, vit is evaluated according to whether the voltage of node i at time t is in the recoverable range, and its value ranges from 0.9 pu to 1.2 pu. λ represents the penalty function, and its calculation process is as follows:(5)λ=10,vit<0.75andΔt>0.1,0,other.(b)
Voltage conformity index can reflect whether voltage in the smart grid can reach a completely stable state. If the voltage at all nodes in the power grid is within a reasonable range, it indicates that the voltage is stable; otherwise, it indicates that the power grid has voltage instability. The calculation process is as follows:(6)f2=∑i=1nfi.In formula (6), the value of fi is as follows:(7)fi=1,other,0,vit<0.75andΔt>0.1.(c)
Voltage qualification rate is the probability of smart grid voltage qualification, and its calculation process is as follows:(8)f3=f2n.Through the above analysis, the potential factors of power grid voltage instability are obtained, which provides a basis for online monitoring of power grid intelligent voltage stability.


### 2.4. Online Monitoring of Power Grid Intelligent Voltage Stability

Based on the analysis of the potential factors of voltage instability, the voltage stability can be monitored online by wide-area measurement. In this process, this study designed a distributed structure, set up the main monitoring station in the dispatching center, and set up monitoring substations at all their levels. Then combined with the voltage stability margin to achieve online monitoring. The specific monitoring process is as follows:Step 1: Identification of abnormal voltage stations based on Pearson coefficient.After the above analysis, the concept of the correlation coefficient is introduced to identify the abnormal voltage station area. In this process, the problem of load uncertainty exists in the node location of the distribution network. At the same time, the fluctuation of voltage data at each node is relatively random, and the variation degree of the fluctuation curve is highly correlated. Therefore, the influence of voltage data fluctuation can be analyzed centrally through the similarity of the fluctuation curve variation trend.It is clear that the topological operation mode of the distribution network is radial, and the amplitude value of voltage data represents the upstream and downstream relationship of data of different nodes of the power network. Therefore, the correlation degree of voltage data can be arbitrarily taken as an index, and described as a Pearson coefficient, and a linear relationship suitable for measuring two fuzzy variables is proposed.Two fuzzy variables are expressed asX and Y, and the relationship between them is defined as [−1, +1]. Assuming that Pearson’s coefficient is ρ, its calculation process is as follows:(9)ρX,Y=Ex−μXy−μYσXσY.In formula (9), ρX,Y represents the description of the attribution relationship between the two fuzzy variables, E represents the parameter variance value, μ represents the mean value of position parameters, and σ represents the parameter amplitude.According to the above calculation formula, different values of Pearson coefficient are described, and the results are shown in Table1.According to the actual value obtained in formula (8), the correlation of different topology nodes of the power grid is analyzed. The corresponding nodes with correlation parameters >0.5 are matched, and the redundant values and data of unassociated nodes are deleted. And the corresponding radio station with the correlation coefficient >0.8 is regarded as the low voltage station area of the distribution network. Combining with the actual situation, the belonging range of the station area is described. In this way, the accurate judgment and identification of abnormal voltage station areas can be realized.Step 2: Collect the information on smart grid topological structure changes (including disconnection, changes in on-load transformer regulation amount, etc.), reactive power output of power generation system and reactive power compensation device, and capacitor switching and switching in the monitoring master station [25, 26]. If the topological structure of the smart grid changes, resulting in the failure of the regulation ability of the power generation system and reactive power compensation device, the monitoring master station needs to recalculate the line impedance between the equivalent bus and nodes in the smart grid, and then transmit it to the monitoring substation.The calculation process of line impedanceZi between equivalent bus and node i in smart grid is as follows:(10)Zi=r+ωA−1/ωCi.In formula (10), r represents resistance, ωA represents inductive reactance, and 1/ωC represents capacitive reactance.Step 3: After receiving relevant information, the monitoring substation at node i calculates voltage Vi, equivalent current Ii and equivalent load Pi at node i, and the process is as follows:(11)Vi=∑i=1nHi×V0,Ii=IΓ−Iτ,Pi=P1+P2.In formula (11), Hi represents the power margin value, V0 represents the bus voltage, IΓ represents the current reaching node i, Iτ represents the transfer current of other load nodes relative to node i, P1 represents the load in the bus, and P2 represents the influence of other load nodes relative to node.Step 4: Calculate the voltage stability margin. For the voltage stability of the smart grid, researchers pay more attention to the distance between the current operating point and the voltage collapse point. The p-V model of load level can be obtained by using the power flow method, but the classification results of the transition process are fuzzy, and it is difficult to get the voltage critical point quickly, so it is difficult to effectively excavate the law and mode of load growth.In practice, when the voltage of the smart grid approaches the stability limit, there is a value of 0 in the Jacobian matrix of convergent power flow. When the voltage of the smart grid shifts from the average state to the stability limit, the minimum eigenvalue of the Jacobian matrix gradually approaches 1. The minimum eigenvalue of the mode is close to 0, indicating that the voltage is closer to the stable state.Based on the above analysis, the model minimum eigenvalue is used as the voltage stability margin index in this study, and the calculation process is as follows:(12)Vμ=mineigjVi.In formula (12) j represents the Jacobian matrix of convergent power flow of smart grid, and eigj represents the modulus of eigenvalues in the matrix.Step 5: The voltage stability index of each node in the power grid is determined by the monitoring substation, including power limit Pmax and power margin Pμ, and then the calculated results are transmitted to the main monitoring station. Combined with the voltage stability margin value, the main monitoring station completes the online monitoring of the intelligent voltage stability of the power grid, and the process is as follows:(13)ΘV=minPmax−PμVμ.Through the above analysis, it is concluded that the monitoring master station combined with the voltage stability margin value can effectively complete the online monitoring of power grid intelligent voltage stability, to complete the online monitoring research of power grid intelligent voltage stability considering potential factors.Table 1
Pearson coefficient is defined.
NumberPearson coefficient value rangeDegree of correlation10∼0.2No relationship20.2∼0.4Weak link30.4∼0.6Strong association40.6∼0.8Strong correlation50.8∼1.0Very strong correlation


## 2.1. Power Grid Intelligent Voltage Data Collection

In the initial stage of analyzing the intelligent voltage stability of the power grid, the corresponding voltage data should be collected. Therefore, in this study, the converter is used as the voltage detection device, and the integrated intelligent column switch is used to collect the intelligent voltage data of the power grid.The intelligent voltage data collection process of the power grid is shown in Figure1.Figure 1
Power grid intelligent voltage data collection process.In Figure1, starting from the current operating point, the acquisition direction of the transformer is adjusted, the voltage transmission instruction is issued according to the specified position, and the voltage monitoring device is deployed to monitor the voltage passing frequency value in the voltage disorder state. In this process, the distance between the voltage transmission path and the central voltage passing through the frequency numerical storage system is shortened, the additional transmission voltage load value is taken into account, the external voltage load is reduced, the power automatic compensation operation of the power grid power generation system is realized, and the idle operation of the device is effectively reduced [7–9]. The voltage values of each node are compared to obtain the corresponding stable voltage data.Integrated intelligent column switch has a variety of sensors, through the signal line and substructure to connect different sensors, realize the switch body and distribution line voltage operating state measurement. The structure of the integrated intelligent column switch is shown in Figure2.Figure 2
Integrated intelligent column switch structure diagram.As can be seen from Figure2, in order to meet the transmission and diffusion of various voltage sensing information, a 37COregX12 butt socket was selected in this study, and an induction switch was installed on the main socket. A variety of lines at the same time to transmit voltage signals, ensure rapid signal transmission, and ensure the safety of staff. The 19-core GX12 socket is used to transmit signal storage and switch control, including load switch status signal and zero-sequence voltage signal, and the 6-core voltage socket is used to transmit all voltage signals.The specific collection steps are as follows:Step 1: The converter realizes voltage stability monitoring by controlling voltage frequency. Therefore, when selecting the converter, it is necessary to strengthen the analysis of the rotor AC excitation power supply of the power grid generation system [10–12].In order to effectively track and collect the power grid voltage, improve the internal storage capacity of the whole power generation system and reduce the detection loss of voltage stability evaluation, it is necessary to run the power generation system and converter generator synchronously to track voltage changes. In this study, a converter device with bi-directional energy flow is selected, and the inertia link is used to describe the data, and the process is as follows:(1)∂dP1dt=P1+P2.In formula (1), P1 represents the rotor power of the converter generator, P2 represents the mechanical power of the power grid generation system, and ∂ represents the operating parameters of the converter.Step 2: There is electromagnetic coupling between the rotor and stator in the power grid generation system, and the voltage will generate the corresponding harmonic potential during the transmission line of the power grid, and induce the conduction current existing in the stator [13–15]. Therefore, when selecting the converter, it is necessary to ensure that the output performance of the converter is in a good state, so as to ensure that the stator inside the power grid system can send high-quality electric energy.Step 3: The internal capacity of the converter will change as the power generation of the grid changes [16]. In order to avoid the interference of the external environment to the power grid generation process, it is necessary to adjust the external converter characteristic input system of the power grid to reduce the input amount of harmonic current, so as to realize the overall regulation of the converter and obtain high-quality voltage data.Step 4: Use of converter, by investigating the tangent vector voltage at zero point for matrix by numerical, and contrast feature vector and the gap between singular vectors, when the critical point of the tangent vector and get the minimum eigenvalue to coincide with right eigenvector value, are capable of measuring the voltage data obtained in equivalent condition, the result of the process is as follows:(2)v+Δn=∑k=1nnkvkcn−k.In formula (2), v represents voltage variable, Δ represents voltage equivalent parameter, n represents the number of parameters involved in the calculation, and n=1,2,⋯,k,⋯, thus obtaining grid voltage equivalent data.Step 5: After obtaining the result that the measured voltage data is in an equivalent state, complete the transmission of voltage data. Therefore, this study designed a ring network transmission network structure, which has high security and reliability, and network expansion is also very convenient.Ring network transmission network adopts Ethernet switching network with optical fiber communication framework, which is mainly used for remote monitoring of the communication of substations. The communication transfer station is installed in the substation, which can save investment and facilitate transmission maintenance [17, 18].In addition, several transmission servers are also set up in the smart grid. The design of the transmission server is as follows: the server adopts symmetrical cluster architecture, composed of a dynamic server cluster, static server cluster, and bus. Thus, the grid intelligent voltage data collection is completed, paving the way for the extraction of grid voltage characteristics.


## 2.2. Extract Grid Voltage Characteristic Quantity

Based on collecting power grid intelligent voltage data by converters, this study uses the wavelet change method to extract power grid voltage characteristic quantity.The wavelet transform method is the most novel, convenient and simple time-frequency resolution method, especially when dealing with unsteady signals. In the case of high-frequency signal, wavelet transform has high time resolution. In the case of low-frequency signal, wavelet transform has high-frequency resolution, that is, wavelet transform has strong superiority and time-frequency localization, making wavelet transform to become a powerful tool for processing power signal [19–21].Assume that the finite signal in the smart grid isvt∈C2L. Where C represents the inductance value of the smart grid, f represents the power supply frequency, l represents the distance between two conductors, and t represents the time variable. Then, the wavelet transform process of this signal is as follows:(3)δl,tτ=∫1Lvtψt−tτ/ldtl.In formula (3), d represents the distance between phase conductors, tτ represents the center value of the time axis, δ represents the wavelet transform coefficient, ψ represents the electric flux of the smart grid, and l∈L.According to formula (3), the time function of the voltage signal processed will be mapped to the time plane of two-dimensional space. This processing method is convenient for extracting the characteristic quantity of grid voltage.


## 2.3. Analysis of Potential Factors of Power Grid Voltage Instability

Smart grids can create dedicated corridors for electricity transmission. The power consumption characteristics of the terminals connected to the power grid are periodic, so the load of the terminals fluctuates. This kind of fluctuation has no law to follow, for example, environmental interference and other factors will affect the voltage and form clutter in the line [22–24]. These influences may affect the quality of power supply at a light level, or cause voltage collapse at a serious level, affecting the voltage stability of the smart grid.Therefore, based on the above research, this study analyzes the potential factors of power grid voltage instability. Based on the practical criterion of voltage instability of smart grid, the quantitative evaluation factors that can reflect the severity of voltage instability are selected, including voltage instability severity factorf1, voltage qualification index f2 and voltage qualification rate f3.(a)
Severity factor of voltage instability, which can reflect the transient voltage stability state in a smart grid. The smaller the value of this indicator, the more stable the transient voltage. The calculation process is as follows:(4)f1=∑i=1nωiλ+Δt∑vit.In formula (4), ωi represents the weight of node i, n represents the number of nodes in the smart grid, Δt represents the step of time domain simulation calculation, vit is evaluated according to whether the voltage of node i at time t is in the recoverable range, and its value ranges from 0.9 pu to 1.2 pu. λ represents the penalty function, and its calculation process is as follows:(5)λ=10,vit<0.75andΔt>0.1,0,other.(b)
Voltage conformity index can reflect whether voltage in the smart grid can reach a completely stable state. If the voltage at all nodes in the power grid is within a reasonable range, it indicates that the voltage is stable; otherwise, it indicates that the power grid has voltage instability. The calculation process is as follows:(6)f2=∑i=1nfi.In formula (6), the value of fi is as follows:(7)fi=1,other,0,vit<0.75andΔt>0.1.(c)
Voltage qualification rate is the probability of smart grid voltage qualification, and its calculation process is as follows:(8)f3=f2n.Through the above analysis, the potential factors of power grid voltage instability are obtained, which provides a basis for online monitoring of power grid intelligent voltage stability.


## 2.4. Online Monitoring of Power Grid Intelligent Voltage Stability

Based on the analysis of the potential factors of voltage instability, the voltage stability can be monitored online by wide-area measurement. In this process, this study designed a distributed structure, set up the main monitoring station in the dispatching center, and set up monitoring substations at all their levels. Then combined with the voltage stability margin to achieve online monitoring. The specific monitoring process is as follows:Step 1: Identification of abnormal voltage stations based on Pearson coefficient.After the above analysis, the concept of the correlation coefficient is introduced to identify the abnormal voltage station area. In this process, the problem of load uncertainty exists in the node location of the distribution network. At the same time, the fluctuation of voltage data at each node is relatively random, and the variation degree of the fluctuation curve is highly correlated. Therefore, the influence of voltage data fluctuation can be analyzed centrally through the similarity of the fluctuation curve variation trend.It is clear that the topological operation mode of the distribution network is radial, and the amplitude value of voltage data represents the upstream and downstream relationship of data of different nodes of the power network. Therefore, the correlation degree of voltage data can be arbitrarily taken as an index, and described as a Pearson coefficient, and a linear relationship suitable for measuring two fuzzy variables is proposed.Two fuzzy variables are expressed asX and Y, and the relationship between them is defined as [−1, +1]. Assuming that Pearson’s coefficient is ρ, its calculation process is as follows:(9)ρX,Y=Ex−μXy−μYσXσY.In formula (9), ρX,Y represents the description of the attribution relationship between the two fuzzy variables, E represents the parameter variance value, μ represents the mean value of position parameters, and σ represents the parameter amplitude.According to the above calculation formula, different values of Pearson coefficient are described, and the results are shown in Table1.According to the actual value obtained in formula (8), the correlation of different topology nodes of the power grid is analyzed. The corresponding nodes with correlation parameters >0.5 are matched, and the redundant values and data of unassociated nodes are deleted. And the corresponding radio station with the correlation coefficient >0.8 is regarded as the low voltage station area of the distribution network. Combining with the actual situation, the belonging range of the station area is described. In this way, the accurate judgment and identification of abnormal voltage station areas can be realized.Step 2: Collect the information on smart grid topological structure changes (including disconnection, changes in on-load transformer regulation amount, etc.), reactive power output of power generation system and reactive power compensation device, and capacitor switching and switching in the monitoring master station [25, 26]. If the topological structure of the smart grid changes, resulting in the failure of the regulation ability of the power generation system and reactive power compensation device, the monitoring master station needs to recalculate the line impedance between the equivalent bus and nodes in the smart grid, and then transmit it to the monitoring substation.The calculation process of line impedanceZi between equivalent bus and node i in smart grid is as follows:(10)Zi=r+ωA−1/ωCi.In formula (10), r represents resistance, ωA represents inductive reactance, and 1/ωC represents capacitive reactance.Step 3: After receiving relevant information, the monitoring substation at node i calculates voltage Vi, equivalent current Ii and equivalent load Pi at node i, and the process is as follows:(11)Vi=∑i=1nHi×V0,Ii=IΓ−Iτ,Pi=P1+P2.In formula (11), Hi represents the power margin value, V0 represents the bus voltage, IΓ represents the current reaching node i, Iτ represents the transfer current of other load nodes relative to node i, P1 represents the load in the bus, and P2 represents the influence of other load nodes relative to node.Step 4: Calculate the voltage stability margin. For the voltage stability of the smart grid, researchers pay more attention to the distance between the current operating point and the voltage collapse point. The p-V model of load level can be obtained by using the power flow method, but the classification results of the transition process are fuzzy, and it is difficult to get the voltage critical point quickly, so it is difficult to effectively excavate the law and mode of load growth.In practice, when the voltage of the smart grid approaches the stability limit, there is a value of 0 in the Jacobian matrix of convergent power flow. When the voltage of the smart grid shifts from the average state to the stability limit, the minimum eigenvalue of the Jacobian matrix gradually approaches 1. The minimum eigenvalue of the mode is close to 0, indicating that the voltage is closer to the stable state.Based on the above analysis, the model minimum eigenvalue is used as the voltage stability margin index in this study, and the calculation process is as follows:(12)Vμ=mineigjVi.In formula (12) j represents the Jacobian matrix of convergent power flow of smart grid, and eigj represents the modulus of eigenvalues in the matrix.Step 5: The voltage stability index of each node in the power grid is determined by the monitoring substation, including power limit Pmax and power margin Pμ, and then the calculated results are transmitted to the main monitoring station. Combined with the voltage stability margin value, the main monitoring station completes the online monitoring of the intelligent voltage stability of the power grid, and the process is as follows:(13)ΘV=minPmax−PμVμ.Through the above analysis, it is concluded that the monitoring master station combined with the voltage stability margin value can effectively complete the online monitoring of power grid intelligent voltage stability, to complete the online monitoring research of power grid intelligent voltage stability considering potential factors.Table 1
Pearson coefficient is defined.
NumberPearson coefficient value rangeDegree of correlation10∼0.2No relationship20.2∼0.4Weak link30.4∼0.6Strong association40.6∼0.8Strong correlation50.8∼1.0Very strong correlation


## 3. Experimental Results and Analysis

In order to verify the practical application performance of the online monitoring method of power grid intelligent voltage stability considering potential factors, the following experiments are designed.


### 3.1. Experimental Design

The structure of the experimental power grid is shown in Figure3.Figure 3
Experimental grid structure diagram.In Figure3, the experimental power grid contains a doubly-fed power generation system, which can analyze the active power generated by the operating state of the power grid and absorb reactive power at the same time, ensuring that the power grid can both output active power and absorb reactive load during the experiment.The power of the experimental grid is stable above 1.5 MW, and the rated voltage generated by the electric field of the power generation system is 220 kV, which is connected to the grid in parallel. The hardware configuration of the monitoring master station is shown in Table2.Table 2
Monitor the hardware configuration parameters of the master station.
EquipmentConfiguration parametersNetwork application serverCPU: ≥12 cores, ≥2.4 GHzOperating system: DebianInterface adapter serverCPU: ≥8 cores, ≥2.4 GHzNic: 1000 Mbps × 4 or higherHBA: Optical fiber interface card ≥4 Gb/sTrunk exchangeAt least four 10 ge optical ports support stacking, line aggregation, and dual power supply redundancy.KVM4-Controller 16KVM analog host, two LCD monitors, two mouse sets, and two keyboard setsDisk arraySupports dual controller IPSAN\FCSAN10 Gbit/s expansion port, fully configured with 8 Gbit/s SFP modulesLoadRunner performance testing tool and black box testing method were used to carry out comparative tests on the method in this study, the data-driven static voltage stability situation assessment and monitoring method in reference [4], and the voltage stability monitoring method based on multiboundary voltage weak region in reference [5].


### 3.2. Analysis of Experimental Results

In the experiment, the fitting degree of voltage monitoring value and actual voltage, transmission delay of monitoring signal, voltage identification rate, and monitoring accuracy were taken as indexes respectively to verify the application performance of different methods.First, the fitting degree of the voltage monitoring values of different methods and the actual voltage are verified. The actual voltage change curve is shown in Figure4.Figure 4
Actual voltage change curve.Analysis of Figure4 shows that the actual voltage increases abnormally between 1.2 s–1.4 s. Then test the voltage monitoring values obtained by different methods, and the results are shown in Figure 5.Figure 5
Statistical results of voltage monitoring values by different methods.It can be seen from Figure5 that there is a small gap between the voltage monitoring curve obtained by using this method and the actual voltage curve, indicating that the voltage monitoring value obtained by this method has a high fitting degree with the actual voltage. However, there are some differences between the voltage values monitored by the methods of reference [4] and reference [5] and the actual voltage, indicating that the monitoring effect of these two methods on voltage stability is not as good as that of the method in this study.Then, the monitoring signal transmission delay of different methods is verified, and the results are shown in Table3.Table 3
Different methods of monitoring signal transmission delay (ms).
Different methodsNumber of experiments/timesMonitor signal transmission delayMethod of this study1090.162090.573080.134090.135080.27Method of reference [4]10230.4520230.1930210.0940260.5350290.44Method of reference [5]10270.4720120.0430160.4740120.9850280.49As shown in Table3, the transmission delay of monitoring signals of the method of reference [4] varies between 210.09 ms–290.44 ms, and that of the method of reference [5] varies between 120.04 ms–280.49 ms. In contrast, the transmission delay of monitoring signals of the method of this study is less, and the global maximum value is only 90.57 ms, which fully indicates that the method of this study can quickly complete the transmission of power grid stability monitoring signals, and the monitoring timeliness is higher.The most direct way to verify the effectiveness of the voltage stability monitoring method is to judge the identification rate of the method, that is, the ratio of the number of unstable voltage signals to the total voltage signals. Therefore, the voltage identification rate is taken as an indicator to verify the application performance of different methods, and the results are shown in Figure6.Figure 6
Comparison results of identification rate of different methods.According to Figure6, with the increase in the number of experiments, the identification rate of the three methods also changes constantly. The identification rate of the method of reference [4] keeps increasing, and the global maximum value can reach 95.2%. The identification rate of the method in reference [5] decreased slightly after rising to 94.7%. However, the identification rate of the method of this study is always above 95%, indicating that method of this study is effective in voltage stability monitoring.Substitute the monitoring results of different methods into formula (8), calculate the average Pearson coefficient of different monitoring methods, and draw the results in Table 4.Table 4
Pearson coefficient statistical table of monitoring results of different monitoring methods.
Different methodsNumber of experiments/timesValue ofρLevelMethod of this study100.90Very strong correlation200.90Very strong correlation300.89Very strong correlation400.94Very strong correlation500.88Very strong correlationMethod of reference [4]100.27Weak link200.26Weak link300.30Weak link400.29Weak link500.17No relationshipMethod of reference [5]100.25Weak link200.27Weak link300.29Weak link400.28Weak link500.12No relationshipAs can be seen from the experimental results in Table4, the Pearson coefficients of the proposed method are all in the range of 0.8∼1.0, while the Pearson coefficients of the two traditional monitoring methods are not more than 0.4.In summary, the voltage monitoring value of method of this study has a high fitting degree with the actual voltage value, and the global maximum value of the transmission delay of the monitoring signal is only 90.57 ms, and the voltage identification rate can always be maintained above 95%. It indicates that the method of this study has high monitoring effectiveness and timeliness for voltage stability.


## 3.1. Experimental Design

The structure of the experimental power grid is shown in Figure3.Figure 3
Experimental grid structure diagram.In Figure3, the experimental power grid contains a doubly-fed power generation system, which can analyze the active power generated by the operating state of the power grid and absorb reactive power at the same time, ensuring that the power grid can both output active power and absorb reactive load during the experiment.The power of the experimental grid is stable above 1.5 MW, and the rated voltage generated by the electric field of the power generation system is 220 kV, which is connected to the grid in parallel. The hardware configuration of the monitoring master station is shown in Table2.Table 2
Monitor the hardware configuration parameters of the master station.
EquipmentConfiguration parametersNetwork application serverCPU: ≥12 cores, ≥2.4 GHzOperating system: DebianInterface adapter serverCPU: ≥8 cores, ≥2.4 GHzNic: 1000 Mbps × 4 or higherHBA: Optical fiber interface card ≥4 Gb/sTrunk exchangeAt least four 10 ge optical ports support stacking, line aggregation, and dual power supply redundancy.KVM4-Controller 16KVM analog host, two LCD monitors, two mouse sets, and two keyboard setsDisk arraySupports dual controller IPSAN\FCSAN10 Gbit/s expansion port, fully configured with 8 Gbit/s SFP modulesLoadRunner performance testing tool and black box testing method were used to carry out comparative tests on the method in this study, the data-driven static voltage stability situation assessment and monitoring method in reference [4], and the voltage stability monitoring method based on multiboundary voltage weak region in reference [5].


## 3.2. Analysis of Experimental Results

In the experiment, the fitting degree of voltage monitoring value and actual voltage, transmission delay of monitoring signal, voltage identification rate, and monitoring accuracy were taken as indexes respectively to verify the application performance of different methods.First, the fitting degree of the voltage monitoring values of different methods and the actual voltage are verified. The actual voltage change curve is shown in Figure4.Figure 4
Actual voltage change curve.Analysis of Figure4 shows that the actual voltage increases abnormally between 1.2 s–1.4 s. Then test the voltage monitoring values obtained by different methods, and the results are shown in Figure 5.Figure 5
Statistical results of voltage monitoring values by different methods.It can be seen from Figure5 that there is a small gap between the voltage monitoring curve obtained by using this method and the actual voltage curve, indicating that the voltage monitoring value obtained by this method has a high fitting degree with the actual voltage. However, there are some differences between the voltage values monitored by the methods of reference [4] and reference [5] and the actual voltage, indicating that the monitoring effect of these two methods on voltage stability is not as good as that of the method in this study.Then, the monitoring signal transmission delay of different methods is verified, and the results are shown in Table3.Table 3
Different methods of monitoring signal transmission delay (ms).
Different methodsNumber of experiments/timesMonitor signal transmission delayMethod of this study1090.162090.573080.134090.135080.27Method of reference [4]10230.4520230.1930210.0940260.5350290.44Method of reference [5]10270.4720120.0430160.4740120.9850280.49As shown in Table3, the transmission delay of monitoring signals of the method of reference [4] varies between 210.09 ms–290.44 ms, and that of the method of reference [5] varies between 120.04 ms–280.49 ms. In contrast, the transmission delay of monitoring signals of the method of this study is less, and the global maximum value is only 90.57 ms, which fully indicates that the method of this study can quickly complete the transmission of power grid stability monitoring signals, and the monitoring timeliness is higher.The most direct way to verify the effectiveness of the voltage stability monitoring method is to judge the identification rate of the method, that is, the ratio of the number of unstable voltage signals to the total voltage signals. Therefore, the voltage identification rate is taken as an indicator to verify the application performance of different methods, and the results are shown in Figure6.Figure 6
Comparison results of identification rate of different methods.According to Figure6, with the increase in the number of experiments, the identification rate of the three methods also changes constantly. The identification rate of the method of reference [4] keeps increasing, and the global maximum value can reach 95.2%. The identification rate of the method in reference [5] decreased slightly after rising to 94.7%. However, the identification rate of the method of this study is always above 95%, indicating that method of this study is effective in voltage stability monitoring.Substitute the monitoring results of different methods into formula (8), calculate the average Pearson coefficient of different monitoring methods, and draw the results in Table 4.Table 4
Pearson coefficient statistical table of monitoring results of different monitoring methods.
Different methodsNumber of experiments/timesValue ofρLevelMethod of this study100.90Very strong correlation200.90Very strong correlation300.89Very strong correlation400.94Very strong correlation500.88Very strong correlationMethod of reference [4]100.27Weak link200.26Weak link300.30Weak link400.29Weak link500.17No relationshipMethod of reference [5]100.25Weak link200.27Weak link300.29Weak link400.28Weak link500.12No relationshipAs can be seen from the experimental results in Table4, the Pearson coefficients of the proposed method are all in the range of 0.8∼1.0, while the Pearson coefficients of the two traditional monitoring methods are not more than 0.4.In summary, the voltage monitoring value of method of this study has a high fitting degree with the actual voltage value, and the global maximum value of the transmission delay of the monitoring signal is only 90.57 ms, and the voltage identification rate can always be maintained above 95%. It indicates that the method of this study has high monitoring effectiveness and timeliness for voltage stability.


## 4. Conclusion

In order to ensure the stability of the output voltage of the smart grid, it is necessary to monitor the voltage value. Therefore, this study designs an online monitoring method for stabilizing the smart voltage of the grid considering potential factors. The method is based on collecting data for intelligent power grid voltage to extract the characteristics of the grid voltage and then the smart grid voltage instability, and based on the practical criterion of gravity, it analyzes the voltage instability factors, voltage eligibility index, and voltage percent of the pass and then is implemented by means of the wide-area measurement of voltage stability state online monitoring.According to the experimental results, the proposed method has a high fitting degree between the voltage monitoring value and the actual voltage value, the transmission delay of the monitoring signal is small, and the voltage identification rate can always remain at a high level, indicating that the proposed method has high monitoring effectiveness and timeliness of voltage stability.In this study, the research on intelligent voltage stability online monitoring of power grid considering potential factors is proposed, and there is still a lot of work to be further studied:(1)
In order to put the intelligent voltage stability online monitoring technology of the power grid into use, a large number of simulation tests are required, and the application software development of the protection algorithm is completed. A large amount of programming work is required for power systems and computer science.(2)
We can refer to the traditional protection scheme and study more new stability control algorithms with the help of the development achievements of communication technology and artificial intelligence, to further improve the security and reliability of intelligent voltage stability of the power grid.



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*Source: 8760582-2022-08-08.xml*