CN105844542B

CN105844542B - WAMS-based online detection method for single large disturbance of power grid

Info

Publication number: CN105844542B
Application number: CN201610207363.1A
Authority: CN
Inventors: 高金兰; 李宏玉; 徐建军; 闫丽梅; 任爽; 刘超; 黄雨晴; 陈永康
Original assignee: Northeast Petroleum University
Current assignee: Han Primary School
Priority date: 2016-03-30
Filing date: 2016-03-30
Publication date: 2020-04-14
Anticipated expiration: 2036-03-30
Also published as: CN105844542A

Abstract

The invention discloses a WAMS-based online detection method for single large disturbance of a power grid, which comprises the following steps: step 1), information is obtained; step 2), disturbance detection; step 3), information fusion; step 4), diagnosis decision. The disturbance detection method has simple and convenient algorithm, higher detection speed and higher accuracy, and particularly utilizes the accurate phase angle information provided by the PMU to detect whether the disturbance occurs or not, thereby solving the problem of judging whether the single large disturbance occurs or not. Meanwhile, the method has a certain error probability aiming at the uploaded information, and verifies whether the disturbance really occurs by combining the data uploaded by the WAMS and the SCADA and utilizing a data fusion theory, so that the problem that the judgment result is wrong when the large disturbance occurs due to the wrong uploaded information is solved.

Description

WAMS-based online detection method for single large disturbance of power grid

Technical Field

The invention relates to the technical field of power grid fault detection, in particular to a power grid single large disturbance online detection method based on a WAMS.

Background

The vigorous construction of projects of west-east power transmission and national networking leads the construction of the power grid of China to be in a new stage of vigorous development. In the 'twelve-five' period, the national power grid is ready to be put into the primary construction of a smart power grid by 5000 million yuan, the smart power grid (smart power grids) means that the power grid is intelligentized, the smart power grid is constructed on the basis of a high-speed bidirectional communication network, and the power grid is enabled to run more safely, reliably, economically and environmentally by using advanced equipment, a measuring method, a sensing technology, a control method, a decision-making system and other technologies, and the smart power grid has the advantages that the power supply can be resisted, the power demand of a user can be guaranteed, various forms of power generation can be accessed, the power market can run efficiently and the like. According to the current construction condition of China, the preliminary construction of an informationized and automated intelligent power grid can be basically completed in 2015, the resource allocation in the power grid is optimized through cross-region interconnection of the power grid, the operation efficiency is improved, and the intelligent power grid is more intelligent.

In today's large scale complex power systems, various types of disturbances are ubiquitous. A disturbance in an electrical power system refers to fluctuations in voltage, current, frequency, power, etc. that occur as a result of sudden changes in certain operating conditions during operation of the system. The disturbance existing in the smart grid can be divided into large disturbance and small disturbance of the grid according to the influence degree. Small disturbances in the power grid, i.e. disturbances with small variations in the electrical quantity and long duration, are usually generated because of small variations in the individual generators and the addition and removal of loads or generator speeds. The large disturbance of the power grid refers to the disturbance that the electric quantity has large variation amplitude but short duration and can influence the operation of the power grid to a great extent. The generation of large disturbances is usually caused by the addition and removal of major components of the power system, such as large-capacity generators or loads, and may also be caused by short-circuit faults. Since the power system is a large and complex dynamic system. The addition and removal of individual generators or loads generally has relatively little effect on the stability of the power system, and so no detailed study of small disturbances is made herein. When a large disturbance occurs to the power system, the whole system inevitably generates state deviation and oscillation to a large extent, the large disturbance is inexhaustible, and a correlation phenomenon occurs. Therefore, the influence range of the large power grid disturbance is continuously enlarged, and if correct measures are not taken to inhibit the disturbance in the first time, the serious consequence of all power failure in the area is likely to be caused.

When disturbance occurs in the power grid, the power system protection device sends a signal to inform dispatching personnel, and related workers can solve the problem of the fault in time, quickly restore the fault and guarantee the stable operation of the system. However, in recent years, power failure events occur in a large area, which brings serious inconvenience to the lives of residents, and causes the results to be many, mainly because a power system protection device is not perfect and has certain defects. Displaying related data: the nearly 7 major blackout events in the world are caused by improper operation of the protection device of the power system, which should become the protection device of the operation maintainer of the power system, but the imperfect device threatens the stable operation of the power system. When a power grid generates large disturbance, if a protection device is not perfect enough and does not make correct action in time, a chain reaction of a power system is necessarily caused, the power grid is rapidly spread, and finally the power grid is broken down [15], so that severe consequences are brought to the smart power grid.

Although typical cases of power failure accidents at home and abroad are analyzed, adverse factors such as incomplete protection systems, wrong actions of protection devices and the like exist, before and after a large disturbance of a power grid occurs, a globally unified power grid state analysis mode is lacked, so that power maintenance personnel cannot find the disturbance in the power grid at the first time, and cannot rapidly make correct and effective countermeasures to inhibit the further propagation of the disturbance. Whether the power grid has disturbance is accurately judged, alarm information is sent out when the disturbance occurs, power maintenance personnel can be reminded, corresponding measures are rapidly adopted for control, the accident range is restrained from further spreading, and the method plays an important role in safe and stable operation of a power system. Disturbance in the power system is found at the first time, and meanwhile, a disturbance point is controlled at a source, so that the smooth operation performance of the power grid can be greatly improved. In conclusion, the fact that whether disturbance occurs in the power grid or not is judged by monitoring the power grid information in real time has important significance for maintaining stable operation of the power system.

The method comprises the steps of detecting large disturbance in the smart grid, judging whether the disturbance occurs or not and determining the precise moment when the disturbance occurs. Because the types of power grid disturbances are more and the degrees of influencing the safe and reliable operation of the power grid are different, research emphasis can be placed on large disturbances which have large influences on the power grid and large influence ranges when the disturbances occurring in the power grid are detected. The general idea of the conventional disturbance detection method is to perform mathematical analysis on some changed electrical quantities and find out a catastrophe point, so as to determine the occurrence and the occurrence time of the disturbance. However, most of the existing methods are long in time consumption and cannot meet the requirement of online detection. With the gradual construction of Wide Area Measurement systems (Wide Area Measurement systems), some researchers have proposed an idea of fully utilizing information uploaded by the Wide Area Measurement systems to perform online detection on large disturbances of power grids. When disturbance occurs, the control center of the WAMS can obtain relevant information such as system state quantity, electric quantity and the like before and after the disturbance and various alarm prompts through PMU measurement information distributed in the whole network. The information which seems to be complex can reflect the running state of the power grid in real time, all data uploaded by the PMU have a strict unified time scale (accurate to millisecond), the section information on the same time point can be reflected, the section information can be directly deduced according to the electric quantity mutation information such as voltage, current, power and the like in the power grid during the disturbance, the characteristics of simplicity and quickness meet the requirement of on-line detection, and therefore a breakthrough is found for detecting whether the disturbance exists in the power system on line. Because the time for uploading data by the wide area measurement system is the shortest of all information sources (the time is in millisecond level), the information in the power grid can be firstly transmitted to power grid maintenance personnel at the initial stage of the disturbance of the power grid, and therefore the online detection of the disturbance is realized. In conclusion, the online detection of the power grid disturbance by using the information collected by the WAMS has profound practical significance.

Because the scale of the power grid is getting bigger and bigger, the research of the large disturbance of the power grid through the data and information of the whole power grid is a current research trend. Because of the constraints of various conditions such as technology and theory, the current research on the technology is still in the initial stage. The online detection of the large disturbance of the power grid is to monitor information data in some power grids, such as electrical quantities (voltage, current, power angle, frequency, and the like), by using a certain tool, and observe whether the data changes or not and time corresponding to the changes. So that the staff can take measures quickly to reduce the adverse effect of large disturbance on the safe operation of the power grid. In summary, the main work of the online detection of the smart grid is to detect whether the disturbance occurs and the precise time when the disturbance occurs.

In recent years, various students have made a lot of research on disturbance detection, and have made certain progress, and the proposed disturbance detection schemes can be roughly divided into two categories: a method based on time-frequency analysis and a method based on non-time-frequency analysis, which are concretely the following methods;

(1) fourier transform method

(2) Wavelet transform method

(3) Time domain analysis method

(4) Mathematical statistics method

(5) Artificial intelligence method

(6) Disturbance detection method based on WAMS

The national power grid makes technical specifications of a real-time dynamic monitoring system of an electric power system in 2006, and promotes the development of a wide-area measurement system. With the rapid development of a wide area measurement system in a national power grid, in addition, the dynamic state of the power grid can be monitored in real time, so that the detection of the power grid disturbance on the basis of the wide area measurement system is a new research direction. The research starting of the large disturbance detection of the power grid is relatively late, most of the existing research methods are difficult to program and high in calculation intensity, and the selected disturbance criterion is single in physical quantity, so that misjudgment is easily caused, and the actual requirements cannot be met. At present, most of the research on disturbance detection is in a theoretical part, the requirement of actual operation is not fully considered, and the acquired information is not fully utilized. Therefore, the power grid disturbance is researched by comprehensively utilizing the information uploaded by the whole network, and a feasible and effective power grid disturbance detection method based on a wide area measurement system is further provided, so that the method has great practical application significance for the safe and stable operation of the power grid.

Disclosure of Invention

The invention is based on one or more of the problems, and provides a power grid single large disturbance online detection method based on WAMS.

The invention solves the technical problems through the following technical scheme:

the WAMS-based power grid single large disturbance online detection method comprises the following steps:

step 1), information acquisition:

phasor information of each monitoring point capable of representing the running state of the power grid is extracted from the WAMS system in real time;

extracting switching value information from an SCADA system;

the electrical quantity information comprises a voltage amplitude and a voltage phase angle;

the switching value information comprises whether a switch acts or not and whether the current of the switch changes to zero or not;

step 2), disturbance detection:

detecting whether disturbance exists in the power grid by adopting a disturbance detection method based on electric quantity change by adopting a support vector machine method;

detecting whether disturbance exists in the power grid or not by using a detection method based on the switching value;

step 3), information fusion:

carrying out information fusion on the detection results of the two detection methods in the step 2) by adopting a D-S evidence theory;

step 4), diagnosis decision:

and analyzing the information fusion result, and finally judging whether the power grid has disturbance.

Further, in the above-mentioned case,

the method for detecting the disturbance based on the change of the electrical quantity by adopting the method of the support vector machine in the step 2) comprises the following steps:

step 2.1.1: carrying out denoising pretreatment on the collected phasor information by adopting a mallat algorithm based on a wavelet analysis theory;

step 2.1.2: dividing the data obtained after preprocessing into data in normal operation and data after disturbance, inputting the data into a support vector machine algorithm for training, obtaining a support vector machine disturbance detection model through training, and dividing the data into two types after training: the judgment value of the information of the normal operation of the power grid is set to be 1; setting a judgment value to be-1 for information except for normal operation;

step 2.1.3: inputting data to be detected into a disturbance detection model of a support vector machine, judging whether disturbance occurs or not, and displaying disturbance occurrence time when the disturbance occurs, wherein when the input data is in a normal operation range, a judgment value is output to be 1, namely no disturbance occurs in a power grid; and when the input data is not in the normal operation range, the output of the judgment value is-1, namely disturbance occurs in the power grid.

Further, in the above-mentioned case,

in the step 2), the detection method based on the switching value is to inquire and judge the switching value action judgment table according to the remote signaling information and the remote sensing information.

Further, in the above-mentioned case,

the information fusion method in the step 3) comprises the following steps:

setting the reliability of the detection result based on the WAMS uploaded electric quantity to be 0.9, and setting the reliability of the detection result based on the SCADA uploaded switching value change condition to be 0.8;

the underlying probability distribution for F is:

the basic probability distribution of (1) is:

wherein X_jiEither of F and

j＝1，....，M，i＝1，2。

further, in the above-mentioned case,

the criterion for analyzing the information fusion result and judging the disturbance detection result in the step 4) is as follows:

compared with the prior art, the power grid large disturbance identification method based on the WAMS provided by the embodiment of the invention has the following beneficial effects:

1. the PMU can provide phase angle information of the electric quantity, and by utilizing the collected phase angle information and combining amplitude information, whether large disturbance occurs can be easily judged.

2. The change conditions of the amplitude and the phase angle before and after disturbance are combined, the support vector machine algorithm is introduced into disturbance detection, and whether large disturbance occurs or not can be rapidly judged.

3. For an actual power grid, data uploaded by a PMU may be error information, and if the uploaded error data is detected, an error conclusion is drawn. Meanwhile, in the current power grid, PMU measuring points are generally only arranged in buses and important lines with voltage levels of 220KV and above, measuring points are few, and power grid information representation is incomplete.

The 4-disturbance detection method has the advantages of simple algorithm, high detection speed and high accuracy, and particularly utilizes accurate phase angle information provided by the PMU to detect whether disturbance occurs or not, so that the problem of judging whether single large disturbance occurs or not is solved. Meanwhile, the method has a certain error probability aiming at the uploaded information, and verifies whether the disturbance really occurs by combining the data uploaded by the WAMS and the SCADA and utilizing a data fusion theory, so that the problem that the judgment result is wrong when the large disturbance occurs due to the wrong uploaded information is solved.

Drawings

FIG. 1 is a flow chart of the WAMS-based online detection method for single large disturbance of a power grid;

FIG. 2 is a detailed flow chart of disturbance detection of the WAMS-based online detection method for single large disturbance of a power grid;

fig. 3 is a system diagram of 3 and 9 node protection in embodiment 1;

FIG. 4 is the variation of the amplitude collected by the PMU in example 1;

FIG. 5 is the variation of the phase angle collected by PMU in example 1;

FIG. 6 is the variation of the amplitude collected by the PMU in example 2;

FIG. 7 is the variation of the phase angle collected by PMU in example 2;

FIG. 8 is a circuit diagram of relay protection of embodiment 3;

FIG. 9 is the variation of the amplitude collected by the PMU in example 3;

fig. 10 shows the phase angle variation obtained by PMU in example 3.

Detailed Description

The present invention will be described in detail below with reference to the accompanying drawings and examples. It should be noted that, if not conflicting, the embodiments of the present invention and the features of the embodiments may be combined with each other within the scope of protection of the present invention.

Example 1: short circuit simulation

Taking a perturbation example of a 3-machine 9-node system, the 3-machine 9-node system is shown in fig. 3. A typical example of several disturbances of a 3-machine 9-node system is selected as input data to be simulated by using MATLAB software. The voltage magnitude and phase angle of the BUS1 BUS continues to be selected as the monitoring parent for disturbance detection in the support vector machine based algorithm.

Short-circuit disturbance occurs on the set Line1, Bus5 Bus side main protection acts, Bus4 side protection does not act, the breaker CB4 is not disconnected, second backup protection of the adjacent Line2 works, and the breaker CB1 is disconnected. Meanwhile, fig. 4 and 5 show the variation of the amplitude and phase angle of the voltage uploaded by the PMU, and the information uploaded by the SCADA is shown in table 3. And judging whether the power grid is disturbed by adopting an information fusion method to obtain a table 1.

TABLE 1SCADA information

The detection process based on the electrical quantity is shown in table 2.

TABLE 2 disturbance detection procedure

And the detection result is that the system generates disturbance alarm at 3.71, and the alarm display time is 3.71S.

The detection result based on the switching value is: through the intelligent switching value information judgment lookup table in the table 3, the Bus5 Bus side main protection, the Line2 second backup protection and the CB1 protection are effective, and disturbance occurs in a power grid.

TABLE 3 Intelligent distinguishing and inquiring table for switch quantity information

And (3) performing information fusion on results of the two disturbance detection methods by adopting a multi-source information fusion theory:

TABLE 4 information fusion data

Table 4 fusion results show that:

the detection result is 3.71 seconds, and the disturbance in the power grid does occur.

Example 2: PMU uploads erroneous information

If the data uploaded from the PMU contains certain error information, the data uploaded from the PMU and the SCADA are shown in fig. 6 and 7 to obtain table 5, and whether disturbance occurs in the power grid is determined by adopting an information fusion method.

TABLE 5SCADA collected information

The detection process based on the electrical quantity is shown in table 6.

TABLE 6 disturbance detection procedure

And the detection result based on the electric quantity is that the system generates disturbance alarm at 2.15, and the alarm display time is 2.15S.

The result of the detection method based on the switching value is: within 2.15 seconds, no action occurs in the protection element and no disturbance occurs in the grid.

The results of the two disturbance detection methods after information fusion by using the multi-source information fusion theory are shown in table 7.

TABLE 7 information fusion data

The fusion result shows that:

the detection result is 2.15 seconds, no disturbance occurs in the power grid, and the PMU uploads error information.

Example 3: single large disturbance detection of oil field power grid

The transformer substation and the transmission voltage class in a certain oil field are relatively complex, and the voltage class of a power grid in the certain oil field has several voltage classes such as 110kV, 66kV and 35 kV. Because of the bulkiness and complexity of the oil field power grid, before the simulation verification is performed on the oil field power grid, a certain variable region of the oil field power grid is subjected to point-line type line simplification, and a protection circuit diagram of a certain oil field is given as in fig. 8.

The power supply system is characterized in that a certain torch transformer supplies power to a wind cloud transformer and a star fire transformer through a double circuit line, the lower side of the wind cloud transformer is provided with a five-north transformer, a ten-north transformer, a nineteen-north transformer, a III-2 north transformer, a twenty-north transformer, a seven-north transformer, the lower side of the star fire transformer is provided with a five-north transformer, a eleven-north transformer, a seventeen-north transformer, a thirteen-north transformer, a nine-middle transformer, a one-middle transformer, a seventeen-north transformer and a II-4-north transformer, wherein a power transmission line for the II-4-north transformer is supplied as a standby line, and.

The method is characterized in that PMUs are installed at wind cloud change and star fire change positions, a certain change area of the whole oil field power grid is monitored, and voltage data of normal operation of the certain change area of the oil field power grid are obtained through calculation by acquiring basic parameters of an actual power grid.

In the embodiment, the voltage amplitude and the phase angle from the wind cloud to the torch tip line are selected as monitoring objects for disturbance detection. The data of the voltage amplitude and the voltage phase angle are trained respectively, a disturbance detection model is established, and the training process is shown in table 8.

TABLE 8 perturbed data training procedure

After the disturbance detection model is established, in order to verify that the method provided by the text has a good detection effect on different types of disturbances, simulation of different disturbances is performed in different places, data before and after the disturbances are detected, and the effectiveness of the method is verified.

First, taking a north ten change place as an example, when a tripping disturbance occurs at the north ten change place, the change conditions of the voltage amplitude and the phase angle collected by the PMU are shown in fig. 9 and 10, and at this time, the information uploaded by the SCADA is shown in table 9.

TABLE 9SCADA gathered information

The detection process based on the electrical quantity is shown in table 10.

TABLE 10 disturbance detection procedure

And the detection result is that the system generates a disturbance alarm at 19:41:07, and the alarm display time is 19:41: 07.

The detection result based on the switching value is: through the intelligent switching value information judgment query table in table 3, the obtained conclusions are main protection at the ten north position, main protection at the wind cloud position, far backup protection from line wind cloud to torch, and effective actions of the circuit breakers CB3 and CB 4.

The results of the two disturbance detection methods after information fusion by using the multi-source information fusion theory are shown in table 11.

TABLE 11 information fusion data

The fusion result shows that:

disturbances do occur in the grid.

The detection result is as follows: when the voltage amplitude and the voltage phase angle of the monitored line are suddenly changed at 19:41:07, and the action of the protection device is effective. Namely, the power grid does generate large disturbance, the system automatically alarms after the disturbance is detected, and the disturbance starting time is displayed to be 19:41: 07.

Through the test of the embodiment, the disturbance detector based on the multi-source information fusion of the switching quantity and the electric quantity can detect the large disturbance of the power grid in the application of the actual power grid. From the result of simulation verification, the disturbance detection method provided by the application is effective and feasible for power grid disturbance detection.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. The WAMS-based power grid single large disturbance online detection method comprises the following steps:

step 1), information acquisition:

extracting the electrical quantity information of each monitoring point representing the power grid operation state from the WAMS in real time;

extracting switching value information from an SCADA system;

step 2), disturbance detection:

2.1, detecting whether disturbance exists in the power grid by adopting a disturbance detection method based on electric quantity change by adopting a support vector machine method, wherein the method comprises the following steps:

step 2.1.3: inputting phasor data to be detected into a support vector machine disturbance detection model, judging whether disturbance occurs or not, and displaying disturbance occurrence time when the disturbance occurs, wherein when the input data is in a normal operation range, a judgment value is output to be 1, namely no disturbance occurs in a power grid; when the input data is not in the normal operation range, the output of the judgment value is-1, namely disturbance occurs in the power grid;

2.2, detecting whether disturbance exists in the power grid or not by using a detection method based on the switching value; the detection method based on the switching value is that the switching value action decision table is inquired and judged according to the remote signaling information and the remote measuring information;

step 3), information fusion:

the basic probability distribution of the disturbance occurrence F is:

no disturbance occurs

The basic probability distribution of (1) is:

wherein, X_jiF or

j＝1，2，...，M，i＝1，2；

step 4), diagnosis decision:

analyzing the information fusion result, and finally judging whether the power grid has disturbance, wherein the criterion for carrying out disturbance detection judgment according to the information fusion result is as follows: