CN114172262A - Intelligent substation sampling data quality comprehensive evaluation method and system - Google Patents

Intelligent substation sampling data quality comprehensive evaluation method and system Download PDF

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CN114172262A
CN114172262A CN202111059818.7A CN202111059818A CN114172262A CN 114172262 A CN114172262 A CN 114172262A CN 202111059818 A CN202111059818 A CN 202111059818A CN 114172262 A CN114172262 A CN 114172262A
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data
substation
state estimation
measurement
transformer substation
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鲁晓秋
孙俭
沈东明
张莹
王法
翟万利
沈杰士
郭磊
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State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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Abstract

The invention provides a comprehensive evaluation method and a comprehensive evaluation system for the quality of sampled data of an intelligent substation, wherein the comprehensive evaluation method comprises the following steps: acquiring merging unit data, measurement and control device data and PMU data in a transformer substation to form multi-source redundant three-phase measurement under unified time scale in the transformer substation; the method comprises the steps of performing three-phase line state estimation of a transformer substation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm; and carrying out comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using a maximum normalized residual error LNR method to obtain a data quality comprehensive analysis result. The intelligent substation sampling data quality analysis comprehensive evaluation method is based on multi-interval, multi-type and acquirable sampling data in the existing integrated monitoring system in the substation, realizes the quality analysis comprehensive evaluation of the sampling data of the intelligent substation, and improves the operation reliability of the substation.

Description

Intelligent substation sampling data quality comprehensive evaluation method and system
Technical Field
The invention relates to the technical field of intelligent substations, in particular to a method and a system for comprehensively evaluating the quality of sampled data of an intelligent substation.
Background
The rapid development of the smart power grid promotes the technological progress of the power grid, and accelerates the application of new theories, new technologies and new equipment in the power system. The intelligent substation is one of important contents for constructing the intelligent power grid and is also a key support point for information interaction of the intelligent power grid. Since the research of the intelligent power grid begins in China, the research of the technology and equipment of the intelligent transformer substation and the application of the technology and equipment in the power grid are very important.
Various information systems such as a data acquisition and monitoring system, an online monitoring system, a fault recording system and the like exist in the intelligent substation, the systems are independently constructed, and higher requirements are provided for the quality of basic data along with the gradual increase of advanced applications of the intelligent substation. The accuracy and reliability of raw data has become a bottleneck problem affecting the development of advanced applications such as distributed state estimation.
At present, an intelligent substation is built according to a three-layer two-network architecture, wherein the three layers refer to a station control layer, an interval layer and a process layer, and station control layer equipment comprises an intelligent substation integrated monitoring host, a data communication network shutdown machine, a database server, a comprehensive application server, a scheduled maintenance terminal and the like; the spacer layer comprises various protection devices, measurement and control devices and the like; the process layer comprises a merging unit, an intelligent terminal and the like. The two networks refer to a process layer network (including GOOSE and SV messages) and a station control layer network (including MMS and GOOSE messages).
The combination unit is adopted to realize the digitization of analog quantities such as current, voltage and the like; the intelligent terminal is adopted to realize the intellectualization of the equipment such as the circuit breaker, the isolating switch and the like. The protection device and the intelligent terminal which are configured in a dual mode are respectively accessed to different process layer networks; a single set of protection (measurement and control) device and intelligent terminal are connected to a single process layer network; the main transformer electric quantity protection is simultaneously accessed to the process layer network of each side. The protection device and the measurement and control device are connected to a substation control layer network by adopting a digital interface supporting IEC61850 standard, relevant information is transmitted to substation control layer equipment of a transformer substation in the form of MMS messages through the network, and interlocking information among the measurement and control devices is also transmitted in the form of GOOSE messages through the network.
According to the framework system of the intelligent substation, the total station information is collected through a merging unit of a process layer network and an intelligent terminal and then is sent to an in-station integrated monitoring background through a measurement and control device, relevant data are marked by a clock synchronization system in a collecting stage, the information coverage is complete, the method has the characteristics of high redundancy and high information integrity, and a good basic platform is provided for comprehensive utilization of the total station information. The data provided by the background in the station at present has a great amount of errors which can be originated from an acquisition device or a transmission medium, and the reliability of the raw data is low, so that the raw data cannot be directly used for carrying out security analysis on the system. In order to improve the situation, on one hand, from the hardware perspective, errors can be reduced by adding measuring equipment capable of ensuring precision, speed and reliability, but the construction cost of the intelligent substation is undoubtedly increased; on the other hand, from the perspective of software, raw data collected by the system can be processed by means of power data analysis and modern state estimation, bad data is eliminated, random interference is eliminated, and accuracy, reliability and integrity of data information are improved.
Compared with the traditional measuring equipment, the synchronous Phasor Measurement Unit (PMU) system of the power system based on the synchronous time synchronization system has quite remarkable characteristics:
(1) the measurement precision meets the practical engineering requirements; the GPS synchronization precision is within 2ps, the phase angle measurement precision can reach 0.1 degree theoretically, and the requirements of power system analysis and control on data synchronization can be met;
(2) the data has accurate time marks, PMU data of different places can be strictly synchronized, and the data is transmitted by adopting a packaging transmission mode;
(3) and (4) directly measuring the state quantity of the power grid. Under the rectangular coordinate system, the measurement (including the measurement of voltage and current phasor) and the state quantity are in a linear relation;
(4) the data updating period is short, the transmission speed is high, and the dynamic change of the system state can be tracked. The PMU in operation transmits 30 phase information per second, i.e. the transmission interval can reach 30 ms.
At present, a 500kV transformer substation is newly built, and 220kV and 110kV transformer substations accessed by large-scale renewable energy sources are provided with synchronous phasor measuring devices. Although the PMU has great advantages in the technical aspect, due to the limitation of economy and technology, the observability of the whole power grid or the whole transformer substation cannot be realized by PMU measuring points, the characteristics of high data redundancy and good integrity of the existing measuring equipment are fully utilized, the technical advantages of the PMU can be effectively combined to monitor and identify bad data of power data, and the realization of state estimation with higher performance is a key problem in the field of state estimation of the current power system.
Disclosure of Invention
The invention aims to provide a comprehensive evaluation method and a comprehensive evaluation system for the quality of sampled data of an intelligent substation, which comprehensively utilize multi-interval and multi-type sampled data in a substation, realize the three-phase line state estimation of the sampled data under a unified time scale according to a clock synchronization system in the substation, provide bad data identification for a sampling device in the substation, pre-judge the occurrence probability of the bad data and the reason of the bad data in a certain time period in advance, automatically push an early warning signal, establish a bad data record report and effectively improve the lean degree of the operation and maintenance of the substation.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a comprehensive evaluation method for quality of sampled data of an intelligent substation comprises the following steps:
acquiring merging unit data, measurement and control device data and PMU data in a transformer substation to form multi-source redundant three-phase measurement under unified time scale in the transformer substation;
the method comprises the steps of performing three-phase line state estimation of a transformer substation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm;
and carrying out comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using a maximum normalized residual error LNR method to obtain a data quality comprehensive analysis result.
Further, the acquiring merging unit data in the substation includes:
and reading PT/CT output data digitally sampled by the merging unit, converting the PT/CT output data into synchronous phasor data on the same time scale through a phasor conversion algorithm to obtain corresponding voltage and current phasors, wherein the merging unit receives clock synchronization and outputs sampling pulses accurately synchronous with the clock synchronization pulses to realize synchronous sampling of the data of the whole station.
Furthermore, when the phasor conversion is carried out, a filter is adopted to filter high-frequency harmonic waves of the system.
Furthermore, under the transient condition of the system, the phasor conversion algorithm adopts a reasonable optimization time window and ensures the data processing speed, thereby ensuring that the delay does not influence the accuracy of phasor data.
Further, before performing the linear state estimation, the quality check is performed on the data stream according to the following five aspects: communication and message format checking, time and timing validation, PMU data state validation, data feature validation, topology-based error detection;
if the data is declared "bad" in any respect, no further checking is required, and if the data is declared "good" or "uncertain", the subsequent data checking continues.
Further, the transformer substation model is in an XML format and is defined by the CIM model.
Further, the substation three-phase line state estimation includes a current state estimation that uses kirchhoff's current law to verify breaker current and a zero-impedance voltage state estimation that is a weighted average of voltage phasor measurements.
Further, the received breaker switch states are verified before state estimation, and substation topology verification is performed by using the current values of all measured data to maintain a substation model that is synchronized with the data being used.
Further, linear state estimation uses synchrophasor measurement information to check and verify system topology, and if a topology error is identified from the PMU data, this information will be used to modify breaker state to update the topology before the next ICCP update.
The utility model provides an intelligent substation sampling data quality comprehensive evaluation system, includes:
the data acquisition module is used for acquiring merging unit data, measurement and control device data and PMU data in the transformer substation to form multi-source redundant three-phase measurement in the transformer substation;
the state estimation module is used for performing three-phase line state estimation of the transformer substation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm;
and the bad data identification module is used for carrying out comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using the maximum normalized residual error LNR method to obtain a data quality comprehensive analysis result.
Compared with the prior art, the invention has the following advantages:
based on multi-interval, multi-type and acquirable sampling data in an existing integrated monitoring system in a station, the quality analysis and comprehensive evaluation of the sampling data of the intelligent substation are realized, the sampling data can be used as a redundant backup for identifying bad data of a sampling device, the occurrence probability of the bad data in a certain period of time and the reason for generating the bad data can be pre-judged in advance, and an early warning signal and a bad data recording report are provided, so that the operation reliability of the substation is improved, and convenience is brought to the post analysis of operation maintenance personnel.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are an embodiment of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts according to the drawings:
fig. 1 is a flowchart of a comprehensive evaluation method for quality of sampled data of an intelligent substation according to an embodiment of the present invention;
fig. 2 is an architecture diagram of a comprehensive evaluation system for quality of sampled data of an intelligent substation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of system data identification according to an embodiment of the present invention;
fig. 4 is a flowchart of a method for determining bad data without a model reference according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a KCL provided by an embodiment of the present invention performing comprehensive and considerable PMU inspection at a substation;
FIG. 6 is a graph of PMU measurement data for different sampling frequencies according to an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating a time scale labeling of the multifunctional measurement and control device according to an embodiment of the present invention;
FIG. 8a is a diagram illustrating Kirchhoff's Current Law (KCL) checking that the sum of the currents injected into the nodes is 0 according to an embodiment of the present invention;
FIG. 8b is a schematic diagram of kirchhoff's law of current (KCL) checking the current across the lines according to one embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the detailed description.
The quality of the sampled data is an important index of the transformer substation, and if errors, jitter or sudden changes exist in the sampled data, the reliability of the actions of multiple relay protection devices may be affected, and the operation safety and efficiency are related.
As shown in fig. 1, the comprehensive evaluation method for the quality of sampled data of an intelligent substation provided by the invention comprises the following steps:
s1, acquiring merging unit data, measurement and control device data and PMU data in the transformer substation to form multi-source redundant three-phase measurement under unified time scales in the transformer substation;
s2, carrying out transformer substation three-phase line state estimation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm;
and S3, performing comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using a maximum normalized residual error (LNR) method to obtain a data quality comprehensive analysis result.
Referring to fig. 2, through a standardized reading interface, the system platform imports or collects merging unit data, SCADA-RTU and PMU measurement data in an offline manner in real time to serve as a data base for substation data identification. The method comprises the steps of mining, integrating and comprehensively analyzing relevant information such as data quality labels, data precision and time sequence, integrating data, breaker switch information, a transformer substation model and the like, analyzing an operation state in real time and the like to realize data abnormity early warning and auxiliary decision making, determining a final warning and display scheme under the condition of normal fault abnormity of transformer substation data by combing logic association such as classification, layering and the like among signals, obtaining a data quality comprehensive analysis result, and providing auxiliary decision making for operation maintenance of measuring equipment and power grid operation. And the transformer substation data identification rewrites the calculated result into a real-time database of the platform through a rewriting interface of the monitoring system platform, the result is displayed on a graphical interface, and the comprehensive display of the alarm event is provided. And intelligent alarm, event information and abnormal analysis decision support are provided for a superior system, and superior scheduling (or centralized control center monitoring personnel) is guided and helped to rapidly process faults. The data identification process of the whole system is shown in fig. 3.
When the system finds bad data, the key point is that the rule of the bad data needs to be further analyzed, the root cause of the problem is found through pattern recognition, and corresponding equipment is pre-warned. Particularly, for sudden change of data, whether the sudden change of data is caused by system topological structure change or system disturbance or caused by a fault mutual inductor needs to be distinguished, so that misjudgment of the system is avoided.
Data quality is a frequently used but poorly defined term. Thus, it is used in different ways and is applied in an inconsistent manner. For this discussion, the following definitions will be used:
data quality: "any data that meets the specified objective and meets all the requirements of use".
The method specifically comprises the following steps:
(1) including any aspect of the data, not just accuracy, corruption, or data loss;
(2) associating data with a use ignores problems that do not affect the intended use.
This definition is very broad and applies almost any situation where data used in an application may adversely affect performance. It is also important to note that data may affect use in one application in one respect, but not another.
With the deployment of synchrophasor data-related applications, PMUs have been widely installed in national grid companies at 330kV and above voltage levels and in important 220kV substations. With the spread of measuring devices, data quality issues have become one of the most important concerns for local and regional utility scheduling. The synchrophasor data is estimated by the PMU and sent to the end user via synchronous communication, typically through several levels of real-time processing. Problems can arise with any process including voltage current transformers, signal conversion equipment, PMU hardware, communication frameworks, Phasor Data Concentrators (PDCs), and end-use applications. Data errors and losses can occur at any stage.
The data quality problem of PMUs can be generally divided into four aspects:
(1) availability: data loss from one or more PMUs or signal loss in a PMU.
(2) Damage: data is corrupted by the transmitting or receiving device, communication or processing.
(3) Timely delivery: inconsistent data rates and delays. Excessive delay results in data loss; varying delays can lead to losses through buffer overload and application processing errors.
(4) Effectiveness and accuracy: measurement errors, such as phase errors due to timing errors, ratio errors and scale errors, excessive noise of the measurement and repeated measurements.
Poor data quality can affect downstream applications. If the application result is not trusted, it cannot be used.
The method of discriminating bad data can be classified into a data algorithm without a model reference and a model-based method. The data algorithm without model reference checks the data in the data stream and may create a data quality label for each measurement to indicate problems found in the data. For example, data tags may be classified as "good", "bad", or "uncertain". Good means that no problems are found in the data; bad means that the measurement is found to be erroneous, making it unusable. Indeterminate means that the data is likely to be erroneous, but the test does not have enough conclusions to determine whether it is good or bad. A frame of data typically has several measurements including phasor magnitude and phase angle, frequency, rate of change of frequency, etc. Evaluating and marking the quality of each measurement maximizes the use of the collected data and simplifies the data processing steps of downstream applications. The method for discriminating bad data without model reference comprises several consecutive modules, as shown in fig. 4. Each module separately examines the data for specific characteristics and records the results in a data quality tag that may be included in the data stream. The downstream application may resolve it to a measurement data quality indicator. The modules are independent of each other, and maintenance and troubleshooting are facilitated. The parameters of all modules in the model-free step are designed to be configurable so that the algorithm can be used in any operational practice. Each module will check and pass the data to the next module. If the data is declared to be "bad" at any stage, no further checking is required. If the data label detected by the module is 'good' or 'uncertain', the data check of other modules is continued.
(1) A communication and message format module: this module processes any error indications received from the communication interface and checks message characteristics such as message format, length and destination. If the basic message is wrong, the whole data flow is not credible, so that the data is judged to be 'bad', and no further check is performed.
(2) A time and timing module: the module verifies the time stamp and synchronicity of the data. For example, the data rate may be inconsistent (i.e., the calculated data rate does not match what is specified in the configuration frame), the delay may be inconsistent or outside of the allowable limits, and the data may be out of order or an abnormal time stamp. Time-problematic data can sometimes be corrected and marked, for example poor time synchronism means that the phase angle measurement is inaccurate, but the amplitude of the phasor may be good.
(3) PMU data state module: the four-bit state word carried by PMU data provides all measured states contained in a data frame, and specifically includes: 1) and (3) data invalidation: set to indicate that the data is bad and unusable; 2) PMU error: set to indicate internal errors such as A/D out of calibration, memory errors, and processor errors; 3) PMU synchronization error: indicating the PMU to detect external time synchronization loss, such as satellite tracking loss or timing input connection failure; 4) sorting in arrival order: the data for a particular PMU is bad, and the PDC configures its own time stamp for the data (in which case the measured phase angle is not available). The module applies these status indications to the quality indicator created for each measurement. They have different effects depending on the measurement. For example, synchronization errors make phase angles unavailable, but do not affect amplitude.
(4) A data characteristic module: the module provides additional verification of various measured values and mainly comprises the following sub-modules:
1) a range check to check if a measurement is within a reasonable range, which can be applied to most measurements with reasonably known limits, e.g., the frequency measurement of the system should be within + -5Hz of normal, the phase angle must be within + -pi;
if the measured value of the measured data (electrical magnitude, phase, frequency, etc. information) is represented by M, U, L is used to represent a reasonable range determined based on theoretical analysis or historical a priori experience, the data quality can be expressed as follows
Figure RE-GDA0003455865750000081
2) Repeating the data check to check whether the measurement quantity continuously repeats the same value within a certain time window T, which may occur due to a problem in the data calculation, data mapping or data verification procedure;
if the measured value of the measured data (information of the amplitude, phase, frequency, etc. of the electrical quantity) is represented by M, D represents a threshold value for allowing measurement deviation, T represents the time for which the measured data exceeds a set threshold value, and T represents the duration of a sampling time window, the data quality can be represented by a repeatability check in the following form
Figure RE-GDA0003455865750000082
3) Noise checking, random noise, is always present in the data but is expected to be much lower than the signal of interest, and sudden high noise above normal power system events may indicate equipment failure, communication errors or other data quality issues, and if noise is passed as data, may cause control or data analysis errors, the noise may resemble a reasonable oscillation but may be detected distinguished by its characteristic of being randomly varying and having a frequency higher than normal PMU measurement capability, in which case it also has a higher amplitude than other power system signals. This detection method is essentially a high pass filter with a delay.
Noise detection is applicable to voltage amplitude, current amplitude and frequency measurements. The main problem in designing high pass filters is setting the filter cut-off frequency and amplitude limits. If the PMU reporting rate is set to 25 frames per second, the Nyquist frequency is limited to 12Hz and the effective measurement limit is about 7 Hz. To detect noise, the cut-off frequency may be configured to be, for example, 5-6Hz, which excludes the oscillation frequency range of 0-5Hz where actual system oscillation typically occurs. The average of the absolute values of the high-pass filtered signals over a period of time of the measurement is calculated and checked against user-defined limits. The threshold is set high enough so that low amplitude noise at 10Hz will not trigger, but noise of greater amplitude (> 10%) will be detected. This threshold and cut-off frequency can be adjusted to more or less noisy conditions. If the limit is exceeded, the value will be considered as noise, and its data quality indicator will be flagged.
(5) A topology based error detection module: the module checks the rationality of the measurement quantities taking into account the topology of the substation. It calculates a value from the local power system configuration (topology) based on the measurements, and if the calculated value deviates significantly from the expected value, the measurement is not qualified. In most cases, it is difficult to determine which specific measurements will cause errors, so all measurements used for the calculation will be marked as "uncertain".
For example, as shown in FIG. 5, Kirchhoff's Current Law (KCL) based current sensing can be used to verify the current measurements of all current going to a particular bus. In practice, a small error ε, e.g., 10A, is used in the application instead of 0A in the KCL equation, because the measurements may have small errors. The current measurements through this topology check can also be used to verify the breaker state in the model-based substation state estimation step, see in particular the description below.
The large-range application of the merging unit in the intelligent substation provides another set of data source, and the data source can be used for verifying the data quality problem by comparing redundant data with RTU (remote terminal Unit) and PMU (phasor measurement Unit) data to find the root of bad data.
The PT/CT output data digitally sampled by the merging unit needs to be subjected to phasor conversion, and is converted into synchronous phasor data on the same time scale to obtain corresponding voltage and current phasors, and the voltage and current phasors can be directly used for comparison with PMU data or used as joint input to carry out a substation state estimation algorithm. When carrying out phasor conversion, a filter is needed to filter high-frequency harmonics of the system. A small computational delay is added during the filtering process. The algorithm needs to adopt a reasonable optimization time window and ensure the data processing speed, thereby ensuring that the delay does not influence the accuracy of phasor data. When the system is in a disturbance state caused by a fault, particularly in the transient state of the system, the accuracy of the phasor conversion algorithm needs to be ensured, so that transient errors are not caused. The total phasor error TVE is guaranteed to reach the relevant standard of the transient operation of the PMU.
The method of synchrophasor calculation is as follows:
and vector values with real-time scales, such as three-phase fundamental voltage, three-phase fundamental current, sequence values, switching states and the like, are measured. The vector values with real-time scale, the disturbance log file, are sent to the data concentrator at a specified rate over the network, as specified by IEEE std 1344-1995 (R2001).
The device samples pulses according to 1PPS synchronous analog quantity and digital quantity signals output by the time synchronization module, and the synchronization error of the sampling pulses is not more than 1 mu s. In the synchronous sampling process, the data sampling pulse is locked by the second pulse of the time synchronization module, so that the synchronous sampling pulse is uniformly distributed in each second, and correspondingly, the time scales corresponding to the phasors are also uniformly distributed in each second.
For the multifunctional measurement and control device of the intelligent station, the sampling pulse synchronism is ensured by the merging unit device, the receiving side of the device calculates the time scale of the synchronous phasor according to the second equal value and the local time in the IEC61850-9-2 message, and directly marks the time scale when the device receives the time scale, so that the time scale precision is ensured.
The multifunctional measurement and control device supports 4kHz synchronous sampling frequency, can synchronously measure three-phase fundamental wave voltage, three-phase fundamental wave current and fundamental wave positive sequence phasor, frequency and switching value signals of the voltage current of a mounting point, and can transmit all primary values of the voltage fundamental wave positive sequence phasor and primary values and frequency of the current fundamental wave positive sequence phasor to an in-station dynamic data concentrator in real time.
The IEEE C37.118 puts high demands on PMU performance under dynamic conditions, and the multifunctional measurement and control device also meets the demands. Synchronous phasor calculation needs to be performed through links such as synchronous sampling, discrete Fourier transform calculation, pre-filtering, phasor calculation, up-sending filtering and the like, and analog sampling and the previous link of the intelligent station are completed by a merging unit.
The analog quantity is subjected to anti-aliasing filtering, so that spectrum aliasing caused by components beyond the Nyquist frequency in the A/D conversion process is avoided. The merging unit introduces a time scale during analog-to-digital conversion, so that the accuracy of a phasor time scale is ensured. The DFT operation result attenuates the frequency spectrum leakage component through the pre-filter, and the phasor calculation precision is improved. Phasor, frequency and frequency change rate calculation results are sent to the filter, so that the influence of out-of-band interference is limited, and the precision is improved.
Analysis of sampling frequency requirements
The sampling rate of sampling data output by a merging unit in the current intelligent substation is 80 points/cycle. The steady state remote measurement requires that the measurement error of alternating voltage and current is not more than 0.2%, and 2-13 harmonic waves need to be calculated. The sampling rate is typically selected to be 48 or 64 points/cycle, according to the sampling theorem, taking into account attenuation of the filter network, etc. Synchronous phasor measurement requires measurement of fundamental phasor, frequency and the like of voltage and current, the phasor amplitude error is less than 0.2%, the phase error is less than 0.5 degrees, and the frequency measurement error is not more than 0.002 Hz. The PMU primarily monitors the fundamental component of the system.
At present, the conventional PMU measuring device in China mainly adopts sampling rates of 96 points/cycle, 200 points/cycle and the like. The sampling rate of the digital PMU is determined by the merging unit, and the PMU measurements with different amplitudes at 80 point/cycle and 200 point/cycle sampling rates are simulated, wherein PMU data at the two sampling rates are shown in FIG. 6, a triangle data point is the sampling rate of 80 points, and a circle data point is the sampling rate of 200 points. The simulation result shows that the measurement accuracy of the fundamental phasor under the two sampling rates is basically consistent. The 80-point/cycle sampling rate can meet the PMU measurement accuracy requirement. The electrical energy calculation requires substantially the same sampling rate as steady state telemetry. Therefore, the sampling rate of 80 points/cycle provided by the current merging unit can meet the precision requirements of three kinds of measurement data.
The high precision synchronous sampling is as follows:
the alternating current sampling of the measurement and control device in the intelligent substation adopts a digital and networking mode. The merging unit collects the alternating current output by the electromagnetic or electronic mutual inductor in a centralized manner, frames are formed according to the IEC61850-9-2 sampling value transmission standard, and then the frames are sent to related devices through the network.
And the merging unit receives clock synchronization, outputs sampling pulses which are accurately synchronous with the clock synchronization pulses, and realizes synchronous sampling of the total station data. In order to ensure the synchronization of the data collected by the whole station, the sampling pulse and the time setting pulse are always kept synchronous. When the system frequency fluctuates, the interlayer device cannot realize frequency tracking sampling of the system by adjusting the sampling frequency. In order to meet the requirements of sampling in a whole period and reducing errors caused by frequency spectrum leakage and a barrier effect, a sampling value sent by the merging unit needs to be processed. In addition, the sampling frequency of the merging unit is 80 points/cycle at present. The spacer layer device is usually calculated by using a fourier algorithm, and the sampling rate is generally 32 points, 64 points and the like, which are not equal. In order not to change the sophisticated algorithm of the original device, the received samples of the merging unit are resampled.
The steady-state and dynamic data time scales are labeled as follows:
the multifunctional measurement and control device also puts forward the requirement of marking time marks on the steady-state telemetering data, so that the master station can use the telemetering data of the same time section to carry out advanced application of state estimation. The functional modules in the multifunctional comprehensive measurement and control device have different requirements for data time scales, the functions requiring accurate data time scales are a steady-state measurement and control function and a dynamic PMU function, but the telemetering data time scale is a steady-state data time scale, the PMU functional data time scale is a dynamic data time scale, and the two functions have different interval requirements for data time scales. If the two functions are respectively subjected to time scale processing, the efficiency is low, and more cache space is occupied. In order to optimize the collected data, the data time scale is shared in the unified processing of the collected data time scale.
The data time scale resolution of PMU is 10ms, the time scale is marked according to 200ms intervals when the remote measurement is stable in the steady state remote measurement, the time scale of the change moment is recorded when the remote measurement is suddenly changed, and the time scale precision requirement is 10 ms. Therefore, the time scales of the measured data in the multifunctional measurement and control device are marked at intervals of 10ms according to the PMU requirements, the data twice in the PMU calculation interval are selected for steady state telemetering to judge whether the data are changed, the data are marked at intervals of 200ms when the telemetering is not changed, and the PMU time scales at the change moment are selected when the telemetering is changed. Therefore, the functional requirements of the PMU are met, the requirements of the steady-state measurement function are met, and the time scale sharing of the collected data is realized. As shown in fig. 7.
The multifunctional measurement and control device adopts a frequency measurement algorithm for compensating proportional phase shift generated by a frequency spectrum leakage item and frequency deviation, can simultaneously meet the requirements of the precision of steady-state measurement and the rapid characteristic of dynamic measurement, and completes the fusion of steady-state measurement, synchronous phasor and electric energy measurement functions through FFT, low-pass filtering and frequency domain integral calculation.
Phasor conversion algorithms commonly employ a Discrete Fourier Transform (DFT) algorithm to calculate phasors. The traditional DFT algorithm is widely applied because the integer multiple harmonics can be eliminated under the static condition, and the phasor of the rated frequency can be accurately calculated. However, under dynamic conditions such as system faults, power oscillation, low-frequency oscillation and the like, the amplitude, frequency and phase angle of voltage and current in the system all change along with the change of time, so that the traditional DFT algorithm cannot accurately calculate and obtain dynamic phasor.
Transient, etc. non-steady state conditions may occur in the power system. Transients can be broadly classified as electromagnetic transients or electromechanical transients. The former is caused by faults and other switching operations, while the latter is caused by dynamic motion of the rotor moving electrical system of the generator and the electrical machine. Phasor measurements under transient conditions are directly relevant for many phasor measurement applications.
An electromagnetic transient phase-to-phase quantity conversion algorithm: these electromagnetic transients are typically very short-lived and do not affect the speed of the generator rotor. When the phasor measurement data window contains parts before and after the jammer signal, the measured phasor is ambiguous. Consider an ideal electromagnetic transient in which the input signal has a step that changes due to some switching operation. When the data window is completely in the early or late measurement phase, the synchrophasors reflect the two states of the system. However, when the data window crosses the transient, the phasor measurement is a combination of states that is not well defined. Phasors calculated at electromagnetic transients often show a step change in phase angle. This sudden change in phase angle may cause the frequency estimate to exhibit a sharp peak when estimating the rate of change of the system frequency. This is indeed frequency observed from the waveform, but may not reflect any change in the machine rotor speed. This phenomenon needs to be noticed when frequency estimates are used.
Phase-to-phase conversion study under electromechanical transient: when synchronous phasor measurement under electromechanical transient state is considered, a signal model under system transient state can be better understood: x is the number of(t)＝X(t)*cos(θ(t)) Where the cosine function acts as a modulator of the input. X(t)Is an "amplitude" signal, and θ(t)Are "phase" signals, are embedded in the input. One particular solution of the formula under steady state conditions is when θ(t)＝2*π*f0*t+θ0Time x(t)＝X0. During an electromechanical transient, the amplitude and phase angle changes slowly relative to the nominal frequency of the system. The phasor magnitude and the rotational speed are almost constant within a short viewing window. Since the rotor may deviate from 0.1-5Hz in modern power systems, the phase angle behavior of the synchronous speed in the phasor estimation window is substantially linear.
The synchronous phasor measurement system can directly measure the amplitude and phase of voltage and current, and phase information in the traditional state estimation algorithm is obtained through nonlinear state estimation according to information such as node voltage, node injection power and branch power. If the state estimation is performed using only the metric information of the synchronized phasor measurement system, the state of the belt estimation system and the input measurement amount become a linear relationship, and the corresponding state estimator is called a "linear state estimator".
Linear state estimation has significant advantages over traditional SCADA-based state estimation. Time-synchronized fast sample rate data streams (typically 10-50 samples per second) allow faster state estimation and observability of system dynamics. Conventional state estimation is not able to quickly solve for the true dynamics of the power system. Furthermore, the linearity of the state estimation of the PMU alone almost always ensures that the system has a solution. Conventional state estimation may sometimes diverge due to its non-linear nature.
Secondary model for synchrophasor data prediction, filtering and smoothing
The voltage phasor of any node in the power system follows a quadratic trajectory in the complex plane for varying the load with a constant power factor. Determining the inverse of a certain vandermonde matrix of second order polynomials results in a set of coefficients that can be inserted (or predicted) into the next data point from the three previous data points. This prediction polynomial is quadratic in nature and can be used to predict future complex voltages of previous complex voltages in the power system. The quadratic prediction model may be used in conjunction with filtering and smoothing algorithms to improve the data quality of the raw synchrophasor data. These algorithms can fill in missing data, reduce gross errors in measurements, and provide optimal values for data points. The quadratic model algorithm may be used for pre-processing of the synchrophasor data before the linear state estimator receives it.
The filtered and smoothed data output from the quadratic module is sent to the linear state estimation module for actual state estimation. The linear state estimation problem can be considered as solving the system state, considering each new measurement set as a new problem relative to the previous measurement set.
The substation three-phase line state estimation comprises a current state estimation and a zero-impedance voltage state estimation. The former uses Kirchhoff's Current Law (KCL) to validate the breaker current, while the latter is actually a weighted average of the voltage phasor measurements.
And (3) current state estimation: in current linear state estimators, we redefine the state as the breaker current. In this model, all nodes and breakers of the same voltage class within the substation constitute a zero impedance power system. The measurement equation is established by applying KCL, assuming that each breaker and branch current phasor measurement is available. For each branch current, if it is a half breaker mode, it is a function of the two breaker currents. This is a function relative to itself for each breaker current. The finally estimated breaker current can be directly used to verify the open and close state of the corresponding breaker to correct possible topology errors. This would be an iterative process. After correcting the bad breaker state, a new correlation matrix is determined in the new topology. In addition, the bad data detection and identification module can collect any abnormal condition as bad data and output and process the corresponding equipment ID. Another major benefit is that the current state estimate is used to determine the breaker state, so that topological errors of the substation can be avoided, thereby facilitating accurate calculation of the system level state estimate.
Zero impedance voltage state estimation: after obtaining the estimated on-off state of each breaker in the substation, it becomes simple and easy to construct a topology for each voltage class. The output will contain the number of busbars for this voltage class as well as all connected elements and branches. We can then estimate the bus voltage from the voltage measurements of all the nodes that make up this bus. All nodes have the same voltage if they are connected by a zero impedance device such as a breaker/switch. This is essentially a weighted average, represented here as a zero impedance voltage state estimate. These states are the voltage of each bus, and the measurements are all voltage phasor measurements at the nodes belonging to the bus. The measurement function equation is shown below: Z-Hx + r, where H is a unit vector, the solution is a weighted average of all voltage measurements. A three-phase voltage state estimation solution is solved similarly. The solution of the weighted least squares state estimate can be easily obtained. Similarly, the bad data detection and identification module will capture any abnormal condition as bad data and output the result and corresponding device ID to the post-processing module.
The checking method of the breaker switch state comprises the following steps:
breaker switch states and measurements typically follow different data flows. Since the breaker state determines the topology of the system, it is necessary to update the model to match the system in order to make a better estimate. Maintaining a model that is synchronized with the data being used becomes a problem, especially for real-time estimation.
Real-time system topologies can cause system disturbances due to planned outages, switching events, scheduling response load changes, and protection and relay actions. If the breaker status is not reported via phasor data, it may be obtained via SCADA using a communication network and protocol such as ICCP. However, since the time interval for ICCP updates may be once every 4 seconds, the system topology may have changed before a new ICCP update is received. This situation makes it possible that the linear state estimation will verify and adjust the PMU data with outdated error models, which may lead to differences in the calculated results.
In addition, in real-time operation, the switch state of the circuit breaker may be misinformed. Therefore, the current state estimation algorithm cannot blindly utilize the breaker switch state, the received breaker switch state needs to be verified before state estimation, and the work can be completed by using the current values of all measurement data to verify the topological structure of the transformer substation.
To address this challenge, the system design implements a solution that addresses the identification of real-time topology changes due to time offsets of breaker state updates. The linear state estimation will use the synchrophasor measurement information to check and verify the system topology. If a topology error is identified from the PMU data, the linear state estimation will use this information to directly modify the breaker state inside the state estimation engine to update the topology before the next ICCP update.
In this case, it is necessary to ensure that the PMU data for updating the topology in real time needs to be very reliable. Thus, in a model-less data quality check module, a topology check based on current measurements may be defined. For example, by Kirchhoff's Current Law (KCL), the sum of the currents injected into the nodes (here the busbars) should be 0, as shown in fig. 8 a. Similarly, the current should be similar across the same line, as shown in FIG. 8 b. If the PMU data passes these checks, the corresponding data will be marked as good and reliable so that the downstream linear state estimation updates the topology with real-time information.
The internal mapping of the circuit breakers and related lines is in the attributes of the substation linear state estimation model. In each PMU updated data frame, the breaker open or closed state will be confirmed by the associated line current (if available). The system will set a near zero tolerance (default 10A) for each transmission line current magnitude. As long as the line current with good data quality meets the specified standards, the linear state estimation can internally process the corresponding line topology analysis, thereby updating the corresponding breaker state to an open or closed state. The thus updated circuit breaker state changes will remain for subsequent PMU data frames until a next circuit breaker state update is received via the ICCP protocol, and then circuit breaker state verification is performed again and its on-off state is updated or overwritten accordingly.
The detection algorithm for the bad data of the state estimation of the transformer substation is as follows:
a very important function of state estimation is to detect and eliminate bad measurement quantities. In this substation linear body estimation, the commonly used chi-square test method is used to detect bad measurements. Once bad data is detected, a maximum normalized residual (LNR) method is applied to identify the most likely bad measurement quantity. If continuous bad measurements are detected from a particular device, it may indicate that a health problem or malfunction exists with the device. Through post processing, can send the warning, the maintenance personal can inspect equipment. In other cases, the characteristics of the anomaly data may be subtle and exhibit a currently unknown size and pattern, and therefore have an uncertain threshold. Therefore, signal processing and anomaly detection should be carefully performed to capture the presently unknown anomaly.
The substation model is stored in XML format and defined by CIM model. CIM models are widely used by the power industry for configuration of energy management system network models and model exchange between power offices. For the linear state estimation of the transformer substation, the required model information from the XML file of the CIM network model is extracted and stored in a local database for retrieval and linkage, and the system needs to access and convert the transformer substation model and partially convert the model with considerable data to build a three-phase state estimation model. In addition, the three-phase state estimation of the transformer substation needs to centralize all phases in a linear state estimation problem, a unified array is used for calculation, and the calculation amount is large. When the topological structure of the substation changes, a new array of state estimation needs to be formed again and calculated. Particularly, when the split bus condition occurs in the transformer substation, an algorithm is required to support parallel computation, and the state estimation of each independent subsystem is computed at the same time.
Based on the same invention concept, the invention also provides a comprehensive evaluation system for the quality of the sampled data of the intelligent substation, which comprises the following steps:
the data acquisition module is used for acquiring merging unit data, measurement and control device data and PMU data in the transformer substation to form multi-source redundant three-phase measurement in the transformer substation;
the state estimation module is used for performing three-phase line state estimation of the transformer substation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm;
and the bad data identification module is used for carrying out comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using the maximum normalized residual error LNR method to obtain a data quality comprehensive analysis result.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (10)

1. The comprehensive evaluation method for the quality of the sampled data of the intelligent substation is characterized by comprising the following steps:
acquiring merging unit data, measurement and control device data and PMU data in a transformer substation to form multi-source redundant three-phase measurement under unified time scale in the transformer substation;
the method comprises the steps of performing three-phase line state estimation of a transformer substation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm;
and carrying out comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using a maximum normalized residual error LNR method to obtain a data quality comprehensive analysis result.
2. The intelligent substation sampling data quality comprehensive evaluation method of claim 1, wherein the obtaining of merging unit data in a substation comprises:
and reading PT/CT output data digitally sampled by the merging unit, converting the PT/CT output data into synchronous phasor data on the same time scale through a phasor conversion algorithm to obtain corresponding voltage and current phasors, wherein the merging unit receives clock synchronization and outputs sampling pulses accurately synchronous with the clock synchronization pulses to realize synchronous sampling of the data of the whole station.
3. The intelligent substation sampling data quality comprehensive evaluation method according to claim 2, characterized in that a filter is adopted to filter high-frequency harmonics of the system during phasor conversion.
4. The intelligent substation sampling data quality comprehensive assessment method according to claim 3, characterized in that under system transient conditions, the phasor conversion algorithm adopts a reasonable optimization time window and guarantees data processing speed, thereby guaranteeing that delay does not affect the accuracy of phasor data.
5. The intelligent substation sampling data quality comprehensive evaluation method of claim 1, wherein before the linear state estimation, the quality of the data stream is checked according to the following five aspects: communication and message format checking, time and timing validation, PMU data state validation, data feature validation, topology-based error detection;
if the data is declared "bad" in any respect, no further checking is required, and if the data is declared "good" or "uncertain", the subsequent data checking continues.
6. The intelligent substation sampling data quality comprehensive evaluation method of claim 1, wherein the substation model is in an XML format and is defined by a CIM model.
7. The intelligent substation sampling data quality comprehensive assessment method of claim 1, wherein the substation three-phase linear state estimation comprises a current state estimation that uses kirchhoff's current law to validate circuit breaker currents and a zero-impedance voltage state estimation that is a weighted average of voltage phasor measurements.
8. The intelligent substation sampling data quality comprehensive assessment method according to claim 1, characterized in that the received breaker switch states are verified before state estimation, and substation topology verification is performed by using the current values of all measurement data to maintain a substation model synchronized with the data being used.
9. The intelligent substation sampled data quality comprehensive assessment method of claim 8, wherein the linear state estimation uses synchrophasor measurement information to check and verify system topology, and if a topology error is identified from the PMU data, this information will be used to modify breaker state to update topology before the next ICCP update.
10. The utility model provides an intelligent substation sampling data quality comprehensive evaluation system which characterized in that includes:
the data acquisition module is used for acquiring merging unit data, measurement and control device data and PMU data in the transformer substation to form multi-source redundant three-phase measurement in the transformer substation;
the state estimation module is used for performing three-phase line state estimation of the transformer substation by adopting multi-source redundant three-phase measurement in the transformer substation and real-time switch states of a transformer substation model and a breaker, and solving a state estimation problem by using a weighted least square algorithm;
and the bad data identification module is used for carrying out comprehensive comparison according to the state estimation result, judging whether bad data exist in the measured quantity by using Chi-square test, and identifying the most possible bad measured quantity by using the maximum normalized residual error LNR method to obtain a data quality comprehensive analysis result.
CN202111059818.7A 2021-09-10 2021-09-10 Intelligent substation sampling data quality comprehensive evaluation method and system Pending CN114172262A (en)

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