CN107065519B - PMU feedback control signal preprocessing method - Google Patents
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Abstract
A preprocessing method for PMU feedback control signals is designed for the problems that PMU signals have time lags in wide-area power system feedback control and the time lags can randomly change in a large range, a preprocessing module before the PMU feedback control signals enter a stability controller is designed, and the preprocessing method for the random time lag PMU feedback control signals based on the optimal time lag matching idea is provided. The method comprises the key steps of determination of the optimal time lag of the controller, reordering of random PMU signals, filtering, data interpolation and sampling, total time lag calculation, selection of optimal time lag feedback control signals and feedback control. The invention can ensure that various wide-area feedback stability controllers still have preset stability control capability when the time lag of the feedback control signal fluctuates greatly.
Description
The technical field is as follows:
the invention relates to a wide-area time-lag stability control method for a large-scale power system, and belongs to the technical field of power system stability control.
Background art:
with the interconnection of large-area power grids, the scale of a power system is continuously enlarged, and the stability problem is increasingly serious. How to ensure that a power system under the ultrahigh-voltage interconnection of a large-area power grid has stronger damping capacity and can keep stable operation under complex and severe disturbance becomes a control problem to be solved urgently for a wide-area smart power grid.
After the large-area power grids are interconnected, the generated disturbance or fault can spread to a plurality of area power grids, the distribution area is very wide, and the influence is also large. The traditional power system stability controller (PSS) adopting a local signal as a feedback signal is limited by the observability of the control signal, and has very limited effect on the aspect of inhibiting interval low-frequency oscillation. A Phasor Measurement Unit (PMU) based on a Global Positioning System (GPS) or a Beidou satellite navigation system (BDS) time service technology enables synchronous measurement of the running state of a power system to be no longer a difficult problem, and a Wide Area Measurement System (WAMS) of the power system based on the PMU is also being improved continuously at present. The utilization of PMU synchronous phasor data for wide area power system stable control has significant advantages compared with the traditional PSS control based on local signals, and clear conclusions are made in a large number of documents. However, the wide-area long-distance transmission of the PMU synchrophasor also causes a problem of time lag of the control signal, and if the influence caused by large fluctuation of the random time lag is not well handled, the stability controller does not work, and may even further deteriorate the stability of the power system.
PMU signals are subjected to time delay large-range random fluctuation, so that the signals sequentially sent by the PMU end are disordered when reaching the controller end; in addition, the controllers designed according to different principles often have a specific time lag value, namely an optimal time lag, when the controllers exert the optimal damping effect. If the time lag fluctuates randomly in a large range according to the traditional control signal preprocessing mode, the maximum stable control capability of the controller cannot be exerted. Therefore, a proper PMU feedback control signal preprocessing method must be designed for the time lag large fluctuation phenomenon faced in the wide-area stability control.
The invention content is as follows:
aiming at the problem of preprocessing PMU feedback control signals caused by time lag large-amplitude fluctuation in the wide area power system stability control, the invention designs a preprocessing method when PMU signals reach a stability controller end so as to ensure that the controller achieves a preset stability effect.
The PMU control signal preprocessing method comprises two stages, seven steps:
the controller design stage:
(1) and determining the optimal time lag of the controller. Adding a designed controller into a power system simulation analysis model, setting time lag of a feedback control signal, calculating a damping index corresponding to the dynamic characteristic after the power system is interfered at different times through simulation analysis, selecting the time lag corresponding to the maximum damping index as the optimal time lag of the controller, and recording the optimal time lag as taum。
(II) a real-time feedback control stage:
(2) random PMU signal reordering. When the random PMU signal reaches the controller, the signal is denoted as Y1, the corresponding time series of measurement is denoted as T1, the PMU signals are reordered according to the time stamp marked by each PMU signal, the ordered signal is denoted as Y2, and the corresponding time series is denoted as T2.
(3) And filtering PMU signals. And filtering high-frequency random noise mixed in the PMU signal Y2 by using a non-causal filter without time delay, wherein the denoised PMU signal is recorded as Y3, and the corresponding time sequence is recorded as T3.
(4) And interpolating and sampling PMU signal data. According to the requirement of the power system stability controller on the input of the feedback signal, interpolation sampling is carried out on the PMU signal Y3, the PMU signal sampling time is consistent with the controller operation frequency, the PMU signal after interpolation sampling is recorded as Y4, and the corresponding time sequence is recorded as T4.
(5) The total skew of the PMU feedback control signal is calculated. Reading real-time GPS time service data TG，TGThe total time lag for each sample point in the PMU signal Y4 is calculated by subtracting the time scale in the time series T4, which is denoted as Δ T.
(6) And selecting an optimal time-lag feedback control signal. Determining the optimal time lag tau in the time lag data sequence delta T and in the step (1)mClosest time lag point Δ TiSelecting and Δ TiCorresponding point Y4 in PMU signal sequence Y4iAs a current TGThe feedback control signal y at the instant.
(7) And (5) feedback control. And (4) inputting the optimal time lag control signal y screened in the step (6) into a power system stability controller.
The invention has the advantages that: the method solves the technical problem of time-lag PMU signal preprocessing in time-lag stability control of a wide-area power system, innovatively provides a signal preprocessing method based on an optimal time-lag matching idea, and can be applied to a generator excitation device controller shown in FIG. 1 and other controllers of other regulating and controlling devices in the power system.
Description of the drawings:
FIG. 1 control architecture diagram of a wide area power system
FIG. 2 is a flow chart of a PMU control signal preprocessing method of the present invention
FIG. 3 is a diagram of a four-machine two-zone power system
FIG. 4 is a graph comparing the effect of stability control at different fixed time lags
FIG. 5 is a graph of random skew over time
FIG. 6 is a comparison of the effect of signal preprocessing on time-lapse PMU feedback control signals
FIG. 7 is a graph comparing the effect of pre-processing of time-lapse PMU feedback control signals on control
The specific implementation mode is as follows:
the technical solution of the present invention is further explained with reference to the accompanying drawings.
Fig. 1 is a diagram showing a control structure of a wide area power system. The power grid connects various power plants and power loads geographically separated by hundreds or even thousands of kilometers into a net shape through alternating current or direct current transmission lines and transformer substations. Therefore, the power energy network which is in great concern to the national civilization brings convenience and benefits to the overall complementary optimization of energy, and meanwhile, the swept area and the influence degree of any disturbance or fault can be enlarged. For long-term research and application popularization, a large number of controllable and adjustable devices have been adopted in an electric power system to enhance the anti-interference and stable operation capability of the system, and the control devices include: the device comprises a traditional generator excitation device, a valve adjusting device, a high-voltage direct-current transmission control device, a newly-developed flexible alternating-current transmission device and the like. The regulation and control effects of the regulation and control devices are not only closely related to the design of hardware technology and control strategy, but also greatly related to the support capability of a power system measurement data platform WAMS and the analysis technology of data. At present, PMUs are installed in power plants, 500kV and above substations and partial 220kV substations in power systems in China, and can accurately measure static and dynamic data of the power systems in real time, so that a solid data base is laid for system stability analysis and control. The PMU feedback control signal preprocessing method is applied to the power system regulation and control device end in FIG. 1, and is used as an intermediate module between the PMU signal transmitted by the WAMS and the regulation and control device stability controller, namely a PMU signal preprocessing module in FIG. 1, so that the normal work of the regulation and control device stability controller is guaranteed.
The flow chart of the PMU control signal preprocessing method of the invention is shown in FIG. 2, which mainly comprises two stages, seven steps:
the controller design stage:
(1) and determining the optimal time lag of the controller. Adding a designed controller into a power system simulation analysis model, setting time lag of a feedback control signal, calculating a damping index corresponding to the dynamic characteristic after the power system is interfered at different times through simulation analysis, selecting the time lag corresponding to the maximum damping index as the optimal time lag of the controller, and recording the optimal time lag as taum。
(II) a real-time feedback control stage:
(2) random PMU signal reordering. When the random PMU signal reaches the controller, the signal is denoted as Y1, the corresponding time series of measurement is denoted as T1, the PMU signals are reordered according to the time stamp marked by each PMU signal, the ordered signal is denoted as Y2, and the corresponding time series is denoted as T2.
(3) And filtering PMU signals. And filtering high-frequency random noise mixed in the PMU signal Y2 by using a non-causal filter without time delay, wherein the denoised PMU signal is recorded as Y3, and the corresponding time sequence is recorded as T3.
(4) And interpolating and sampling PMU signal data. According to the requirement of the power system stability controller on the input of the feedback signal, interpolation sampling is carried out on the PMU signal Y3, the PMU signal sampling time is consistent with the controller operation frequency, the PMU signal after interpolation sampling is recorded as Y4, and the corresponding time sequence is recorded as T4.
(5) The total skew of the PMU feedback control signal is calculated. Reading real-time GPS time service data TG，TGThe total time lag for each sample point in the PMU signal Y4 is calculated by subtracting the time scale in the time series T4, which is denoted as Δ T.
(6) And selecting an optimal time-lag feedback control signal. Determining the optimal time lag tau in the time lag data sequence delta T and in the step (1)mClosest toTime lag point Δ TiSelecting and Δ TiCorresponding point Y4 in PMU signal sequence Y4iAs a current TGThe feedback control signal y at the instant.
(7) And (5) feedback control. And (4) inputting the optimal time lag control signal y screened in the step (6) into a power system stability controller.
The four-machine two-zone power system shown in fig. 3 is a benchmark test system for studying zone low-frequency oscillation, the generators G1 and G2 are located in zone 1, the generators G3 and G4 are located in zone 2, and the two zones are interconnected by a long tie line. In the simulation, it is assumed that the output power of 4 generators in a normal operation state is 700MW, and since the load in the area 1 is light and the load in the area 2 is heavy, the double-circuit connecting line 7-9 needs to transmit about 300MW of active power from the area 1 to the area 2, and the node 8 is a long-distance transmission line intermediate substation. According to the small disturbance stability characteristic value analysis, the system has 3 low-frequency oscillation modes, the low-frequency power oscillation exists between G1 and G2 in the region 1 and G3 and G4 in the region 2, the oscillation frequency is 0.5370Hz, and the damping value is less than 0. Since the damping value is negative, when the system is disturbed, the relative power angle of the generator, the power of the tie line, etc. will oscillate sharply for a long time, as shown by the interval relative power angle curve in fig. 4 when "no controller" is provided.
Assuming that a stabilizing controller K is installed on an excitation control device of the generator G2, and system dynamic simulation analysis is carried out by setting different fixed time lags, as can be seen from FIG. 4, the stabilizing controller K has the best damping control effect when the time lag is 500ms, so the optimal time lag is set to be taum＝500ms。
Then, according to the communication delay normal distribution characteristic of the PMU signal under the WAMS actual operation condition, the mean value of the two-way signal transmission delay in the feedback control in the simulation test is 200ms, the standard deviation is 200ms, fig. 5 shows the situation that the random time lag changes with time, and it is assumed that the lower limit of the time lag is 50ms and the upper limit of the time lag is 1000 ms. In order to simulate the noise effect in PMU signals, Gaussian white noise is added to the measurement data of system dynamic simulation. Transmitting the simulated random time-lag PMU feedback control signal to an excitation control device end of a generator G2, and processing through the following steps:
(1) random PMU signal reordering. When the random PMU signal reaches the controller, the signal is denoted as Y1, the corresponding time series of measurement is denoted as T1, the PMU signals are reordered according to the time stamp marked by each PMU signal, the ordered signal is denoted as Y2, and the corresponding time series is denoted as T2.
(2) And filtering PMU signals. And filtering high-frequency random noise mixed in the PMU signal Y2 by using a non-causal filter without time delay, wherein the denoised PMU signal is recorded as Y3, and the corresponding time sequence is recorded as T3.
(3) And interpolating and sampling PMU signal data. According to the feedback signal input requirement of the power system stability controller, interpolation sampling is carried out on the PMU signal Y3, the PMU signal sampling time is consistent with the controller operation frequency, the PMU signal after interpolation sampling is recorded as Y4, and the corresponding time sequence is recorded as T4.
(4) The total skew of the PMU feedback control signal is calculated. Reading GPS time service data TG，TGThe total time lag for each sample point in the PMU signal Y4 is calculated by subtracting the time scale in the time series T4, which is denoted as Δ T.
(5) And selecting an optimal time-lag feedback control signal. Determining the optimal time lag tau in the time lag data sequence delta T and in the step (1)mClosest time lag point Δ TiSelecting and Δ TiCorresponding PMU signal sequence Y4 midpoint Y4iAs a current TGThe feedback control signal y at the instant.
And finally, continuously inputting the optimal time lag control signal screened out in real time into a stability controller in the excitation control device of the generator G2. After the PMU feedback control signal is preprocessed by the method, the stability controller can normally exert a damping effect (equivalent to the effect when the random time lag is 500ms in FIG. 4), even if the random time lag fluctuates randomly in a large range as shown in FIG. 5.
To further illustrate the importance of the method in the wide area power system stability control, it is compared with other 3 possible PMU signal selection strategies in this case, specifically as follows:
replacement policy 1-order of reception: feedback control is carried out according to the sequence of PMU signals reaching the stability controller without queuing again;
replacement policy 2-sending order: queuing the received disordered PMU signals again, and performing feedback control according to the sending sequence of the PMU end;
if the time lag is small or remains unchanged, the PMU feedback control signals selected by the 3 alternative strategies are basically consistent with the method of the invention. When the skew is large and fluctuates greatly, the PMU feedback control signals selected by different strategies may have a large difference, and the effect in this case is shown in fig. 6. As can be seen in fig. 6: the time lag of the feedback control signal under the strategy of time lag matching is closer to the optimal time lag by 500ms, the time lag under the strategy of replacing strategy 1, receiving sequence and the latest principle of replacing strategy 3 is closer to the lower time lag limit by 50ms, wherein the feedback control signal under the strategy of receiving sequence has more mutation, and the time lag under the strategy of replacing strategy 2, sending sequence is very close to the upper time lag limit by 1000 ms.
Fig. 7 is a dynamic response diagram of a relative power angle between the system sections corresponding to fig. 6, where, for the same stability controller, the damping effect difference under different PMU feedback control signal selection strategies is large: the damping effect under the strategy of 'time lag matching' is the best, the strategy of 'receiving sequence' and 'latest principle' is the second time, and the strategy of 'sending sequence' makes the system lose stability. Therefore, the PMU signal is utilized to perform feedback control in the random time-lag environment with large fluctuation, and the selection strategy of the feedback control signal in the random time-lag signal preprocessing link must be taken into account.
Claims (1)
1. A PMU feedback control signal preprocessing method includes the following steps:
(1) determining the optimal time lag of the controller; adding the designed controller into a simulation analysis model of the power system, setting time lag of feedback control signals, and performing simulation analysis when the time lag is differentCalculating the dynamic characteristic of the power system subjected to interference, calculating the damping index corresponding to the dynamic characteristic, selecting the time lag corresponding to the maximum damping index as the optimal time lag of the controller, and recording the time lag as taum；
(2) Reordering random PMU signals; when the random PMU signal reaches the controller, the signal is marked as Y1, the corresponding measurement time sequence is marked as T1, the PMU signals are reordered according to the time scale marked by each PMU signal, the ordered signal is marked as Y2, and the corresponding time sequence is marked as T2;
(3) filtering PMU signals; a non-causal filter without time delay is adopted to filter high-frequency random noise mixed in a PMU signal Y2, the denoised PMU signal is recorded as Y3, and a corresponding time sequence is recorded as T3;
(4) interpolating and sampling PMU signal data; according to the requirement of the power system stability controller on the input of the feedback signal, performing interpolation sampling on a PMU signal Y3 to ensure that the PMU signal sampling time is consistent with the action frequency of the controller, recording the PMU signal after interpolation sampling as Y4 and recording a corresponding time sequence as T4;
(5) calculating the total time lag of the PMU feedback control signal; reading real-time GPS time service data TG，TGSubtracting the time scales in the time sequence T4 to calculate the total time lag of each sampling point in the PMU signal Y4, and recording the time lag data sequence as delta T;
(6) selecting an optimal time lag feedback control signal; determining the optimal time lag tau in the time lag data sequence delta T and in the step (1)mClosest time lag point Δ TiSelecting and Δ TiCorresponding point Y4 in PMU signal sequence Y4iAs a current TGThe optimal time lag feedback control signal y of the moment;
(7) feedback control; and (4) inputting the optimal time lag feedback control signal y screened in the step (6) into a power system stability controller.
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