[0052] Hereinafter, the present invention will be further described in detail with reference to the drawings and examples.
[0053] figure 1 Step 1 describes that after the example classification process is started, a certain example is taken from the complete set of expected faults, and the SEEAC algorithm is applied to calculate the temporary stability margin of the example.
[0054] figure 1 Step 2 describes the identification rule 1 of the stable case. If the case is calculated by the SEEAC algorithm for the temporary stability margin, the stability margin η SE (τ) is greater than the threshold ε 1 (τ), and its fault clearing time τ is less than or equal to the threshold ε 2 , It is recognized as a stable calculation example, and step 14 is executed, otherwise, step 3 is executed.
[0055] figure 1 In step 3, a calculation method that reflects the degree of time variation of the research case is disclosed: apply the SEEAC algorithm to obtain the critical clear time of the case Use it to replace the fault clearing time τ of this example, apply SEEAC and DEEAC algorithms to calculate the temporary stability margin respectively, compare the differences of the three pairs of intermediate results obtained in the calculation process, and take the maximum value to reflect the time-varying degree of the example.
[0056] The mathematical expressions of the three pairs of intermediate results and their differences are:
[0057] Acceleration area calculated by SEEAC and DEEAC algorithm: Use formula (1) to characterize the difference between them;
[0058] Deceleration area calculated by SEEAC and DEEAC algorithm: Use formula (2) to characterize the difference between them;
[0059] Acceleration area and deceleration area calculated by DEEAC algorithm: Use formula (3) to characterize the difference between them;
[0060] Finally, formula (4) is used as an expression of the time-varying degree of the calculation example.
[0061] Δ A inc ( t c SE ) = | A inc DE ( t c SE ) - A inc SE ( t c SE ) | max { A inc SE ( t c SE ) , A inc DE ( t c SE ) } - - - ( 1 )
[0062] Δ A dec ( t c SE ) = | A dec DE ( t c SE ) - A dec SE ( t c SE ) | max { A dec SE ( t c SE ) , A dec DE ( t c SE ) } - - - ( 2 )
[0063] Δη ( t c SE ) = | A dec DE ( t c SE ) - A inc DE ( t c SE ) | max { A inc DE ( t c SE ) , A dec DE ( t c SE ) } - - - ( 3 )
[0064] σ 1 = max { Δ A inc ( t c SE ) , Δ A dec ( t c SE ) , Δη ( t c SE ) } - - - ( 4 )
[0065] It’s worth noting that with σ 1 When reflecting the time-varying degree of the study example, its value does not change with the change of the fault clearing time.
[0066] figure 1 Step 4 in step 4 describes the identification rule 2 of stable cases, if the stability margin η obtained by the SEEAC algorithm SE (τ) is greater than the threshold ε 3 (τ), and the time-varying degree σ obtained from step 3 1 Less than or equal to the threshold ε 4 , The calculation example is recognized as a stable calculation example, and step 14 is executed, otherwise, step 5 is executed.
[0067] figure 1 Step 5 in Step 5 discloses another calculation method that reflects the time-varying degree of the research case: apply the DEEAC algorithm to calculate the temporary stability margin of the case to obtain the stability margin η DE (τ), by comparing η SE (τ) and η DE The difference between (τ) reflects the time-varying degree of the calculation example, as shown by formula (5):
[0068] σ 2 ( τ ) = | η DE ( τ ) - η SE ( τ ) | max { | η SE ( τ ) | , | η DE ( τ ) | } - - - ( 5 )
[0069] It’s worth noting that with σ 2 When (τ) reflects the time-varying degree of the study case, its value will be affected by the fault clearing time.
[0070] figure 1 Step 6 in step 6 describes the identification rule 3 of stable cases, if the stability margin η obtained by DEEAC algorithm DE (τ) is greater than the threshold ε 5 (τ), and the time-varying degree σ obtained from step 5 2 (τ) is less than or equal to the threshold ε 6 , The calculation example is recognized as a stable calculation example, and step 14 is executed, otherwise, step 7 is executed.
[0071] It should be noted that steps 2, 4, and 6 are used to identify stable calculation examples. The above-mentioned main technical methods have been reflected in the patent application "Quick Screening Method for Predicted Fault Sets in Power System Transient Stability Assessment" (Publication No. 103336994A). It is the basis of the present invention.
[0072] figure 1 Step 7 in step 7 describes the identification rule 4 of stable cases, if the stability margin η obtained by the SEEAC algorithm SE (τ) is greater than the threshold ε 7 、The stability margin η obtained by DEEAC algorithm DE (τ) is greater than the threshold ε 8 , And the fault clearing time τ of this example is less than or equal to the threshold ε 9 , And the time-varying degree σ obtained from step 3 1 Less than or equal to the threshold ε 10 , The calculation example is recognized as a stable calculation example, and step 14 is executed, otherwise, step 8 is executed.
[0073] figure 1 Step 8 in step 8 describes the instability case identification rule 1. If the stability margin η obtained by the SEEAC algorithm SE (τ) is less than the threshold ε 11 , And the stability margin η obtained by DEEAC algorithm DE (τ) is less than the threshold ε 12 , The calculation example is recognized as an unstable calculation example, and step 14 is executed, otherwise, step 9 is executed.
[0074] figure 1 Step 9 in step 9 describes the instability case identification rule 2. If the stability margin η obtained by the SEEAC algorithm SE (τ) is less than the threshold ε 13 , And the stability margin η obtained by DEEAC algorithm DE (τ) is less than the threshold ε 14 (τ), and the time-varying degree σ obtained from step 3 1 Less than or equal to the threshold ε 15 , The calculation example is identified as an unstable calculation example, and step 14 is executed, otherwise, step 10 is executed.
[0075] figure 1 Step 10 describes the identification rule 3 of instability calculation examples, if the stability margin η obtained by the SEEAC algorithm SE (τ) is less than the threshold ε 16 And greater than the stability margin η obtained by the DEEAC algorithm DE (τ), meanwhile, the fault clearing time τ of this example is greater than or equal to the threshold ε 17 And the time-varying degree σ obtained from step 5 2 (τ) is less than or equal to the threshold ε 18 , The calculation example is recognized as an unstable calculation example, and step 14 is executed, otherwise, step 11 is executed.
[0076] figure 1 Step 11 in step 11 describes the identification rules for suspected stable cases, if the stability margin η obtained by the SEEAC algorithm SE (τ) is greater than the threshold ε 19 , And the stability margin η obtained by DEEAC algorithm DE (τ) is greater than the threshold ε 20 , And the fault clearing time τ of this example is less than or equal to the threshold ε 21 , The calculation example is identified as a suspected stable calculation example, and step 14 is executed, otherwise, step 12 is executed.
[0077] figure 1 Step 12 in step 12 describes the identification rules for suspected instability cases, if the stability margin η obtained by the SEEAC algorithm SE (τ) is less than the threshold ε 22 , And the stability margin η obtained by DEEAC algorithm DE (τ) is less than the threshold ε 23 , At the same time, the time-varying degree σ obtained from step 3 1 Less than or equal to the threshold ε 24 , The calculation example is identified as a suspected instability calculation example, and step 14 is executed, otherwise, step 13 is executed.
[0078] Ε in the above steps 2 , Ε 4 , Ε 6 ~ε 13 , Ε 15 ~ε 24 Is the static threshold, ε 1 (τ), ε 3 (τ), ε 5 (τ), ε 14 (τ) are dynamic thresholds. They are optimized based on a large number of typical calculation examples of different actual systems and based on reliability as the first principle. They are robust for different systems, different working conditions and different faults. They are robust in different systems, models and No change under failure. Their values are shown in Table 1:
[0079] Table 1 Threshold of each parameter
[0080]
[0081]
[0082] In Table 1, when τ≥1, ε 3 (τ), ε 5 (τ), ε 14 The value of (τ) is 0.923, 0.980, -0.940, respectively. It should be noted that in step 2, only calculation examples with τ≤0.26 may be filtered, so in ε 1 (τ) does not need to consider τ> in the expression 0.26 situation.
[0083] figure 1 Step 13 describes that after the above steps, the case has not been identified as stable, unstable, suspected to be stable, and suspected to be unstable. Then it is recognized as a critical case , And go to step 14.
[0084] figure 1 Step 14 describes that if all calculation examples in the complete set of faults are expected to be identified as the corresponding category, the calculation case classification ends, otherwise, step 1) is executed to take the next calculation example for processing.
[0085] As an embodiment of the present invention, Hainan (data in 2009), Shandong (data in 2004 and 2012, recorded as Shandong A and Shandong B respectively), Zhejiang (data in 2012 and 2013, recorded as Zhejiang A and Zhejiang B, respectively) ), Jiangxi (data in 2011), Henan (data in 2011), Xinjiang (data in 2012), and China Southern Power Grid (data in 2012). The complete set of faults (1652 examples in total), test the robustness and effectiveness of the method of the present invention.
[0086] figure 2 Shown are the actual stability margins (η IE (τ)) is arranged in ascending order, and it can be seen that it almost covers the entire [-1,1] interval, thus confirming the robustness and rationality of the selection in this embodiment.
[0087] After the transient stability severity of the calculation examples is classified by the method of the present invention, the distribution of the actual stability calculation examples and the actual instability calculation examples in the five categories is shown in Table 2:
[0088] Table 2
[0089]
[0090] It can be seen from Table 2 that in the total concentration of predicted failures, 92.05% (=87.99%+4.06%) of the actual stable calculation examples are identified as stable or suspected stable under the premise of ensuring high accuracy; 85.11% (=80.64%+4.47) %) actual instability cases are identified as instability or suspected instability under the premise of ensuring high accuracy; for stable and instability cases, the classification framework guarantees 100% recognition accuracy; for suspected cases For stable and suspected instability cases, the classification framework guarantees their 97.98 % ( = ( 1 - 28.45 % X 0.21 % 28.45 % X 0.21 % + 71.55 % X 4.06 % ) X 100 % ) , 95 . 69 % ( = ( 1 - 71.55 % X 0 . 08 % 71.55 % X 0 . 08 % + 28 . 45 % X 4 . 47 % ) X 100 % ) Recognition accuracy rate; in harsh online analysis situations, only all the examples are concentrated 9.81 % ( = 71.55 % X 7.87 % + 28.45 % X 14.68 % 100 % X 100 % ) The calculation examples need to apply the IEEAC algorithm for detailed analysis, while a large number of other examples only need to be analyzed by the SEEAC and DEEAC algorithms with extremely fast calculation rates.
[0091] In addition, among the 1652 examples tested by simulation, the actual stability margins (η IE The distribution of (τ)) is as follows:
[0092] 1040 examples are correctly identified as stable examples, and the η of these examples IE (τ) arranged in ascending order, such as image 3 (a), where η IE (τ)>0.90 cases accounted for 70.29%, η IE (τ)>0.75 cases accounted for 90.58%: most of the cases identified as stable, their actual stability margins are very high.
[0093] 49 cases were identified as suspected stable cases, only 1 case was misidentified, and its η IE (τ)=-0.08.
[0094] 162 examples were identified as critical examples, and the η of these examples IE (τ) arranged in ascending order, such as image 3 As shown in (b), 80.25% of the calculation examples have η IE (τ)∈(-0.50,0.50): Most of the examples identified as critical classes, their actual stability margin is around 0.
[0095] 22 cases were identified as suspected instability cases, and 1 case was misidentified, its η IE (τ)=0.59.
[0096] 379 examples are correctly identified as unstable examples, and the η of these examples IE (τ) arranged in ascending order, such as image 3 (c), where η IE (τ) IE (τ)
[0097] It can be seen that the designed classification framework achieves a reasonable classification of the severity of transient stability of the case.
[0098] Use formula (6) to define the acceleration effect of the case classification framework:
[0099]
[0100] In formula (6), t IEEAC The average time required for the complete IEEAC algorithm to process a calculation example after the initial calculation of the power flow, etc. is completed; t SORTING After the initial calculation of the characterizing power flow, etc., the average time required to obtain the classification information of a case is obtained by the case classification framework. When the observation time of a calculation example processed by the complete IEEAC algorithm is set to 1s, 5s, and 10s respectively, the acceleration effect of the above 1652 calculation examples is 9.56, 12.17, 14.01 in order.
[0101] In summary, the present invention realizes a reasonable classification of the transient stability severity of each case in the complete set of expected faults at a relatively small computational cost. According to the classification results, most cases can be reliably screened out, which greatly reduces the need to perform detailed transients. The expected number of failures (calculated amount) of stability analysis has great theoretical significance and engineering practical value for realizing the transient stability analysis of the power grid in the case of considering the uncertain factors and the online situation.
[0102] Although the present invention has been disclosed as above in preferred embodiments, the embodiments are not intended to limit the present invention. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the content defined by the claims of this application as the standard.
