JP3442658B2 - Power system out-of-step prediction method - Google Patents
Power system out-of-step prediction methodInfo
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- JP3442658B2 JP3442658B2 JP19287498A JP19287498A JP3442658B2 JP 3442658 B2 JP3442658 B2 JP 3442658B2 JP 19287498 A JP19287498 A JP 19287498A JP 19287498 A JP19287498 A JP 19287498A JP 3442658 B2 JP3442658 B2 JP 3442658B2
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- power system
- series data
- time series
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Description
【０００１】[0001]
【発明の属する技術分野】本発明は、電力系統の脱調に
よる広域事故波及を防止するための脱調予測方式に関
し、特に、中間領域における電力系統の脱調予測方式の
改良に関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a step-out prediction method for preventing a wide area accident spread due to a step-out in a power system, and more particularly to improvement of a step-out prediction method for a power system in an intermediate region.
【０００２】[0002]
【従来の技術】近年の電力系統では、電源と負荷の遠隔
化，系統構成の複雑化，重潮流化が進み、長距離送電線
が多数出現している。このような大規模・複雑化した基
幹系統では、ルート断などの稀頻度の重大事故が発生す
ると、擾乱による電力系統動揺が事故除去後にも継続
し、やがて脱調に至る中間領域での安定度が問題となっ
てきている。この中間領域での安定度は数秒から数十秒
に及ぶ電力系統の動揺であり、電力系統の大規模・複雑
化にしたがってこの傾向がさらに顕在化し、電力系統動
揺後の脱調に至る過程が複雑化していくと予想される。
このような状況下において、系統擾乱発生による電力動
揺後の脱調を、如何に早く確実に予測・検出して、広域
事故波及を防止するかは極めて重要な課題である。2. Description of the Related Art In recent power systems, many remote power transmission lines have appeared due to remote power sources and loads, complicated system configuration, and heavy power flow. In such a large-scale and complicated backbone system, when a rare accident such as a route breakage occurs, the power system sway due to disturbances continues even after the accident is removed, and the stability in the intermediate region that eventually leads to step out Is becoming a problem. The stability in this intermediate region is the fluctuation of the power system that lasts from several seconds to several tens of seconds, and this tendency becomes more apparent as the power system becomes larger and more complex, and the process leading to the step out after the power system fluctuation occurs. It is expected to become more complicated.
Under such circumstances, how quickly and reliably predict / detect a step-out after power fluctuation due to occurrence of system disturbance to prevent widespread accident spread is a very important issue.
【０００３】従来の中間領域における安定度対策用の脱
調予測方式には、例えば、 (a)電力振幅方式と (b)リア
ルタイム予測演算方式がある。前者の (a)電力振幅方式
は、系統擾乱による送電線の電力動揺波形をとらえ、各
振動波形の振幅を求めて、これらの各振動波形の正方向
および負方向の振幅がそれぞれ３波以上連続して増加し
たとき、系統が脱調傾向にあると判定して脱調を予測す
る方式である。一方、後者の (b)リアルタイム予測演算
方式は、電力動揺を、各系統を代表する電気所の母線電
圧位相角差をリアルタイムに求め、この位相角差の変化
を正弦波などで模擬した予測演算により将来の位相角を
予測し、予め、多数の系統構成や系統運用に応じて整定
した位相角の閾値と比較し、閾値を超えたら脱調と判定
する方式である。[0003] Conventional out-of-step prediction methods for stability measures in the intermediate region include, for example, (a) power amplitude method and (b) real-time prediction calculation method. In the former (a) power amplitude method, the power fluctuation waveform of the transmission line due to system disturbance is captured, the amplitude of each vibration waveform is obtained, and the positive and negative amplitudes of each of these vibration waveforms are three or more consecutive waves. Then, it is a method of predicting out-of-step by determining that the system has a step-out tendency when it increases. On the other hand, the latter (b) real-time predictive calculation method calculates the power fluctuation in real time by calculating the bus voltage phase angle difference of the electrical station representing each system, and simulating the change in this phase angle difference with a sine wave. Is a method of predicting a future phase angle, comparing it with a threshold value of the phase angle set beforehand according to a large number of system configurations and system operations, and determining a step out if the threshold value is exceeded.
【０００４】[0004]
【発明が解決しようとする課題】しかし、上記従来の脱
調予測方式の (a)電力振幅方式は、送電線の電力動揺が
複雑な振動モードを有するため確実な予測が難しく、し
かも、予測判定が遅いという問題がある。また、後者の
(b)リアルタイム予測演算方式は、閾値の整定に系統構
成や系統運用を考慮するため、控えめな閾値による過剰
制御の可能性がある。その上、電力系統の構成・運用が
変更されると、想定事故条件，すなわち脱調予測条件の
見直しを行う必要があり、その都度、閾値を整定し直さ
なければならない、という欠点がある。However, in the above-mentioned conventional step-out prediction method (a), the power amplitude method, it is difficult to make a reliable prediction because the power fluctuation of the transmission line has a complicated vibration mode. There is a problem that is slow. Also, the latter
(b) Since the real-time predictive calculation method considers the system configuration and system operation in the settling of the threshold value, there is a possibility of excessive control with a conservative threshold value. In addition, if the configuration and operation of the power system is changed, it is necessary to review the assumed accident condition, that is, the out-of-step prediction condition, and the threshold value must be reset each time.
【０００５】本発明は、上記従来の問題点，欠点を解消
するために行ったものであり、電力系統の構成・運用が
変更されても脱調を判定するための閾値を設定し直さな
くてもよく、すなわち、電力系統の構成・運用の変化に
依存しない方式で、かつ、複雑な系統の振動発散現象
を、短時間で予測することのできる電力系統の脱調予測
方式であり、系統擾乱発生による電力動揺後の脱調を、
早く確実に予測・検出して、事故波及を防止することを
目的とするものである。The present invention was carried out in order to solve the above-mentioned conventional problems and drawbacks, and it is not necessary to reset the threshold value for judging out-of-step even if the configuration and operation of the power system are changed. In other words, it is a method that does not depend on changes in the configuration and operation of the power system, and is a power system out-of-step prediction method that can predict complex system vibration divergence phenomena in a short time. Step out after power fluctuation due to generation,
The purpose is to predict and detect quickly and surely to prevent accident spread.
【０００６】[0006]
【課題を解決するための手段】本発明の電力系統の脱調
予測方式は、時系列モデルの定常性の条件を脱調予測に
応用することにより目的を達成するものであり、時系列
モデルとして線形自己回帰モデル（以下、ＡＲモデルと
略称する）を用い、時系列モデル作成用の入力用データ
として電力系統の脱調時に変化する各発電機内部位相角
を用いた。これにより、時系列モデルの定常性の条件に
基づいて時系列データが発散傾向（脱調方向）にあるか
収束傾向（安定方向）にあるかを判定して脱調を予測す
るようにしたことを特徴とするものである。A step-out prediction method for a power system according to the present invention achieves the object by applying the condition of stationarity of a time-series model to step-out prediction. A linear autoregressive model (hereinafter abbreviated as AR model) was used, and the internal phase angle of each generator that changed when the power system was out of step was used as input data for creating the time series model. By doing so, it is possible to predict the out-of-step by determining whether the time-series data has a diverging tendency (step-out direction) or a convergence tendency (stable direction) based on the stationarity condition of the time-series model. It is characterized by.
【０００７】[0007]
【発明の実施の形態】以下に、本発明の具体例について
図１により説明する。図１は本発明の脱調予測方式の流
れ図である。図において、１〜６はステップ番号であ
る。まず、事故の発生を検出（ステップ１）してから、
単純または多機系統の各発電機内部位相角の時系列デー
タの脱調予測演算部への取り込みを開始（ステップ２）
する。次に、時系列データを用いてＡＲモデルの２つの
パラメータ（ａ1,ａ 2 ）を、最小二乗法により推定（ス
テップ３）する。さらに、推定された２つのパラメータ
（ａ1,ａ2 ）により構成される特性方程式から特性根
（解）を求め（ステップ４）、事故発生の１周期後から
予測を開始する（ステップ５）。その特性根（ベクト
ル）の大きさが１未満ならば安定と判定し、１以上なら
ば脱調（不安定）と判定（ステップ４）する。BEST MODE FOR CARRYING OUT THE INVENTION Specific examples of the present invention will be described below.
This will be described with reference to FIG. FIG. 1 shows the flow of the step-out prediction method of the present invention.
It is a figure. In the figure, 1 to 6 are step numbers
It First, after detecting the occurrence of an accident (step 1),
The time series data of the internal phase angle of each generator of simple or multi-machine system
Start loading data into the out-of-step prediction calculator (step 2)
To do. Next, using time series data, two AR models
Parameter (a1, a 2) Is estimated by the method of least squares (S
Step 3) In addition, the estimated two parameters
(A1, a2) To the characteristic root
(Solution) is found (Step 4), and one cycle after the accident
The prediction is started (step 5). Its characteristic root
If the size of R) is less than 1, it is judged to be stable, and if it is 1 or more,
If it is out of sync (unstable), it is determined (step 4).
【０００８】以下、本発明についてさらに詳しく説明す
る。時間を通して順次的に発生した観測値の集合である
時系列データは、定常性と呼ばれる概念に基づいて、定
常なデータと非定常なデータとに大別され、ある値に収
束する時系列データは定常なデータであり、持続振動す
るデータ、あるいは、発散する時系列データは非定常な
データである。発電機の動揺は振動的に振る舞うことが
多く、多機系統においては多数の振動モードを有するた
め複雑な挙動を示す。発電機が安定ならば、動揺が減衰
し、ある値に収斂していくが、不安定ならば、単調発散
するか振動発散する。そこで、発電機の動揺現象を定常
性の概念に当てはめ、定常性の条件を脱調予測条件に応
用し、時系列データとして電力系統の脱調時に変化する
発電機内部位相角を用い、単純および多機系統の各発電
機の内部位相角をリアルタイムに監視し、取得された時
系列データが非定常なデータであれば不安定（脱調）と
判定するようにしたものである。The present invention will be described in more detail below. Time-series data, which is a set of observation values that occur sequentially over time, is roughly classified into stationary data and non-stationary data based on the concept called stationarity, and time-series data that converges to a certain value is The data is stationary, and the data that continuously oscillates or the time-series data that diverges is non-stationary data. The sway of a generator often behaves in an oscillatory manner, and in a multi-machine system, it has a number of vibration modes and therefore exhibits complicated behavior. If the generator is stable, the vibration will be attenuated and will converge to a certain value, but if it is unstable, it will diverge monotonically or oscillate. Therefore, we applied the sway phenomenon of the generator to the concept of stationarity, applied the condition of stationarity to the out-of-step prediction condition, and used the internal phase angle of the generator that changes during step-out of the power system as time-series data. The internal phase angle of each generator in the multi-machine system is monitored in real time, and if the acquired time series data is unsteady data, it is determined to be unstable (step out).
【０００９】時系列データから安定／不安定を判定する
ための算出モデルとして、本発明では、自己回帰（Ａ
Ｒ：Ａｕｔｏ Ｒｅｇｒｅｓｓｉｖｅ）モデルを用い
る。ＡＲモデルでは、最小二乗法の評価関数がパラメー
タベクトルに関して線形であるため、線形最小二乗法に
基づいて容易に求めることができる。一方、ＭＡ（移動
平均）モデル、ＡＲＭＡ（自己回帰移動平均）モデル、
ＡＲＩＭＡ（自己回帰和分移動平均）モデルなどの他の
モデルは、最小二乗法の評価関数がパラメータベクトル
に関して線形でないため、非線形最小二乗法によって求
めなければならない。これは、収束計算によって解くた
めに計算時間がかかる。本発明の脱調予測ではオンライ
ンでパラメータを求めることから、計算時間の短いＡＲ
モデルが適している。ＡＲモデルは次の（１）式で示さ
れる。In the present invention, as a calculation model for determining stable / unstable from time series data, autoregression (A
R: Auto Regressive (R) model is used. In the AR model, since the evaluation function of the least squares method is linear with respect to the parameter vector, it can be easily obtained based on the linear least squares method. On the other hand, MA (moving average) model, ARMA (autoregressive moving average) model,
Other models, such as the ARIMA (autoregressive integrated moving average) model, must be determined by the nonlinear least squares method because the least squares evaluation function is not linear with respect to the parameter vector. This is computationally time consuming to solve by a convergent calculation. In the step-out prediction of the present invention, since the parameters are calculated online, the AR having a short calculation time is used.
The model is suitable. The AR model is expressed by the following equation (1).
【００１０】[0010]
【数１】 ｙ(k) ＋a1ｙ(k−1)＋……＋ an ｙ(k− na ) ＝ｗ(k) ……（１） ただし、ｙ(k) ：観測値の時系列データ ｗ(k) ：白色雑音の時系列データ ａi ：ＡＲモデルのパラメータ ｎa ：ＡＲモデルの次数[Formula 1] y (k) + a 1 y (k−1) + …… ＋ a n y (k−n a ) = w (k) …… (1) where y (k) is the observed value Series data w (k): White noise time series data a i : AR model parameters n a : AR model order
【００１１】モデルの次数としては、計算効率を考える
となるべく低いことが望ましい。しかし、１次のＡＲモ
デルでは指数関数的に発散・収束する動きは表現できる
が、振動的な発散・収束は表現できない。中間領域にお
ける発電機の動揺は、振動的な発散と減衰であるため、
振動を捕らえることのできる最低の次数として２次とな
る。従って、上記の（１）式のｎa ＝２となり、観測値
の時系列データとして発電機内部位相角を用いると、次
の（２）式で示される。It is desirable that the order of the model is as low as possible in consideration of calculation efficiency. However, although the first-order AR model can express exponentially divergent / convergent motions, it cannot express oscillatory divergent / convergent motions. The sway of the generator in the middle region is oscillatory divergence and damping, so
The second order is the lowest order that can capture vibration. Therefore, n a = 2 in the above equation (1), and using the generator internal phase angle as the time series data of the observed value is represented by the following equation (2).
【００１２】[0012]
【数２】 ｙ(k) ＋a1ｙ(k−1)＋a2ｙ(k−2)＝ｗ(k) ……（２） ただし、ｙ(k) ：発電機内部位相角の時系列データ ｗ(k) ：白色雑音の時系列データ[Formula 2] y (k) + a 1 y (k−1) + a 2 y (k−2) = w (k) (2) where y (k) is the time series data of the generator internal phase angle. w (k): White noise time series data
【００１３】ステップ２のデータ読み込みの場合、定常
な時系列データからＡＲパラメータを推定するには、デ
ータ数が多いほど雑音の影響を低減化できるので望まし
いが、例えば、事故発生直後の発電機動揺は状態が大き
く変動するので、過去のデータとして参照すると現状の
時系列データの特徴を正確に表さない可能性がある。そ
こで、例えば、事故開始１秒前からのデータを用いてＡ
Ｒパラメータを推定する。In the case of reading the data in step 2, it is desirable to estimate the AR parameter from the steady time-series data, because the larger the number of data is, the more the influence of noise can be reduced. Since the state changes greatly, there is a possibility that the characteristics of the current time-series data may not be accurately represented when referred to as past data. So, for example, using data from 1 second before the accident
Estimate the R parameter.
【００１４】ステップ３では、上記（２）式によって２
つのパラメータ（ａ1 ，ａ2 ）を、最小二乗法によって
推定する。パラメータの推定を行う場合、サンプリング
タイムは、短ければ短いほどよいわけではない。一般
に、上界はサンプリング定理によって規定され、下界は
計算機の精度，同定対象の着目する周波数帯域との関係
などから決定される。発電機の動揺周期は、１秒弱程度
から３秒程度であるため、サンプリングの上界はサンプ
リング定理より０．５秒弱となる。そこで、波形を再現
できる０．１秒のサンプリングタイムを用いた。In step 3, 2 is obtained by the above equation (2).
One parameter (a 1 , a 2 ) is estimated by the method of least squares. When estimating parameters, the shorter the sampling time, the better. In general, the upper bound is defined by the sampling theorem, and the lower bound is determined by the accuracy of the computer, the relationship with the frequency band of interest of the identification target, and the like. Since the oscillation period of the generator is about 1 second to about 3 seconds, the upper bound of sampling is less than 0.5 second according to the sampling theorem. Therefore, a sampling time of 0.1 second that can reproduce the waveform was used.
【００１５】ステップ４では、推定された２つのパラメ
ータ（ａ1,ａ2 ）により次に示す特性方程式を構成し、
この特性方程式の解（特性根）を算出する。In step 4, the characteristic equation shown below is constructed by the estimated two parameters (a 1 , a 2 ),
The solution (characteristic root) of this characteristic equation is calculated.
【００１６】[0016]
【数３】ｘ2 ＋a1ｘ＋a2＝０[Formula 3] x 2 + a 1 x + a 2 = 0
【００１７】この特性方程式によって求められた特性根
ｘによって系統の安定と不安定を予測するのであるが、
ステップ５では、事故発生直後と事故除去直後の過渡領
域は予測の対象から外し、事故発生後、動揺波形が事故
発生直前の内部位相角と同じ位相角を通過した回数が２
回に達したところ、すなわち、事故発生後の動揺波形の
１周期経過後から、求められた特性根ｘによって予測を
開始する。ステップ６では、特性方程式によって求めら
れた根ｘの絶対値｜ｘ｜が１未満ならば安定と判定し、
１以上ならば不安定すなわち脱調と判定する。例えば、
０．１秒刻みの判定を行い、フラグによって安定／不安
定を示す。The stability and instability of the system are predicted by the characteristic root x obtained by this characteristic equation.
In step 5, the transient region immediately after the accident occurs and immediately after the accident is eliminated is excluded from the target of prediction, and the number of times the shaking waveform has passed the same phase angle as the internal phase angle immediately before the accident occurs is 2 after the accident occurs.
When the number of times is reached, that is, after one cycle of the shaking waveform after the occurrence of the accident, the prediction is started with the obtained characteristic root x. In step 6, if the absolute value | x | of the root x obtained by the characteristic equation is less than 1, it is determined to be stable,
If it is 1 or more, it is judged to be unstable, that is, out of step. For example,
Judgment is made every 0.1 seconds, and flag indicates stable / unstable.
【００１８】図２，図３は本発明の適用例の説明図であ
り、図２は発電機４機系統の場合、図３は多機系統の例
として発電機１８機系統の適用例である。いずれも、発
電機内部位相角の動揺波形の２波目の立ち上がりで、発
電機の脱調の予測を開始し、判定フラグによって直ちに
脱調を判定することができる。2 and 3 are explanatory views of an application example of the present invention. FIG. 2 shows an application example of a four-generator system, and FIG. 3 shows an application example of an 18-generator system as an example of a multi-machine system. . In either case, the step-out prediction of the generator can be started at the second rising edge of the fluctuation waveform of the generator internal phase angle, and the step-out can be immediately determined by the determination flag.
【００１９】上記のように、本発明によれば、例えば、
４機系統あるいは１８機系統のいずれにおいても、同一
の脱調予測条件で脱調を予測することができるため、系
統構成に依存しない効果がある。また、発電機内部位相
角の動揺波形の１周期後の立ち上がりにおいて発電機の
脱調を予測することができるため、脱調を予測する時点
が早いという優れた効果がある。As described above, according to the present invention, for example,
In either the four-machine system or the eighteen-machine system, the out-of-step can be predicted under the same out-of-step prediction condition, so that there is an effect that does not depend on the system configuration. Further, it is possible to predict the out-of-step of the generator at the rising edge of the sway waveform of the generator internal phase angle after one cycle, and therefore there is an excellent effect that the out-of-step is predicted at an early time.
【００２０】上記の本発明は、以下の点でオンラインに
適している。
（１）ＡＲパラメータを２次元の行列計算で容易に推定
できる。
（２）特性方程式は２次方程式であるため、特性根を解
の公式により容易に求めることができる。
（３）線形最小二乗法を用いるため収束計算は不要であ
る。The above-described present invention is suitable for online in the following points. (1) The AR parameter can be easily estimated by a two-dimensional matrix calculation. (2) Since the characteristic equation is a quadratic equation, the characteristic root can be easily obtained by a solution formula. (3) Since the linear least squares method is used, the convergence calculation is unnecessary.
【００２１】[0021]
【発明の効果】以上説明したように、本発明を実施する
ことにより、単純系統または多機系統の系統構成の差ま
たは系統構成に変更があっても、同一の脱調予測条件で
脱調を予測することができるため、系統構成に依存しな
いという実用上極めて優れた効果がある。さらに、発電
機内部位相角の動揺波形の２波目の立ち上がりにおいて
発電機の脱調を予測することができるため、脱調を予測
する時点が早いという効果がある。As described above, by carrying out the present invention, even if there is a difference in the system configuration of the simple system or the multi-machine system or there is a change in the system configuration, it is possible to carry out the step-out under the same step-out prediction condition. Since it can be predicted, it has an extremely excellent practical effect of not depending on the system configuration. Further, the step-out of the generator can be predicted at the rising of the second wave of the fluctuation waveform of the generator internal phase angle, so that there is an effect that the step-out is predicted earlier.
【図１】本発明の脱調予測の流れ図である。FIG. 1 is a flow chart of step-out prediction according to the present invention.
【図２】本発明の適用例（４機系統）の説明図である。FIG. 2 is an explanatory diagram of an application example (four-machine system) of the present invention.
【図３】本発明の適用例（１８機系統）の説明図であ
る。FIG. 3 is an explanatory diagram of an application example (18 machine system) of the present invention.
【符号の説明】 １〜６ ステップ番号[Explanation of symbols] 1 to 6 step number
───────────────────────────────────────────────────── フロントページの続き (72)発明者 谷口 治人 東京都狛江市岩戸北２─11─１財団法人 電力中央研究所 狛江研究所内 (56)参考文献 特開 平３−212124（ＪＰ，Ａ) 特開 平８−126205（ＪＰ，Ａ) 特開 昭60−13439（ＪＰ，Ａ) 山下、井上、亀田、谷口，自己回帰モ デルによる脱調予測方式の提案，電力中 央研究所報告，日本，財団法人電力中央 研究所，1998年10月15日，Ｔ97068，ｐ. ｉ〜ｉｉｉ (58)調査した分野(Int.Cl.7，ＤＢ名) H02J 3/00 - 5/00 H02H 3/48 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Haruhito Taniguchi 2-11-1 Iwatokita, Komae City, Tokyo 11-11 Central Research Institute of Electric Power Industry, Komae Research Institute (56) Reference Japanese Patent Laid-Open No. 3-212124 (JP, A) ) JP-A-8-126205 (JP, A) JP-A-60-13439 (JP, A) Yamashita, Inoue, Kameda, Taniguchi, Proposal of out-of-step prediction method by autoregressive model, Central Research Institute of Electric Power Industry, Japan, Central Research Institute of Electric Power Industry, October 15, 1998, T97068, pi-iii (58) Fields investigated (Int. Cl. 7 , DB name) H02J 3/00-5/00 H02H 3 / 48
Claims (2)
式において、 電力系統内の各発電機内部位相角を時系列データとして
観測し、事故発生を検出したとき、当該時系列データを
自己回帰モデル作成用の入力データとしてパラメータを
求め、得られたパラメータにより構成した特性方程式か
ら特性根を求め、その特性根に時系列モデルの定常性の
条件を適用して前記時系列データが発散傾向か収束傾向
かを判定して脱調を予測するようにしたことを特徴とす
る電力系統の脱調予測方式。1. In a step-out prediction method for a power system in an intermediate region, each generator internal phase angle in the power system is observed as time series data, and when an accident is detected, the time series data is used as an autoregressive model. A parameter is obtained as input data for creation, a characteristic root is obtained from a characteristic equation composed of the obtained parameters, and the stationary condition of the time series model is applied to the characteristic root, and the time series data is divergent or convergent. A power system out-of-step prediction method characterized in that it is determined whether or not there is a tendency to predict out-of-step.
式において、 電力系統内の各発電機内部位相角を時系列データとして
観測し、事故発生を検出したとき、前記観測値の時系列
データの読み込みを開始する。次に、その時系列データ
を用いて２次の自己回帰モデルの２つのパラメータを最
小二乗法により推定する。さらに、推定された２つのパ
ラメータにより構成した特性方程式の特性根を求め、事
故発生の動揺波形の１周期後から予測判定を開始し、前
記特性根の絶対値が１未満ならば安定と判定し１以上な
らば脱調と判定するようにしたことを特徴とする電力系
統の脱調予測方式。2. In the step-out prediction method for the power system in the intermediate region, each generator internal phase angle in the power system is observed as time series data, and when an accident is detected, the time series data of the observed value is Start reading. Next, the two parameters of the quadratic autoregressive model are estimated by the least squares method using the time series data. Further, the characteristic root of the characteristic equation composed of the estimated two parameters is obtained, and the prediction judgment is started from one cycle of the shaking waveform of the accident occurrence. If the absolute value of the characteristic root is less than 1, it is judged to be stable. A power system out-of-step prediction method characterized in that if it is 1 or more, it is determined to be out of step.
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| JP19287498A JP3442658B2 (en) | 1998-07-08 | 1998-07-08 | Power system out-of-step prediction method |
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|---|---|---|---|
| JP19287498A JP3442658B2 (en) | 1998-07-08 | 1998-07-08 | Power system out-of-step prediction method |
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| JP3442658B2 true JP3442658B2 (en) | 2003-09-02 |
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| JP19287498A Expired - Fee Related JP3442658B2 (en) | 1998-07-08 | 1998-07-08 | Power system out-of-step prediction method |
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| JP4502321B2 (en) * | 2004-09-09 | 2010-07-14 | 財団法人電力中央研究所 | Power system step-out prevention control method, apparatus, and program |
| CN100445901C (en) * | 2007-01-25 | 2008-12-24 | 上海交通大学 | Dynamic cost control method for industrial process of procedure based on AR(p)model |
| CN101963643B (en) * | 2009-07-22 | 2013-04-10 | 南京南瑞继保电气有限公司 | Method for judging out-of-step oscillation of power system |
| CN103235199B (en) * | 2013-05-15 | 2015-05-13 | 武汉大学 | Out-of-step separation judgment method based on frequency difference of buses at two ends of branch |
| CN106300332B (en) * | 2015-06-09 | 2019-02-15 | 中国电力科学研究院 | A kind of off-the-line Measures program method of multigroup asynchronous oscillation mode |
| CN111864747A (en) * | 2020-08-13 | 2020-10-30 | 国网黑龙江省电力有限公司电力科学研究院 | Power grid oscillation splitting judgment method taking voltage as criterion |
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Non-Patent Citations (1)
| Title |
|---|
| 山下、井上、亀田、谷口，自己回帰モデルによる脱調予測方式の提案，電力中央研究所報告，日本，財団法人電力中央研究所，１９９８年１０月１５日，Ｔ９７０６８，ｐ．ｉ〜ｉｉｉ |
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