A Probabilistic Approach to Small Signal Stability Analysis of Power Systems with Correlated Wind Sources

Yue, Hao; Li, Gengyin; Zhou, Ming

doi:10.5370/jeet.2013.8.6.1605

Cited by 13 publications

(18 citation statements)

References 27 publications

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“…This paper adopts the current mainstream wind turbine, the doubly fed induction generator (DFIG) [24][25][26], to study the impact of wind power integration on small signal stability. The 2MW wind turbine built in the software DIgSILENT/PowerFactory was used to do simulations.…”

Section: Model Of Dfig Wind Turbinementioning

confidence: 99%

Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary

Liu

et al. 2014

Sustainability

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Abstract:Up until now, study results on the impact of large-scale wind power integration on small signal stability have often been in conflict. Sometimes, the conclusions are even completely opposite, making people unable to agree on which is right. The reason behind this phenomenon is that most of these studies are based on a certain grid and typical working conditions, so conclusions are reached by comparing changes in oscillation mode, one by one. This study method lacks a broader perspective, and often reflects only a part of the grid conditions. However, the small signal stability region boundary describes the critical operating range of power system small signal stability as a whole, making possible an overall evaluation of the system from a more macro perspective. Thus it is more suitable for analysis of the impact of large-scale wind power integration on small signal stability. Based on the above, using the model of wind farm integration to the single-machine infinite bus power system, this paper studies the impact of wind power integration scale and the coupling strength with synchronous generator on small signal stability through the comparison of the stability region boundaries, thus providing a new method and support for analyzing the impact of wind power integration on small signal stability.

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Section: Model Of Dfig Wind Turbinementioning

confidence: 99%

Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary

Liu

et al. 2014

Sustainability

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“…Soleimanpour and Mohammadi [10] used the two-point estimating method to make a similar calculation. Based on the 2 m + 1 points estimating method, Cornish-Fisher expansion and considering the correlation of wind speed, Yue et al [11] studied the probability of small-signal stability of the power system after wind power integration and adopted the Monte Carlo method to verify the results. Overall, these studies considered the random fluctuation of wind speed; the conclusions can therefore more fully describe the small-signal stability of a power system with wind power integration.…”

Section: Introductionmentioning

confidence: 99%

Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain

Liu

et al. 2015

Sustainability

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Abstract:In the traditional studies on small-signal stability probability of a power system with wind farms, the frequency of wind speed was often assumed to obey to some extent a particular probability distribution. The stability probability that is thus obtained, however, actually only reflects the power system stability characteristics on long time scales. In fact, there is a direct correlation between the change of wind speed and the current state of wind speed, resulting in the system stability characteristics in different time periods having a great difference compared with that of long time scales. However, the dispatchers are more concerned about the probability that the power system remains stable in the next period or after several periods, namely the stability characteristics of the power system in a short period or multi-period. Therefore, research on multi-period small-signal stability probability of a power system with wind farms has important theoretical value and practical significance. Based on the Markov chain, this paper conducted in-depth research on this subject. Firstly, the basic principle of the Markov chain was introduced, based on which we studied the uncertainty of wind power by adopting the transition matrix and the wind speed−power output transformation model and established the probability distribution OPEN ACCESSSustainability 2015, 7 4583 model of multi-period wind power. Then the boundary-based small-signal stability probability evaluation method was used to establish an evaluation model of multi-period small-signal stability probability of power system with wind farms. Finally, taking the power system with two wind farms as an example, we analyzed its small-signal stability probability and studied the influence of the initial states of wind speed and different periods on the probability of stability. This study provides a new method and support for analyzing the small-signal stability probability of a power system with wind farms.

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“…The common deterministic approach has been widely applied in small signal stability analysis (SSSA) though it has been less credited due to conservativeness [3], [4]. In addition, the conventional deterministic approach fails to independently take into account the uncertainties existing in both wind power and the loads.…”

mentioning

confidence: 99%

“…As a consequence, numerous probabilistic approaches have been suggested to account for the uncertainties arising from the loads and wind power in SSSA. They include numerical methods such as the Monte Carlo Simulation (MCS) method [4], [5]; approximate methods such as the Point Estimation Method (PEM) [3], [6] and probabilistic collocation method (PCM) [7]; and analytical methods such as the cumulant based method [8]. The aforementioned methodologies have a major limitation in analysing power systems integrated with dependent wind power sources since they lack the ability to independently represent the complex dependencies existing between multiple wind power sources.…”

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confidence: 99%

See 1 more Smart Citation

A Robust Pair Copula-Point Estimation Method for Probabilistic Small Signal Stability Analysis with Large Scale Integration of Wind Power

Kanyingi¹,

Wang²,

Li³

et al. 2017

JOCET

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Abstract-In this paper, a robust Pair Copula-Point Estimation Method is proposed to probabilistically analyse the impact of wind power complex dependencies and uncertainties in both wind power and loads towards a power system's small signal stability. The proposed approach is adopted to probabilistically analyse the small signal stability of the IEEE 39-bus test system at three different wind power penetration levels concurrently considering light and heavy loading conditions. Increasing wind power penetration, uncertainties in both wind power and loads and the complex dependencies between wind power sources are probabilistically established to negatively impact the system's small signal stability. The worst case is observed when the level of wind power penetration is highest alongside consideration of the uncertainties arising from both wind power and the loads, the complex dependence existing between the wind power sources and at heavy loading. Thus, increasing penetration of uncertain and strongly dependent wind power sources and the uncertainty existing in power system loads should be intensively studied as they present risks towards a power system's small signal stability.Index Terms-Dependence, probabilistic small signal stability analysis (PSSSA), pair copula, uncertainty.

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A Probabilistic Approach to Small Signal Stability Analysis of Power Systems with Correlated Wind Sources

Cited by 13 publications

References 27 publications

Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary

Impact of Large-Scale Wind Power Integration on Small Signal Stability Based on Stability Region Boundary

Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain

A Robust Pair Copula-Point Estimation Method for Probabilistic Small Signal Stability Analysis with Large Scale Integration of Wind Power

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