Importance Sampling of Injected Powers for Electric Power System Security Analysis

Perninge, Magnus; Lindskog, Filip; Söder, Lennart

doi:10.1109/tpwrs.2011.2162654

Cited by 36 publications

(17 citation statements)

References 21 publications

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“…5. The crossover probability is shown as in (14): (14) where P c is crossover probability, P cmax and P cmin are, respectively, the maximum and minimum of the crossover probability, f max and f avg are, respectively, the maximum fitness and the average fitness in the group, and f ' is the greater fitness of two individuals in crossover.…”

Section: Mutation Operationmentioning

confidence: 99%

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Power failure risk assessment and management based on stochastic line failures in distribution network including distributed generation

Quan

Liao

et al. 2018

IEEJ Transactions Elec Engng

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All kinds of power interruption, which cause heavy economic loss to the electrical power company and users, occur in the operating process of distribution networks. Among them, stochastic line fault has a high probability of occurrence. Distributed generation (DG) as an environmental and independent power source has drawn worldwide attention. However, the introduction of DGs has brought increase in the number of system components and expansion of the scale of power systems, which make risk assessment and management more difficult. Therefore, a new method of risk assessment for power failure and control method of distribution network with DG based on stochastic lines failure is presented in the paper. In order to improve the accuracy and rapidity of evaluation, an assessment method with a new island partition strategy is proposed, in which two new parameters are defined to weaken the dependence on the network structure during the course of island partition. This paper also establishes a risk management model based on line maintenance and replacement for selecting an optimized risk management scheme, and applies the genetic algorithm (GA) to calculate the same. The presented method can provide the reference for planning and operating staff, and the example result proves its favorable guiding benefits. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Mutation Operationmentioning

confidence: 99%

“…DG2 island alternative tree contains the load nodes 5, 4, 3, 2, 1, 36, 37, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 6, 7, 8, 40, 41, 9, 42, 43, and 10. DG3 island alternative tree contains the load nodes 19,20,21,22,23,24,25,26,27,18,17,16,14,13,12,57, and 58.…”

Section: Case Of Fast Islands Partition Simulation Studymentioning

confidence: 99%

Power failure risk assessment and management based on stochastic line failures in distribution network including distributed generation

Quan

Liao

et al. 2018

IEEJ Transactions Elec Engng

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“…Actually, when constraint (14) is replaced by (15), the linear objective function (9) can only achieve its maximum at its boundaries, which are defined by (14) and other linear constraints in (10)- (13). Therefore, this relaxation of constraint (15) still gives the same optimal solutions for (9)- (14).…”

Section: Ecqp Modelmentioning

confidence: 99%

“…To accurately and efficiently assess power system security, large amounts of machine learning tools such as decision trees [9], extreme learning machine [10], and support vector machines [11] provide promising solutions. To alleviate the high computational complexity in these methods, several other technical measures, such as probabilistic neural network classifier [12] and important sampling methods [13], were introduced. However, the success of security assessment relies on the training set, which is determined by the performance of pattern classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Security Level Classification Method for Power Systems under N-1 Contingency

Zhang³

et al. 2017

Energies

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Security assessment is crucial for the reliable and secure operation of power systems. This paper proposes a security level classification (SLC) method to analyze the security level of power systems both qualitatively and quantitatively. In this SLC method, security levels are graded according to a comprehensive safety index (CSI), which is defined by integrating the system margin index (SMI) and load entropy. The SMI depends on the operating load and the total supply capacity (TSC) under N-1 contingency, and the load entropy reflects the heterogeneity of load distribution calculated from entropy theory. In order to calculate the TSC under N-1 contingency considering both of the computational accuracy and speed, the TSC is converted into an extended conic quadratic programming (ECQP) model. In addition, the load boundary vector (LBV) model is established to obtain the capacity limit of each load bus, and thus detect potential risks of power systems. Finally, two modified practical power systems and the IEEE 118-bus test system are studied to validate the feasibility of the proposed SLC method.

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“…However, precise calculation of polynomial coefficients may be difficult as the relationship between the uncertain parameters and the output of interest evolves with time. In another study, the uncertainty due to wind generation is incorporated using importance sampling [14] and fast sensitivity analysis approach [15], however, are not applicable for online transient stability assessment of a large power systems. Recently, a stochastic process-based load model was proposed using a structure preserving network [16].…”

mentioning

confidence: 99%

Fast Unscented Transformation-Based Transient Stability Margin Estimation Incorporating Uncertainty of Wind Generation

Hua

Mishra

Ledwich

2015

IEEE Trans. Sustain. Energy

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Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semischeduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.Index Terms-Kalman filter (KF), stability margin, uncertainty, unscented transformation (UT), wind energy.

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Importance Sampling of Injected Powers for Electric Power System Security Analysis

Cited by 36 publications

References 21 publications

Power failure risk assessment and management based on stochastic line failures in distribution network including distributed generation

Power failure risk assessment and management based on stochastic line failures in distribution network including distributed generation

A Security Level Classification Method for Power Systems under N-1 Contingency

Fast Unscented Transformation-Based Transient Stability Margin Estimation Incorporating Uncertainty of Wind Generation

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