Determination of probabilistic risk of voltage collapse using radial basis function (RBF) network

Arya, L. D.; Titare, L.S.; Kothari, D.P.

doi:10.1016/j.epsr.2005.09.011

Cited by 22 publications

(7 citation statements)

References 14 publications

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“…Risk assessment by computing risk indices based on over-limit probability and severity to recognise system weakness more realistically is entailed in this research. With regard to transmission line overload, literature study reveals the study of static RBSA with 'N-1' contingency [18], risk visualisation using Poisson distribution [8,17], online static RBSA with forecasted operating condition [19], possibility and severity of risk occurrence [20]. Risk assessment is performed in four steps [8]: (i) describe an index that represents system risk, (ii) select a system state and calculate its probability, (iii) evaluate the outcome of the system state and (iv) calculate the risk index.…”

Section: Background On Rbsa Studiesmentioning

confidence: 99%

“…Adding an interconnection can alleviate the overloading on existing lines though finding the exact location is out of the scope of this paper. An increase of load at bus 16 accounts for a marginal increase in risk index for line [16][17][18][19]. The positive aspect is seen in the form of low overall risk index of the total system with consideration of spatio-temporal dependency.…”

Section: Case Bmentioning

confidence: 99%

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Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Khuntia

Rueda

Meijden

2019

IET Renewable Power Generation

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Location of wind power plants and demand centres are not always close by; hence, the transmission of energy puts a burden on existing grid infrastructure. This unwanted burden necessitates transmission lines to operate more and more frequently close to their operating limits. To alleviate such situations, this research addresses the advantages of modelling spatio-temporal dependence of load and wind power using vine copula. Probabilistic AC optimal power flow is performed on a modified IEEE 39-bus system with significant wind penetration. Real load and wind power data from a U.S. utility is mapped onto the test-case to achieve realistic results. Load flow calculation can help in performing steady-state voltage and overload evaluations for post-disturbance system conditions. Because the security level of a power system is determined by the likelihood and severity of security violation. In this research, the probability of line overload is calculated from load flow and the severity function describes the risk of line overloading. Two case studies depicting future operating conditions of massive wind power penetration with reduced fossil fuel and nuclear power generation are considered. Simulation results prove the advantage of addressing spatio-temporal dependency to quantify the overload risk index, which is treated as a security indicator.

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Section: Background On Rbsa Studiesmentioning

confidence: 99%

Section: Case Bmentioning

confidence: 99%

Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Khuntia

Rueda

Meijden

2019

IET Renewable Power Generation

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“…As contingency occurs randomly and it does not take place at any specific time, probability method is used to compute the likelihood of the contingency. Various probabilistic methods have been applied in risk assessment, the methods include Poisson distribution [1,5], Monte Carlo [6] and using computational intelligence such as Radial Basis Function (RBF) [6]. The impact associated with security violation can be considered in terms of the severity of violation and monetary loss due to outage.…”

Section: Risk Based Security Assessment (Rbsa)mentioning

confidence: 99%

Risk based static security assessment in a practical interconnected power system

Marsadek

Mohamed

Nizam

et al. 2008

2008 IEEE 2nd International Power and Energy Conference

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In security assessment of power systems, presently, the deterministic approach is the most widely used method. However, in the current competitive environment, the drawbacks become salient; hence security assessment based on the concept of risk is required. Risk assessment is an approach that quantitatively captures the factors that determine security level, namely likelihood and the severity of events. Risk index is used to quantify the degree of risk of the given operating condition. This paper presents risk based static security assessment on a practical interconnected power system at various loading condition using a risk index. The result captures the uncertainty in the occurrence of contingency and the variation of risk index as the load changes.

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“…The probability of a transmission line outage that can cause a security violation is referred to as the event likelihood, and it is assumed to have the characteristics of a Poisson process [11]- [13]. A Poisson process counts the arrival of events within a pre-defined time interval.…”

Section: A Probability Of Transmission Line Outagementioning

confidence: 99%

“…To speed up the risk assessment procedure, contingency screening is implemented in [11] and [12], but this method can be very time consuming for a large-sized power system. Determining the probabilistic risk of voltage collapse using the radial basis function (RBF) network implemented in [13] only calculates the probability of voltage collapse. Online voltage collapse risk quantification using fuzzy techniques, as employed in [14], do not take into account the probabilistic nature of power system operation.…”

Section: Introductionmentioning

confidence: 99%

Risk-based voltage collapse assessment using generalized regression neural network

Marsadek

Mohamed

Nopiah

2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system.

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Determination of probabilistic risk of voltage collapse using radial basis function (RBF) network

Cited by 22 publications

References 14 publications

Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Risk based static security assessment in a practical interconnected power system

Risk-based voltage collapse assessment using generalized regression neural network

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