Software implementation of online risk-based security assessment

Ni, Ming; McCalley, James D.; Vittal, Vijay; Greene, Scott; Ten, Chee‐Wooi; Ganugula, Vijaya; Tayyib, T.

doi:10.1109/tpwrs.2003.814909

Cited by 86 publications

(5 citation statements)

References 10 publications

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“…As of to date, there is no valid conclusion regarding what limit of risk index is acceptable and unacceptable to be used in practice. However, discussions and proposals of some approaches for determining appropriate risk level and system's condition at certain risk level are presented in [16]- [18].…”

Section: A Risk Of Voltage Collapsementioning

confidence: 99%

Voltage collapse risk index prediction for real time system's security monitoring

Aminudin

Rahman²,

Razali

et al. 2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

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Risk based security assessment (RBSA) for power system security deals with the impact and probability of uncertainty to occur in the power system. In this study, the risk of voltage collapse is measured by considering the L-index as the impact of voltage collapse while Poisson probability density function is used to model the probability of transmission line outage. The prediction of voltage collapse risk index in real time requires precise, reliable and short processing time. To facilitate this analysis, Artificial Intelligent using Generalize Regression Neural Network (GRNN) technique is proposed where the spread value is determined using Cuckoo Search (CS) optimization method. To validate the effectiveness of the proposed method, the performance of GRNN with optimized spread value obtained using CS is compared with heuristic approach. Keywords-Risk based security assessment, transmission line outage, generalize regression neural network, cuckoo search optimization, L-index, voltage collapse.

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Section: A Risk Of Voltage Collapsementioning

confidence: 99%

Voltage collapse risk index prediction for real time system's security monitoring

Aminudin

Rahman²,

Razali

et al. 2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

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“…In the risk assessment of power system, the current research results can be divided into system-level and component-level. The risk assessment on trolly wires [2] and transformers [3] are in the component-level, while the system-level case includes risk assessment on the transient stability analysis [4], voltage stability analysis [5] and security range [6]. Literature [7] proposed a series functions to represent the severity of power flow overloading, generatrix low voltage, voltage instability, cascading overload, which is applied in the risk assessment of transmission system, has not in the field of distribution network.…”

Section: Risk Assessmentmentioning

confidence: 99%

Risk Identification based on Hidden Semi-Markov Model in Smart Distribution Network

Chang¹,

Sheng²,

Song³

2013

EPE

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The smart distribution system is the critical part of the smart grid, which also plays an important role in the safe and reliable operation of the power grid. The self-healing function of smart distribution network will effectively improve the security, reliability and efficiency, reduce the system losses, and promote the development of sustainable energy of the power grid. The risk identification process is the most fundamental and crucial part of risk analysis in the smart distribution network. The risk control strategies will carry out on fully recognizing and understanding of the risk events and the causes. On condition that the risk incidents and their reason are identified, the corresponding qualitative / quantitative risk assessment will be performed based on the influences and ultimately to develop effective control measures. This paper presents the concept and methodology on the risk identification by means of Hidden Semi-Markov Model (HSMM) based on the research of the relationship between the operating characteristics/indexes and the risk state, which provides the theoretical and practical support for the risk assessment and risk control technology.

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“…To accurately assess the potential risks of the electrical system, researchers have conducted a substantial quantity of research [12] based primarily on reliability‐based risk assessment techniques. Among them, reliability‐based power system risk assessment is a mature research field that includes analytical methods [13], Monte Carlo simulation methods [14] etc. Ref.…”

Section: Introductionmentioning

confidence: 99%

Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

Xiao,

Jiang,

Liang

et al. 2023

The Journal of Engineering

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During the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.

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Software implementation of online risk-based security assessment

Cited by 86 publications

References 10 publications

Voltage collapse risk index prediction for real time system's security monitoring

Voltage collapse risk index prediction for real time system's security monitoring

Risk Identification based on Hidden Semi-Markov Model in Smart Distribution Network

Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

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