Risk based voltage security assessment

Wan, Hua; McCalley, James D.; Vittal, Vijay

doi:10.1109/59.898097

Cited by 129 publications

(8 citation statements)

References 9 publications

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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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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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“…Calculating risk value. Risk-based assessment [16][17][18] can give comprehensive measurement of likelihood and severity for the uncertainties in power system. The basic equation is (…”

Section: Improved Opa Modelmentioning

confidence: 99%

A Blackout Model Based on Optimal Risk Index and High-Risk Line Identification

Ding¹,

Liu²

2011

AMR

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This paper summarizes the defects of both OPA model and its improved models and then proposes a new model in order to simulate real power system roundly. There is an inner loop based on DC power flow optimization to minimize the change in generation or load shed and an outer loop based on the risk theory. In the outer loop, whether to improve the line capacity and how to improve are determined by risk index of line improvement (RILI) and line improvement degree function, respectively. A new prevention strategy against cascading failure is proposed by identifying the high-risk lines and determining optimal RILI which minimize line improvement cost and load loss. In the end, simulation based on 39-bus New England System reveals that new model is closer to real power system than the original one by comparing outage probability and load loss between the two models, and risk-based assessment interval and line improvement delay play important roles in prevention against power system outages, and improvement based on risk assessment can reduce outage probability to varying degrees, which illustrates the practical significance of optimal RILI.

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“…In [48], a distinction is made between online and offline security assessments and various alternatives of severity assessment are presented, including economic and control aspects. In [49], a severity factor definition is proposed to quantify the impact of a contingency in terms of voltage collapse. The method is used to estimate the expected future cost due to voltage instability.…”

Section: Introductionmentioning

confidence: 99%

Investigating Various Severity Factor Behaviors for Operational Risk Assessment

Nazir

Bollen

2022

Electricity

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Operational risk assessment is a stochastic approach to quantify the operational security of power systems. In this study, the interrelation between severity factor and operational risk, two important parameters in operational risk assessment, is analyzed. Four different definitions of severity factor, based on the results from network studies, are proposed and applied, resulting in different values for the operational risk indices. The behavior of these indices is analyzed under varying operating conditions and compared with the behavior of the severity factor for individual contingencies. This study confirms the importance of the severity factor definition and shows that multiple operational risk indices are required to obtain a clear picture of the operational security of a transmission system. One risk index may increase while another decreases.

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Risk based voltage security assessment

Cited by 129 publications

References 9 publications

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

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

A Blackout Model Based on Optimal Risk Index and High-Risk Line Identification

Investigating Various Severity Factor Behaviors for Operational Risk Assessment

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