Two full capacity generators—Why is the calculated emergency power system probability of failure on demand so high?

2020 ◽  
Author(s):  
Arthur M. (Art) Dowell
Keyword(s):  
Power System ◽  
Probability Of Failure ◽  
On Demand ◽  
Full Capacity ◽  
Emergency Power

Advanced Emergency Power System Using Thermal Battery for Future Aircraft

2006 ◽  
Author(s):  
Jae Byoung Cha ◽  
Dong Kyoo Choi ◽  
Sun Wook Park ◽  
Kwang Chae Jung ◽  
Sang Baek Lee ◽  
...  
Keyword(s):  
Power System ◽  
Thermal Battery ◽  
Emergency Power

scholarly journals Study on emergency power control strategy for AC/DC hybrid power system containing VSC-HVDC

2018 ◽  
Author(s):  
Lin Liu ◽  
Zhenda Hu ◽  
Rong Ye ◽  
Zhangsui Lin ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Power System ◽  
Power Control ◽  
Control Strategy ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Emergency Power

scholarly journals Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Ahmed H. Aburawwash ◽  
Moustafa Mohammed Eissa ◽  
Azza F. Barakat ◽  
Hossam M. Hafez
Keyword(s):  
Genetic Algorithm ◽  
Accurate Determination ◽  
Probability Of Failure ◽  
System Structure ◽  
Control Element ◽  
Algorithm Optimization ◽  
On Demand ◽  
Uncertainty Sources ◽  
Iec 61508 ◽  
Proposed Model

A more accurate determination for the Probability of Failure on Demand (PFD) of the Safety Instrumented System (SIS) contributes to more SIS realiability, thereby ensuring more safety and lower cost. IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas. However, these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources, which, including high redundant systems architectures, cannot be assessed, have perfect proof test assumption, and are neglegted in partial stroke testing (PST) of impact on the system PFD. On the other hand, determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time. This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem. A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor (DC) and common cause failures (CCF). In order to simulate the proof test effectiveness, the Proof Test Coverage (PTC) factor has been incorporated into the formula. Additionally, the system PFD value has been improved by incorporating PST for the final control element into the formula. The new developed formula is modelled using the Genetic Algorithm (GA) artificial technique. The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables. The proposed model has been applicated on SIS design for crude oil test separator using MATLAB. The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality. Furthermore, the cost and associated implementation testing activities are reduced.

scholarly journals Power system flexibility improvement with a focus on demand response and wind power variability

2020 ◽  
Vol 14 (6) ◽  
pp. 1095-1103 ◽  
Author(s):  
Akbar Dadkhah ◽  
Behrooz Vahidi ◽  
Miadreza Shafie‐khah ◽  
João P.S. Catalão
Keyword(s):  
Power System ◽  
Wind Power ◽  
Demand Response ◽  
On Demand ◽  
Power System Flexibility

Probability of failure on demand of safety systems: impact of partial test distribution

2012 ◽  
Vol 226 (4) ◽  
pp. 426-436 ◽  
Author(s):  
Florent Brissaud ◽  
Anne Barros ◽  
Christophe Bérenguer
Keyword(s):  
Probability Of Failure ◽  
Test Time ◽  
Time Interval ◽  
System Failures ◽  
On Demand ◽  
Component Failure Rates ◽  
Safety Systems ◽  
The Impact ◽  
Architecture Component ◽  
Approximate Equations

In accordance with the IEC  61508 functional safety standard, safety-related systems operating in a low demand mode need to be proof tested to reveal any ‘dangerous undetected failures’. Proof tests may be full (i.e. complete) or partial (i.e. incomplete), depending on their ability to detect all the system failures or only a part of them. Following a partial test, some failures may then be left latent until the full test, whereas after a full test (and overhaul), the system is restored to an as-good-as-new condition. A partial-test policy is defined by the efficiency of the partial tests, and the number and distribution (periodic or non-periodic) of the partial tests in the full test time interval. Non-approximate equations are introduced for probability of failure on demand (PFD) assessment of a Moo N architecture (i.e. k-out-of- n: G) systems subject to partial and full tests. Partial tests may occur at different time instants (periodic or not) until the full test. The time-dependent, average, and maximum system unavailability (PFD(t), PFDavg, and PFDmax) are investigated, and the impact of the partial test distribution on average and maximum system unavailability are analysed, according to system architecture, component failure rates, and partial test efficiency.

scholarly journals Unavailability of K-out-of-N: G Systems with non-identical Components Based on Markov Model

2019 ◽  
Vol 2 (1) ◽  
pp. 25-35
Author(s):  
Ayodeji Akinsoji Okubanjo ◽  
Olasunkami oriola Akinyemi ◽  
Oluwadamilola Kehinde Oyetola ◽  
Olawale omopariola Olaluwoye ◽  
Olufemi Peter Alao
Keyword(s):  
Markov Model ◽  
Probability Of Failure ◽  
Process Industry ◽  
Redundant System ◽  
System Architectures ◽  
On Demand ◽  
Proposed Model ◽  
Challenging Tasks ◽  
Average Probability ◽  
Analytical Formulas

The process industry has always been faced with the challenging tasks of determining the overall unavailability of safety instrumented systems (SISs). The unavailability of the safety instrumented system is quantified by considering the average probability of failure on demand. To mitigate these challenges, the IEC 61508 has established analytical formulas for estimating the average probability of failure on demand for K-out-of-N (KooN) architectures. However, these formulas are limited to the system with identical components and this limitation has not been addressed in many researches. Hence, this paper proposes an unavailability model based on Markov Model for different redundant system architectures with non-identical components and generalised formulas are established for non-identical k-out-of-n and n-out-of-n configurations. Furthermore, the proposed model incorporates undetected failure rate and evaluates its impact on the unavailability quantification of SIS. The accuracy of the proposed model is verified with the existing unavailability methods and it is shown that the proposed approach provides a sufficiently robust result for all system architectures.  

Study of the Power Balance and Reliability of Power Plants Based on Renewable Energy in the Power System of the Ryazan Region

2020 ◽  
Vol 67 (1) ◽  
pp. 3-10
Author(s):  
Evgeniy I. Lopatin
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Gas Turbine ◽  
Electricity Generation ◽  
Power Plants ◽  
Organic Waste ◽  
Thermal Power ◽  
Probability Of Failure ◽  
Power Balance ◽  
Reliability Indicators

On the territory of the Ryazan region, there are currently five 5 power plants with an installed electric capacity of 3,759 megawatt, including two gas turbine thermal power plants, which partially use an alternative fuel of the first generation (biogas) obtained from recycled organic waste through their processing. Experiments on conversion the gas turbine power plants to alternative fuels are being carried out. (Research purpose) The research purpose is to determine the power balance and reliability indicators of power plants based on renewable energy in the power system of the Ryazan region. (Materials and methods) Authors have investigated two gas turbine thermal power plants in the Ryazan power system in Sasovo and Kasimov. The reliability indicators of the gas turbine station equipment were calculated. (Results and discussion) Due to the commissioning of the second stage of the gas turbine power plant in Sasovo and its transfer to alternative sources, authors predict an increase in the growth of electricity generation. Authors considered the system reliability of the gas turbine station. (Conclusions) Analysis of power and electricity balances of power plants based on renewable energy in the power system of the Ryazan region showed that their share in the total electricity generation does not exceed one percent. It was determined that the probability of failure-free operation of electrical equipment lies in the range from 0.9 to 0.98. It was found that the probability of failure-free operation of the equipment included in the high voltage switchgear (10 kilovolts) lies below the standard and is up to 0.93. It was revealed a high probability of failure-free operation of step-up transformers up to 0.99, switching and protective equipment as part of a low voltage switchgear from 0.93 to 0.98. It was found that the transfer of gas-turbine stations to alternative fuel (biogas) obtained from recycled organic waste, will not cause a decrease in the reliability of power supply, since the estimated probability of failure-free operation of the equipment corresponds to or exceeds the normative value of 0.95, and the recovery time does not exceed 2.82 hours.

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