Data reconciliation and fault detection by means of plant-wide mass and energy balances

Sunde, Svein; Berg, Øivind

doi:10.1016/s0149-1970(03)00015-5

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Real-time measurement on the power plant is contain random error and gross error, as stated in equation (4). The impact of existence of random error and gross error is increasing the uncertainty of monitoring result on power plant performance [31,32].…”

Section: E Gross Error Detectionmentioning

confidence: 99%

“…Data reconciliation techniques have two main components, e.g modeling and selection optimization processes. Data reconciliation have been widely used in the power plant [4]- [6], on gas turbines and combined cycle ower generation units [7] and coalfired generation units [8]- [10]. In this paper, we will present data reconciliation on the power plant of heat exchanger component, which is preheater, low pressure economizer (LP Eco), high pressure economizer 1 (HP Eco 1), low pressure evaporator (LP Eva), high pressure economizer 2 (HP Eco 2), high pressure evaporator (HP Eva), high pressure superheater 1 (HP SH 1), and high pressure superheater 2 (HP SH 2) which is satisfy the mass and energy balance in steady state condition.…”

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confidence: 99%

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Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO)

Pratiwi

Biyanto

2017

IJOS

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AbstractThe innaccurate on process data in PLTGU Gresik does not satisfy the mass and energy balance. Data reconciliation techniques can effectively improve precision and reduce measurement error on process variable estimation of data plant through modeling and optimization techniques. In this paper, we propose PSO (Particle Swarm Optimization) algorithm to solve the data reconciliation problem for precise improvement and error minimization. As a result, the standard deviation of data measurement and reconciliation is different on each variable heat exchanger component, so that indicates random errors on measurement. Based on the result, PSO algorithm is capable generate reliable data and minimizing error with sum square error is equal to 1.153. It means PSO algorithm is compatible with the instrument system on PLTGU Gresik. Moreover, data reconciliation is applied then followed with detection gross error using statistical test that is Global Test. As the result, there is not gross error on the measurement.

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Section: E Gross Error Detectionmentioning

confidence: 99%

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confidence: 99%

Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO)

Pratiwi

Biyanto

2017

IJOS

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“…mass and energy, are evaluated using single bilinear FDD residual vectors. (Sunde & Berg, 2003) successfully achieved the notion of fault detection by way of plant-wide mass and energy balances for a 3,300 MW t boiling water reactor turbine cycle. The balance equations were implemented as constraints to a minimisation problem.…”

Section: Historical Developmentmentioning

confidence: 99%

An energy perspective on modelling, supervision, and control of large-scale industrial systems: Survey and framework

Schoor

Uren

Wyk

et al. 2014

IFAC Proceedings Volumes

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Energy is a universal concept that can be used across physical domains to describe complex large-scale industrial systems. This brief survey and framework gives a perspective on energy as a unifying domain for system modelling, supervision, and control. Traditionally, modelling and control problems have been approached by adopting a signal-processing paradigm. However, this approach becomes problematic when considering non-linear systems. A behavioural viewpoint, which incorporates energy as basis for modelling and control, is considered a viable solution. Since energy is seen as a unifying concept, its relationship to Euler-Lagrange equations, state space representation, and Lyapunov functions is discussed. The connection between control and process supervision using passivity theory coupled with a system energy balance is also established. To show that complex industrial systems comprising multiple energy domains can be modelled by means of a single electric circuit, its application to a large-scale thermo-hydraulic system is presented. Next, a simple non-linear transmission impedance electric circuit is used to illustrate how energy can be used to not only describe a system, but also serve as basis for system optimisation. An energy-based framework is proposed whereby energy is used as a unifying domain to work in, to analyse, and to optimise large-scale industrial systems.

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“…Sunde and Berg [23] published a paper in which the thermal balance of the steam turbines of a boiling water reactor power plant was modeled using the Cycle-TEMPO software for diagnostic purposes.…”

Section: Introductionmentioning

confidence: 99%