Optimal Sensor Location in a Linear Distributed Parameter System

Omatu, Sigeru; Soeda, Takashi

doi:10.1016/s1474-6670(17)66839-3

Cited by 13 publications

(11 citation statements)

References 7 publications

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“…4. A similar conclusion can be drawn under different initial conditions Po(x, y) [16]. This shows that the proposed method is applicable to a wide variety of distributed parameter systems.…”

Section: (~L~( Z O ' ) H~( T ) / R ( T ) = Max C P L Z (~' ) H 2 ( T supporting

confidence: 78%

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Optimization of sensor location for linear distributed parameter systems

Ohmatsu

Koide

Soeda

1978

Electrical Engineering Japan

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Section: (~L~( Z O ' ) H~( T ) / R ( T ) = Max C P L Z (~' ) H 2 ( T supporting

confidence: 78%

“…Under the above assumptions with respect to q ( d , there exists a fundamental solution W(t, x, y) for a~c t ,~,~) i a t =~~~( t ,~,~) (17) Ax such that W(t, x, y) has the following properties:…”

mentioning

confidence: 99%

Optimization of sensor location for linear distributed parameter systems

Ohmatsu

Koide

Soeda

1978

Electrical Engineering Japan

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“…Different methodologies have been applied for solving optimal sensor placement problems. Some commonly used methods include optimality criteria based on the error covariance matrix (19)(20)(21)(22)(23) and observability matrix or Gramian (18,24). Importance has been given to relevant aspects such as measurement cost, sensor failure, redundancy and process information (25)(26)(27)(28).…”

Section: Literature Surveymentioning

confidence: 99%

A multi-objective optimization approach to optimal sensor location problem in IGCC power plants

Sen

Diwekar

2016

Applied Energy

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Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. Practical issues present in an IGCC power plant, such as harsh physical conditions and variability in process parameters, make the optimal sensor location problem an especially complicated one. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. This work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinear stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.

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“…The most well-known techniques for control engineers consist in positioning the sensors maximising the determinant of the information matrix [3] or an associated measure, minimising the trace of an estimate error covariance matrix [4], maximising a measure associated with the observability gramian proposed in [5], and more recently in [6,7].…”

Section: Model Reduction For Updating Proceduresmentioning

confidence: 99%