Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO) - Wahyu T Pratiwi; Totok R. Biyanto (Lembaga Penelitian dan Pengabdian kepada Masyarakat, 2017)
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.