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access icon free Calibration of erroneous branch parameters utilising learning automata theory

© The Institution of Engineering and Technology
Received 29/08/2015, Accepted 30/05/2016, Revised 18/05/2016, Published 06/10/2016

Incorrectness of simulation model parameters can lead to erroneous results in power system operation and planning studies. Therefore, model parameters should be verified according to measurements obtained from the actual system. Mismatches between simulation results and corresponding field measurements can be considered as a sign for the necessity of model verification. Inaccurate branch parameters in simulation models can lead to misguided results in protection, operation and planning studies. An efficient algorithm is proposed in this study for detection and correction of erroneous branch parameters. First, suspicious parameters are selected utilising a sensitivity-based probabilistic method. Applying learning automata theory, selection probabilities will be modified according to simulation responses. A novel formulation is proposed for calculating selection probabilities according to simulation responses. Afterwards, selected parameters are modified by utilising an iterative Newton–Raphson scheme. Unlike state estimation-based methods, required sensitivities are calculated numerically. Therefore, the explicit mathematical formulation of measurements dependency on model parameters is unnecessary. The procedure of erroneous parameter detection and correction will be continued until the value of sum of squared errors (SSE), the objective function in optimisation procedure, becomes sufficiently small. Simulation results demonstrate the high efficiency of the proposed method.

Inspec keywords: optimisation; power system state estimation; Newton-Raphson method; power system planning; probabilistic automata; probability; power system economics; learning automata; calibration; power engineering computing; power system protection

Other keywords: sum of squared errors; learning automata theory; power system operation; power system planning; erroneous branch parameter correction; mathematical formulation; erroneous branch parameter detection; sensitivity-based probabilistic method; iterative Newton-Raphson scheme; field measurement; erroneous branch parameter calibration; objective function; model verification; simulation model parameter incorrectness; power system protection; optimisation procedure

Subjects: Optimisation techniques; Optimisation techniques; Other topics in statistics; Power system planning and layout; Interpolation and function approximation (numerical analysis); Measurement standards and calibration; Interpolation and function approximation (numerical analysis); Other topics in statistics; Automata theory; Power system management, operation and economics; Power engineering computing; Power system protection

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