Electric-power-system robust state estimation method and apparatus thereof

Abstract

An embodiment of the invention provides an electric-power-system robust state estimation method based on a subsection nonlinear weight function and an apparatus thereof. The method comprises the following steps of constructing a node admittance matrix of an electric power system, a measurement amount measurement equation and a zero injection equality constraint; calculating a jacobian matrix and a measurement-amount residual error of the electric power system and acquiring a weight of each measurement amount; and updating a state variable, determining whether an iteration algorithm reaches a convergence threshold, and if the iteration algorithm reaches the convergence threshold, acquiring a final node voltage amplitude and a phase position of the electric power system. In the invention, bad data detection and identification, and state estimation are simultaneously performed; state estimation calculating is performed, and simultaneously a measurement amount weight can be changed; a bad data weight in iteration is continuously reduced and an influence brought by the bad data is restrained; a calculation amount is effectively reduced and work efficiency is increased; a residual error of the measurement amount is directly introduced into a subsection nonlinear weight function, and standardized residual error calculating is avoided so that a calculating speed can be greatly increased and calculating time is saved; and the method and the apparatus are suitable for an engineering application.

Description

technical field [0001] The invention relates to a power system state estimation technology, in particular to a method and device for estimating a robust state of a power system based on a segmented nonlinear weight function. Background technique [0002] The most widely used traditional power system state estimation is the weighted least squares estimation algorithm. Its model is simple and its calculation method is convenient. Under the assumption that the measurement error obeys an ideal normal distribution, it can obtain the least variance and unbiased estimation results. [0003] However, in power system measurement, in addition to random errors and systematic errors, there are inevitably some bad data that are very different from the true value, and these bad data will have a serious adverse effect on the estimation results. Bad data mixed in the measurement data will make the estimation results of the weighted least squares method lose reliability and accuracy. In ord...

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Problems solved by technology

However, this method requires multiple round-robin inspections in the two links of state estimation and bad data detection and identification. The workload is huge, the calculation time is long, and due to the inherent defects of the residual error inspection method, the identification reliability is not high enough.

[0004] Existing robust state estimation methods include weighted least absolute absolute value (WLAV) and weighted least absolute value robust state estimation based on variable substitution interior point method, etc. The solution process is complex and the amount of calculation is large, so it cannot be applied into the real-time estimation of large power systems

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