Online Adaptive Parameter Estimation of a Finite Control Set Model Predictive Controlled Hybrid Active Power Filter

Abstract

This paper presents a novel strategy for online parameter estimation in a hybrid active power filter (HAPF). This HAPF makes use of existing capacitor banks which it combines with an active power filter (APF) in order to dynamically compensate reactive power. The equipment is controlled with finite control set model predictive control (FCS-MPC) due to its already well-known fast dynamic response. The HAPF model is similar to a grid-connected LCL-filtered converter, so the direct control of the HAPF current can cause resonances and instabilities. To solve this, indirect control, using the capacitor voltage and the inverter-side current, is applied in the cost function, which creates high dependency between the system parameters and the equipment capability to compensate the load reactive power. This dependency is evaluated by simulations, in which the capacitor bank reactance is shown to be the most sensitive parameter, and, thus, responsible for inaccuracies in the FCS-MPC references. In order to minimize this problem without increasing the complexity of the FCS-MPC algorithm, an estimation technique, based on adaptive notch filters, is proposed. The proposed algorithm is tested in a laboratory prototype to demonstrate its ability to follow variations in the HAPF capacitor reactance, effectively correcting the reactive power reference and providing dynamic reactive power compensation. During the tests, the proposed algorithm was capable of keeping the supplied reactive power within a 1% error, even in a situation with 33% variation in the HAPF capacitor reactance.