Artificial Intelligence Based Three-Phase Unified Power Quality Conditioner

Power quality is an important measure of the performance of an electrical power system. This paper discusses the topology, control strategies using artificial intelligent based controllers and the performance of a unified power quality conditioner for power quality improvement. UPQC is an integration of shunt and series compensation to limit the harmonic contamination within 5 %, the limit imposed by research-519 standard. The novelty of this paper lies in the application of neural network control algorithms such as model reference control and Nonlinear AutoregressiveMoving Average (NARMA)–L2 control to generate switching signals for the series compensator of the UPQC system. The entire system has been modeled using MATLAB 7.0 toolbox. Simulation results demonstrate the applicability of MRC and NARMA-L2 controllers for the control of UPQC.
The better controllability, higher efficiency, higher current carrying capability, and fast switching characteristics of static power converters are promoting major changes in controlling the power flow of transmission and distribution systems. On the other hand the nonlinear characteristics of these switching devices introduce many undesirable features such as low power factor, poor voltage regulation, zerosequence currents, imbalances, and harmonics. Traditionally passive filters, synchronous condensers, capacitors, and phase advancers were used to improve the power quality. The undesirable features such as lower efficiency, bulkiness, fixed compensation, resonance, and electromagnetic interference of traditional compensators urged power electronics and power system engineers to develop an adjustable and dynamic solution for power quality problems. Active power filters (APF) were introduced in order to compensate reactive power, to cancel current harmonics, to correct current imbalances and to control zero-sequence currents

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