Impact of Improved Unified Power Quality Conditioner Allocation in Radial Distribution Network

Abstract

This paper presents the investigative study on the Unified Power Quality Conditioner (UPQC) impact on Radial Distribution System (RDS). The architecture of Power Angle Controlled UPQC named Improved Unified Power Quality Conditioner (I-UPQC) was implemented in RDS. The problem of power loss, under-voltage, and reactive power burden on shunt inverters are the significant issues addressed in this study. The allocation of I-UPQC by placing it at each bus of the RDS one node at each iteration, excluding the swing bus, is studied by considering its impact on each bus of the radial network. The Power Loss Index (PLI) and Degree of Under Voltage Mitigation Node (DUVMN) values of all the buses are calculated analytically using distribution framework expressions of I-UPQC. Hence, the bus having the highest PLI value, and the minimum permissible node voltage is the most favourable. The determination of the candidate bus for I-UPQC was achieved by the load flow algorithm. The results obtained in this study on IEEE 33 and 69 bus system shows 3.9% and 4.2% power loss reduction respectively for both networks. Also, the minimum bus voltage was improved to 0.954 p.u. and 0.955 p.u. in each case for both networks, after the allocation of I-UPQC in RDS, compared to the base case. Consequently, the VA burden on shunt inverter was reduced by reactive power compensation of the series inverter. The results and simulation obtained in MATLAB / SIMULINK environment and discussion to support the concept developed are also presented. The results from the study confirmed that the concept of I-UPQC placement impacted the operation of RDS compared to the other connected UPQC model.

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“…Gunawan et al 8 presented four new independent optimization algorithms for solving single-objective problems in renewable energy sources. They used the IEEE 30-node test system to compare the proposed metaheuristic optimization with alternatives, including particle swarm, moth flame optimization, and gray wolf optimization.…”