This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both seriesactive and shuntactive power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.
Power quality is one of the major concerns in the present era. It has become important, especially with the introduction of sophisticated devices, whose performance is very sensitive to the quality of power supplied. Power quality problem is an occurrence manifested as a nonstandard voltage, current, or frequency that results in a failure of end use equipment [
UPQC can also be operated in different possible configurations for singlephase (2wire) and 3phase (3wire and 4wire) networks, diverse compensation approaches, and recent developments are also found in the field [
The control signal given to the UPQC plays a significant role in establishing a better operation of the device. Conventional control schemes are widely used in addition to the control schemes derived from artificial intelligence which is also recorded in the literature. Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also applied. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is also growing [
In another attempt [
In [
The simulation is done using MATLAB and Simulink Toolbox for both control schemes used for the generation of control signals for the UPQC. The rest of the paper is organized in such a way that UPQC is revisited in Section
UPQC is one of the custom power devices used at the electrical power distribution systems to improve the power quality of distribution system customers [
The schematic diagram of a threephase UPQC is shown in Figure
Schematic diagram of the UPQC.
The control scheme of threephase UPQC is shown in Figure
Control scheme of UPQC.
The fundamental description of the UPQC with an ideal voltage source and the fuzzy logic is modeled as an ideal current source. The UPQC source voltage (
In Stage 2 the supply voltage magnitude has reduced to
Fuzzy logic controller for system under consideration.
In FLC, basic control action is determined by a set of linguistic rules. These rules are determined by the system. Since the numerical variables are converted into linguistic variables, mathematical modelling of the system is not required in FLC.
The FLC comprises three parts: fuzzification, interference engine, and defuzzification.
Membership function values are assigned to the linguistic variables, using seven fuzzy subsets: NB (Negative Big), NM (Negative Medium), NS (Negative Small), ZE (Zero), PS (Positive Small), PM (Positive Medium), and PB (Positive Big). The partition of fuzzy subsets and the shape of membership function adapt the shape up to appropriate system. The values of input error
In this system the input scaling factor has been designed such that input values are between −1 and +1. The triangular shape of the membership function of this arrangement presumes that for any particular input there is only one dominant fuzzy subset. The input error
Several composition methods such as MaxMin and MaxDot have been proposed in the literature. In this paper MaxMin method is used. The output membership function of each rule is given by the minimum operator and maximum operator. Table
Fuzzy rules base.




NL  NM  NS  ZE  PS  PM  PL  
NL  NL  NL  NL  NM  NS  NS  ZE 
NM  NL  NM  NM  NM  NS  ZE  ZE 
NS  NM  NM  NS  NS  ZE  ZE  PS 
ZE  NS  NS  ZE  ZE  ZE  PS  PS 
PS  NS  ZE  ZE  PS  PS  PM  PM 
PM  ZE  ZE  PS  PM  PM  PM  PL 
PL  ZE  PS  PM  PM  PL  PL  PL 
As a plant usually requires a nonfuzzy value of control, a defuzzification stage is needed. To compute the output of the FLC, height method is used and the FLC output modifies the control output. Further, the output of FLC controls the switch in the inverter.
The FLC uses a rule base as shown in Table
Membership functions of
This is a method that cannot be used for asymmetrical output membership functions and can be used only for symmetrical output membership functions. Weighting each membership function in the obtained output by its largest membership value forms this method. The evaluation expression for this method is
From Figure
Weighted average method.
Ant Colony Optimization (ACO) algorithm is essentially a system based on agents which simulate the natural behavior of ants, including mechanisms of cooperation and adaptation. It is designed to reproduce the ability of ant colonies to determine the shortest paths to food. Real ants can indirectly communicate by pheromone information without using visual cues and are capable of finding the shortest path between food sources and their nests. The ant deposits pheromone on the trail while walking, and the other ants follow the pheromone trails with some probability which are proportioned to the density of the pheromone. Through this mechanism, ants will eventually find the shortest path. Artificial ants imitate the behavior of real ants of how they forage the food but can solve much more complicated problems than real ants can.
ACO algorithms are based on the following ideas:
Each path followed by an ant is associated with a candidate solution for a given problem.
When an ant follows a path, the amount of pheromone deposited on that path is proportional to the quality of the corresponding candidate solution for the target problem.
When an ant has to choose between two or more paths, the path(s) with a larger amount of pheromone have a greater probability of being chosen by the ant.
Figure
The proposed flow diagram for the ACOFL control technique.
In order to test the performance of the UPQC using the proposed FLC, it has been simulated for a 400 V, 50 Hz threephase AC supply using MATLAB/Simulink. A threephase diode rectifier feeding an RLC load is considered as nonlinear load. The maximum load power demand is considered as 11 kW + j12 kVAR. The value of source resistance
Output waveform without UPQC connected to the system.
Output waveform with UPQC connected to the system controlled by PI controller.
Output waveform with UPQC connected controlled by ACOFLC technique.
THD when UPQC is not connected to the system.
THD when UPQC is tuned by PI.
THD when UPQC is tuned by ACOFLC technique.
By observing Figure
To substantiate this in Figure
Voltage sag mitigation performance.
System without UPQC  UPQC with PI controller  System with ACOFLC controller 

Voltage sag persists  Voltage sag mitigated  Voltage sag mitigated with high accuracy 
Due to the operation of the UPQC, the shunt AF current (ica) compensates the reactive power and the harmonics of the load; thus the source current follows the sinusoidal reference currents. The THD of the supply current is 3.19%, while the load current THD is 12.73% as shown in Figures
Total harmonic distortion for the proposed system.
THD without UPQC  THD with PI controller  THD with ACOFLC controller 

4.17%  0.49%  0.13% 
The confrontation in improving the power quality has become a promising area of research amongst power system and power electronic engineers and researchers. With the ever increasing advent of nonlinear loads and also due to high frequency switching characteristics, suitable conditioners are always a demand. Unified Power Quality Conditioner (UPQC) is one of the promising power electronic circuit modules to overcome voltage sag and total harmonic distortion problems, as the circuit is modeled using both seriesactive and shuntactive power filters. Thus the benefits of both the power filters are integrated for better power quality mitigation is realized. This paper considers the advantages of the fuzzy logic and proposes a new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system. The reference signal generated by the fuzzy logic controller was given as input to the UPQC switching module. Exhaustive simulation experiments using MATLAB/Simulink have been done and based on the results and output received it is observed that the proposed fuzzy logic controller is better in improving the power quality by minimizing the voltage sag and total harmonic distortion when compared to the conventional PI controller. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. In this research the UPQC is only simulated for minimizing the voltage sag and total harmonic distortion in the distribution system but the main purpose of UPQC is to compensate for voltage imbalance, reactive power, negativesequence current, and harmonics and these parameters will be considered in the near future.
The authors declare that there is no conflict of interests regarding the publication of this paper.