The MC-UPQC includes two series Voltage Source Converters (VSCs), where power is transferred from one supply to another to eliminate "sag, swell, dropout and transient response." Hybrid Meta-Heuristic Algorithm The Beetle-based Swarm-Beetle Optimization Algorithm (BS-BOA) is developed from the combination of the Beetle Swarm Optimization (BSO) and the Butterfly Optimization Algorithm (BOA). The MC-UPQC includes two series Voltage Source Converters (VSCs), where power is transferred from one supply to another to eliminate "sag, swell, dropout and transient response." The hybrid meta-heuristic algorithm Beetle Swarm-Beetle-based Optimization Algorithm (BS-BOA) is developed from the combination of Beetle Swarm Optimization (BSO) and Butterfly Optimization Algorithm (BOA).
Literature Survey
In (Liu et al. 2021) have suggested that Time-Dependent Spectral Features (TDSFs) detect power quality. Although there are several functions in MC-UPQC, it makes the MC-UPQC improve the power quality. ANN (Senthilnathan and Annapoorani 2016) has the ability to compensate for heave and deflection, and it also reduces power quality issues in the 3-feeder system.
The phase-locked loop-based control technique (Koroglu et al. 2016) captures and distinguishes power quality disturbances in a multi-supplier system. ANN (Kumar et al. 2018) depends only on the knowledge of line currents and also enhances the power frame quality. ANN It helps to minimize. harmonics in the supply currents and also the load voltage.
ANN It generates the reference. currents for the shunt converter and reference voltages for the series converter.
Improved Power quality in MC-UPQC 1 Line Diagram of MC-UPQC
Control Strategy of MC-UPQC
Consequently, a control strategy is used to detect the behavior and current performance of a particular distribution system. In addition, MC-UPQC depends on control techniques to improve the power quality of the system (Vinotha et al. 2019) (Jose et al. 2014). Here, the performance of MC-UPQC is achieved based on reference voltage signals or current signals.
The developed control scheme produces the received signals in series and avoids the "voltage source converter of MC-UPQC". The control method extracts harmonic and reactive components, voltage and current unbalance, harmonics and swell, voltage sags, source voltage distortions and load currents of both feeders.
Control Strategy of Shunt VSC
The power is received from the source for stabilizing the DC capacitor voltage through the shunt VSC. The quadrature components of the feeder current are applied to zero and the direct component of the feeder current is applied to load direct components. The compensation currents from the shunt VSC are directly subjected to the controller section, as shown in Fig.
Control Strategy of Series VSC
The output of the "PWM generator offset voltage" is provided as a direct input to the VSC series control section as in Fig.
Optimized fuzzy controller-based MC-UPQC 1 Designed Model
Fuzzy Logic Controller
The output, as well as input variables of the data bus or the controlled system, is composed of a description of a fuzzy set. The memberships of fuzzy are modeled based on Eq. 10) Here, the term PN1,2,3NH, which NH represents the number of rules, the fuzzy sets are described by. FG1, 2, , the count of fuzzy variables is denoted by nh, the check whether the output variable is defined V.
Using the provided rule base, the fuzzy controller calculates the specific input signals for describing the control action. This requires only a smaller amount of actual loss for regulating the voltage considered as the FLC's output. The fuzzy logic output is then taken, and then it is forwarded to the feeder currentdq0.
The MSRF oriented currents are directly applied to the relay and a control signal is detected to shunt the VSC control circuit.
Hybrid Beetle Swarm with Butterfly Optimization Algorithm
FG1, 2, , the number of fuzzy variables is denoted nh, the control or output variable is defined as V. The term U1,U2,Unh defines the vector of the input variables. Here, the "sensory modality is denoted by d, the power exponent based on modality is denoted" by b, odor magnitude is denoted by g, and stimulus intensity is denoted by J. The limits of band d are in the range 0 to 1. The variation of absorbance is characterized by the parameter b. The parameter d describes the behavior of the BOA and the speed of convergence. in the practical format it is described by the system characteristics to be optimized.
The properties of butterflies in relation to the search algorithm can be described in the following ways: eq 13) describes the search phase in which the butterfly performs the butterfly movement*. In the above equation, the optimal solution found between all iterations is denoted by meh*a random number of intervals varying between 0 and 1 and described by "the solution vector yj for the jthfly in the iteration count is denoted" byylj, and gj denotes the flavor of the butterfly.
In the above equation, learning factors are defined by d1,d2,d3, the current position to obtain the optimal solution Qclj, and the current global optimum is represented by Qhlj, respectively.
Designed BS-BOA Begin
Optimized Fuzzy Logic Controller
To further improvise the performance of the LLC, the adjustment of the membership limit is introduced here, which is done by the proposed BS-BOA. Rather than the conventional FLC, the optimized FLC improves its performance by adjusting the membership limits through the proposed BS-BOA. The solution encoding of membership limit optimization in FLC based on the entire rules is shown in Fig.
Here the membership limits of each language variable of fuzzy are considered as the parameters for optimization to be done by the proposed BS-BOA. The main purpose of the BS-BOA based MC-UPQC to improve the power quality using an optimized Fuzzy Controller is to reduce the THD. In the equation above, the "THD is described as the ratio of the sum of the powers of all harmonic components to the power of the fundamental frequency." It is represented mathematically in Eq.
9 shows the membership function performance with respect to "error voltage, error voltage change, and output voltage" generated by the BS-BOA based FLC.
Results
- Experimental setting
- Simulation Model
- Performance analysis of MC-UPQC with feeder 1 and feeder 2
- Performance evaluation of MC-UPQC connection with feeder 1and feeder 2 with different heuristic- based FLC
- Harmonic Analysis
- Convergence analysis
- Comparison of the proposed and conventional MC-UPQC with the existing methods
Performance of proposed MC-UPQC in terms of (a) General MC-UPQC system, (b) FLC-MC-UPQC, (c) NN-MC-UPQC and (d) BS-BOA-FLC-MC-UPQC for feeder 1. Performance of proposed MC-UPQC in terms of (a) General MC-UPQC system, (b) FLC-MC-UPQC, (c) NN-MC-UPQC, and (d) BS-BOA-FLC-MC -UPQC for feeder 2. Performance of proposed MC-UPQC in terms of (a) BOA-FLC-MC-UPQC, (b) BS-BSO-FLC-MC-UPQC and (c) BS-BOA-FLC-MC- UPQC using feeder 1.
The proposed BS-BOA-FLC-MC-UPQC offers better results based on the lowest cost. Here, using the proposed BS-BOA-FLC-MC-UPQC, the cost function is minimized while increasing the number of iterations. Therefore, the proposed BS-BOA-FLC-MC-UPQC performs better for all iterations compared to other heuristic algorithms.
Thus, it is proved that the proposed BS-BOA-FLC-MC-UPQC is better than the other conventional algorithms.
Conclusion
Power Quality Improvement Using Fuzzy Logic Controller Based Unified Power Flow Controller (UPFC), Indonesian Journal of Electrical Engineering and Computer Science. Investigating the effectiveness of neural network-based unified power quality conditioning, IEEE Transactions on Power Delivery, 26(1). Performance analysis of a unified multi-inverter power quality stabilizer with an EPLL-based controller at the medium voltage level, International Transactions on Electrical Energy Systems.
Control algorithms for unified power quality switching based on three-level converters, International Transactions on Electrical Energy Systems, 25(10). A unified power quality controller based on neuro-fuzzy controller with PQ theory, International Electrical Engineering Journal. Power quality improvement with a unified power quality controller using ANN with hysteresis control, International Journal of Computer Applications, 6: 9-15.
New Control Method in Unified Power Quality Conditioner (UPQC) for Harmonic Distribution Using PSO-Fuzzy Logic.
Reviewed Manuscript Title
Optimal Operation of Multi-Converter-UPQC for Power Quality Improvement in Distribution System using Optimized Fuzzy Logic Controller
- Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and all the fonts are too small
- Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, and all the fonts are too small
- Figure 11, Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, and all fonts are too small
- In Figure 11, Figure 12, Figure 13, and Figure 14, the authors cannot explain the types of system disturbances based on the performance of the method and proposed MC-UPQC as
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THD of the source voltage, THD of the injection/series voltage and THD of the load voltage in Figure 15 and Figure 16, because all the images and fonts on the screen are too small to be seen by normal eyes. In Table 6 and Table 7, all THD values of the new configurations and methods proposed by the authors (proposed MC-UPQC and BS-BOA-FLC-MC-UPQC) are 8.803%, which means they are still above the IEEE 519 standard 5%. In the final part, the effectiveness of UPQC regardless of the proposed combinations and methods in general, i.e. a) reduction of the THD of the load voltage and improvement (stabilization) of the load voltage magnitude due to voltage drops, swells and interruptions on the source bus and (b) reduction of the THD of the source current due to the existence of a non-linear load on the load side.
The author failed to tell the reader about the improvement of all these parameter values, so the main goal of applying UPQC to a system finally should not be achieved.
Cybernetics and Systems
Rencanakan 4
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