An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance. In this research, a novel control technique-based Hybrid-Active Power-Filter (HAPF) is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor (PF) and Total–Hormonic Distortion (THD) and the performance of a system. This work proposed a soft-computing technique based on Particle Swarm-Optimization (PSO) and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods. Moreover, the control algorithms are implemented for an instantaneous reactive and active current (I_{d}-I_{q}) and power theory (Pq0) in SIMULINK. To prevent the degradation effect of disturbances on the system's performance, PS0-PI is applied in the inner loop which generate a required dc link-voltage. Additionally, a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions. The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system. Therefore, the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller (FLC) are optimal that will detect reactive power and harmonics much faster and accurately. The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI (Voltage Source Inverter), and thus the simulation has been taken in SIMULINK/MATLAB. The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation. As a result of the comparison, it can be concluded that the PSO-based Adaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.

The electronic devices draw nonsinusoidal current that causes harmonics in the system. The harmonics may cause severe problems in the power system like power loss and economic loss. While a non-linear load is attached to the PCC (Point of Common-Coupling), harmonics are produced that can harm electronic devices commonly used on commercial and industrial scales [

While current and voltage are decreasing in the power system, installing a hybrid type filter combines both active and passive filters implemented in [

The key motivation behind the proposed implementation is that the PI controller is incapable of reacting to an abrupt change in error signal because it can only determine instantaneous values without considering the rise and fall of error. Hence, the proposed implementation is more realistic and capable of practical application. The main contributions of this research work are as follow:

Simulation of HSAPF for three-phase power system using Pq0 and I_{d}-I_{q} control theory in SIMULINK.

To optimize the PI controller parameters using PSO that minimizes a objective function by providing robust parameters to PI and can also regulate DC link voltage that helps maintain the charging and discharging of capacitors.

Implementation of PSO-based Adaptive Fuzzy PI system to eliminate harmonics in a power system, based on gains acquired by PSO, and its performance compared with other techniques.

This part of the paper is summarized in sections, with Section-2 summarizing a literature review. Section 3 examines the control techniques used for harmonics mitigation. In Section 4, results will be discussed, and a comparison of a given technique with previous systems such as Pq0, Id-Iq PSO-PI, based on THD, DC-Link Voltage regulation will be summarized. In the Section 5, the conclusion based future-works will be discussed.

By referring [

HSAPF combines single tuned pasive filters in the series arrangement with the active filter used for harmonic current compensation, as shown in

The I_{d}-I_{q-}based control technique for reference current calculation contains three functional blocks and is carried out in MATLAB using the three-phase power system. The dc-link capacitor voltage has been compared to a dc reference value value, and than error is given to the PI for zero steady-state error that is further used to track a reference value of current signal. The I_{d}-I_{q} currents are obtained from Park's transformation that passed through the eighth order butter-worth filter to eliminate dc components from the non-linear load.

The conventional PI controller has been implemented with _{best}) in problem space. PSO algorithm accelerates every particle at each time towards P_{best} and G_{best} with random-weighted acceleration [^{th} ilteration. In further, C1, C2 are the acceleration constants, inertia, r1, r2 are the random numbers that lie between 0,1. The PSO proposed PI technique depends on objective function (OF) that monitors optimizing search. The PSO technique assigns random K_{p} and K_{i} and evaluates function. Whenever we have designed a controller for any system, one or more than one parameter is selected. Four performance index criteria are usually used to minimize the error function. These are given below

ISE integrates square of error signal over time, as given in _{p}, K_{i} values as

J is performance index, tsim is represented as simulation time that would be a large value so that system output response reaches steady-state value and T = 10sec is selected. W is the weight factor. Vector K = [K1, K2,…….Kn] is control system parameters i.e., PI parameters.[ K_{p}, K_{i}]T represents the parameters that have to be optimized and e =

Its inertial weight determines the PSO convergence behavior. A large inertial weight causes the optimization to converge slowly, while the low inertial weight causes local trapping.

As a result, the inertia weight should be selected to achieve a better search-utilization trade-off. In the implemented model, an inertial weight expression decreases linearly from a large to a small value over some iterations, improving the technique's performance.
_{p} and K_{i} values are obtained from PSO are 1.093, 15.091 corresponding to Swarm at GBEST.itae = 5.9165. The presence of these PSO gains in any system makes it stable and free of oscillations. The chosen parameters for PSO can be observed below in

Used parameters | Notation | Value |
---|---|---|

Size of population | S | 60 |

Max. iteration | N | 10 |

Search space dimension | P | 2 |

The acceleration constant | C_{1}, C_{2} |
2 |

Constant of inertia | W_{max}, W_{min} |
0.9,0.2 |

In this section, by using gains obtained from PSO, adaptively tuning of PI controller has been carried out using FLC. The following quantization factor can transform the error ‘e’ and rate of change ‘ec’ from the fundamental to the fuzzy universe ke = 3/

Seven sets of fuzzy are used to change these crisp inputs into linguistics variables, i.e., NB (Negative Big), NM (Negative- Middle), NS (Negative-Small), Zero's (ZE), Positive-Small value (PS), Positive-Middle value (PM), PB (Positive Big). Fuzzy rules for inputs and output is given in [

Rule# | Effect of error ‘e’ and ‘ec’ | Effect of ‘Kp’ and ‘Ki’ on system | Impact of defined rules on results |
---|---|---|---|

1 | e, ec are positive big | Select Small Ki and large Kp for the system. | To overcome large overshoot and fast response speed |

2 | e is positive medium value change in error is positive big value. | Select large Kp and minimum Ki for the system. | The system reaches a steady state instantly. |

3 | e varies from value of positive small to zero and ec varies from positive big to small value | Select Ki and large Kp for the system largely. | Maintain the stability of the system by reducing static error. |

4 | e varies from zero to negative small and ec positive big to zero, | Select medium Ki and large Kp for the system. | Minimize overshoot and static error |

5 | e varies from negative small to zero and ec zero to negative medium | Select small Kp increases Ki up to Positive Big value for the system. | Oscillations will be avoided in the system. |

6 | e varies from zero to positive minimum and ec varies from negative big to a smaller value | Select Kp value that is increasing gradually and Ki that decreasing for the system. | It may result in avoiding overshoot and fast response speed in the system. |

7 | e varies from positive small to zero and ec decreasing slowly. | Select large Kp and Ki for the system. | It may result in a system that is stable and free of oscillations. |

…

The PI parameters sum output by FLC and the PSO-PI parameters is the adaptive adjustment of the PI controller for the dc voltage regulator.

As

This part has demonstrated the effectiveness of the given HSAPF due to non-linear loads. by using the simulation results generated in MATLAB. The load is three phase bridge rectifier attached to its DC side with a resistive load.

The characteristics of the load current are used to design the RL tuned passive filter. A single-tuned passive filter is used to remove low-order harmonics as they have a maximum impact. The design simulation parameters are in

System parameters | Values |
---|---|

Phase voltage | 220 V |

Frequency | 50 Hz |

1 mΩ, 3 mH | |

470 μF | |

622 V | |

Load rating | 10,000 VA |

Bridge rectifier non-linear load | 42.2 Ω, 34.56 mH, 393 μF (720VAR) |

It can be seen that the resultant waveform is non-sinusoidal that causes the source current to be distorted. So, there is a need to make them sinusoidal that improve power quality by implementing different techniques. The Source voltage remains sinusoidal due to the balanced load. As in

Three scenarios are investigated for this non-linear load Case 1: when I_{d}-I_{q} is implemented alone with HSAPF for calculating the reference currents. Case 2: when HAPF is attached with I_{d}-I_{q} based PSO-PI controller. Case 3: HAPF is attached with I_{d}-I_{q} based adaptive Fuzzy PI controller for estimating maximum reference current.

After implementing the HSAPF based on the I_{d}-I_{q} technique on MATLAB/Simulink, the waveform of the Source current (I_{s}) becomes sinusoidal under non-linear load with THD 2.03% at a fundamental frequency of 50 Hz as given in _{d}-I_{q} control technique based on HSAPF. The harmonics in source voltage are minimized by impedance at the source side, so it can be concluded that no distortion on source voltage is observed.

The conventional controller is mainly for DC link voltage regulation to minimize switching loss of VSI and to charge the DC capacitor upon discharging. From _{d}-I_{q} technique, which is far better than Pq0 technique.

The HSAPF based on the PSO-PI control technique is used for dc voltage regulator to improve transient response due to non-linear load. The PI controller's performance depends on its gains; the best gains produce better results and improve system response. _{d}-I_{q} technique. The results show that the PSO algorithm is an efficient technique that helped provide better performance than the conventional PI controller by improving steady-state response by providing the best gains to the PI controller. _{d}-I_{q} control technique performed better by injecting compensating current thus, source voltages and currents are in phase has helped improve THD of the system to 1.98% as given in

The results concluded that PSO has successfully tuned the PI controller by giving the best gains, so that performance of I_{d}-I_{q}-based HSAPF is further improved. As in

This research aims to mitigate current harmonics on the source side up to the minimum value caused by non-linear load so that power quality in the system can also be improved.

In _{s}) is presented. It has been concluded that the given technique based HSAPF can keep the actual voltages and current by reducing the stability time (T_{st}) of the voltage regulator to 0.04 sec by using the PSO-based adaptive Fuzzy PI controller for harmonics mitigation.

The proposed model aids in the visualization of data in situations where wireless applications cannot operate independently of traditional database transactions. Data is accessed from both traditional centralized and distributed sources, as well as wireless applications, at the same time. It includes mobile transactions/queries as well as dissemination applications. As stated in our previous work [

Contents Providers (CP)s offer the data to be read out and revised by each user.

Dissemination Operators (DO)s are in control to push the data. Moreover, CPs feed DOs along with the required disseminated data.

Mobile Support Stations (MSS)s are the conventional platforms to help bi-directional wireless communication among wireless users.

Dissemination Controller (DC) directs data to be disseminated from the MSSs, CPs to DOs.

Functional units of dissemination design are separated into the 4 stages of the cellular network as follows.

The Database level: In this level, the CPs supplies the data to be disseminated, which they maintain and manage. These may be legacy systems that support conventional relational databases and SQL access.

The Signaling level: In this level, one or more DC is required to specify what and when data items are put in the wireless dissemination channels.

The Network level: At this level, MSSs provide the services and infrastructure needed to support the normal two-way wireless phone/data usage and the User level where Wireless Clients (WC)'s exists.

Implemented techniques | Parameters | |||
---|---|---|---|---|

THD |
Tst |
Ts (sec) | PF | |

Pq_{O} based control technique (conventional) |
3.34 | 0.07 | 5e-08 | 0.91 |

I_{d}-I_{q} implementation |
2.03 | 0.06 | 5e-08 | 0.973 |

PSO-PI based I_{d}-I_{q} implementation |
1.98 | 0.05 | 5e-08 | 0.951 |

PSO based adaptive Fuzzy-PI system I_{d}-I_{q} system |
1.72 | 0.04 | 5e-08 | 0.982 |

In this paper, a techno-economical HSAPF has been implemented to overcome the power quality issue by using an appropriate soft computing technique. Initially, Pq0 and

The error measured DC voltage of VSI and the set value is minimized in the case of a PSO-based adaptive Fuzzy PI.

The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.

It's economical, easy to carry out the technique.

Three different control techniques are analyzed in this paper for a three-phase HSAPF. The proposed design reduces THD to the lowest possible levels in all test cases. It can observed that the THD of source current in the case of PSO-based adaptive Fuzzy PI has reduced to 1.72%, while in the case of PSO-PI, it is reduced to 1.98% that lies the IEEE, 519–1992 harmonic standard. Therefore, it could be inferred from the comparative analysis that the adaptive Fuzzy PI system tuned by PSO has accurate results with a less THD, with minimum T_{st}, and a power factor nearer to unity. This work can be further extended in the future as instead of using PI controller, FOPI based PSO-GA technique can be implemented for further improvement in the performance of HSAPF. Moreover, a practical implementation based on a real hardware platform of this proposed technique can be considered in future work to test the results in MATLAB of the proposed technique.

The authors are thankful to King Saud University, Riyadh, Saudi Arabia, for supporting this research work, through Researchers Supporting Project number RSP-2021/184.