The integration of renewable energy sources is growing at a high rate because of electricity load demand and technological innovation. They're fed to the network via a power converter system that includes a DC/AC converter, a Maximum Power
A battery energy storage system (BESS) is an electrochemical device that charges (or gathers) energy from the grid or a power plant, then discharges it as needed to deliver electricity or other grid services. When it comes to load levelling, the energy storage system can help with voltage management as well as minimizing low voltage network disturbances. When it comes to the island network, it is regarded to be a useful solution for supplying critical loads when distribution lines are entirely cut off, allowing the network to continue running and so increasing the reliability of the power system.
Precise power quality (PQ) study encompasses the full power and electricity supply chain, from generation through distribution and evaluation. Voltage sags, breakdowns, swelling, harmonics, and imbalances all occur in the distribution system
This manuscript presents a hybrid control scheme for the PQ improvement for hybrid RES such as PV, wind energy hybrid scheme in grid and off grid mode. The suggested method is the consolidation of Barnacles Mating Optimization Algorithm (BMO) and Artificial Neural Network (ANN) and hence it is said to be as HBMOANN control scheme. The remaining segment of this manuscript is designed as: Segment 2 explains the power quality improvement topology with hybrid renewable energy sources. Segment 3 illustrates the proposed hybrid approach using ABMOANN Control Scheme. Segment 4 presents the results obtained through simulation using different algorithms. Finally, Section 5 concludes the proposed research work.
Wind turbine is designed to convert the wind energy into electric energy, with all control circuits and gearbox that convert the rotational low speed into electric power. Understanding of wind properties is very important for wind energy exploitation. Speed of wind is highly variable both geometrically from place to place and temporally, seasonal and in hourly means. These variations are called synoptic variations, and they have a peak at around 4 days. wind speed will be determined by the seasonal, synoptic and diurnal effects, which varies on a time, with turbulence fluctuations superimposed.
Wind turbine captures energy with the following equation
Where: P = Mechanical power in the moving air (Watt).
Tip speed ratio (TSR) of a wind turbine is defined as:
Where:
PV cells are clustered into significant part to arrange PV modules, which can be related in blend of series and lined up with plan of PV exhibits, later that is worked as PV electrical source.
The VI highlight condition of a PV cell is conveyed as module photocurrent (I_{ph})as follows
Where,
I_{ph } photocurrent (A);
I_{sc } short circuit current (A);
K_{i } short circuit current of cell at 25◦C and 1000W/m^{2};
T  temperature operated in kelvin;
Irradiation of solar in W/m^{2}
Reverse saturation current Module I_{rs} is given by
Where,
q=electron charge=1.6×10^{−19} C;
V_{oc }= open circuit voltage in volts;
N_{s }= number of cells connected in series;
n = Diode ideality factor;
k = Boltzmann’s constant = 1.3805 × 10^{−23} J/K.
The I_{0} saturation current of module varies with the Temperature of cell, which is shown by
Here: N_{p}: Parallel connection of PV modules in number; R_{s}: Resistance in series (Ω); R_{sh}: resistance in shunt (Ω); V_{t}: Thermal voltage of diode (V)
The closer AC waveform supplying voltages at the bus with rated currents, frequency
The various PQ improvement techniques are tabulated in Table 1 which provides complete overview of existing technology to mitigate the PQ issues and its contributions.




1. 
Applicability of PSO Technique to Mitigate Power Quality Problems 
Selective Harmonic Elimination (SHE) technique based on PSO algorithm. 
Eliminated 5th, 7th, 11th, and 13th output voltage and selected lower order harmonics 
2 
Compensators in Microgrid for Power Quality Enhancement 
Virtual fundamental impedance (VFI) loop and variable harmonic impedance (VHI) loop 
The active powerfrequency (Pω) droop controller and reactive powervoltage (QV) droop controller’s performances are supported by VFI loop 
3 
Role of APC in Power Quality Improvement 
In microgrid, the AC bus is interfaced with renewable energy sources by an active power conditioner (APC). Hence, for the power quality enhancement, this APC has to be controlled 
The current from the microgrid to become balanced and sinusoidal by making the APC to compensate the nonlinear load current 
4 
A novel control strategy with PI controllers and hysteresis control is projected 
Compensating the current harmonics, correcting the power factor, and balancing the PCC supply voltage 
THD of microgrid current to 3% Correction of power factor With the APC control, the degree of unbalance is less than 0.8% which is below the permitted level of 2% by international standards. 
5 
Power Quality Improvement with Controllers 
DG employs a PI controller to generate the unbalance compensation reference for the microgrid DGs 
The repetitive based voltage controller plays a major role in the synchronization of the microgrid and also generating and dispatching power in the microgrid under nonlinear and unbalanced load 
6 
Facts Devices in Power Quality Improvement 
DSTATCOM that recognizes positive sequence admittance and negativesequence conductance to regulate positivesequence voltage and to overcome negativesequence voltage. 
Operation of DSTATCOM with other devices, multiple DSTATCOMs are implemented together to restore the voltage 
The magnitude of voltage in a bus could diverge from their rated value. These variations are mostly permitted for tiny proportion but if they exceed few limits and described as disruptions. When magnitudes of the three voltages is not equal and are not 120 degrees apart then the system is called as unbalanced 3 phase power system. Furnaces, traction systems, and other large inductive machines, also big singlephase loads drawing currents indicate the voltage unbalances between two phases some equipment may also be connected between two phases such that current is only drawn on two out of the three. For the other equipment’s connected to the similar supply, this causes the higher loaded phases to experience a higher value of voltage drop, reduction in the voltage on those phases, or on any one phase. The general singlephase loads when unevenly distributed, the unbalance in the voltage can be observed across a 3phase system. This is originally balanced at the time of construction but when additional circuits and any equipment added to it this unbalance occur more often. The voltage unbalance is because of the degradation or the PFC capacitor bank unit failure, and a fault can produce the temporary voltage unbalances in the supply network. These factors have increased because of the reactive PF abnormality in maximum voltage lines. The reactance to resistance ratio (X/R) is low in the line, when activated by micro grid at the Distribution level, and the voltage gets impact by increasing the active PF. With all these reasons the variation of voltage on MG will be maintained with in
At grid voltage, this leads no sinusoidal waveforms. The optimal prevalence of nonlinear power electronic devices together with a maximum of sensitive loads causes in several elements along security with appropriate function of electronic factors. Harmonic is nothing but maximize the overall losses for system. The maximal harmonics is comparatively released through active/passive filters or shortens by using of suitable modulation strategy in power electronics switches command. Moreover, low order harmonics are much efficient to filter without shortening concurrently the signal on fundamental frequency. To overcome this issues, harmonic cancellation methods are existing, but it is not normally costefficient also conceptually hard to execute. Here, the 2 vital calculations are described to choose the amount of harmonic distortion, also deal with it. First, the THD as RMS percentage of harmonic frequency components among the base frequency component for voltage and current as represented in
The second calculations we developed to cope with harmonic distortion is described by the set of equations in (35) as follows
The structure of the BMO algorithm involves three main steps that start with the initialization of the barnacles, then the mating process, and finally, the reproduction of the offspring. These steps are mathematically implemented in the next sections.
In the BMO, barnacles are randomly initialized based on control variables number N and the number of barnacles n as follows:
where the barnacles X should be within the boundary limits as:
where X_{lb} and X_{ub} are the lower and upper vector bounds, respectively, and can be expressed as:
Forecasting process of artificial NN is employed to augment the wind velocity including optimal wind velocity factor. This Wind velocity factor is deemed as input, whereas probability of the available wind is deemed as network outcome. When the procedure of learning that is determined and regulated by weights, the outputs is obtained by nonlinear task of the input. ANN is used to capture unpredictable events of the wind power; therefore, the method guarantees the maximal wind power usage. Here, proposed hybrid approach solution of decreased the total cost. In this manuscript, a hybrid control scheme for PQ improvement in grid connected hybrid RES such as PV energy, wind energy and Battery is proposed. The proposed method is the consolidation of Hybrid Barnacles Mating Optimization Algorithm (HBMO) and Artificial Neural Network (ANN), hence it is called HBMOANN method. The major intention of this work is “improve the power quality based on the grid connected and independent microgrid mode based on total harmonics and unbalanced condition”. The ABMO method is taught using inputs, namely previously available immediate sources of energy. The present time's necessary load need depending on the desired reference power sources. The proposed method's simulation analysis is conducted using the provided test cases with various nonlinear load combinations.
In this manuscript, a hybrid controller scheme for the PQ improvement in the gridintegrated hybrid RES viz, photovoltaic, wind energy and Battery is proposed. The proposed method is the combination of Barnacles Mating Optimization Algorithm (BMO) and the Artificial Neural Network (ANN), hence it is called HBMOANN control scheme. Here, the major purpose this work is “improve the power quality based on the grid connected and independent mode of micro grid based on total harmonics and unbalanced condition”. The BMO method is tuned with the inputs i.e. recent instantaneous energy of the obtainable sources and the necessary demand requirement, based on the intended target power sources
F1 Best 2.58E−87 2.04E−01 3.63E−14 4.72E−149 Worst 3.04E−72 1.64E+00 1.09E−05 1.43E−124 Mean 1.60E−73 7.05E−01 7.85E−07 5.37E−126 Std 6.20E−73 3.65E−01 2.68E−06 2.61E−125 F2 Best 4.80E−58 7.50E−02 2.59E−03 5.18E−79 Worst 4.81E−50 3.13E−01 4.16E−01 5.53E−67 Mean 2.37E−51 1.81E−01 7.03E−02 2.17E−68 Std 8.82E−51 6.57E−02 9.65E−02 1.01E−67 F3 Best 1.35E+04 3.48E+03 1.31E+01 5.45E−140 Worst 7.34E+04 1.88E+04 8.22E+02 3.83E−110 Mean 4.91E+04 1.19E+04 1.14E+02 2.96E−111 Std 1.45E+04 3.68E+03 1.52E+02 9.60E−111 F4 Best 4.10E−01 8.23E+00 6.72E−01 3.65E−79 Worst 8.78E+01 3.12E+01 4.56E+00 5.33E−59 Mean 5.42E+01 1.93E+01 2.36E+00 2.32E−60 Std 2.50E+01 5.90E+00 1.09E+00 9.85E−60 F5 Best 2.74E+01 8.92E+01 1.13E+01 2.68E+01 Worst 2.87E+01 3.28E+03 1.73E+02 2.80E+01 Mean 2.81E+01 5.22E+02 5.63E+01 2.72E+01 Std 4.25E−01 8.82E+02 4.38E+01 3.12E−01 F6 Best 8.61E−02 9.48E−02 1.59E−12 9.14E−03 Worst 8.93E−01 2.11E+00 1.01E−04 5.81E−01 Mean 4.24E−01 8.55E−01 3.94E−06 8.29E−02 Std 2.15E−01 4.87E−01 1.85E−05 1.28E−01 F7 Best 5.37E−05 4.00E−02 1.04E−02 1.59E−04 Worst 1.85E−02 1.74E−01 5.85E−02 2.80E−03 Mean 3.82E−03 8.80E−02 2.76E−02 9.00E−04 Std 4.85E−03 3.29E−02 1.23E−02 6.61E−04 F8 Best −1.91E+03 −1.76E+03 −1.56E+03 −1.74E+03 Worst −1.63E+03 −1.32E+03 −1.04E+03 −1.64E+03 Mean −1.86E+03 −1.53E+03 −1.28E+03 −1.68E+03 Std 9.89E+01 1.11E+02 1.26E+02 2.67E+01 F9 Best 0.00E+00 5.85E−01 4.18E+01 0.00E+00 Worst 0.00E+00 8.91E+00 1.33E+02 0.00E+00 Mean 0.00E+00 3.68E+00 7.53E+01 0.00E+00 Std 0.00E+00 1.86E+00 2.43E+01 0.00E+00 F10 Best 8.88E−16 2.00E+01 3.79E+00 8.88E−16 Worst 7.99E−15 2.00E+01 2.03E+01 8.88E−16 Mean 4.44E−15 2.00E+01 1.94E+01 8.88E−16 Std 2.29E−15 7.76E−04 2.96E+00 4.01E−31 F11 Best 0.00E+00 1.83E−02 9.29E−13 0.00E+00 Worst 1.40E−01 1.38E−01 4.43E−02 0.00E+00 Mean 4.66E−03 4.94E−02 1.39E−02 0.00E+00 Std 2.55E−02 2.32E−02 1.43E−02 0.00E+00 F12 Best 4.72E−03 1.67E−05 4.84E−17 5.72E−03 Worst 4.43E−02 1.04E−01 4.15E−01 2.13E−01 Mean 1.39E−02 3.61E−03 2.42E−02 4.17E−02 Std 8.54E−03 1.89E−02 8.02E−02 4.02E−02 F13 Best 2.69E−01 8.52E−02 1.10E−02 4.64E−02 Worst 1.25E+00 8.96E−01 3.61E+00 2.97E+00 Mean 7.56E−01 2.58E−01 9.06E−01 5.49E−01 Std 2.50E−01 1.94E−01 1.11E+00 6.74E−01 F14 Best 9.98E−01 9.98E−01 9.98E−01 9.98E−01 Worst 1.08E+01 9.98E−01 1.64E+01 7.87E+00 Mean 3.06E+00 9.98E−01 5.22E+00 2.45E+00 Std 3.58E+00 4.52E−16 3.74E+00 1.95E+00 F15 Best 3.09E−04 3.94E−04 3.07E−04 3.08E−04 Worst 2.81E−03 2.07E−02 2.04E−02 2.04E−02 Mean 8.21E−04 5.76E−03 2.45E−03 1.97E−03 Std 6.88E−04 7.25E−03 6.08E−03 5.00E−03 F16 Best −1.03E+00 −1.03E+00 −1.03E+00 −1.03E+00 Worst −1.03E+00 −1.03E+00 −1.03E+00 −1.03E+00 Mean −1.03E+00 −1.03E+00 −1.03E+00 −1.03E+00 Std 6.78E−16 6.78E−16 6.78E−16 6.78E−16 F17 Best 3.98E−01 3.98E−01 3.98E−01 3.98E−01 Worst 3.98E−01 3.98E−01 3.98E−01 3.98E−01 Mean 3.98E−01 3.98E−01 3.98E−01 3.98E−01 Std 1.22E−05 3.65E−06 1.69E−16 1.69E−16 F18 Best 3.00E+00 3.00E+00 3.00E+00 3.00E+00 Worst 3.00E+00 3.00E+01 3.00E+00 3.00E+00 Mean 3.00E+00 3.90E+00 3.00E+00 3.00E+00 Std 1.72E−04 4.93E+00 0.00E+00 0.00E+00 F19 Best −3.00E−01 −3.00E−01 −3.00E−01 −3.00E−01 Worst −3.00E−01 −3.00E−01 −3.00E−01 −3.00E−01 Mean −3.00E−01 −3.00E−01 −3.00E−01 −3.00E−01 Std 1.13E−16 1.13E−16 1.13E−16 1.13E−16 F20 Best −3.32E+00 − −3.32E+00 − −3.32E+00 − −3.32E+00 − Worst 5.61E−02 3.20E+00 3.20E+00 3.09E+00 Mean −2.61E+00 −3.28E+00 −3.27E+00 −3.26E+00 Std 1.03E+00 5.83E−02 6.03E−02 8.17E−02 F21 Best −1.02E+01 −1.02E+01 −1.02E+01 −1.02E+01 Worst −8.82E−01 −2.63E+00 −2.63E+00 −2.63E+00 Mean −8.13E+00 −5.00E+00 −5.98E+00 −5.84E+00 Std 2.78E+00 3.46E+00 3.55E+00 2.30E+00 F22 Best −1.04E+01 −1.04E+01 −1.04E+01 −1.04E+01 Worst −2.76E+00 −2.75E+00 −1.84E+00 −2.75E+00 Mean −6.99E+00 −6.63E+00 −6.74E+00 −7.10E+00 Std 3.07E+00 3.66E+00 3.78E+00 3.01E+00 F23 Best −1.05E+01 −1.05E+01 −1.05E+01 −1.05E+01 Worst −2.80E+00 −2.42E+00 −2.43E+00 −2.43E+00 Mean −8.45E+00 −6.06E+00 −7.49E+00 −7.06E+00 Std 2.77E+00 3.75E+00 3.61E+00 3.41E+00
In this manuscript, a hybrid control scheme for PQ improvement in grid connected hybrid RES such as PV energy, wind energy and Battery is proposed. The proposed method is the consolidation of Hybrid Barnacles Mating Optimization Algorithm (HBMO) and Artificial Neural Network (ANN), hence it is called HBMOANN method. The major intention of this work is “improve the power quality based on the grid connected and independent microgrid mode based on total harmonics and unbalanced condition”. The HBMO method is taught using inputs, namely previously available immediate sources of energy. The present time's necessary load need depending on the desired reference power sources. The proposed method's simulation analysis is conducted using the provided test cases with various nonlinear load combinations.












0.96 
4.50% 
10% 
0.94 
5.60% 
12% 


0.96 
4.00% 
9.30% 
0.95 
4.27% 
10.11% 


0.97 
3.26% 
7.13% 
0.96 
3.15% 
8.41% 


0.98 
2.15% 
4.30% 
0.97 
2.73% 
5.13% 


0.99 
1.20% 
3.60% 
0.98 
1.40% 
3.80% 
This study presents the Grid connected mode PI control with total harmonics of Voltage is 4.50% and Total harmonics distortion of current is 10%. At Islanded mode the PI control total harmonics of Voltage is 5.6% And Total harmonics distortion of current is 12%. And with the voltage magnitude at the bus of point of common coupling (PCC) is 0.96 and 0.94 respectively. The response time of HBMO_ANN control is much better than other existing PI control algorithms. Both the grid current total harmonic distortion (THD) and inverter injected current THD are decreased to 3.60% and 3.80%, respectively, in both the mode of operations. Here, HBMO_ANN based control illustrates enhanced performance in terms of inverterinjected current quality.