A unique method of a PLC controller based performance evaluation of optimization algorithms and power quality methods in a grid-connected hybrid wind-solar system

Abstract

The electricity demand is growing due to increasing population and fast industrial development . To meet the worldwide energy requirement, the greatest possibilities are the wind-solar energy sources due to excessive accessibility, simplicity in use, and non-polluting in nature . The integration of these resources offers higher advantages, but the quality of the power scheme gets influenced by the different characteristics of wind and solar energy. Thus in the proposed work, an augmented controller and rectifier have been designed to improve the power quality in which the source current gets optimized by using the Hybrid Bat-Dragonfly optimization algorithm. The quality of the optimized power will be enhanced through the Five Legged Power Converter (FLPC) by converting the DC into AC by using a three-phase bridge rectifier without any power loss. The occurrences of harmonics in the current are reduced using a sieve optimized algorithm design.

Keywords

Five legged power converter (FLPC)fault detection segregation measure hybrid Bat-Dragon fly optimization algorithm PLC controller three-phase bridge rectifier

Introduction

Electricity is a major factor needed for the industrialization, urbanization, and economic development of any country. To protect the earth for the coming generations, many are thinking for sustainable energy solutions due to the rising worry for global warming and the fast exhaustion of petrochemical reserves (Sioshansi and Denholm, 2013). Wind and solar energy systems are the most important energy sources (Dhal, 2021; Roy et al., 2022). With increased flow of wind energy to the power grid, the instability and intermittency of wind energy harm the secure process of the power grid. Thus, in generating large-scale renewable energy, improving the quality of renewable energy production, particularly for wind and solar energy is very important. The wind and solar energy are the most wealthy renewable energy resource and usually has an adverse wind correlation (Shezan et al., 2017). A sensible choice for obtaining the constant supply of power from the renewable energy source is to combine the wind and solar energy sources so that when one energy source is unavailable or insufficient to meet the Load demand, other energy source will compensate for the shortage of the other energy source, because the wind and solar energy characteristics are complementary to each other (Dhal, 2021; Malik et al., 2020; Roy et al., 2022). Currently, two primary techniques of solar generation are photovoltaic (PV) and concentrated solar power (CSP) technologies. The combination of solar and wind power with batteries helps to stabilize energy production and also improves the overall hybrid system’s economic effectiveness (Galindo Noguera et al., 2018). The Optimal Power Flow (OPF) (Yu and Rosehart, 2012) is used to deliver improved quality electrical power with higher efficiency. Usually deterministic techniques are used to resolve the optimization issues (Ghorbani et al., 2018). Some innovative methods like, Particle swarm optimization (PSO), bacterial foraging algorithm, Genetic algorithm (GA) resolves the deterministic problems. In wind solar schemes, the optimization problems are solved by probabilistic approaches (Tang et al., 2008) to obtain the better results and accuracies in uncertainties (Biswas et al., 2017). describes the problems of optimal power flow in the wind speed (Gonal and Sheshadri, 2021) at a great level (Huang and Huang, 2014). describes a fuzzy logic control which depends on PSO algorithms offered for fine-tuning the PI control factors of the renewable energy scheme. A improved gains of the PID regulator in a green energy system can be obtained by using Fuzzy-PSO algorithm. Based on the echolocation behavior of the bat, the Bat Algorithm got developed, which possess quick convergence and strong overall searching ability. BA has a superior optimal solution (Rahmani and Amjady, 2017) in comparison to PSO. A Hybrid BA presented in Cao and Yan (2017), solves the problems in the disordered maps integration (Gonal and Sheshadri, 2021) and whole random model in the bat algorithm. Bat algorithm, possess reduced computational time, with small accuracy. Dragonfly algorithm (Gonal and Sheshadri, 2021) has great accuracy, but increased computational time (Mahmud and Nejadpak, 2019).

To solve the problems in the disordered maps (Gonal and Sheshadri, 2021) integration and whole random model in the bat algorithm, a Hybrid BA, is illustrated in Cao and Yan (2017). Bat algorithms possess a reduced amount of computational time, however a lesser amount of accuracy (Gonal and Sheshadri, 2021). Dragonfly algorithm has great accuracy, but large computational time (Mahmud and Nejadpak, 2019).

The probabilistic method, linear programing, and graphic construction methods are certain optimization techniques developed for hybrid wind-solar energy systems. To cope with the scarcity of electricity generation, the energy storage system was used to deliver a nonstop electricity supply, which consists of battery banks, fuel cells, etc. Hence, additional method is used for attaining wind-solar energy framework (Zhang, 2013).

In distant areas, for sizing procedures, the situation is difficult to get longstanding environment situations like wind velocity, incidence of solar irradiance. Therefore, artificial intelligence methodologies, used for substituting predictable sizing methods. Grid-connected systems impact the power quality parameters (Zhang et al., 2013).

Furthermore, the consistency of the system gets affected due to fluctuating surroundings of wind-solar energies, which can be reduced by expecting and planning perfectly (Sharma, 2014). Therefore, several techniques and algorithms established for predicting the weather fluctuations which permit the system operator to change additional existing power producing units, if some scarcity increases, which reduces the fluctuations. Energy storing devices are used to compensate the deficiency and to store the energy when large quantity of power is produced (YerraSreenivasa et al., 2012).

Though, fluctuating solar irradiance and changing wind velocity results in voltage variation is the primary concern. The variations in voltage primarily get influenced by the nature of the load, its magnitude and on the size and capacity of the unified grid. The voltage fluctuation issue can be resolved by using electric filters like static synchronous compensators, integrated power quality conditioners, and dynamic voltage controllers (Gonal and Sheshadri, 2020). Moreover, an optimal sizing method is necessary to efficiently forecast solar irradiation and wind velocity to maintain sufficient electricity to meet the requirement (Pourmousavi et al., 2015).

Hence, the combined wind-solar scheme consists of wind and solar in optimal combination for the better optimization with the superior quality to efficient and economical use of the electricity produced from the hybrid system.

Literature review

Indira-Gandhi et al. (2018). focused on AC / DC microgrid energy leadership, and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm explored its optimization. MOPSO delivered favorable efficiency and got considered as the best replacement for improving the use of electrical energy in distant regions. It got implemented by identifying the objective energy cost functions and the likelihood of power loss. However, it has less optimization in size.

Loukriz et al. (2016) presented that enhancing the effectiveness of the photovoltaic system based on new high-energy monitoring algorithms (MPPT) is the most promising option because of its low price and simple operation without updating devices. Many MPPT techniques are there with a set step size. However, the efficiency of standard algorithms gets decreased when atmospheric circumstances alter quickly. A fresh Incremental Conductance, IC MPPT algorithm variable step size has been suggested in this article. Even though the output voltage THD values are not improved.

Kumar et al. (2013) Biogeography-based optimization (BBO) algorithm is created to predict the optimum wind/PV hybrid power systems, sizing coefficient in distant fields. The BBO algorithm used to evaluate optimal component sizing and operational approach by minimizing complete hybrid power system costs while ensuring power availability. However, it has optimized in size and cost when compared with others.

Daraban et al. (2014) presented a modified GA (genetic algorithm) MPPT (maximum energy point tracking). When partial shading affects photovoltaic systems, a GMPPT algorithm is needed to boost the system’s capacity for energy harvesting, this work presented a fresh GMPPT algorithm: a P&O algorithm (perturb and observe) is incorporated into the GA feature and generates a single algorithm. By inserting a simple MPPT algorithm (P&O) within the GA structure, the number of iterations and population size are cut, resulting in a shorter time for the MPP (maximum PowerPoint) and optimizing the parameters of the algorithm and the final solution. However, the calculation of a function has not better random numbers.

Cruz et al. (2013) had suggested adapting the bee-inspired optimization algorithm to solve the problem of data clustering. The algorithm got to run for separate datasets, and the findings acquired showed high-quality clusters and solution variety while automatically determining an appropriate amount of clusters. However, it had less quality in the analysis of parametric sensitivity.

Adarsh et al. (2016) presented a fresh meta-heuristic optimization algorithm, a chaotic bat algorithm to fix the problem of economic dispatch that incorporates several limitations on equality and inequality, such as power balance, banned job areas, and ramp rates. For some issues, transmission losses and several fuel choices got considered. It has poor quality when compared with other algorithms.

Daely and Shin (2016) applied the Dragonfly Algorithm (DA) to estimate node place randomly deployed in the specified region. The location area is to forecast the location of each unknown node in a specified region. The distance measurement among the unidentified node and beacon node in range-based localization is a significant component for further estimating unknown node location even though it improves the processing speed of DA and will rise in high complexity.

Wen et al. (2013), an extensive comparison was made between a direct matrix converter (MC), an indirect MC, and a back-to-back dc-link voltage converter for the asynchronous motor drive of 15 kW permanent magnet. The comparison includes examining the passive components, including the EMI input filter, the silicon chip area required for a defined maximum thermal charge of the power semiconductors, total loss and achievable efficiency, prediction of the resulting volume and weight of the passive components and to end, a study of the communication between converter effectiveness, volume and mass even though it raises expenses and decreases the system’s lifetime.

Bernal-Perez et al. (2013) included an HVDC diode-based rectifier technical feasibility study to connect big offshore wind farms to an onshore voltage converter (VSC). The wind farm uses a disseminated control algorithm where all wind turbines add voltage and frequency control to the offshore grid while allowing for optimal tracking of wind turbine power. Also, a big amount of harmonics (input current) are generated by the diode rectifier that influences the effectiveness of the utility scheme.

Hojabri et al. (2011). presented easy and complete viewing, and the examination method for a matrix converter is provided created on the modulation matrix’s singular value decomposition (SVD). The modeling technique described provides a fresh restriction between the profit of the matrix converter and its factor of input energy. The modulation matrix’s SVD results in a unified modulation method that achieves a matrix converter’s complete capacity. However, for unbalanced or distorted input voltages or unbalanced loads, the input present and output voltage are distorted.

Thus from the above discussion, it is revealed that (Indira-Gandhi et al., 2018) has less optimization in size (Loukriz et al., 2016), the output voltage THD values are not improved (Kumar et al., 2013) it has to optimize in size and cost when compared with others (Daraban et al., 2014), calculation of a function has not better random numbers (Cruz et al., 2013), had less quality in the analysis of parametric sensitivity (Adarsh et al., 2016), Had bad performance relative to other algorithms (Daely and Shin, 2016). Improves the processing speed of DA and will rise in high complexity (Wen et al., 2013). Increases expenses and decreases the system’s lifetime (Bernal-Perez et al., 2013), manufactures a large number of harmonics (input present) affecting the efficiency of the utility scheme (Hojabri et al., 2011) distorting the voltage input and output. Thus, from all the above issues, a controller needs to be developed to solve all these problems.

PLC controller for hybrid wind-solar power system

The main components of the Wind Solar Hybrid System are the wind aero generator and tower, solar photovoltaic panels, batteries, cables, charging devices, and inverter. Power quality is one of the primary limitations of power systems transmission and distribution. The quality of the energy scheme is influenced by the solar and wind energy (Gonal and Sheshadri, 2020) that cause voltage fluctuations, high harmonic distortion, and less transient stabilities. Optimization is also a significant problem in the hybrid wind-solar energy system. Ideally, high power quality produces a perfect power supply accessible as a sheer sinusoidal waveform, noise-free, and is always within tolerances of voltage and frequency. It’s taken into account as the term which is used to define the power of electricity, and it used to drive the electrical load and ability to drive function used in electric power. To this end, an effective controller needs to get developed that effectively improves the effectiveness of the wind-solar hybrid system (Gonal and Sheshadri, 2021)) by optimizing the energy and improving the quality of power.

As day moves, the demand for current is increasing massively (Gonal and Sheshadri, 2020). Hybrid systems prove to be a promising source, as it combines wind and PV resources for electricity production (Gonal and Sheshadri, 2020). Thus, our work has proposed a PLC controller in which the power generated from a hybrid wind-solar power system is received then optimized by the Hybrid optimization algorithm called hybrid Bat-Dragon fly algorithm on the generation side. The output from the PLC controller gets passed to the five-legged power converter, which converts DC to AC and produces the final output without power loss as well as enhances the power quality in the hybrid power system. Then, the Hybrid Bat-Dragon fly optimization algorithm got proved as the best optimization algorithm for a hybrid wind-solar power system by comparing the following parameters of the optimized output with the existing optimization algorithms (Gonal and Sheshadri, 2021): such parameters are Cost Optimization, Sizing Optimization, Speed/Time, THD, K_d, K_c, K_p, and Thd. Similarly, the five-legged power converter is also proved to be the best overall the existing converters to improve the power quality in a hybrid power system by comparing it with all those existing methods in terms of Power Factor, Switching Losses, Voltage Fluctuations, Electrical Power Efficiency, Harmonics, Network Unbalance, Reactive Power, Transients (Fast Disturbances), Flicker, Oscillations (Resonances), Voltage Variations. By comparing the hybrid Bat-Dragon fly algorithm and five-legged power converter with existing methodologies, it is proved that our proposed methodologies are the best among all. The Flow Process of the controller is shown in Figure 1, in the generation side the power generated from the Hybrid wind-solar power system, the wind and solar are optimized by using the hybrid Bat-Dragonfly algorithm in a PLC Controller and the power quality of the optimized power flow from the hybrid system in the generation side is improved in FLPC at the distribution side.

Figure 1.

Flow process of proposed Controller.

Hybrid Bat-Dragon-fly algorithm

The need for RES hybrid operation is increasing quickly in remote areas and islands. It is, therefore, essential to optimize hybrid wind-solar systems (Gonal and Sheshadri, 2021). Solar systems supplied by the hybrid operation of the Bat Algorithm and Dragonfly Algorithm are the suggested method of optimization in the hybrid wind-solar system (Gonal and Sheshadri, 2021).

Bat algorithm

The Bat algorithm design has done through the echolocation behavior of bats (Gonal and Sheshadri, 2021) in their food location, which is developed by Yang and used to solve the problems of optimization. Each near bat employs the same way in the initial population by updating its position in echolocation performance (Gonal and Sheshadri, 2021). To develop echoes, powerful ultrasound waves get radiated on the bat echolocation perceptual system (Gonal and Sheshadri, 2021).

Dragonfly algorithm

Some small animals hunted by the dragonflies are generally stated to as small hunters (Gonal and Sheshadri, 2021). In front of all other species, Small Dragonfly has come. Dragonflies have unusual swarming behavior. Migration and hunting are the two targets of the swarm of the dragonfly. Bat algorithm has less computational time but has less accuracy, and the dragonfly algorithm has great accuracy (Gonal and Sheshadri, 2021) but consumes large computational time.

The two effective optimization algorithms are combined in this suggested approach into a single powerful algorithm called the hybrid Bat-Dragonfly algorithm that is highly accurate and consumes less computational time. This algorithm is used to optimize the wind, solar system energy flow connected to the grid. The output from the solar PV panel is DC and gets transformed into AC by a three-phase inverter (Gonal and Sheshadri, 2021). The energy scheme was interfaced with the three-phase grid. Thus, the conversion used for the synchronization operation of Park and Clarke (Gonal and Sheshadri, 2021). This transformation involves the control of the inverter’s PID controller (Gonal and Sheshadri, 2021).

In this suggested methodology, the PID controller is designed by the HBDF algorithm. Here, the objective function is to maximize the proportional and integral gain of the PID controller (Gonal and Sheshadri, 2021). The Bat algorithm initially receives the error values, and then the Dragonfly algorithm is provided with the output of the Bat algorithm values. Therefore, Dragonfly’s algorithm provides the highest value of this comparison. Therefore, the PID controller (Gonal and Sheshadri, 2021) should track this relation optimally. On the basis of this reference current, the pulses are produced for the inverter.

The highly optimized power in the PLC controller is then passed to the five-legged power converter (FLPC), converting the DC to AC and also enhancing the power quality.

Optimized five legged power converter (FLPC)

Based on a time-invariant power converter, a five-legged harmonic remover avoids elevated harmonic distortion and increases power efficiency in power plants. The energy loss, fault, and part of high frequency are the parameters found in this scheme. The power quality and the transient stability is maintained by the system combating with high harmonic distortion (Gonal and Sheshadri, 2020). The Figure 2 shows the five legged power quality convertor (FLPC) with an optimized filter. Originally, using a three-phase bridge rectifier, alternating power from the wind farm gets transformed into direct current with zero energy loss and then modified to block the high-frequency portion in the nonlinear direct current condenser (Gonal and Sheshadri, 2020). Fault detection segregation measure (FDSM) (Gonal and Sheshadri, 2020) subsequently detects the fault in the measure. Following the removal of the fault, the direct current allowed DC to be converted into AC via a Diode-based DC to AC converter (Gonal and Sheshadri, 2020). Finally, the alternating current experiences harmonics, so that a bridge-based C-type filter (Gonal and Sheshadri, 2020) is configured to suppress harmonics (Gonal and Sheshadri, 2020). Therefore, the suggested scheme has achieved better energy quality.

Figure 2.

Five legged power quality convertor with an optimized filter.

Three Phase Bridge Rectifier

A three-phase bridge rectifier (Gonal and Sheshadri, 2020) used to transform AC that sometimes flows back into DC (Gonal and Sheshadri, 2020), flowing in one direction. It consists of six thyristor and uses the devices like IGBT’s, MOSFET’s, SCR’s, etc., to vary the power at various voltages. By activating these gadgets (Gonal and Sheshadri, 2020), the output power at the load (Gonal and Sheshadri, 2020) will be varied appropriately. The three-phase bridge rectifier circuit’s diagrammatic representation is shown in Figure 3. The Input and the output waveform of a three phase full wave rectifier is shown in Figures 4 and 5.

Figure 3.

Three-phase bridge rectifier.

Figure 4.

Waveform of a Three-phase bridge rectifier (Gonal and Sheshadri, 2020).

Figure 5.

Three-phase full wave rectifier (Gonal and Sheshadri, 2020).

Therefore, in the three-phase bridge rectifier, the entire AC (Gonal and Sheshadri, 2020) derived from the renewable resources is converted to DC without any power loss, although it includes high frequency. Therefore, to obtain better power quality, the high frequency present in the supply must be removed because the presence of high frequency makes the transformer operate with higher core losses and hence lower efficiency.

Nonlinear capacitor

Within the transformed direct current (DC), the non-linear capacitor (Gonal and Sheshadri, 2020) used to remove the high-frequency constituents remains. Surface-based packages (Gonal and Sheshadri, 2020) are small, and condenser terminals are used for shortage linking. Because of the terminals and their simple automatic assembly, these modules avoid undesirable high-frequency impacts (Gonal and Sheshadri, 2020). The capacitance value depends on the applied voltage. Figure 6 shows the nonlinear condenser diagram with a rectifier. It also smoothens the rectifier’s DC. The main function (Gonal and Sheshadri, 2020) of the nonlinear condenser suppresses the source’s high frequency and smoothen rectifier’s output waveform.

Figure 6.

Rectifier with capacitor.

Fault detection segregation measure

Figure 7 shows the circuit schematic of FDSM, which removes the fault in the system without breakdown. This circuit is included to demonstrate the yield of the undefined spectator when a fault are detected and disconnected.

Figure 7.

Simple circuit with FDSM.

Typically, in large power systems, a circuit breaker is installed to disconnect the power supply when a fault (Gonal and Sheshadri, 2020) occurs, but, to increase reliability, a smaller region should be disconnected where the fault is present. The circuit breaker cannot bear while incorporating other sources such as wind turbines and PV panels (Gonal and Sheshadri, 2020). Therefore, FDSM is used to removing the fault from the system when a fault occurs, the circuit breakers cannot bear the other sources like wind turbines and solar PV. The main circuit gets connected to the load, once the fault is restored. If the circuit mistake is permanent, then the after some time defective circuit gets detached.

The assured voltage regulation is obtained by knowing the restricting resistance expressed by the equations.

\frac{Δ V}{V_{p}} = \frac{R_{e}}{R_{e} + R_{l}} \times 100

(1)

Here $V_{p}$ represents the voltage in the power system,

$R_{e}$ , equivalent resistance,

$R_{l}$ , limiting resistance (Gonal and Sheshadri, 2020).

The fault gets rectified utilizing the restrictive resistance, whole process will not be interrupted due to the fault, instead, a defective circuit gets shut down. Then the DC should be converting into AC through the converter.

DC to AC converter

The inverter is a circuit which converts the DC voltage into appropriate AC voltage (Gonal and Sheshadri, 2020), which is synchronized with the grid network.

The primary DC to AC converter, shown in Figure 8. The DC output of the fault detection segregation measure is converted to provide the AC electric power, using the inverter, reduces the complexities created during process and also the expenditure.

Figure 8.

Basic DC to AC converter.

Although, the waveforms hold definite harmonics. The output waveforms of the inverter are sinusoidal.

Optimized C type filter

The resonance is the main issue of a practical notch filter; for acceptable performance, alternative filters are to be considered. The C-type filters are a good option as they can be tuned for several harmonic frequencies and prevent most of the resonance problems. Some assumptions are to be made during the design of the C-type filter, such as the resistance in the reactors, and the dielectric losses in the condensers are to be neglected. Basic system nominal voltage, frequency, and reactive power capacity are mandatory to be known at specified fundamental system frequency. Such data can be easily obtained from the distribution system’s power flow review. The basic structure of a C-type filter is shown in Figure 9.

Figure 9.

Optimized C-type filter.

The filter is made up of two condensers, one inducer, and one damping resistor. To reduce the power loss, the value of L2 and C2 tuned to the fundamental frequency. The value of the main condenser can be easily calculated with the knowledge of base voltage and the reactive power requirement at nominal frequency. C1 calculation is very simple because the inductor-capacitor path works as a short circuit at the fundamental frequency. The reactance of the main capacitor is given by equation (2).

X_{c_{1}} = \frac{V_{b}}{Q_{c}}

(2)

Where V_b is the base voltage, and Q_c is the reactive power rating.

The other three parameters of the filter can be estimated with the knowledge of the main capacitor value by making use of optimization techniques.

Sieve optimization algorithm

The harmonic C-type filter is designed optimally using the optimization algorithm. The PSOGSA exhibited extraordinary search capabilities in solving different optimization issues, but still, it has many drawbacks, like tendency to get stuck in the local minima and slow convergence. The position-updating equation of the search agent’s velocity V ⁿ_t+1 is given as follows to add on sieve optimization algorithm in the filter design.

V_{t + 1}^{j} = k_{1} \cdot V_{t}^{n} + c_{x} \cdot k_{2} \cdot a_{t}^{n} + c_{γ} . k_{3} . a_{t}^{n} (X_{opt} - X_{t}^{n})

(3)

where, \forall_{j} \in N_{max}

Here, v_tⁿ, a_tⁿ, X_tⁿ represents the velocity, acceleration, and position, respectively. The uniformly distributed k random numbers k₁, k₂, k₃ are in the range 0–1. C_x and $c_{γ}$ are the positive coefficients. X_best is the best solution, and N_max is the maximum number of iterations.

In the proposed algorithm, initially, the network data, branch lengths, loading data, non-linearity level, voltage harmonics, and operational limits are given as input. The iteration gets commenced by simulation of the Monte-Carlo function. The normal probability distribution function is obtained for the uncertain parameters using the MC simulations. The iteration is continued until the last MC is achieved. The fundamental and harmonic load flow is calculated along with the objective function. The optimization is done using equation 3. The obtained result thus optimizes the filter parameters and generates the signal with gained voltage.

Thus the harmonics are nullified in the alternating current, and the energy is transmitted to the grid. Thus in our proposed work, the power quality from the hybrid power system is efficiently enhanced by optimizing the power and converts the alternating current (AC) acquired from the wind farm to direct current (DC) with zero power loss in an effectual mode (Gonal and Sheshadri, 2020).

Result analysis

This section ensures the efficiency of our proposed work by analyzing the results and comparing those proposed results with the prior methodologies.

System specification

The proposed system has been implemented in MATLAB/SIMULINK to demonstrate competent power utility. The system specifications are (Table 1);

Table 1.

System specifications.

Platform (Gonal and Sheshadri, 2020)	MATLAB 2018b (Gonal and Sheshadri, 2020)
Operating System	Microsoft Windows 7
CPU	Intel core i5
Primary Memory	8 GB RAM

Experimental results

The experimental output of the proposed augmented controller rectifier with an optimized filter, as illustrated in Figure 7. It shows that the hybrid power source, that is, solar and wind power, is passed to the PLC controller. The power in the controller got optimized by using a hybrid Bat-Dragon fly optimization algorithm, and the optimized power in the controller is passed to the Five Legged power quality converter, and the converter output is a converted DC-AC output with no energy loss. Also, the sieve optimization-based filter efficiently reduces the harmonics. The simulation output of our overall proposed work with enhanced voltage is given in Figure 10.

Figure 10.

Simulation output of our proposed work.

Figure 8 gives the output with optimized voltage and current obtained by the proposed PV-wind hybrid technique with enhanced energy efficiency. Figure 11 shows the input power of the Hybrid wind-solar energy system.

Figure 11.

Hybrid wind-solar energy system input power.

The proposed rectifier receives the input from the Hybrid Bat-Dragon fly optimization algorithm-based controller. Then is fed to the filter through an optimized filter based FLPC, and the amplified output obtained is shown in Figure 12.

Figure 12.

Curve representation of optimized power from the proposed work.

Comparison results

To evaluate the optimization capability of the Hybrid Bat-Dragon fly optimization algorithm and the power quality of the five-legged power quality converter, the proposed methodologies got compared with the corresponding existing methodologies.

Comparison of proposed augmented controller rectifier with existing methods

The comparison analysis of our proposed technique with existing is described below (Table 2). Here, we compared the performance of execution time, frequency, steady-state error, and total harmonic distortion with existing techniques such as

Table 2.

Comparison of Proposed method with Existing.

Power converters	Execution time	THD	Frequency	Steady-state oscillations
Proposed)	0.5	0.99	0.19	2.014
OASC (Bernal-Perez et al., 2013)	0.83	3.24	0.7368	2.736
HC-MPPT (Wen et al., 2013)	1.21	3.83	0.5381	2.981
Fuzzy (Hojabri et al., 2011)	0.73	1.78	0.7368	3.138

Execution time

From the Figure 13.A comparison shows that the execution time of our proposed converter got highly reduced as compared with Fuzzy, HC-MPPT, and OASC methods.

Figure 13.

Comparison of the execution time of the proposed method with existing methodologies.

Total harmonic distortion

Total harmonic distortion (THD) is the degree of a signal’s harmonic distortion and is described as the proportion between the totality of all harmonic mechanisms energy and the fundamental frequency.

The Figure 14.a shows the comparison that the THD of our proposed converter got highly reduced by 0.98% when compared with prevailing approaches.

Figure 14.

Comparison of the THD of the proposed method with existing methodologies.

Frequency

The Figure 15 comparison shows that the Frequency of our proposed converter got highly reduced when compared with existing systems.

Figure 15.

Comparison of the frequency of the proposed method with existing methodologies.

Steady-state error

The Figure 16 comparison shows that the steady-state error of our proposed method gets highly reduced when compared with other techniques.

Figure 16.

Comparison of steady-state error of the proposed method with existing methodologies.

Comparison of proposed algorithm with existing optimization algorithms

Hybrid Bat-Dragonfly Algorithm, compared for analyzing the optimizing capacity with other optimization algorithms, and they are Multi-Objective Particle Swarm Optimization (MOPSO), Incremental conductance (Inc.) algorithm, Genetic algorithm, Modified Genetic algorithm, Bee-inspired algorithms, Bio geography-based optimization (BBO), Bat algorithm, Dragonfly algorithm, Rastrigin optimization algorithm, and sphere optimization algorithm (Table 3).

Table 3.

Comparison of proposed algorithms with existing optimization algorithms.

Optimization algorithm	K_p	K_i	K_d	THD	COST	TIIME	SIZE
Proposed method	0.04	0.034	0.46	0.45	0.821	0.034	0.034
MOPSO (Indira-Gandhi et al., 2018)	0.078	0.078	0.44	0.78	0.978	0.078	0.078
Modified GA (Daraban et al., 2014)	0.11	0.11	0.41	0.51	1.11	0.11	0.11
Bee Inspired optimization (Cruz et al., 2013)	0.38	0.38	0.38	0.58	1.38	0.38	0.38
Bio geography based optimization (Kumar et al., 2013)	0.42	0.42	0.39	0.52	1.42	0.42	0.42
Bat Algorithm (Adarsh et al., 2016)	0.52	0.52	0.38	0.52	1.52	0.52	0.52
Dragonfly Algorithm (Daely and Shin, 2016)	0.49	0.49	0.392	0.59	1.49	0.49	0.49
Sphere	0.50	0.45	0.397	2.09	-	-	-
Rastrigin	0.48	0.47	0.38	1.83	-	-	-

K_p

The proportional profit (K_p) is the proportion of the output response to the error signal. By increasing the proportional gain, the speed of the control system response is usually improved.

Ki

Excessive gain can make the axis oscillate. The Integral Gain regulates how much of the Control Output is produced during position control, respectively, owing to the accumulated position error.

K_d

A derivative defines the path or speed of a signal trace shift at a precise stage in time. The derivative expression in the above PID equation, therefore, considers how quickly, or at what pace, mistake (or PV as we address next) is evolving at the present moment. The Figure 17 shows the comparison of Kp value of proposed method with existing methodologies.The figure 18., shows the comparison of Ki value of proposed method with existing methodologies. The Figure 19., shows the comparison of Kd value of proposed method with existing methodologies.

Figure 17.

Comparison of K_p value of proposed method with existing methodologies.

Figure 18.

Comparison of Ki value of proposed method with existing methodologies.

Figure 19.

Comparison of K_d value of proposed method with existing methodologies.

Total harmonic distortion (THD)

Total harmonic distortion (THD) (Gonal and Sheshadri, 2021) is the degree of a signal’s harmonic distortion and got described as the proportion between the sum of the energy of all harmonic elements and the fundamental frequency by our proposed optimized filter. The Figure 20 shows the comparison of THD value of the proposed method with existing methodologies. The Figure 21 shows the comparison of cost optimization of the proposed the method with existing methodologies. Similarly, the Figure 22 shows the comparison of time optimization of the proposed method with existing methodologies and the Figure 23 shows the comparison of size optimization of the proposed method with existing methodologies.

Figure 20.

Comparison of THD value of the proposed method with existing methodologies.

Figure 21.

Comparison of cost optimization of the proposed the method with existing methodologies.

Figure 22.

Comparison of time optimization of the proposed method with existing methodologies.

Figure 23.

Comparison of size optimization of the proposed method with existing methodologies.

Thus, from all the above results, it has clearly shown that our proposed work has efficiently optimized the output using the power quality, which is also increased in our proposed work than the existing systems.

FFT analysis

The Figure 24 shows the FFT analysis of PLC based HBDFA with THD of 0.01% which is the very lowest THD in the industry.

Figure 24.

FFT analysis of PLC based HBDFA.

Conclusion

Power quality is an essential one in a hybrid wind-solar power system, and it gets enhanced for better efficient performance. The power value in a hybrid wind-solar system gets highly enhanced in our proposed work by using the augmented controller and rectifier design. In the PLC controller, the current from the power system got highly optimized by using the efficient optimization algorithm, Hybrid Bat- Dragon fly optimization algorithm and the optimized power in the controller get passed to the Five Legged power converter, which highly enhanced the power quality by incorporating an optimized algorithm based filter which efficiently reduces the harmonics in the signal. Thus, the proposed novel hybrid PV-wind system efficiently proved as the best method among all the existing methods by comparison.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Veeresh S Gonal

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