J. Cent. South Univ. (2021) 28: 153-167

DOI: https://doi.org/10.1007/s11771-021-4593-1

Optimized PI controller for 7-level inverter to aid grid interactive RES controller

GAYATHRI DEVI K S, SUJATHA THERESE P

Noorul Islam Center for Higher Education, Kumaracoil, Thuckalai, Tamil Nadu 629180, India

Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Abstract: With the huge rise of energy demand, the power system in the current era is moving to a new standard with increased access to renewable energy sources (RESs) integrated with distribution generation (DG) network. The RESs necessitate interfaces for controlling the power generation. The multilevel inverter (MLI) can be exploited for RESs in two diverse modes, namely, the power generation mode (stand-alone mode), and compensator mode (statcom). Few works have been carried out in optimization of controller gains with the load variations of the single type such as reactive load variation in different cases. Nevertheless, this load type may be unbalanced hence, to overcome such issues. So, a sophisticated optimization algorithm is important. This paper aims to introduce a control design via an optimization assisted PI controller for a 7-level inverter. In the present technique, the gains of the PI controller are adjusted dynamically by the adopted hybrid scheme, grey optimizer with dragon levy update (GD-LU), based on the operating conditions of the system. Here, the gains are adjusted such that the error between the reference signal and fault signal should be minimal. Thus, better dynamic performance could be attained by the present optimized PI controller. The proposed algorithm is the combined version of grey wolf optimization (GWO) and dragonfly algorithm (DA). Finally, the performance of the proposed work is compared and validated over other state-of-the-art models concerning error measures.

Key words: PI controller; renewable energy source (RES); distribution generation; utility grid; GD-LU model; voltage analysis

Cite this article as: GAYATHRI DEVI K S, SUJATHA THERESE P. Optimized PI controller for 7-level inverter to aid grid interactive RES controller [J]. Journal of Central South University, 2021, 28(1): 153-167. DOI: https://doi.org/ 10.1007/s11771-021-4593-1.

1 Introduction

The current harmonics of the non-linear load may cause voltage harmonics, which leads to a severe PQ issue in the power network [1-3]. APF is widely deployed for compensating the unbalancing of load and current harmonics at the distribution level, which leads to an increased cost of hardware [4-9]. Generally, the grid-interfacing inverter could be efficiently exploited for injecting active power, and accordingly, the load current executes the vital functions like active power transmission [10, 11]. Besides, the objectives could be attained either simultaneously or individually with sufficient control of “grid-interfacing inverter” [12-16].

The PQ parameters at the PCC can be strictly sustained within the utility standards with no extra hardware cost [17-19]. Besides, there are specific challenges while integrating solar and wind systems directly with the grid [20, 21]. Because of the rise in air pollution concerning global warming, fossil fuel supply, and their rising cost have made it essential to focus on renewable energy source (RES) [22-24]. The high-level penetration of sporadic RES in distribution generation (DG) systems can affect networks related to stability, PQ, and voltage regulatory issues [25, 26]. Therefore, DG systems must follow strict regulatory and technical procedures to ensure the efficient, reliable, and secure operation of the network [27-29].

Nowadays, for raising the exploitation of grid linked PV, RESs [30-33] are widely deployed due to their rise in load demand utility. RESs act in micro-grid as well as in the smart grid. Micro-grids are integrated energy systems consisting of loads and distributed energy resources (DERs), which is the combination of DG and RESs, whereas the smart grid is capable of controlling and integrating DG storage and load [34]. For grid connection of RES, grid-tie inverter-grid integration is exploited [35]. The inverter consumes energy from the grid when RES is inadequate and this energy is supplied until sufficient RES is generated. The connection and disconnection of the grid with RES could be performed within 100 ms [36]. The most important function of a converter in the PV array-based grid system is to regulate the phase and magnitude of PV output by obtaining feedback from the utility grid [37-39].

The grid interactive converter/inverter undergo multiple load variations such as reactive load, unbalanced load, and neutral load conditions. However, the controller gains remain untuned to attend such different load conditions. Few works have performed optimization of such controller gains by considering load variations of single type such as reactive load variation at different instances. However, the load type can be unbalanced at any instant. So, a sophisticated optimization algorithm is an essential pre-requisite. This paper intends to address these challenges. Accordingly, the major contribution can be given as follows: This paper proposes a grid-interactive converter topology with a 7-level inverter and adaptive PI controller. To make the controller adaptive, a novel hybrid variant of the optimization algorithm is proposed. The proposed algorithm combines the grey wolf optimization and levy update function of dragonfly optimization to determine the controller gains of the adopted controller.

The arrangement of the paper is specified as Section II portrays the review. Section III explains the system description and section IV portrays the optimal tuning of gain parameters by the proposed hybrid algorithm. Section V illustrates the outcomes and the paper is concluded by section VI.

2 Literature review

2.1 Related works

In 2018, KIM et al [20] have analyzed the measures taken by the KEPCO for prevailing over the drawbacks in rising RES penetration that aided the actions taken by the government for expanding the RES. For facilitating the incorporation of RES into the upcoming KEPCO, the government has declared an assured interconnection strategy for RES at a range of 1MW. Finally, the investigational outcomes have illustrated the superiority of the introduced scheme over the other existing schemes in terms of determining the optimal sizing and location of a renewable complex.

In 2017, VIGNEYSH et al [21] have introduced a multi-objective control approach by employing the AFPI controller for GIC. The implemented controller exploited the adaptive and robust nature of the FLC and PI controller, by which the GIC performance could be enhanced effectively. Here, in the introduced model, the PI controller gains were adjusted dynamically by the FLC oriented control system based on the operational conditions of the system. Besides, the adopted scheme aided in providing a speedy response and minimal settling time and overshoot during interruptions.

In 2015, SWAMY et al [33] have introduced a new model, which intended to assess the perspective of high RES integration in the “Greek island of Syros”, which followed the scheduled interconnection with the NGS. At present, Syros handled oil-fired APS, which emitted huge quantities of carbon discharges. Interconnections between various islands in mainland and Cyclades have eradicated the exploitation of APS and it has reinforced the power network of islands and it has allowed the deployment of high solar and wind potential. Finally, the impact of interconnection was briefly examined concerning solar and wind energy.

In 2019, BALDZNELLI et al [23] have established a scheme for hybridizing flywheels with rSOCs that improved the rapid peak-shaving and ramping capabilities with enhanced power quality. Moreover, a widespread technique was introduced for computing the performances and feasibilities of storage systems. In this work, the flywheel+rSOC has minimized the limitations of the power grid and it has smoothened the power peaks to a large extent. At last, the adopted scheme was examined by carrying out the experimentations concerning variability in seasonal performances.

In 2019, PRABAHARAN et al [24] have developed a novel scheme for integrating grids in a solar PV system employing a modified MPPT approach with a multilevel inverter and multi-output converter. The exploitation of MLI improved the quality of current signal and output voltage, thus minimizing the passive filter’s size. Besides, it had eradicated the necessity of huge transformers for integrating grids. Finally, the outcomes have demonstrated the enhancement of the suggested model in terms of grid voltage and current.

In 2019, KUSAKANA [27] has established a new approach that portrayed the optimal modeling of energy management in grid-interactive WECS with battery storage. Accordingly, an arithmetical approach defining the optimal functions of the system was developed. The performance of the proposed scheme was examined in the South African environment via Matlab. Also, better cost minimization was found to be offered by the suggested model when compared to the other schemes.

In 2017, BIFARETTI et al [28] have introduced the MPC approach, which was dependent on the MLIP framework and it was deployed to a residential MG case. The attained outcomes have confirmed the potential capabilities of grid-connected MG’s. Moreover, the grids could be balanced in an efficient way, which allowed better penetration of RES. From the investigational outcomes, the potential of the adopted scheme was validated in terms of real applicability.

In 2019, SEDAGHATI et al [29] have established a novel power and control management scheme for a grid-connected MG that involved a 3-phase load and HRES system. Here, PV was the most important energy resource, whereas the BSS and SC have provided a transient and steady load demand owing to its different power densities. Additionally, SOFC resource was chosen to maintain the BSS with a full charge, by which the system reliability could be increased. Finally, the outcomes had demonstrated the superior performance of the suggested system and more particularly, revealed its capability in maintaining the load.

In 2019, ARYA [40] has introduced a cascaded fuzzy fractional-order (FO) PI-FOPID (CFFOPI- FOPID) controller and a control strategy for automatic generation control (AGC) problem solution in an electric power system. The proposed controller parameters were determined to employ a recent stochastic imperialist competitive algorithm (ICA). The controller guarantees the deviation in area frequency and tie-line power under load disturbances to zero in a minimum definite time. Finally, the robustness test illustrates the controller’s robustness, random change in power demand, and additional imperative nonlinearities.

In 2019, ARYA [41] has investigated the impact of energy storage hydrogen aqua electrolyzer (HAE)-fuel cell (FC) units on AGC of interconnected power systems. For this, a fuzzy tilt integral derivative with filter plus double integral (FTIDF-II) intelligent control technique was introduced. Sensitivity analysis substantiates that the proposed controller was robust as well as it executes well at variations in the system parameters and random load perturbations.

In 2019, ARYA [42] has analyzed the effect of energy storage systems on the AGC of interconnected conventional and restructured energy systems. Two‐area non-reheat thermal PS by additional generations from wind turbine system (WTS) and dish-stirling solar thermal system (DSTS) was explored extensively. To validate the effectiveness of the approach, it was tested on a two‐area non-reheat thermal system having governor dead band (GDB) nonlinearity, restructured multisource thermal gas systems, and reheat thermal. An ICA optimized fuzzy PID‐filter‐ (1+PI) controller known as FPIDF‐ (1+ PI) was employed as an additional controller. The robustness analysis illustrates that the ICA‐ optimized controller with ESSs executes efficiently and robustly.

In 2019, ARYA [43] has introduced a hybrid fuzzy fractional order proportional integral- fractional order proportional derivative (FFOPI- FOPD) controller to find AGC profitably in isolated and interconnected multi-area power systems. Initially, the proposed technique was implemented in the 1-area thermal system. Then it was extended to 2-area hydro-thermal and 3-area thermal power systems widespread to state its potential and extensibility. The importance of the method was predicted by comparing the results with the other traditional control methodologies and FPI/FFOPI controller. The sensitivity analysis proves that the recommended controller was strong and performs a reliable operation.

In 2019, ARYA [44] has introduced intelligent multi-stage fuzzy assisted PID with a filter FPIDF-(1+PI) controller to improve the conduct of AGC of the power system. Firstly, the two-area photovoltaic-reheat thermal system was appraised by utilizing the imperialist competition algorithm, and the parameters of the FPIDF-(1+PI) controller were optimized. The outcome of the proposed controller was compared with other traditional optimization techniques to prove its supremacy. Finally, under large changes in system parameters and random load demands, the robustness of the proposed controller with or without CES was verified. Therefore, the proposed method ensures excellent and durable results to provide reliable and high-quality electrical power to the end-user.

In 2014, GUPTA et al [45] have developed bacterial foraging optimization (BFO) control technique to design the integrated control gain for AGC in an interconnected multi-area system. Here they were considering three cases of disturbances and the controller was optimized by utilizing BFO. Finally, the outcomes show the system connected to BFOA responds quicker than the conventional methods.

In 2016, DAHIYA et al [46] have investigated the comparative performance of the optimal controller for AGC of electric power generating systems. For this study, from the literature different single, multi-area models with and without nonlinearities were simulated under sudden load perturbation. To validate the performance of the optimal controller, the AGC controller was compared with I/PI controller optimized adopting the conventional techniques. The comparison was made in terms of different performance indices, settling time, peak undershoot/overshoot, the minimum damping ratio, and system eigenvalues. Finally, the sensitivity analysis shows the robustness of the optimal gains of an optimal controller.

In 2018, ARYA [47] has introduced the fuzzy aided integer order proportional integral derivative with filter-fractional order integral (FPIDN-FOI) controller which was tuned by ICA to reduce the oscillations in power systems. For this, the method was implemented on two-area non-reheat/reheat thermal, PV-thermal/hydrothermal, and multi- source hydrothermal systems. Additionally, the proposed controller was compared to other existing controllers. Finally, the sensitivity analysis was carried out to reveal the robustness of the proposed controller parameters.

In 2012, ARYA et al [48] have presented a fuzzy logic-based PI controller (FLPI) for LFC of four-area interconnected reheat thermal power system. To restore the frequency and tie-line power smoothly to its nominal value within less time was the main intention of the FLPI controller. Finally, the outcomes show the robustness of the proposed controller with varying system parameters.

2.2 Review

Table 1 shows the reviews on-grid interconnected RESs. At first, the Kriging method was introduced in Ref. [20], which maximizes the power quality and it also offers higher reliability. However, cost variations have to be focused more. FLC was exploited in Ref. [21] that offers optimal voltage gain and it also provides improved robustness, but it has to focus more on overshoot. GIS was used in Ref. [22] that offers better savings of energy and it does not include optical vision. However, it needs an analysis of feasible delays. Besides, a-posteriori scheme was implemented in Ref. [23] that offers improved storage efficiency and provides better balancing of energy; anyhow, it needs more consideration on the inversion cycle. PWM technique was presented in Ref. [24] that offers increased quality of signals with the reduced count of components, but, sudden variations at irradiation conditions have to be examined. Moreover, the MPPT controller was implemented in Ref. [27] that provides minimal cost along with optimal storage. Anyhow, economic factors have to be concerned more. Besides, the MPC model was suggested in Ref. [28] which offers better penetration of RES and it could offer better stability. However, it requires consideration of fast system dynamics. The fuzzy approach was introduced in Ref. [29], which is more robust and it also offers improved tracking. However, weather conditions have to be concerned. CFFOPI-FOPID controller elucidated in Ref. [40] offers improved robust and stable operation. But the settling time was longer and it provides high oscillations. FTIDF-II controller presented in Ref. [41] offers less computational time and provide low tie-line power deviation. Yet, the computational cost was high as well as the controller may be stuck in local minima. Also, FPIDF‐(1+PI) exploited in Ref. [42] was more superior and provide improved robust. However, there was a variation in the system parameters due to wear/tear. Moreover, the FFOPI- FOPD introduced in Ref. [43] provides a fast and smooth response and also it offers less oscillatory outcomes. Nevertheless, the settling time was more. ICA optimized multi-stage FPIDF-(1+PI) exploited in Ref. [44], which provides very quick settling time, a minimum undershoots, and oscillation. The BFO implemented in Ref. [45] provides a faster response, but the system reliability was less and the settling time and peak time were more. An optimal controller investigated in Ref. [46] provides less oscillation and robust behavior; however, it was less stable. FPIDN-FOI controller introduced in Ref. [47] produces no oscillation, yet it takes more time to attain the required generation. FLPI controller employed in Ref. [48] is very simple and easy to implement; however, it needs more consideration on settling time and peak overshoot.

3 System description

The diagrammatic illustration of the DG interfaced with a 3-phase 4-wire distribution network through the 7-level inverter is shown in Figure 1. A VSC of 3-leg, 3-phase, 2-level is exploited in this work. The RES oriented DG unit is linked to the dc-link via a PCU. The RES oriented DG units from the utility grid are decoupled by the dc-link capacitor (C_dc1 and C_dc2) and they permit the automatic control of converter on two sides of dc-link [49]. The 7-level inverter output is linked to the utility grid via an LCL filter. A damping resistor R_d is serially linked with the filter capacitor, C_f, to offer passive damping. A variety of unbalanced and balanced non-linear/ linear local loads are linked to PCC.

Table 1 Reviews on conventional grid interconnected RES

Boost converter control in MPPT: Boost converter (or any dc-dc converter) connects PV array with the load. MPPT algorithm modifies the duty ratio (of this converter) such that PV array is operated at a voltage (or current) corresponding to maximum PowerPoint.

PV array: A photovoltaic system, also the PV system or solar power system is a power system designed to supply usable solar power using photovoltaics.

VSC control: A voltage source converter is a device, which is used to convert DC into AC. It is a converter in which the DC voltage always has one polarity and the power reversal takes place through the reversal of the current polarity.

7-level inverter: The 7-level multilevel inverter is obtained by cascading three full-bridge inverter circuits. The three full-bridge inverters are connected in series and a single-phase output is taken. Each full-bridge is fed from separate DC source.

VDC inverter: VDC stands for volts of direct current. This requires the power supplies of sensitive electronics to have a rectifying circuit that converts the alternating current to direct current. The voltage and polarity of alternating current take the form of a sine wave.

Figure 1 System model of the proposed framework

Utility grid: A utility grid is usually a commercial electric power distribution system that takes electricity from a generator (e.g., fossil fuel boiler and generator, diesel generator, wind turbines, water turbine, etc.), transmits it over a certain distance, and then takes the electricity down to the consumer through a distribution.

Figure 2 shows the dual loop d-axis control of a 7-level inverter using the proposed PI controller. Likewise, Figure 8 shows the q₀ axis control of the 7-level inverter for compensating the reactive power load and the unbalanced/neutral load current. The inputs to the PI controller are the voltage that is attained by minimizing the error between reference signal and fault signal [50]. Depending on these input waveforms, the PI controller gains are adjusted dynamically. Therefore, the gains of the PI controller should be fine-tuned, such that the error between the reference signal and the fault signal should be minimal. For this purpose, the GD-LU algorithm is introduced in this work for fine-tuning the gains of the PI controller.

Figure 2 Picture of q₀ axis control of 7-level inverter by proposed PI controller

4 Optimal tuning of gain parameters by proposed hybrid algorithm

4.1 Solution encoding and objective function

In the present work, for precise error minimization, it is planned to tune the proportional and integral gain parameters of the PI controller, such that the error between the reference signal and fault signal gets minimal. The input gain solution given to the proposed algorithm is shown in Figure 3. The defined objective function (OF) of the present work is given in Eq. (1), where R_s denotes the reference signal and F_s indicates the fault signal.

(1)

Figure 3 Solution encoding

4.2 Proposed GD-LU optimization

Though the conventional GWO approach includes various advantages; it is encountered with certain drawbacks such as bad local searching ability, slow convergence. Hence, to overcome the shortcomings of the conventional algorithm, the concept of DA [51] is incorporated in the GWO algorithm to introduce a hybrid algorithm. The algorithm describes the grey wolves’ hunting character and their headship hierarchy [52]. The wolves α, β and δ are the major wolves that focus on the process of hunting. Among these wolves, α is considered the leader that makes decisions relating to hunting process, sleeping location, time to awake, etc., whereas, β and δ hold the 2nd and 3rd levels that help α in taking decisions. Besides, the final level of wolves is concerned as ζ, which concerns on eating. The encircling characteristics are modeled as p Eq. (2) and Eq. (3), where M and L denote coefficient vectors, J_p indicates prey’s position vector, J denotes position vector of grey wolves and it specifies current iteration. Eq. (4) and Eq. (5) denote the model for M and L, where is a parameter which is minimized steadily from 2 to 0 in entire iterations. Here, ra₁ and ra₂ specify the random vectors that lie in [0, 1] and it_max denotes the maximum iteration.

(2)

(3)

(4)

(5)

According to the new hybrid concept, the evaluation of the hunting character of wolves (D_α, D_β and D_δ) takes place based on the Levy update equation of DA as given in Eq. (6), Eq. (7) and Eq. (8), where z signifies the dimension of the position vectors. The mothematical model of J₁, J₂, and J₃ are given in Eq. (9), Eq. (10) and Eq. (11). Accordingly, the final position updating the evaluation of wolves is specified in Eq. (12). The pseudo-code of the adopted GD-LU model is specified by Algorithm 1 and its flowchart representation is given in Figure 4.

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Algorithm 1 Proposed GD-LU algorithm

5 Results and discussions

5.1 Simulation procedure

The proposed model for the optimal PI controller was implemented in MATLAB and the corresponding outcomes were achieved. The experimental investigation has been conducted fault by connecting the load at the time interval of 0.08-0.2 s. In the present work, the voltage analysis and current analysis were held concerning time by comparing it with other conventional schemes like DA [52] and GWO [51]. Moreover, voltage analysis was carried out on various phases such as abc phase, ab phase, boost voltage, and dc voltage. Here, the V_dc voltages are measured across the capacitor placed near the PV array, whereas V_ab and V_abc are measured immediately after the 7-level inverter and utility grid respectively and the V_dc boost voltage is measured in the PV array. Also, the analysis was held concerning “rise time, settling time, settling minimum, settling minimum, overshoot, undershoot, peak and peak time” for validating the performance of the adopted GD-LU model.

Figure 4 Flowchart of the proposed GD-LU model

5.2 Current analysis

The current analysis for the proposed GD-LU model over conventional schemes like DA and GWO is given in Figure 5. During the analysis, the fault was induced at the time of 0.08 s, and the minimized distortion of the wave is observed. Therefore, the technique that attains the minimal distortion after 0.8 s was considered a better one. From the observed outcomes, the signal attained by the proposed GD-LU model after 0.08 s is found to be a perfect sine wave signal as given in Figure 5(c). Tables 2-4 show the error between the reference and phase current I_a, I_b, I_c respectively for the proposed GD-LU model with conventional schemes such as DA and GWO.

Figure 5 Analysis of abc phase current by proposed model and traditional models:

Table 2 Error between reference and I_a current

5.3 Voltage analysis

Figures 6-9, demonstrate the analysis of ab phase voltage, abc phase voltage, dc boost voltage,and DC voltage respectively for the suggested GD-LU technique over conventional schemes such as DA and GWO. An enhanced system must reveal minimal distortion after the fault has been induced to it. Here, on observing the graphs from Figure 6, it can be noted that the signals attained by conventional DA and GWO after 0.08 s are not a perfect sine wave. However, a perfect sine wave has been attained by the proposed GD-LU model after 0.08 s as shown in Figure 6. Similarly, concerning Figure 8, a high distortion is exhibited under the existing DA model; whereas, the proposed model has attained an error-free sine wave after 0.08 s. Besides, on analyzing the DC voltage from Figure 9, the signal attained by the proposed GD-LU model is found that with minimal distortion, the traditional DA model shows a higher distortion after 0.08 s. Thus, the enhancement of the adopted model is confirmed from the analysis outcomes. Table 5 shows the error between reference and phase voltage V_ab for the proposed GD-LU technique with conventional schemes such as DA and GWO. Tables 6-8 show the error between reference and phase voltage of V_a, V_b, V_c respectively by the proposed GD-LU model and conventional schemes such as DA and GWO. The errors between the reference and V_dc boost voltage by the proposed GD-LU model and conventional schemes such as DA and GWO are shown in Table 9. Table 10 shows the error between reference and V_dc voltage by the proposed GD-LU model with conventional schemes such as DA and GWO.

Table 3 Error between reference and I_b current

Table 4 Error between reference and I_c current

Figure 6 Analysis on ab phase voltage by proposed model and traditional models

Figure 7 Analysis of abc phase voltage by proposed model and the traditional models:

Figure 8 Analysis of DC boost voltage: Proposed model over the Traditional models

Figure 9 Analysis of DC voltage by proposed model and the traditional models

Table 5 Error between reference and V_ab voltage

Table 6 Error between reference and V_a voltage

Table 7 Error between reference and V_b voltage

Table 8 Error between reference and V_c voltage

Table 9 Error between reference and V_dc boost voltage

Table 10 Error between reference and V_dc voltage

5.4 Steady-state response

The steady-state response of the present method for attaining optimal PI controller using the GD-LU model was evaluated in this section. Here, the responses like “rise time, settling minimum, settling time, overshoot, settling maximum, peak, peak time, and undershoot” were achieved as shown in Table 11. The computed time responses should be minimum, which proves the betterment of the proposed model. On analyzing Table 11, the settling time of the adopted GD-LU model is 86.78% and 3.76% better than the existing DA and GWO models. Likewise, the undershoot response of the implemented scheme is almost 0 s and the peak time is about 3 s. Also, the peak response of the adopted model is 22.99% superior to the existing DA model. Thus, the enhanced outcomes prove the effectiveness of the adopted GD-LU model.

Table 11 Steady-state analysis by proposed work and conventional models

6 Conclusions

This paper has presented a novel optimization assisted control design for a 7-level inverter that guaranteed the dynamic performance in control generation. Moreover, a new hybrid algorithm termed as GD-LU was employed to overcome the barriers in traditional models. The main investigations of the work are revealed as follows:

1) The performance of adopted work is compared with other existing models concerning current and voltage analysis.

2) On analyzing the outcomes, the settling time of the adopted GD-LU model was 86.78% and 3.76% better than the existing DA and GWO models.

3) Likewise, the undershoot response of the implemented scheme was almost 0 s and the peak time was about 3 s.

4) Also, the peak response of the adopted model was 22.99% superior to the existing DA model. Thus, the enhanced outcomes prove the efficacy of the implemented GD-LU model.

Nomenclature

RES

Renewable energy sources

GD-LU

Grey optimizer with dragon levy update

DG

Distribution generation

VSC

Voltage source converter

GWO

Grey wolf optimization

DA

Dragon fly algorithm

DG

Distributed generation

PQ

Power-quality

PV

Photo voltaic

APF

Active power filters

KEPCO

Korea electric power corporation

AFPI

Adaptive fuzzy PI

GIC

Grid-interactive converter

FLC

Fuzzy logic control

NGS

National grid system

APS

Autonomous power system

rSOCs

Reversible solid oxide cells

MPPT

Maximum power point tracking

WECS

Wind energy conversion system

MPC

Model predictive control

MLIP

Mixed linear integer programming

HRES

Hybrid RES

BSS

Battery storage system

SC

Super-capacitor

GIS

Gases insulation substation

PWM

Pulse width modulation

PCU

Power conditioning unit

EPLL

Enhanced phase-locked loop

LPF

Low pass filter

Contributors

GAYATHRI DEVI K S conceptualized and designed the study, reviewed identified articles to determine if they met defined study inclusion and exclusion criteria, critically reviewed the manuscript, and approved the final manuscript as submitted. SUJATHA THERESE P reviewed identified articles to determine if they met defined study inclusion and exclusion criteria, critically reviewed the manuscript, and approved the final manuscript as submitted. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Conflict of interest

Authors state no conflict of interest.

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(Edited by YANG Hua)

中文导读

七级变频器的PI控制器的优化以辅助电网交互式RES控制器

摘要：随着能源需求的巨大增长，当前的电力系统急需适应新的要求，能将再生能源接入电网从而整合利用可再生能源。多级变频器(MLI)可在两种不同的模式下实现这个功能，即发电模式（独立模式）和补偿器模式(STATCOM)。目前，对于不同工况的单一类型负荷的变化，如无功负荷的变化，优化控制器增益的研究很少。这类负荷的稳定性不好的问题急需解决。因此，一个适用的优化算法尤显重要。本文介绍了一种基于优化辅助PI控制器的七级变频器控制设计算法。在所提出的算法中，PI控制器的增益是根据系统的运行条件，通过采用混合方案，即龙形更新的灰色优化器(GD-LU)，对增益进行动态调整，使参考信号与故障信号之间的误差最小，以便所提出的优化PI控制器可以获得更好的动态性能。提出的算法是灰狼优化(GWO)和蜻蜓算法(DA)的组合版本。最后，对所提出算法的性能与其他先进的模型就误差方面进行了比较和验证。

关键词：PI控制器；可再生能源(RES)；配电发电；公共电网；GD-LU模型；电压分析

Received date: 2020-03-07; Accepted date: 2020-10-15

Corresponding author: GAYATHRI DEVI K S, Research Scholar; E-mail: ksgayathridevi2@gmail.com; ORCID: https://orcid.org/ 0000-0002-8065-8640