A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

K. Thanigaivel; S. Ramesh; K. Karunanithi

doi:10.1556/1848.2022.00556

International Review of Applied Sciences and Engineering

Volume/Issue:: Volume 14: Issue 3

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

Authors:

K. Thanigaivel

K. Thanigaivel Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, India

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thanigaivelme@gmail.com https://orcid.org/0000-0002-0725-261X

S. Ramesh

S. Ramesh Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, India

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, and

K. Karunanithi

K. Karunanithi Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, India

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Pages:: 325–341

Online Publication Date:: 20 Mar 2023

Publication Date:: 10 Oct 2023

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2022.00556

Keywords:: transient stability; grid connected hybrid system; voltage profile; global solution; load; fault; UPFC performance; controllers

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Abstract

In this paper, a novel hybrid technique is proposed for transient stability analysis on grid connected Wind-Diesel-PV hybrid system. The proposed hybrid methodology is combination of the dwarf mongoose optimization algorithm (DMO) and the recalling enhanced recurrent neural network (RERNN) named DMO-RERNN. The main purpose of this work is to consider various elements on hybrid system for the analysis of transient stability according to different conditions. The voltage profile of hybrid system is enhanced using the proposed unified power flow controller (UPFC), which also has higher performance improving transient performance compared to the conventional ANN, PI and fuzzy-sliding mode controller. Considering the proposed technique, DMO is used to find the optimal global solution for the fault predicted by the RERNN approach. The proposed system is executed on MATLAB work platform; its performance with existing systems is analyzed. The result proves that the proposed hybrid technique based UPFC controller provides better results compared with other existing technique. The efficiency of the PI is 82.136, ANN is 77, Fuzzy Sliding Mode is 65.097% and proposed technique is 97.99038%.

Abstract

1 Introduction

In central power system, an advanced Distributed Generation (DG) technology, like fuel cell (FC), wind turbine (WT), photovoltaic (PV) are used as an alternate energy. According to the requirement of the situation, DG achieves customers' demand in grid-connected and extra power generation given to grid to increase reliable power distribution. Unreliable and PQ problems arise in the power generation of wind and PV due to the undesired environmental characteristics variation of wind speed as well as solar radiations. To enhance the dependability of power distribution, the sources are needed to connect with typical power generating sources [1, 2]. Wind–diesel-photovoltaic systems are highly available hybrid system, where the wind turbine is linked to the diesel generator along photovoltaic system to deliver power in rural areas. Generally, by choosing synchronous generator (SG), performance improvement is achieved to operate diesel generator [3–7]. When compared with SG, the induction generator is much better because of its rugged characteristics and it is essential to consider the reactive power during the operation [8–10].

Induction generators and load-connected photovoltaics are considered as difficult, wherever the induction generators and loads require the reactive power delivered to SG and PV. The SG and PV inverters cannot satisfy the demand and generate a gap between demand as well as output of reactive power. It causes voltage stability problems. The capacitor banks are utilized to enhance voltage stability and manage reactive power [11–13]. The fixed capacitor does not deliver the reactive power requirement. Henceforth, the reactive power compensation is achieved by alternatively using AC transmission system devices, like Static VAR Compensator (SVC), Unified Power Flow Controller (UPFC), Static Synchronous Compensator (STATCOM), [14–21]. These devices are utilized under the study of the angular and voltage stability of power system [22]. The reduced order linear system is achieved by the feedback linearization dynamic stability design technique by transforming the dynamics of the nonlinear system into a linear one. Here, a linear controller is considered in the design; due to this autonomous hybrid system becomes stable and has zero dynamics.

The controllers are modeled by utilizing the adaptive control methods [23]. In the robotic application as well as the semi-active suspension system, the sliding mode control (SMC) is utilized to make different structure [24]. The main feature of this SMC is to maintain the stability. The controller may be managing the modeling incorrectness. A fuzzy-PI control is recommended by many researchers that is another adaptive control method. A Hybrid DMO-RERNN based UPFC Controller is proposed to obtain superior performance subject to accuracy, overshoot and settling time. The foremost purpose of this work is to control reactive power using proposed DMO-RERNN based UPFC Controller for the better damping control on wind–diesel-photovoltaic hybrid system. The organization of this work is shown in Fig. 1.

Fig. 1.

Organization of work

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Objectives and Contribution:

(a)A novel hybrid system is proposed for transient stability analysis on grid connected Wind-Diesel-PV hybrid system using hybrid system. The hybrid technique is a combination of dwarf mongoose optimization algorithm (DMO) and the recalling enhanced recurrent neural network (RERNN) named DMO-RERNN technique. At MATLAB/Simulink working platform, the proposed system proficiency is evaluated by relating with existing schemes.
(b)Considering the proposed technique, DMO is used to find optimal global solution for the fault predicted by the RERNN approach.
(c)The efficiency of the PI is 82.136, ANN is 77, Fuzzy Sliding Mode is 65.097% and the proposed technique is 97.99038%.

2 Recent research work

In previous studies, the stability analysis in self-governing hybrid system using different techniques and aspects were explained. Some of them are revised here;

Wang et al. [25] presented an online power system transient stability assessment (TSA) issue using two-class classification problem. Here, a core vector machine (CVM) was used to tackle limitation via large phasor measurement unit (PMU) data. At first, offline training and online application framework were developed under selection, offline training, online application. PMU big data generation was involved by time domain simulation, training and testing of CVM model. Simultaneously, the online application procedure included the collection of PMU-specific big data in real time using the PMU data center interface and feature calculation program. The CVM-based assessment algorithm was much better than other support vector machines based on time consumption and space complexity.

Kassem and Abdelaziz [26] have suggested a firefly optimization approach to design the optimal control voltage stability of stand-alone hybrid renewable production unit depends on reactive power. The PMIG operated by wind turbine, also with the help of diesel engine, the synchronous generator was operated. In the system, the terminal bus voltage was stabilized via managing the reactive power by utilizing a STATCOM. By altering the total reactive power it was possible to stabilize the terminal voltage caused by the influence of the reactive power. The main objective was accomplished by restricting the STATCOM phase angle. System performance efficiency was obtained by comparing system control including model predictive, robust H, and classical PI control. Haruni et al. [27] have suggested a dynamic process along control approaches of hybrid Wind-Diesel-battery energy storage base power distribution amid the hybrid system components were achieved through the control strategies developed the system. The load and power demand generated from the wind turbine were managed by the battery energy storage system. During low wind condition a diesel generator was utilized in the wind energy conversion system to deliver sufficient power. A power generation strategy was implemented in case of low wind condition.

Shezan et al. [28] have explained the world's massive population, specifically in the emerging countries, was living in the rural areas. Actually, the rural areas were geographically isolated as regards grid connection. The complexities in providing these facilities were solved by renewable energy on off-grid hybrid energy system. Therefore, a system was modeled and simulated to help less community deeming an average load demand. Jamil et al. [29] have suggested increasing power quality for efficient power transmission on grid-connected solar PV-wind power systems. The hybrid system included a renewable energy according to PV power generation system and wind energy conversion system. The system was influenced by common disturbances on AC loads and power output. These disturbances were the reason for power mismatch, voltage instability and power quality problems. These limitations were overcome by neglecting the gap with an adjustable reactive power source. Lee and Wang [30] have elucidated autonomous hybrid renewable energy power generation/energy storage system linked with isolated loads utilizing time-domain simulation. The frequency domain analysis of the hybrid system contains a diesel engine generator. The essential amount of hydrogen for FCs was generated using an aquatic electrolyzer by absorbing part of the energy produced by photovoltaic or wind turbines. According to different operating points and disturbance condition, a time domain technique was developed to obtain balance condition in system power frequency. Baghdadi et al. [31] have illustrated the investigation of efficiency of hybrid PV-Wind-Diesel-Battery configuration as a function of hourly measurements of the Adrar climate. In the beginning of the designing the system was optimized via HOMER software. Based on the renewable resources potential and energy demand the optimization process took place. Next, a mathematical model was developed according to different operation strategies to validate the efficient energy management.

Agarala et al. [32] have realized simple and innovative control system called automatic reactive power support (ARS) for single and combined renewable sources to improve system stability. Permanent Magnet Synchronous Generator (PMSG) and Doubly Fed Induction Generator (DFIG) were deemed from wind generators in this paper for comparison. Moreover, an improvement of transient stability was carried out, cultivating the critical clearance of three-phase fault on power system. Tian et al. [33] have clarified dual-stream CNN algorithm on deep learning, take the power of every node and line as input, quickly recognize the key oscillation modes of power system, and make qualitative assessment. D. Rakesh Chandra et al. [34] have postulated that the effect of DFIG on TS of grid-connected power system has been examined, to improve the TS of system. Here, the proposed TS analysis was executed on Reliability Test System (RTS)-24 bus system.

2.1 Background of research work

Recent studies show that hybrid system transient stability analysis was a major factor. System transient stability was affected using sudden load removal, line switching operations; system failure, etc. In such case, a data mining algorithm called core vector machine (CVM) based assessment algorithms was considered and has advantages, such as least time consumption and space complexity. To provide sufficient powers to the load power generation, a technique was utilized which was able to function during low wind condition. Different approaches were utilized to operate under dynamic and steady state condition. Even though the above-mentioned techniques have advantages, some limitations in achieving system power-frequency balance condition. The Static VAR Compensator (SVC) and UPFC were considered as the best device in reactive power compensation and voltage maintenance support. SVC was utilized to recover the power quality issues on the grid connected hybrid system according to reactive power control. However, the SVC and STATCOM exhibit better performance but it was difficult to maintain the system stability. These restrictions have inspired to do this investigation work.

3 System modelling of a hybrid power system components

Hybrid Power System (HPS) is a stand-alone electricity generation system utilized to supply power to the grid or on-site. By integrating various energy sources into one supply system, it leads hybridization technology to make use of local obtainable renewable energy sources to generate electric power. The proposed hybrid system is displayed in Fig. 2. Here, the induction generator and SG are used to deliver active with reactive power to the induction generator and the load using proposed DMO-RERNN-based UPFC controller [35]. Photovoltaic energy is considered in the proposed system to enhance the system performance. According to target power factor, the reactive power delivered or absorbed via photovoltaic system. In WECS, SG with IEEE type 1 excitation system is deemed and operates a local network for the induction generator. The mathematical modelling and various subsystems like wind diesel PV, FACTS controllers (SVC, STATCOM and UPQC) are defined as below:

Q_{P V} + Q_{S G} + Q_{C O N} = Q_{L} + Q_{I G}

(1)

where the reactive power produced by PV denoted as

Q_{P V}

, reactive power produced SG is represented as

Q_{S G}

, the reactive power generated by controller denoted as

Q_{C O N}

, the reactive power needed using load is mentioned as

Q_{L}

, the needed reactive power using induction generator is defined as

Q_{I G}

Fig. 2.

Block diagram of proposed system

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

A change occurs in the load _C causes all the parameters in the reactive power, which is represented as:

Δ Q_{P V} + Δ Q_{S G} + Δ Q_{C O N} = Δ Q_{L} + Δ Q_{I G}

(2)

The excessive reactive power influences the system voltage by maximizing the induction generator electromagnetic energy absorption and emerging the reactive load consumption of system. The excessive reactive power of system is expressed as:

Δ Q_{P V} + Δ Q_{S G} + Δ Q_{C O N} - Δ Q_{L} - Δ Q_{I G}

(3)

By rewriting the equation, the differential and Laplace forms are represented as:

Δ Q_{P V} + Δ Q_{S G} + Δ Q_{C O N} - Δ Q_{L} + Δ Q_{I G} = \frac{d}{d t} (Δ Ε_{m}) + D_{V} Δ V

(4)

\begin{array}{l} Δ V (S) = \frac{K_{V}}{1 + S T_{V}} [Δ Q_{S G} (S) + Δ Q_{C O N} (S)] \\ + Δ Q_{P V} (S) - Δ Q_{L} (S) - Δ Q_{I G} (S)] \end{array}

(5)

here

T_{V} = (2 H_{r} / D_{V}) V^{O} a n d K_{V} = (1 / D_{V})

The dynamic behaviors of WTG, DEG, PV are analyzed utilizing high-order mathematical model with nonlinearity and it consist of power conditioners and controllers. In case of transient analysis, the mathematical model of system components is assumed [36].

3.1 Modelling of photovoltaic system

The photovoltaic cell is also called as p-n junction device that is utilized to convert the count of incidence solar radiation into electric power. A photovoltaic small signal equivalent mode is considered to deliver the reactive power to the proposed system [37].

F = V + J Y_{S} I

(6)

F \sin (δ) = Y_{S} I \cos (ϕ)

(7)

where

ϕ

denotes the power angle amid grid and inverter output,

δ

represents load angle amid grid voltage (V) and inverter output voltage (F).

The active power given to the grid by the converter may be illustrated as:

P = V I \cos (ϕ) = \frac{V F}{Y_{S}} \sin (δ)

(8)

The reactive power given to the grid by the PV is expressed as:

Q = \frac{V F}{Y_{s}} \cos (δ) - \frac{V^{2}}{Y_{S}} = \frac{V}{Y_{S}} (V \cos (δ) - V)

(9)

3.2 Modelling of wind energy system

In the proposed hybrid system, WT is deemed as an important power source and the electric power generated through the wind turbine is based on wind speed [38]. Wind speed is separated into three groups: rated speed, cutoff speed, and cutout speed. WT power is largely based on the rate of change of kinetic energy:

P = \frac{d E}{d t} = \frac{1}{2} \frac{d m_{a}}{d t} v_{W}^{2}

(10)

here

\frac{d m_{a}}{d t} = ρ a v_{W}

describes the mass flow rate,

a

denotes wind area,

ρ

denotes air density. Equation (10) can be rewritten as:

P = \frac{1}{2} ρ a v_{W}^{3}

(11)

According to the flow of upstream and downstream wind powers, the removed mechanical power is decided and can be set as:

P_{W} = \frac{1}{2} ρ a v_{W} (v_{U}^{2} - v_{D}^{2})

(12)

here

v_{U}

describes the velocity of wind in upstream,

v_{D}

signifies velocity of wind in downstream and above equation is rewritten as:

P_{W} = \frac{1}{2} ρ a r_{p} v^{3}

(13)

where

P_{W}

refers to mechanic power on air flow,

r_{p}

denotes power coefficient depending tip speed ratio and blade pitch angle.

3.3 Modelling of diesel generator

When the load requirement is not compromised using other renewable energy system or the batteries then it is essential to consider diesel generator on HRES. The diesel generator is selected based on the sort and nature of load. The engine generator rated capacity needed to install is based on the following criteria such as,

In case of directly connecting the diesel generator to the load, the rated capacity of generator should be similar.
When using the diesel generator from battery charger, it important to note that the generated current of the generator must not be superior to C_Ah/5 A.

The diesel generator general efficiency is expressed as:

η_{o v e r a l l} = η_{b r a k e t h e r m a l} \times η_{g e n e r a t o r}

(14)

here,

η_{b r a k e t h e r m a l}

defines the brake thermal of diesel engine. At hybrid power system the diesel generator is designed to achieve the required demand. Due to absence of peak demand the diesel generator is utilized to obtain load requirements and to charge battery.

3.4 Induction generator model

The reactive power generated from SG and the power demand of induction generator based on variation of the wind speed. The svc controller state space equation is expressed in a standard form which is given below:

y = [A] \bar{y} + [B] \bar{u} + [C] \bar{p}

(15)

\bar{y} = {[Δ F_{f d} Δ V_{a} Δ V_{f} Δ Ε_{q}^{/} Δ Β_{S V C} Δ B_{S V C}^{/} Δ α Δ V]}^{T}

(16)

\bar{u} = [Δ V_{r e f}]

(17)

\bar{p} = {[Δ Q_{L} Δ P I W]}^{T}

(18)

The calculation of matrix $[A], [B], [C]$ is similar order represents $\bar{x} ., \bar{u} a a n d \bar{p}$ .

3.5 Mathematic model of SG

SG is considered to be the primary source of power and alternative to grid power, and SG is used to deliver reactive power. When connected to WT, the rotor speed is secured by properly controlling the SG. The SG equation is expressed as:

Q_{S G} = \frac{(F_{q}^{/} V \cos δ - V^{2})}{X^{/} d} (T r a n s i e n t)

(19)

The above equation is rewritten as,

Δ Q_{S G} = \frac{V \cos δ}{Y^{/} d Δ F^{/} q} + \frac{F^{/} q \cos δ - 2 V}{Y^{/} d Δ^{/} V}

(20)

Using Laplace equation may be rewritten as,

Δ Q_{S G} (S) = K_{a} Δ F^{/} q (S) + K_{b} Δ V (S)

(21)

here

K_{a} = (V \cos δ / Y^{/} d)

(22)

K_{a} = (F^{/} q \cos δ - 2 V / Y^{/} d)

(23)

3.6 Modelling of excitation system

The excitation system is utilized to generate direct current and it is used to drive the synchronous machine. The excitation system has the ability to manage the field voltage along field current, which is defined using individual time constant automatic high gain AVR System. Several sorts of excitation systems are available, in which, IEEE excitation system is considered in the hybrid system. The mathematical expression for the excitation system is defined by

\overset{•}{F_{f d}} = (- F_{f d} + K_{A} (V_{r e f} - V) / T_{A}

(24)

3.7 Flux linkage of SG

The flux linkage equation for round rotor synchronous motor is expressed as:

\frac{d}{d t} (Δ F_{q}^{/}) = \frac{(Δ F_{f d} - Δ F_{q})}{T_{d o}^{/}}

(25)

F_{q} = (\frac{Y_{d}}{Y_{d}^{/}}) F^{/} q - (\frac{Y_{d} - Y_{d}^{/}}{Y^{/} d V \cos δ})

(26)

The Laplace transform is applied in order to obtain a small incremental change which is expressed as:

(1 + S T_{G}) Δ F^{/} q (s) = K_{e} Δ F_{f d} (s) + K_{f} Δ V (s)

(27)

here

\begin{array}{l} T_{G} = (Y_{d}^{/} T_{d o} / Y_{d}), K_{e} = (Y_{d}^{/} / Y_{d}) \\ a n d K_{f} = ((Y_{d} - Y_{d}^{/}) \cos δ / Y_{d}) \end{array}

3.8 Unified power flow control

It is utilized to manage reactive power compensation, and it is utilized to manage reactive power coordinates using robust control models in the proposed hybrid system. Figure 3 implicates that block diagram of UPFC controller. The UPFC's series and shunt impedances are considered as pure reactance [39]. The shunt voltage sources are represented as

P_{s h}

Q_{s h}

and the series voltage sources are denoted as

P_{i}

Q_{i}

P_{j}

and

Q_{j}

. In UPFC, the injected power is depending on injected voltages together with bus voltages. The load flow analysis is deemed according to the buses

i

and

j

. The reactive power fed by the UPFC controller is expressed as:

\frac{d Q_{j}}{d t} = \frac{d Q_{j}}{d δ} \frac{d δ}{d t} + \frac{d Q_{j}}{d V_{m 2 p}} \frac{d V_{m 2 p}}{d t}

(28)

Fig. 3.

Block diagram of UPFC controller

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

From the above equation, it is known that reactive power fed by UPFC is based on

V_{m 2 p}

and angle

δ

and at the point of connection they are proportional to the voltage.

Δ Q_{U P F C} = K_{j} Δ δ (S) + K_{k} Δ V (S)

(29)

4 Proposed hybrid technique

The transient stability of grid connected hybrid system is achieved by the Hybrid DMO-RERNN technique which is the joined execution of DMO and RERNN. Here, the DMO is utilized to find global optimum solution for the proposed system and RERNN is used to predict the fault in the system.

4.1 Dwarf mongoose optimization algorithm (DMO)

DMO is a novel metaheuristic method based on adaptive behavior of dwarf mongoose. The proposed DMO is used to determine global optimum solutions for various optimization issues [40]. The optimization process is obtained by designing three structural transformations, such as alpha group, babysitters, scout group [41].

Step 1: Initialization

The DMO parameters, such as $p e e p$ , mongoose population $(n_{p o p})$ , baby sitters $(b s)$ and exchange criteria are initialized.

Step 2: Fitness calculation

The fitness value of the mongoose is calculated by assigning the time counter “

s

” value and the alpha based value is calculated as:

α = \frac{f i t_{i}}{\sum_{i = 1}^{n} f i t_{i}}

(30)

Step 3: Evaluation of sleeping mound

The sleeping mound is calculated in this step which is illustrated as:

s m_{i} = \frac{f i t_{i + 1} - f i t_{i}}{\max {| f i t_{i + 1} - f i t_{i} |}}

(31)

Step 4: Average value and movement vector calculation

In this step, the average value of sleeping mound and movement vector is determined using the below equation:

φ = \frac{\sum_{i = 1}^{n} s m_{i}}{n}

(32)

\vec{M} = \sum_{i = 1}^{n} \frac{Y_{i} \times s m_{i}}{Y_{i}}

(33)

Step 5: Position update

The baby sitter is exchanged if the fitness function value is lesser than or similar with alpha value. The scout mongoose next best position is updated.

Step 6: Termination
Once the best solution is found out the process will terminate. Table 1 tabulates the pseudo code for DMO algorithm.

Table 1.

Pseudocode for DMO algorithm

4.2 Recalling enhanced recurrent neural network (RERNN)

The structure of RERNN contains Input, State, Hidden, Output, and Delay Layer. Figure 4 depicts RERNN using several inputs and outputs. The steps and structure of RERNN are briefly described as below,

Step 1: Initiation

Fig. 4.

The RERNN structure with several inputs and outputs

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Initiate EV parameters, as count of nodes, vectors weight, count of hidden nodes and count of iteration.

Step 2: Random generation

After the initiation method, the input vectors are generated randomly. At same time, the input parameter of the EV system, like SOC of battery, engine speed, etc. are created randomly.

Step 3: Check iteration

The iteration of the method is less when compared with maximal repetition then the data process will terminate.

Step 4: Find learning rate by generalized Armijo search

The learning rate is determined according to Armijo search method and the equation is expressed as:

e (W^{K} + l_{r} P^{K}) \leq e (W^{K}) + α_{1} l_{r} e_{W}^{K} {(P^{K})}^{T}, α_{1} \geq 0

(34)

Step 5: Calculate new weight

It is utilized to find out the new weight and it is computed depending on following equation,

W^{k + 1} = W^{k} + l_{R} p^{k}

(35)

Step 6: Verify the maximum iteration

It attains the maximal iteration process will stop or the iteration value will maximize and goes with step 6.

Step 7: Calculate direction

The calculation of directional learning method takes place with conjugate gradient descent method.

P^{k} = - F_{w}^{k} + β P^{k - 1}

(36)

β = \frac{α F_{w}^{k} {(P^{k - 1})}^{t}}{P^{k - 1} (P^{k - 1} - F_{W}^{k})}, α \in (0,1)

(37)

Figure 5 portrays flow chart of RERNN method.

Fig. 5.

Flowchart of RERNN

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

5 Result and discussion

Here, the transient response of grid connected Wind-Diesel- photovoltaic hybrid system is described. The system is analyzed under two cases: (i) constant irradiation with grid fault and (ii) irradiation variation with grid fault.

Case 1: Constant Irradiation with grid fault

The analysis of distribution generation active power and reactive power is represented in Fig. 6. In Fig. 6 (a), the DGs active power value is 2,500 W and it remains constant with slight variation. Figure 6 (a) displays the reactive power of DGs is 1,400 W and remains constant with slight variation. The analysis of distribution generation current and voltage is shown in Fig. 7. In Fig. 7 (a), the current of DGs during constant irradiation varies from −3 to 3 A. In Fig. 7 (b), the voltage of the DGs during constant irradiation deviates from −500 to 500 A. Figure 8 displays the analysis of distribution generation grid active and reactive power comparison. Under constant irradiation the grid active of the DG is 5 W through the time interval of 0–0.15 time/sec and decrease to 0.3 W and again increases to 3.8 W at 0.15 time/sec. Figure 8 (a) represents the active power value slightly deviate at 0.16 to 0.205 time/sec and remains constant at 4.9 A for the remaining time period. The comparison of grid active power of DMO-RERNN with existing PI, ANN, Fuzzy sliding mode is illustrated in Fig. 8 (b). It is proved that the grid active power of DMO-RERNN is higher than the existing methods.

Fig. 6.

Analysis of distribution generation (a) active power (b) reactive power

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 7.

Estimation of distribution generation (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 8.

Estimation of distribution generation (a) grid active power (b) grid active power comparison

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

The performance of grid current with voltage is represented in Fig. 9. During constant irradiation the grid current value differs from −1,000 to 1,000 A and there is a deviation present at 0.15 to 0.2 time/sec which is shown in Fig. 9 (a). During constant irradiation the grid voltage varies from −3,000 to 3,000 V and during the time period of 0.15–0.2 time/sec which is displayed in Fig. 9 (b). The PV current and voltage analysis during constant irradiation is represented in Fig. 10. In Fig. 10 (a), the current produced from the PV increases from 0 to 11 A at 0 to 0.02 time/sec leftovers stable for the remaining time period. Figure 10 (b) portrays PV voltage increase 0–5,100 W at 0 to 0.02 time/sec leftovers stable with slight variation for the remaining time period.

Fig. 9.

Estimation of grid (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 10.

Analysis of PV (a) current (b) power

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

The estimation of PV power and comparison of PV power demonstrated in Fig. 11. In Fig. 11 (a), the power value of PV emerges from 0 to 5,500 W at 0 to 0.02 time/sec leftovers constant for the rest of the time. Figure 11 (b) illustrates that the DMO-RERNN power is superior to existing systems. The analysis of wind current and voltage is depicted in Fig. 12. In Fig. 12 (a), wind current oscillates from −3–3.5 A during the interval of 0–0.02 time/sec. In Fig. 12 (b), the voltage produced from wind oscillates from −450 to 450 at 0 to 0.02 time/sec. The analysis of wind active power and the comparison are represented in Fig. 13. In Fig. 13 (a), the wind active power maximizes from 0 to 2,700 W at 0 to 0.02 time/sec. again, it drops to 2,000 W and increases to 2,100 W at 0.02 to 0.03 time/sec and remains constant for the rest of the time. The comparison graph shows that the wind active power of DMO-RERNN is higher than the existing PI, ANN and fuzzy sliding mode represented in Fig. 13 (b).

Case 2: Irradiation variation with grid fault

Fig. 11.

Analysis of PV (a) Power (b) Power comparison in case 1

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 12.

Estimation of Wind (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 13.

Estimation of Wind (a) active power (b) active power comparison

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

The analysis of distribution generation active and reactive power during irradiation variation with grid fault is demonstrated in Fig. 14. In Fig. 14 (a), the DGs active power value is 2,500 W and it remains constant with slight variation. In Fig. 14 (b), the reactive power of the DGs is 1,400 W and remains constant with slight variation. The analysis of distribution generation current and voltage during irradiation variation with grid fault is shown in Fig. 15. In Fig. 15 (a), the current of the DGs during irradiation variation varies from −3.5 to 3.5 A. In Fig. 15 (b), the voltage of the DGs during irradiation variation deviates from −500 to 500 A. The analysis of distribution generation grid active power and the comparison of grid active power are shown in Fig. 16. Under irradiation variation condition the grid active power of the DG is 5 W at 0 to 0.15 time/sec and decreases to 3 W. Then it increases to 3.2 W at 0.16 time/sec again it emerges to 4 W and then to 5 W in the time duration of 0.21–0.28 time/sec. For the remaining time period it remains constant which is displayed in Fig. 16 (a). The grid active power of DMO-RERNN with existing PI, ANN, Fuzzy sliding mode is illustrated in Fig. 16 (b). It is proved that the grid active power of DMO-RERNN is higher to existing systems.

Fig. 14.

Analysis of distribution generation (a) active (b) reactive power

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 15.

Analysis of distribution generation (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 16.

Analysis of distribution generation (a) grid active power (b) comparison of grid active power

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

The analysis of grid current and voltage represented in Fig. 17. In Fig. 17 (a), during irradiation variation the grid current value differs from −1,000 to 1,000 A and there is a deviation present at 0.15 to 00.25 time/sec. The grid voltage varies from −3,000 to 3,000 V during the time period of 0.15–0.27 time/sec is displayed in Fig. 17 (b). Figure 18 displays the analysis of reactive power, in which the reactive power value is 4 W at 0 to 0.15 time/sec, then there is sudden drop to 0.9 W AT 0.15 time/sec. Again, the reactive power rises to 2.6 A and it gradually rises with the presence of slight oscillation for the remaining time period. The analysis of PV current and voltage during irradiation variation condition is represented in Fig. 19. In Fig. 19 (a), the current produced from PV increases 0–6.4 A at 0 to 0.02 time/sec leftovers stable during 0.02–0.2 time/sec. Again, it rises to 9 A during 0.2–0.21 time/sec leftovers stable at 0.21 to 0.3 time/sec then increases to 11 A leftovers stable. Figure 19 (b) shows PV voltage increase 0–350 W at 0 to 0.02 time/sec and there is an oscillation for the remaining time period. The analysis of PV current and voltage during irradiation variation condition is represented in Fig. 19. In Fig. 19 (a), the current produced from the PV increases from 0 to 6.2 A at 0 to 0.02 time/sec leftovers stable at 0.02 to 0.2 time/sec and it increases gradually during the time duration of 0.2–0.3 time/sec. Figure 19 (b) shows that PV voltage increase from 0 to 350 V at 0 to 0.02 time/sec there is a slight deviation for the remaining time period.

Fig. 17.

Analysis of grid (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 18.

Estimation of Reactive power

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 19.

Analysis of PV (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

The PV power analysis and comparison of PV power is illustrated in Fig. 20. In Fig. 20 (a), the power value of PV emerges from 0 to 2,300 W during 0–0.02 time/sec leftovers stable during 0.02–0.15 time/sec, then it gradually increases for the rest of the time. Figure 20 (b) illustrates that the DMO-RERNN method power is higher to existing systems like proportionate integral (PI), artificial neural network (ANN) and fuzzy sliding mode.

Fig. 20.

Analysis of PV (a) Power (b) power comparison in case 2

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

The analysis of wind active power and the comparison are represented in Fig. 21. In Fig. 21 (a), the wind active power maximizes 0–2,800 W at 0 to 0.02 time/sec, again, it drops to 2,000 W and increases to 2,100 W at 0.02 to 0.03 time/sec and remains constant for the rest of the time. The comparison graph shows that the wind active power of DMO-RERNN is higher than other existing methods, such as PI, ANN and fuzzy sliding mode which represented in Fig. 21 (b). The analysis of wind current and voltage during irradiation variation condition is shown in Fig. 22. In Fig. 21 (a), the wind current oscillates from −3–3.5 A during the interval of 0–0.02 time/sec. In Fig. 21 (b), the wind voltage oscillates from −450 to 450 at 0 to 0.02 time/sec. Efficiency comparison of source power is shown in Table 2. For 100 iterations first order statistic is given in Table 3. Computation time with numerous numbers of trails of proposed and existing systems is shown in Table 4.

Fig. 21.

Analysis of Wind (a) active power (b) active power comparison

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Fig. 22.

Analysis of Wind (a) current (b) voltage

Citation: International Review of Applied Sciences and Engineering 14, 3; 10.1556/1848.2022.00556

Table 2.

Efficiency comparison of proposed and existing system

Solution techniques	Efficiency (%)
PI	82.136
ANN	77.26588
Fuzzy Sliding Mode	65.097
Proposed technique	97.99038

Table 3.

For 100 iterations first order statistic assessment

Solution	Mean	Median	SD
PI	0.8890	0.8736	0.0061
ANN	0.8514	0.8018	0.0198
Fuzzy Sliding Mode	0.61038	0.5317	0.00543
Proposed system	0.5117	0.4720	0.00417

Table 4.

Computation time with several number of trails of proposed and existing systems

Solution techniques	Computation time with several number of trails (sec)
Solution techniques	100	150	200	250	500
PI	60.0398	70.1257	83.2906	75.9023	75.8707
ANN	57.1107	68.0273	79.0373	69.96800	67.65823
Fuzzy Sliding Mode	48.1740	51.2133	71.0483	60.00126	57.80132
Proposed technique	31.5799	47.0637	65.3690	59.1155	56.0975

6 Conclusion

The hybrid system for proper management of reactive power as well as voltage stability enhancement is incorporated with UPFC controller and is simulated through MATLAB platform. The proposed DMO-RERNN based UPFC controller helps to improve the transient stability. The performance of the proposed DMO-RERNN based UPFC controller is analyzed under diverse operating conditions. The simulation outcomes show that transient stability incorporated with proposed DMO-RERNN based UPFC controller provides better result than the conventional ANN, PI and fuzzy-SMC. The system is analyzed under two cases like constant irradiation with grid fault and irradiation variation with grid fault. The system parameters like active and reactive power, DG current and voltage, grid current and voltage, are also analyzed in this proposed work. From this analysis, the proposed work provides the best solution with higher efficiency. Exactly, attention must be paid to transient stability of multi-inverter, multi-machine hybrid systems. The damping effect must no longer be ignored while executing TSA is required. Frequency hopping must also be deemed for determining CCA and CCT. This carries a novel impulse to relay protection design.

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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International Review of Applied Sciences and Engineering

Print ISSN:: 2062-0810

Online ISSN:: 2063-4269

Editorial Board

Mohammad Nazir AHMAD, Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

Murat BAKIROV, Center for Materials and Lifetime Management Ltd., Moscow, Russia

Nicolae BALC, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Umberto BERARDI, Toronto Metropolitan University, Toronto, Canada

Ildikó BODNÁR, University of Debrecen, Debrecen, Hungary

Sándor BODZÁS, University of Debrecen, Debrecen, Hungary

Fatih Mehmet BOTSALI, Selçuk University, Konya, Turkey

Samuel BRUNNER, Empa Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

István BUDAI, University of Debrecen, Debrecen, Hungary

Constantin BUNGAU, University of Oradea, Oradea, Romania

Shanshan CAI, Huazhong University of Science and Technology, Wuhan, China

Michele De CARLI, University of Padua, Padua, Italy

Robert CERNY, Czech Technical University in Prague, Prague, Czech Republic

Erdem CUCE, Recep Tayyip Erdogan University, Rize, Turkey

György CSOMÓS, University of Debrecen, Debrecen, Hungary

Tamás CSOKNYAI, Budapest University of Technology and Economics, Budapest, Hungary

Anna FORMICA, IASI National Research Council, Rome, Italy

Alexandru GACSADI, University of Oradea, Oradea, Romania

Eugen Ioan GERGELY, University of Oradea, Oradea, Romania

Janez GRUM, University of Ljubljana, Ljubljana, Slovenia

Géza HUSI, University of Debrecen, Debrecen, Hungary

Ghaleb A. HUSSEINI, American University of Sharjah, Sharjah, United Arab Emirates

Nikolay IVANOV, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Antal JÁRAI, Eötvös Loránd University, Budapest, Hungary

Gudni JÓHANNESSON, The National Energy Authority of Iceland, Reykjavik, Iceland

László KAJTÁR, Budapest University of Technology and Economics, Budapest, Hungary

Ferenc KALMÁR, University of Debrecen, Debrecen, Hungary

Tünde KALMÁR, University of Debrecen, Debrecen, Hungary

Milos KALOUSEK, Brno University of Technology, Brno, Czech Republik

Jan KOCI, Czech Technical University in Prague, Prague, Czech Republic

Vaclav KOCI, Czech Technical University in Prague, Prague, Czech Republic

Imre KOCSIS, University of Debrecen, Debrecen, Hungary

Imre KOVÁCS, University of Debrecen, Debrecen, Hungary

Angela Daniela LA ROSA, Norwegian University of Science and Technology, Trondheim, Norway

Éva LOVRA, Univeqrsity of Debrecen, Debrecen, Hungary

Elena LUCCHI, Eurac Research, Institute for Renewable Energy, Bolzano, Italy

Tamás MANKOVITS, University of Debrecen, Debrecen, Hungary

Igor MEDVED, Slovak Technical University in Bratislava, Bratislava, Slovakia

Ligia MOGA, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Marco MOLINARI, Royal Institute of Technology, Stockholm, Sweden

Henrieta MORAVCIKOVA, Slovak Academy of Sciences, Bratislava, Slovakia

Phalguni MUKHOPHADYAYA, University of Victoria, Victoria, Canada

Balázs NAGY, Budapest University of Technology and Economics, Budapest, Hungary

Husam S. NAJM, Rutgers University, New Brunswick, USA

Jozsef NYERS, Subotica Tech College of Applied Sciences, Subotica, Serbia

Bjarne W. OLESEN, Technical University of Denmark, Lyngby, Denmark

Stefan ONIGA, North University of Baia Mare, Baia Mare, Romania

Joaquim Norberto PIRES, Universidade de Coimbra, Coimbra, Portugal

László POKORÁDI, Óbuda University, Budapest, Hungary

Roman RABENSEIFER, Slovak University of Technology in Bratislava, Bratislava, Slovak Republik

Mohammad H. A. SALAH, Hashemite University, Zarqua, Jordan

Dietrich SCHMIDT, Fraunhofer Institute for Wind Energy and Energy System Technology IWES, Kassel, Germany

Lorand SZABÓ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Csaba SZÁSZ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Ioan SZÁVA, Transylvania University of Brasov, Brasov, Romania

Péter SZEMES, University of Debrecen, Debrecen, Hungary

Edit SZŰCS, University of Debrecen, Debrecen, Hungary

Radu TARCA, University of Oradea, Oradea, Romania

Zsolt TIBA, University of Debrecen, Debrecen, Hungary

László TÓTH, University of Debrecen, Debrecen, Hungary

László TÖRÖK, University of Debrecen, Debrecen, Hungary

Anton TRNIK, Constantine the Philosopher University in Nitra, Nitra, Slovakia

Ibrahim UZMAY, Erciyes University, Kayseri, Turkey

Andrea VALLATI, Sapienza University, Rome, Italy

Tibor VESSELÉNYI, University of Oradea, Oradea, Romania

Nalinaksh S. VYAS, Indian Institute of Technology, Kanpur, India

Deborah WHITE, The University of Adelaide, Adelaide, Australia

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Issues per Year	3
Founder	Debreceni Egyetem
Founder's Address	H-4032 Debrecen, Hungary Egyetem tér 1
Publisher	Akadémiai Kiadó
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	2062-0810 (Print)
ISSN	2063-4269 (Online)