# A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems

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## Abstract

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## 1. Introduction

_{2}a year [3,4]. Aside from the benefits, RES-DGs change the radial distribution network (DN) into a meshed network and cause bidirectional power flow, which changes the fault current level (FCL) [5], resulting in conventional protection systems facing new challenges, most notably the overcurrent relay coordination (ORC) problem [6,7,8,9]. Overcurrent relay (OCR) measures the FCL and sends a trip signal after a typical operating time. The faulty portion is isolated from the healthy system if proper coordination is sustained between the primary and backup OCRs [10]. This relay coordination is maintained by delaying the upstream relays with a suitable time called the coordination time interval (CTI). Optimal relay settings, such as the pickup current (Ip) and time multiplier setting (TMS), play a vital role in achieving optimum ORC. These relay settings are fixed at predefined FCL and connected load [11,12]. The integration of wind farms (WF) into distribution systems is intermittent, depending on the operating conditions of the WTG, which mainly depend on the stochastic behavior of wind speed and direction. This results in changes in the FCL with WD, which affect the relay setting, thereby causing miscoordination problems [13,14]. Thus, the relay settings should be adaptively updated in line with the operating conditions of the WTG. To determine optimal relay settings, two approaches are used: conventional approaches (CA) [15,16,17,18,19,20,21] and optimization approaches (OA) [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40].

_{p}and TMS is predetermined and the second is optimized [22,23]. In complex networks, the results of LP may be trapped in local minima. Hence, the ORC problem is formulated as nonlinear programming (NLP), where both settings of the relay are optimized at the same time, which is often formulated with metaheuristic techniques such as particle swarm optimization (PSO) [25], seeker optimization algorithm (SOA) [26], genetic algorithm (GA) [27], evolutionary optimization algorithm (EOA) [28], teaching learning-based algorithm (TLBO) [29], ant colony optimization (ACO), gravitational search algorithm (GSA) [30], symbiotic organism search optimization technique (SOSO) [31], and extended continuous domain ant colony optimization [32]. Some hybrid optimization approaches (HOAs) were also proposed in the literature by combining the benefits of different OAs. It is seen that HOAs produce good results in the form of less computational time, better accuracy, and reliability toward global optimization [33,34]. Some examples are the hybrid electro-search algorithm and cuckoo optimization (HES–CO) [35], hybrid PSO–LP [36], combined genetic algorithm and simulated annealing (GA–SA) [37], hybrid biogeography-based optimization and differential evolution (BBO–DE) [38], hybrid CS–GA [5], and hybrid CS–LP [39].

#### Contributions and Paper Organization

- The delay in updating the relay settings and the coordination with other relays can cause the malfunctioning of OCRs. A considerable delay time is evaded when updating the relay settings by predicting the wind speed and FCL variation in advance.
- The hybrid ANFIS–SARIMA is devised for predicting periodic and nonperiodic wind series.
- An efficient optimization model HHO–LP is established for the existing constraints.
- A significant reduction in the overall tripping time of relays is achieved.
- The is no record of miscoordination or limit violation.

## 2. Problem Formulation

#### 2.1. Objective Function

_{OT}is the overall operation time of relays for fault isolation, ${\mathrm{t}}_{\mathrm{if}}^{\mathrm{p}}$ and ${\mathrm{t}}_{\mathrm{ijf}}^{\mathrm{b}}$ represent the operation time of the i-th primary relay and j-th backup relay, respectively, for a fault at location f, and F, I, and J denote the set of fault points, the total primary relays, and the backup relays, respectively.

_{P}i and I

_{P}j are the pickup currents of the primary and backup relays.

#### 2.2. OCR Coordination Constraints

_{p}are assessed within the lower and upper bounds given as

_{p}should be more than the maximum load current and less than the minimum short-circuit current value. During temporary faults, the relays should not operate and, therefore, there is a minimum limit for the operation time of relays.

## 3. Proposed Methodology

#### 3.1. EEMD

- (1)
- Initialize the number of ensembles (M) and the amplitude of the added white noise; set i = 1.
- (2)
- Add a white noise series to the original wind speed series x(t).$${x}_{i}(t)=x(t)+{n}_{i}(t),$$
_{i}(t) denotes the i-th added white noise series, and x_{i}(t) denotes the series with the added white noise. - (3)
- Decompose the series xi(t) into J IMFs cij(t) (j = 1, 2, …, J) using the EMD method, where cij(t) is the j-th IMF after the i-th trial, and J is the number of IMFs.
- (4)
- If i < M, then go to Step (2) with i = i + 1. Repeat Steps (2) and (3) with different white noise series.
- (5)
- Calculate the ensemble mean cj(t) of the M trials for each IMF of the decomposition as the final results.$${c}_{j}(t)=\frac{1}{M}{\displaystyle \sum _{i=1}^{M}{c}_{ij}(t),i=1,2,\dots \dots ,M,j=1,2,\dots \dots j.},$$
_{j}(t), (j = 1, 2, …, J) is the j-th IMF component using the EEMD method.

#### 3.2. ANFIS

_{1,i}is the membership degree of fuzzy set {A

_{1}, A

_{2}} or {B

_{1}, B

_{2}}, and μ(x) or μ(y) is the membership function.

_{Ai}(x) is the Gaussian function, and c

_{i}and σ

_{i}are the mean and standard deviation of the membership function, respectively.

_{3,i}is the output of Layer 3, and W

_{i}is the incentive strength of rule i.

#### 3.3. SARIMA

^{s}) denote nonperiodic and periodic autoregressive polynomials, respectively, Q(B) and V(B

^{s}) denote nonperiodic and periodic moving average polynomials, respectively, Z

_{t}denotes the wind speed series, et represents the white noise series, d is the level of integration, D is the level of periodic integration, s is the order of periodicity, and B is the back-shift operator. More details about SARIMA can been found in [42].

#### 3.4. Hybrid ANFIS–SARIMA Model

- (i)
- The WSS is decomposed into IMFs, and one residual series is given as$$\mathrm{S}(\mathrm{t})={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{I}}_{\mathrm{i}}(\mathrm{t})+{\mathrm{R}}_{\mathrm{n}}(\mathrm{t})},$$
_{i}(t) represents the IMFs, and R_{n}(t) is the residual series. - (ii)
- The periodic and nonperiodic series of I
_{i}(t) and R_{n}(t) are defined as P_{j}(t) and N_{i}(t), respectively. Thus, the original wind speed series can be given as$$\mathrm{S}(\mathrm{t})={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{N}}_{i}(\mathrm{t})+{\displaystyle \sum _{\mathrm{j}=\mathrm{m}+1}^{\mathrm{n}}{\mathrm{P}}_{\mathrm{j}}(\mathrm{t})+{\mathrm{R}}_{\mathrm{n}}(\mathrm{t})}},$$_{i}(t) is the nonperiodic WSS, and P_{j}(t) is the periodic WSS. - (iii)
- For P
_{j}(t), the SARIMA model is implemented and the results are defined as $\widehat{\mathrm{P}}$_{j}(t), Whereas, for N_{i}(t) and R_{n}(t), the ANFIS model is implemented and the results are defined as ${\widehat{\mathrm{N}}}_{\mathrm{i}}$(t) and ${\widehat{\mathrm{R}}}_{\mathrm{n}}$(t). The sum of results of ANFIS–SARIMA is the forecasted wind speed given as$$\widehat{\mathrm{S}}(\mathrm{t})={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{m}}{\widehat{\mathrm{N}}}_{\mathrm{i}}(\mathrm{t})+{\displaystyle \sum _{\mathrm{j}=\mathrm{m}+1}^{\mathrm{n}}{\widehat{\mathrm{P}}}_{\mathrm{j}}(\mathrm{t})+{\widehat{\mathrm{R}}}_{\mathrm{n}}(\mathrm{t})}}.$$ - (iv)
- On the basis of the predicted wind speed in Equation (20), the wind power can be expressed in the form of wind power flux or kinetic energy flux given as$${\mathrm{P}}_{(\mathrm{W}\mathrm{T})}=\frac{1}{2}{\rho}_{(\mathrm{t})}{\mathrm{C}}_{\mathrm{p}}\mathrm{A}{\mathrm{S}}^{3}(\mathrm{t}),$$$${\rho}_{(\mathrm{t})}=\frac{{\mathrm{P}}_{(\mathrm{t})}}{{\mathrm{R}}_{\mathrm{s}}\times {\mathrm{T}}_{(\mathrm{t})}},$$
_{(t)}is the density of air, P_{(t)}is the atmospheric pressure, R_{s}is the specific gas density, and T_{(t)}is the atmospheric temperature. A is the rotor swept area, C_{p}is the coefficient of maximum power, and S(t) is the forecasted wind speed. If the wind hits the turbine at an angle φ_{(t)}, as shown in Figure 2, then the azimuthal angle variation in the airflow can be considered as cosφ_{(t)}, and Equation (21) can be written as$${\mathrm{P}}_{(\mathrm{W}\mathrm{T})}=\frac{1}{2}{\rho}_{(\mathrm{t})}{\mathrm{C}}_{\mathrm{p}}\mathrm{A}{\left[\mathrm{S}(\mathrm{t})\mathrm{cos}{\phi}_{(\mathrm{t})}\right]}^{3}.$$

_{(t)}and $\overline{\phi}$

_{(t)}are the temporal means of the wind speed and wind direction, respectively, while s’

_{(t)}and φ’

_{(t)}are perturbations or fluctuations about their respective means. Hereafter, for simplicity the notation (t) is removed from all terms. Substituting Equation (24) into Equation (23) and performing Taylor’s expansion and neglecting higher-order terms [46], we have

- (v)
- The squirrel-cage induction generator (SCIG) and doubly fed induction generator (DFIG) are used almost exclusively in the energy conversion stage of the induction generator wind power system. In this study, SCIG was used. The most commonly used system topology is an SCIG directly connected to the power grid, as shown in Figure 3. This topology implies a constant frequency and voltage of the SCIG that establishes a fixed-speed operation. In such a system, the SCIG relies on the grid (or capacitor bank) to provide reactive power, which is necessary to build electromagnetic excitation for the rotary field. The generating mode of the SCIG is triggered by driven torque, which acts opposite to the generator speed within the super-synchronous speed operation region. Due to the absence of a power electronics interface, such a system can only serve the grid support applications, wherein just limited control (pitch-angle control) can be applied.

- (vi)
- The electrical power transferred to the grid is given as$${\mathrm{P}}_{\mathrm{e}}={\eta}_{\mathrm{g}\mathrm{b}}{\eta}_{\mathrm{g}\mathrm{n}}{\eta}_{\mathrm{p}}({\mathrm{P}}_{\mathrm{W}\mathrm{T}}).$$$${\mathrm{I}}_{\mathrm{W}\mathrm{T}\mathrm{G}}=\frac{{\mathrm{P}}_{\mathrm{e}}}{\sqrt{3}\times \mathrm{cos}\varphi \times {\mathrm{V}}_{\mathrm{L}}},$$
_{L}is the line voltage. - (vii)
- The fault current from a three-phase fault in a squirrel-cage induction machine is calculated using the network shown in Figure 5 [48]. The short-circuit current value at t = 0 is given as$${\mathrm{I}}_{\mathrm{S}\mathrm{C}}=\frac{{\mathrm{E}}^{\prime}}{{\mathrm{R}}_{\mathrm{s}}+\mathrm{j}{\mathrm{X}}^{\prime}},$$$${\mathrm{E}}^{\prime}={\mathrm{V}}_{\mathrm{s}}-({\mathrm{R}}_{\mathrm{L}}+\mathrm{j}{\mathrm{X}}_{\mathrm{L}}){\mathrm{I}}_{\mathrm{W}\mathrm{T}\mathrm{G}}-({\mathrm{R}}_{\mathrm{s}}+\mathrm{j}{\mathrm{X}}^{\prime}){\mathrm{I}}_{\mathrm{W}\mathrm{T}\mathrm{G}},$$$${\mathrm{X}}^{\prime}={\mathrm{X}}_{\mathrm{s}}+\frac{{\mathrm{X}}_{\mathrm{m}}{\mathrm{X}}_{\mathrm{r}}}{{\mathrm{X}}_{\mathrm{m}}+{\mathrm{X}}_{\mathrm{r}}},$$
_{s}is the stator resistance, X_{s}and X_{r}are the leakage reactance of the stator and rotor, respectively, X_{m}is the magnetizing reactance, and R_{L}and X_{L}are the resistance and reactance of the line connecting the WTG and grid. Substituting Equation (29) into Equation (28), the short-circuit current for a particular instance can be given as$${\mathrm{I}}_{\mathrm{S}\mathrm{C}}=\frac{{\mathrm{V}}_{\mathrm{s}}-({\mathrm{R}}_{\mathrm{L}}+\mathrm{j}{\mathrm{X}}_{\mathrm{L}}){\mathrm{I}}_{\mathrm{W}\mathrm{T}\mathrm{G}}}{{\mathrm{R}}_{\mathrm{s}}+\mathrm{j}{\mathrm{X}}^{\prime}}.$$_{WTG}calculated in Equation (16), which depends upon the wind speed. The total fault current from a wind farm is the sum of fault currents from all the WTGs.$${\mathrm{I}}_{\mathrm{S}{\mathrm{C}}_{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}}={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{I}}_{\mathrm{S}\mathrm{C}}}={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}\left(\frac{{\mathrm{E}}^{\prime}}{{\mathrm{R}}_{\mathrm{s}}+\mathrm{j}{\mathrm{X}}^{\prime}}\right)}.$$

#### 3.5. Hybrid HHO–LP Optimization Algorithm

_{p}makes the ORC a nonlinear problem (NLP). A hybrid HHO–LP is proposed to solve this NLP by converting it into a linear programming (LP) problem. The basic technique involves the decomposition of the ORC problem into two subproblems. In the first subproblem, a random value is assigned to I

_{p}within its limits. This is only for the first iteration. Later on, its value is updated by the HHO. This converts the NLP into an LP. HHO calls the second subproblem in each iteration, which optimizes the TMS variable by using the standard LP method. This process continues until the convergence of the solution to an optimal value. Detailed descriptions of HHO and LP are given below.

#### 3.5.1. Harris Hawks Optimization

_{(t)}and P

_{(t+1)}are the position vectors of hawks at t and t + 1 iterations, respectively, P

_{rand(t)}is a randomly selected hawk position from the current population, P

_{avg(t)}is the average position of hawks, Pbest(t) is the prey position, LL and UL are the lower and upper limits of the position variables, and r

_{1}, r

_{2}, r

_{3}, and r

_{4}re random values selected from the range [0, 1].

_{max}is the total number of iterations, and E

_{0}and E are the initial and current escape energies of prey randomly selected taken from [−1, 1]. During the attack of hawks in the exploitation phase, the prey has (r) probability of escaping. On the basis of the escape energy and the escape probability of prey, the hawks can espouse one of the four strategies tabulated in Table 1.

_{(t)}is the difference between the position vectors of the prey and hawk, and r

_{5}is a randomly selected number within the range [0, 1].

_{m(t)}is the average position of hawks.

#### 3.5.2. Linear Programming

_{p}is fixed as extracted from HHO. The linear programming subproblem is called repeatedly by HHO to compute the value of TMS and the fitness of each hawk corresponding to the I

_{p.}A penalty relative to the severity of violation is added to the fitness value of each hawk if it violates the inequality coordination constraint. The complete algorithm of the proposed methodology for forecasting wind coupled with HHO–LP optimization algorithm is shown in Figure 6. In this study, short-term wind forecasting is used. The predicted wind speed, wind direction, and metrological variables are compared with the actual ones, and the predicted data are modified if the error exceeds the limits. On the basis of the predicted wind dynamics, the fault current is calculated in advance, and HHO–LP optimizes the ORC problem. The algorithm continuously checks if there is any change in wind data during a specified time interval; then, the fault current is calculated accordingly, and the relay settings are updated. The total time from forecasting to the upgrading of relay settings is given as

_{F}is the time taken by ANFIS–SARIMA to calculate the predicted wind power and fault current, ΔT

_{HHO–LP}is the time consumed by HHO–LP to compute the optimum values of relay variables, and ΔT

_{t}is the time required to transfer the values of I

_{p}and TMS to the relay.

## 4. Case Studies

_{p}for a minimization of the total operation time of primary and backup relays for the fastest fault isolation.

#### 4.1. Test System Specification

#### 4.1.1. IEEE-8 Bus System

_{p}were 1.1 × I

_{Load}and 1.5 × I

_{Load}. The CTI can take a value between 0.2 and 0.5 (REFF). However, for analysis of the IEEE-8 bus system, it was taken as 0.3. The minimum and maximum operation times of the relay were kept as 0.1 and 2.5, respectively, to assure the reliability of the proposed protection scheme.

#### 4.1.2. Jhimpir Wind-Farm-Integrated Substation

_{p}, these limits were 1.1 × I

_{Load}and 1.5 × I

_{Load}respectively. The CTI, in this case was set to 0.3. The proposed algorithm was implemented, and the relay settings were updated after every 5 min. The TMS and I

_{p}for all relays using PSO–LP and HHO–LP updated at each interval are given in Table 9. The operation time of primary–backup relay pairs and the CTI for the 12 intervals of one hour are reflected in Figure 17. The results show that the proposed algorithm reduced the overall operation time of relays and also maintained the CTI between primary–backup relay pairs, which ensured the reliability and security of the power system. The overall performance in terms of an improvement in the reduction of time in all cases is given in Table 10. The computation time was reduced in the proposed algorithm because the relay settings were determined earlier on the basis of the predicted fault current level. If the difference between the predicted and actual fault current due to wind-speed variation was greater than 2%, the actual values were then updated, and the HHO–LP algorithm was implemented during the current interval to optimize the relay settings on the basis of the actual FCL.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

Parameters | WF-1 | WF-2 | Parameters | WF-1 | WF-2 |
---|---|---|---|---|---|

Number of machines | 20 | 10 | Rated wind speed | 13 m/s | |

Nominal power of each machine | 3 MW | L_{S} | 0.0397 pu | ||

Generating voltage | 0.690 kV | L_{r} | 0.0397 pu | ||

Frequency | 50 Hz | L_{m} | 1.354 pu | ||

H_{(s)} | 0.95 |

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**Figure 1.**The architecture of the adaptive neural network-based fuzzy inference system (ANFIS) with type-3 reasoning mechanisms.

**Figure 6.**The hybrid ANFIS–seasonal autoregression integrated moving average (SARIMA) forecasting algorithm coupled with the hybrid Harris hawks optimization and linear programming (HHO–LP) optimization algorithm.

**Figure 7.**Variation in wind speed for one day during (

**A**) winter, (

**B**) summer, (

**C**) spring, and (

**D**) autumn.

**Figure 8.**Variation in the angle of attack for one day during (

**A**) winter, (

**B**) summer, (

**C**) spring, and (

**D**) autumn.

**Figure 9.**Variation in air density for one day during (

**A**) winter, (

**B**) summer, (

**C**) spring, and (

**D**) autumn.

**Figure 11.**Fault currents in primary and backup relays without distributed generation (DG) and with (DG) for both cases.

**Figure 12.**Primary and backup relay pair characteristics for relay pair 6 (

**a**) with conventional settings, and (

**b**) with settings updated using the proposed algorithm.

**Figure 14.**Primary and backup relay pairs as a function of operation time with a wind speed of 20 m/s and an angle variation of 10° using conventional settings and those updated with PSO–LP and HHO–LP.

**Figure 15.**CTI obtained with conventional settings and those updated with PSO–LP and HHO–LP for IEEE-8 bus system for all cases.

**Figure 17.**Operation time of primary–backup relays for all relay pairs on 15 July 2019 at (

**a**) 10:00 a.m., (

**b**) 10:05 a.m., (

**c**) 10:10 a.m., (

**d**) 10:15 a.m., (

**e**) 10:20 a.m., (

**f**) 10:25 a.m., (

**g**) 10:30 a.m., (

**h**) 10:35 am, (

**i**) 10:40 a.m., (

**j**) 10:454 a.m., (

**k**) 10:50 a.m., and (

**l**) 10:55 a.m.

No | Strategies | Escape Energy (E) | Escape Probability (r) |
---|---|---|---|

1 | Soft siege (SS) | E ≥ 0.5 | r ≥ 0.5 |

2 | Soft siege with progressive rapid dives (SSPRD) | E ≥ 0.5 | r < 0.5 |

3 | Hard siege (HS) | E < 0.5 | r ≥ 0.5 |

4 | Hard siege with progressive rapid dives (HSPRD) | E < 0.5 | r < 0.5 |

Errors | FTSVM [50] | MPM [51] | CNN–RBFNN [52] | SELM [53] | Hybrid ANFIS–SARIMA |
---|---|---|---|---|---|

ME | 0.0947 | 0.0850 | 0.0764 | 0.0658 | 0.0625 |

MAE | 1.3412 | 1.3210 | 1.3072 | 1.1064 | 0.9643 |

MSE | 16.512 | 15.5413 | 13.7651 | 11.5614 | 11.0312 |

RMSE | 3.2314 | 3.1150 | 2.8574 | 2.4358 | 2.3519 |

ESD | 4.6864 | 4.5754 | 4.1523 | 3.9525 | 3.8798 |

Fault | Pair | PR | BR | Fault | Pair | PR | BR | Fault | Pair | PR | BR |
---|---|---|---|---|---|---|---|---|---|---|---|

F1 | 1 | 1 | 6 | F3 | 7 | 3 | 2 | F6 | 14 | 6 | 5 |

2 | 8 | 7 | 8 | 10 | 11 | 15 | 6 | 14 | |||

3 | 8 | 9 | F4 | 9 | 4 | 3 | 16 | 13 | 8 | ||

F2 | 4 | 2 | 1 | 10 | 11 | 12 | F7 | 17 | 7 | 5 | |

5 | 2 | 7 | F5 | 11 | 5 | 4 | 18 | 7 | 13 | ||

6 | 9 | 10 | 12 | 12 | 13 | 19 | 14 | 1 | |||

13 | 12 | 14 | 20 | 14 | 9 |

**Table 4.**Relay settings obtained with particle swarm optimization (PSO)–LP and HHO–LP for IEEE-8 bus system at a wind speed of 10 m/s. TMS, time multiplier setting.

Relay | Conventional | PSO–LP [55] | HHO–LP | |||
---|---|---|---|---|---|---|

TMS | I_{p} (kA) | TMS | I_{p} (kA) | TMS | I_{p} (kA) | |

1 | 0.7 | 0.114 | 0.6 | 0.124 | 0.471 | 0.156 |

2 | 0.8 | 0.249 | 0.806 | 0.249 | 0.685 | 0.249 |

3 | 0.724 | 0.187 | 0.729 | 0.187 | 0.597 | 0.187 |

4 | 0.7 | 0.213 | 0.648 | 0.21 | 0.464 | 0.27 |

5 | 0.7 | 0.142 | 0.6 | 0.142 | 0.399 | 0.193 |

6 | 0.743 | 0.171 | 0.677 | 0.171 | 0.592 | 0.171 |

7 | 0.7 | 0.155 | 0.6 | 0.155 | 0.448 | 0.211 |

8 | 0.8 | 0.164 | 0.827 | 0.164 | 0.815 | 0.163 |

9 | 0.7 | 0.13 | 0.6 | 0.131 | 0.522 | 0.177 |

10 | 0.8 | 0.12 | 0.767 | 0.12 | 0.742 | 0.121 |

11 | 0.8 | 0.203 | 0.731 | 0.203 | 0.712 | 0.202 |

12 | 0.8 | 0.183 | 0.913 | 0.183 | 0.894 | 0.183 |

13 | 0.7 | 0.138 | 0.647 | 0.187 | 0.635 | 0.187 |

14 | 0.7 | 0.183 | 0.605 | 0.249 | 0.594 | 0.249 |

**Table 5.**Operation time (TOP) of primary and backup relays for all pairs of IEEE-8 bus system with conventional settings and those updated with PSO–LP and HHO–LP.

Pair | PR | BR | Conventional | PSO–LP [55] | HHO–LP | |||
---|---|---|---|---|---|---|---|---|

TOP_{PR} | TOP_{BR} | TOP_{PR} | TOP_{BR} | TOP_{PR} | TOP_{BR} | |||

1 | 1 | 6 | 1.362 | 1.643 | 1.197 | 1.497 | 1.010 | 1.309 |

2 | 8 | 7 | 1.322 | 2.199 | 1.367 | 1.885 | 1.345 | 1.645 |

3 | 8 | 9 | 1.322 | 2.051 | 1.367 | 1.764 | 1.345 | 1.768 |

4 | 2 | 1 | 1.502 | 2.036 | 1.513 | 1.812 | 1.286 | 1.586 |

5 | 2 | 7 | 1.502 | 2.203 | 1.513 | 1.888 | 1.286 | 1.648 |

6 | 9 | 10 | 1.424 | 1.588 | 1.223 | 1.522 | 1.174 | 1.476 |

7 | 3 | 2 | 1.427 | 1.725 | 1.437 | 1.738 | 1.177 | 1.477 |

8 | 10 | 11 | 1.360 | 1.738 | 1.304 | 1.588 | 1.264 | 1.544 |

9 | 4 | 3 | 1.463 | 1.636 | 1.348 | 1.647 | 1.049 | 1.349 |

10 | 11 | 12 | 1.541 | 1.495 | 1.408 | 1.706 | 1.369 | 1.671 |

11 | 5 | 4 | 1.484 | 1.707 | 1.272 | 1.572 | 0.939 | 1.240 |

12 | 12 | 13 | 1.362 | 1.774 | 1.555 | 1.855 | 1.523 | 1.820 |

13 | 12 | 14 | 1.362 | 1.881 | 1.555 | 1.856 | 1.523 | 1.822 |

14 | 6 | 5 | 1.314 | 2.043 | 1.197 | 1.751 | 1.047 | 1.344 |

15 | 6 | 14 | 1.314 | 2.512 | 1.197 | 2.595 | 1.047 | 2.548 |

16 | 13 | 8 | 1.310 | 1.577 | 1.326 | 1.628 | 1.302 | 1.602 |

17 | 7 | 5 | 1.281 | 1.860 | 1.098 | 1.594 | 0.897 | 1.207 |

18 | 7 | 13 | 1.281 | 2.174 | 1.098 | 2.338 | 0.897 | 2.295 |

19 | 14 | 1 | 1.310 | 1.997 | 1.242 | 1.776 | 1.220 | 1.551 |

20 | 14 | 9 | 1.310 | 1.796 | 1.242 | 1.544 | 1.220 | 1.520 |

**Table 6.**Primary and backup relay operation times with variable wind turbine generator (WTG) capacity and location.

WTG Size and Location | PSO–LP [55] | HHO–LP | ||
---|---|---|---|---|

TOP_{PR} | TOP_{BR} | TOP_{PR} | TOP_{BR} | |

20 WTGs of 1.5 MVA each at bus 3 | 17.17 s | 23.85 s | 15.88 s | 21.36 s |

15 WTGs of 2.5 MVA each at bus 4 | 15.25 s | 22.44 s | 13.44 s | 19.57 s |

20 WTGs at bus 3 and 10 WTGs at bus 4 each of 1.5 MVA | 28.17 s | 37.27 s | 24.73 s | 33.16 s |

15 WTGs at bus 3 and 10 WTGs at bus 6 each of 1.5 MVA | 26.47 s | 35.57 s | 23.93 s | 32.43 s |

**Table 7.**Performance improvement in terms of overall operation time of relay obtained using proposed HHO–LP.

WTG Integration | Conventional Settings | PSO–LP [55] | Proposed Approach HHO–LP | |||
---|---|---|---|---|---|---|

Bus No. | Size (MW) | Operation Time (s) $\sum}({\mathit{t}}_{\mathit{p}}+{\mathit{t}}_{\mathit{b}})$ | Operation Time (s) $\sum}({\mathit{t}}_{\mathit{p}}+{\mathit{t}}_{\mathit{b}})$ | Time Reduction (%) | Operation Time (s) $\sum}({\mathit{t}}_{\mathit{p}}+{\mathit{t}}_{\mathit{b}})$ | Time Reduction (%) |

3 | 30 | 52.14 | 41.02 | 21.327 | 37.24 | 28.577 |

4 | 37.5 | 48.76 | 37.69 | 22.703 | 33.01 | 32.301 |

3, 6 | 60, 30 | 72.28 | 62.038 | 14.169 | 56.36 | 22.026 |

3, 4 | 30, 15 | 76.06 | 65.44 | 13.963 | 57.89 | 23.889 |

3, 6 | 22.5, 15 | 75.44 | 62.04 | 17.623 | 56.36 | 25.292 |

Pair | PR | BR1 | BR2 | Pair | PR | BR1 | BR2 | Pair | PR | BR1 | BR2 | Pair | PR | BR1 | BR2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | R1 | R32 | R36 | 10 | R10 | R33 | R36 | 19 | R19 | R34 | R36 | 28 | R28 | R35 | R36 |

2 | R2 | R32 | R36 | 11 | R11 | R33 | R36 | 20 | R20 | R34 | R36 | 29 | R29 | R35 | R36 |

3 | R3 | R32 | R36 | 12 | R12 | R33 | R36 | 21 | R21 | R34 | R36 | 30 | R30 | R35 | R36 |

4 | R4 | R32 | R36 | 13 | R13 | R33 | R36 | 22 | R22 | R34 | R36 | 31 | R31 | R35 | R36 |

5 | R5 | R32 | R36 | 14 | R14 | R33 | R36 | 23 | R23 | R34 | R36 | 32 | R32 | R36 | -- |

6 | R6 | R32 | R36 | 15 | R15 | R33 | R36 | 24 | R24 | R35 | R36 | 33 | R33 | R36 | -- |

7 | R7 | R32 | R36 | 16 | R16 | R33 | R36 | 25 | R25 | R35 | R36 | 34 | R34 | R36 | -- |

8 | R8 | R32 | R36 | 17 | R17 | R34 | R36 | 26 | R26 | R35 | R36 | 35 | R35 | R36 | -- |

9 | R9 | R33 | R36 | 18 | R18 | R34 | R36 | 27 | R27 | R35 | R36 |

**Table 9.**Relay settings for 12 intervals of one hour from 10:00 a.m.–11:00 a.m. on 15 July 2019 obtained using PSO–LP and the proposed HHO–LP.

10:00 a.m. | 10:05 a.m. | 10:10 a.m. | 10:15 a.m. | 10:20 a.m. | 10:25 a.m. | |||||||||||||||||||

PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | |||||||||||||

Pair | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{P} | TMS | I_{P} | TMS | I_{P} | TMS | I_{p} |

1 | 0.17 | 0.38 | 0.11 | 0.47 | 0.11 | 0.23 | 0.14 | 0.15 | 0.13 | 0.49 | 0.14 | 0.54 | 0.15 | 0.11 | 0.14 | 0.36 | 0.13 | 0.40 | 0.11 | 0.39 | 0.16 | 0.41 | 0.12 | 0.34 |

2 | 0.16 | 0.21 | 0.13 | 0.39 | 0.17 | 0.17 | 0.12 | 0.39 | 0.16 | 0.36 | 0.12 | 0.39 | 0.11 | 0.23 | 0.11 | 0.14 | 0.11 | 0.23 | 0.14 | 0.42 | 0.18 | 0.18 | 0.12 | 0.24 |

3 | 0.13 | 0.50 | 0.11 | 0.36 | 0.11 | 0.33 | 0.12 | 0.13 | 0.17 | 0.13 | 0.12 | 0.14 | 0.18 | 0.18 | 0.11 | 0.20 | 0.16 | 0.38 | 0.14 | 0.12 | 0.11 | 0.38 | 0.10 | 0.22 |

4 | 0.13 | 0.30 | 0.13 | 0.13 | 0.14 | 0.40 | 0.11 | 0.35 | 0.16 | 0.55 | 0.14 | 0.60 | 0.11 | 0.46 | 0.11 | 0.26 | 0.12 | 0.44 | 0.11 | 0.20 | 0.14 | 0.19 | 0.11 | 0.12 |

5 | 0.13 | 0.36 | 0.11 | 0.12 | 0.13 | 0.52 | 0.13 | 0.23 | 0.16 | 0.51 | 0.12 | 0.57 | 0.11 | 0.48 | 0.11 | 0.29 | 0.17 | 0.52 | 0.14 | 0.36 | 0.13 | 0.41 | 0.12 | 0.29 |

6 | 0.14 | 0.42 | 0.13 | 0.48 | 0.16 | 0.18 | 0.11 | 0.32 | 0.12 | 0.24 | 0.13 | 0.26 | 0.13 | 0.44 | 0.12 | 0.19 | 0.11 | 0.49 | 0.14 | 0.18 | 0.17 | 0.52 | 0.13 | 0.22 |

7 | 0.14 | 0.40 | 0.10 | 0.20 | 0.11 | 0.22 | 0.13 | 0.12 | 0.11 | 0.18 | 0.11 | 0.20 | 0.16 | 0.29 | 0.12 | 0.21 | 0.12 | 0.24 | 0.13 | 0.16 | 0.12 | 0.37 | 0.12 | 0.31 |

8 | 0.17 | 0.36 | 0.11 | 0.18 | 0.17 | 0.42 | 0.14 | 0.26 | 0.17 | 0.50 | 0.12 | 0.55 | 0.18 | 0.13 | 0.14 | 0.33 | 0.13 | 0.52 | 0.10 | 0.13 | 0.14 | 0.18 | 0.12 | 0.26 |

9 | 0.15 | 0.32 | 0.11 | 0.55 | 0.14 | 0.51 | 0.11 | 0.36 | 0.17 | 0.16 | 0.13 | 0.17 | 0.15 | 0.28 | 0.14 | 0.37 | 0.12 | 0.27 | 0.10 | 0.38 | 0.14 | 0.34 | 0.11 | 0.34 |

10 | 0.12 | 0.45 | 0.11 | 0.32 | 0.19 | 0.29 | 0.11 | 0.24 | 0.17 | 0.16 | 0.13 | 0.18 | 0.16 | 0.53 | 0.11 | 0.14 | 0.18 | 0.12 | 0.11 | 0.40 | 0.15 | 0.19 | 0.14 | 0.18 |

11 | 0.13 | 0.54 | 0.12 | 0.39 | 0.16 | 0.32 | 0.14 | 0.36 | 0.12 | 0.27 | 0.14 | 0.30 | 0.18 | 0.42 | 0.12 | 0.37 | 0.18 | 0.42 | 0.11 | 0.17 | 0.16 | 0.54 | 0.13 | 0.19 |

12 | 0.12 | 0.45 | 0.13 | 0.31 | 0.16 | 0.49 | 0.11 | 0.11 | 0.11 | 0.24 | 0.14 | 0.27 | 0.10 | 0.50 | 0.11 | 0.40 | 0.18 | 0.50 | 0.13 | 0.13 | 0.17 | 0.12 | 0.13 | 0.35 |

13 | 0.18 | 0.21 | 0.11 | 0.29 | 0.11 | 0.13 | 0.13 | 0.23 | 0.15 | 0.51 | 0.13 | 0.56 | 0.16 | 0.47 | 0.12 | 0.20 | 0.11 | 0.35 | 0.10 | 0.12 | 0.17 | 0.35 | 0.12 | 0.25 |

14 | 0.16 | 0.34 | 0.13 | 0.44 | 0.17 | 0.15 | 0.12 | 0.19 | 0.15 | 0.32 | 0.13 | 0.36 | 0.16 | 0.33 | 0.13 | 0.30 | 0.15 | 0.50 | 0.10 | 0.28 | 0.10 | 0.18 | 0.12 | 0.42 |

15 | 0.16 | 0.16 | 0.13 | 0.53 | 0.12 | 0.29 | 0.12 | 0.33 | 0.13 | 0.17 | 0.12 | 0.19 | 0.18 | 0.27 | 0.11 | 0.13 | 0.12 | 0.54 | 0.12 | 0.13 | 0.13 | 0.55 | 0.12 | 0.38 |

16 | 0.19 | 0.20 | 0.11 | 0.31 | 0.15 | 0.55 | 0.14 | 0.16 | 0.11 | 0.53 | 0.10 | 0.58 | 0.16 | 0.26 | 0.11 | 0.16 | 0.13 | 0.32 | 0.10 | 0.14 | 0.15 | 0.55 | 0.12 | 0.12 |

17 | 0.12 | 0.30 | 0.11 | 0.55 | 0.10 | 0.27 | 0.13 | 0.18 | 0.16 | 0.27 | 0.10 | 0.30 | 0.19 | 0.38 | 0.12 | 0.39 | 0.12 | 0.21 | 0.12 | 0.11 | 0.14 | 0.14 | 0.13 | 0.41 |

18 | 0.17 | 0.49 | 0.13 | 0.24 | 0.10 | 0.28 | 0.12 | 0.14 | 0.10 | 0.48 | 0.13 | 0.53 | 0.15 | 0.25 | 0.10 | 0.39 | 0.10 | 0.53 | 0.14 | 0.23 | 0.13 | 0.38 | 0.11 | 0.14 |

19 | 0.14 | 0.55 | 0.12 | 0.26 | 0.10 | 0.33 | 0.11 | 0.19 | 0.12 | 0.29 | 0.14 | 0.32 | 0.12 | 0.19 | 0.11 | 0.30 | 0.19 | 0.31 | 0.10 | 0.17 | 0.14 | 0.53 | 0.14 | 0.33 |

20 | 0.14 | 0.22 | 0.12 | 0.37 | 0.15 | 0.50 | 0.12 | 0.39 | 0.11 | 0.30 | 0.13 | 0.33 | 0.17 | 0.41 | 0.12 | 0.38 | 0.16 | 0.53 | 0.12 | 0.15 | 0.15 | 0.27 | 0.13 | 0.30 |

21 | 0.12 | 0.54 | 0.11 | 0.16 | 0.12 | 0.32 | 0.11 | 0.25 | 0.19 | 0.42 | 0.12 | 0.47 | 0.11 | 0.21 | 0.11 | 0.25 | 0.17 | 0.13 | 0.11 | 0.33 | 0.15 | 0.47 | 0.12 | 0.41 |

22 | 0.16 | 0.46 | 0.13 | 0.35 | 0.18 | 0.30 | 0.10 | 0.29 | 0.11 | 0.40 | 0.12 | 0.44 | 0.18 | 0.22 | 0.10 | 0.38 | 0.12 | 0.30 | 0.11 | 0.19 | 0.18 | 0.54 | 0.10 | 0.38 |

23 | 0.17 | 0.54 | 0.12 | 0.50 | 0.16 | 0.19 | 0.14 | 0.42 | 0.13 | 0.23 | 0.13 | 0.26 | 0.10 | 0.27 | 0.10 | 0.37 | 0.15 | 0.38 | 0.11 | 0.23 | 0.14 | 0.20 | 0.14 | 0.39 |

24 | 0.10 | 0.17 | 0.13 | 0.12 | 0.16 | 0.12 | 0.10 | 0.29 | 0.18 | 0.19 | 0.14 | 0.21 | 0.12 | 0.46 | 0.11 | 0.42 | 0.13 | 0.49 | 0.10 | 0.24 | 0.12 | 0.20 | 0.14 | 0.12 |

25 | 0.14 | 0.28 | 0.13 | 0.53 | 0.17 | 0.18 | 0.11 | 0.13 | 0.12 | 0.37 | 0.13 | 0.41 | 0.17 | 0.44 | 0.12 | 0.27 | 0.15 | 0.50 | 0.14 | 0.22 | 0.15 | 0.17 | 0.12 | 0.25 |

26 | 0.17 | 0.18 | 0.13 | 0.26 | 0.15 | 0.32 | 0.10 | 0.17 | 0.18 | 0.12 | 0.13 | 0.13 | 0.19 | 0.23 | 0.11 | 0.30 | 0.14 | 0.45 | 0.12 | 0.33 | 0.12 | 0.11 | 0.12 | 0.30 |

27 | 0.14 | 0.44 | 0.14 | 0.26 | 0.14 | 0.45 | 0.14 | 0.40 | 0.14 | 0.24 | 0.13 | 0.27 | 0.12 | 0.48 | 0.11 | 0.29 | 0.17 | 0.33 | 0.13 | 0.42 | 0.10 | 0.49 | 0.11 | 0.27 |

28 | 0.18 | 0.39 | 0.14 | 0.46 | 0.13 | 0.12 | 0.13 | 0.27 | 0.11 | 0.21 | 0.11 | 0.23 | 0.14 | 0.37 | 0.11 | 0.35 | 0.10 | 0.48 | 0.11 | 0.18 | 0.16 | 0.28 | 0.10 | 0.39 |

29 | 0.12 | 0.40 | 0.11 | 0.38 | 0.15 | 0.48 | 0.11 | 0.33 | 0.11 | 0.50 | 0.12 | 0.55 | 0.13 | 0.29 | 0.11 | 0.17 | 0.15 | 0.37 | 0.13 | 0.11 | 0.14 | 0.33 | 0.11 | 0.33 |

30 | 0.11 | 0.28 | 0.13 | 0.19 | 0.15 | 0.35 | 0.12 | 0.40 | 0.17 | 0.45 | 0.11 | 0.49 | 0.15 | 0.39 | 0.11 | 0.12 | 0.11 | 0.39 | 0.12 | 0.19 | 0.13 | 0.25 | 0.14 | 0.31 |

31 | 0.18 | 0.46 | 0.12 | 0.22 | 0.18 | 0.28 | 0.13 | 0.12 | 0.17 | 0.36 | 0.13 | 0.40 | 0.14 | 0.16 | 0.12 | 0.18 | 0.14 | 0.22 | 0.13 | 0.24 | 0.17 | 0.46 | 0.12 | 0.20 |

32 | 0.23 | 0.43 | 0.19 | 0.41 | 0.28 | 0.26 | 0.24 | 0.20 | 0.32 | 0.19 | 0.17 | 0.50 | 0.24 | 0.25 | 0.23 | 0.27 | 0.21 | 0.64 | 0.25 | 0.25 | 0.29 | 0.25 | 0.18 | 0.36 |

33 | 0.23 | 0.37 | 0.23 | 0.16 | 0.19 | 0.72 | 0.18 | 0.51 | 0.18 | 0.69 | 0.22 | 0.31 | 0.27 | 0.31 | 0.25 | 0.21 | 0.25 | 0.42 | 0.20 | 0.33 | 0.22 | 0.45 | 0.17 | 0.50 |

34 | 0.33 | 0.18 | 0.22 | 0.48 | 0.22 | 0.45 | 0.19 | 0.55 | 0.24 | 0.49 | 0.17 | 0.56 | 0.30 | 0.23 | 0.20 | 0.35 | 0.19 | 0.67 | 0.18 | 0.46 | 0.34 | 0.21 | 0.23 | 0.29 |

35 | 0.30 | 0.26 | 0.25 | 0.43 | 0.21 | 0.52 | 0.21 | 0.38 | 0.26 | 0.34 | 0.18 | 0.55 | 0.22 | 0.50 | 0.18 | 0.40 | 0.21 | 0.52 | 0.19 | 0.44 | 0.27 | 0.30 | 0.24 | 0.22 |

36 | 0.23 | 0.43 | 0.17 | 0.15 | 0.19 | 0.53 | 0.24 | 0.28 | 0.27 | 0.29 | 0.21 | 0.36 | 0.29 | 0.19 | 0.13 | 0.55 | 0.21 | 0.47 | 0.33 | 0.12 | 0.21 | 0.27 | 0.15 | 0.35 |

Pair | 10:30 a.m. | 10:35 a.m. | 10:40 a.m. | 10:454 a.m. | 10:50 a.m. | 10:55 a.m. | ||||||||||||||||||

PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | PSO–LP | HHO–LP | |||||||||||||

TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{p} | TMS | I_{P} | TMS | I_{P} | TMS | I_{P} | TMS | I_{p} | |

1 | 0.11 | 0.28 | 0.11 | 0.35 | 0.14 | 0.54 | 0.10 | 0.32 | 0.18 | 0.30 | 0.13 | 0.42 | 0.16 | 0.43 | 0.11 | 0.18 | 0.11 | 0.32 | 0.13 | 0.29 | 0.15 | 0.47 | 0.10 | 0.31 |

2 | 0.14 | 0.49 | 0.11 | 0.24 | 0.18 | 0.37 | 0.10 | 0.22 | 0.18 | 0.22 | 0.14 | 0.23 | 0.13 | 0.45 | 0.13 | 0.34 | 0.14 | 0.17 | 0.14 | 0.43 | 0.15 | 0.39 | 0.12 | 0.40 |

3 | 0.11 | 0.22 | 0.13 | 0.42 | 0.15 | 0.20 | 0.13 | 0.26 | 0.17 | 0.47 | 0.13 | 0.18 | 0.16 | 0.33 | 0.14 | 0.39 | 0.12 | 0.13 | 0.11 | 0.14 | 0.14 | 0.36 | 0.10 | 0.25 |

4 | 0.12 | 0.50 | 0.11 | 0.24 | 0.18 | 0.27 | 0.13 | 0.39 | 0.18 | 0.35 | 0.11 | 0.33 | 0.13 | 0.19 | 0.13 | 0.38 | 0.10 | 0.17 | 0.13 | 0.18 | 0.12 | 0.13 | 0.12 | 0.34 |

5 | 0.14 | 0.29 | 0.11 | 0.31 | 0.19 | 0.54 | 0.11 | 0.39 | 0.10 | 0.54 | 0.13 | 0.37 | 0.18 | 0.38 | 0.12 | 0.39 | 0.11 | 0.46 | 0.11 | 0.39 | 0.19 | 0.12 | 0.11 | 0.24 |

6 | 0.13 | 0.44 | 0.14 | 0.14 | 0.11 | 0.31 | 0.11 | 0.40 | 0.14 | 0.47 | 0.13 | 0.13 | 0.11 | 0.49 | 0.14 | 0.22 | 0.18 | 0.33 | 0.12 | 0.12 | 0.14 | 0.48 | 0.13 | 0.18 |

7 | 0.19 | 0.42 | 0.14 | 0.15 | 0.15 | 0.53 | 0.12 | 0.19 | 0.14 | 0.37 | 0.13 | 0.29 | 0.14 | 0.16 | 0.11 | 0.37 | 0.13 | 0.31 | 0.10 | 0.24 | 0.18 | 0.20 | 0.13 | 0.12 |

8 | 0.17 | 0.44 | 0.13 | 0.23 | 0.18 | 0.11 | 0.11 | 0.28 | 0.19 | 0.11 | 0.12 | 0.20 | 0.14 | 0.23 | 0.11 | 0.19 | 0.16 | 0.35 | 0.13 | 0.21 | 0.16 | 0.18 | 0.13 | 0.18 |

9 | 0.14 | 0.32 | 0.10 | 0.34 | 0.12 | 0.49 | 0.12 | 0.25 | 0.12 | 0.35 | 0.13 | 0.29 | 0.19 | 0.36 | 0.13 | 0.27 | 0.13 | 0.26 | 0.14 | 0.35 | 0.16 | 0.55 | 0.12 | 0.26 |

10 | 0.18 | 0.53 | 0.10 | 0.41 | 0.18 | 0.31 | 0.11 | 0.22 | 0.11 | 0.45 | 0.10 | 0.24 | 0.16 | 0.39 | 0.14 | 0.12 | 0.15 | 0.40 | 0.13 | 0.18 | 0.18 | 0.32 | 0.12 | 0.23 |

11 | 0.18 | 0.55 | 0.11 | 0.31 | 0.14 | 0.37 | 0.12 | 0.25 | 0.13 | 0.51 | 0.13 | 0.27 | 0.15 | 0.25 | 0.10 | 0.18 | 0.16 | 0.47 | 0.11 | 0.19 | 0.18 | 0.39 | 0.12 | 0.34 |

12 | 0.17 | 0.15 | 0.12 | 0.25 | 0.11 | 0.47 | 0.13 | 0.29 | 0.11 | 0.29 | 0.11 | 0.26 | 0.11 | 0.33 | 0.12 | 0.14 | 0.16 | 0.49 | 0.10 | 0.41 | 0.16 | 0.31 | 0.12 | 0.20 |

13 | 0.19 | 0.18 | 0.10 | 0.27 | 0.17 | 0.53 | 0.12 | 0.22 | 0.16 | 0.20 | 0.10 | 0.41 | 0.17 | 0.12 | 0.10 | 0.16 | 0.17 | 0.40 | 0.11 | 0.19 | 0.14 | 0.29 | 0.12 | 0.37 |

14 | 0.19 | 0.14 | 0.13 | 0.40 | 0.15 | 0.41 | 0.12 | 0.34 | 0.19 | 0.36 | 0.12 | 0.25 | 0.17 | 0.17 | 0.10 | 0.22 | 0.16 | 0.37 | 0.11 | 0.17 | 0.15 | 0.44 | 0.14 | 0.16 |

15 | 0.13 | 0.28 | 0.13 | 0.18 | 0.17 | 0.49 | 0.10 | 0.16 | 0.12 | 0.17 | 0.10 | 0.31 | 0.13 | 0.20 | 0.12 | 0.16 | 0.16 | 0.49 | 0.14 | 0.13 | 0.18 | 0.53 | 0.13 | 0.35 |

16 | 0.13 | 0.34 | 0.11 | 0.42 | 0.12 | 0.38 | 0.12 | 0.16 | 0.14 | 0.44 | 0.11 | 0.15 | 0.11 | 0.52 | 0.13 | 0.29 | 0.15 | 0.43 | 0.11 | 0.28 | 0.11 | 0.31 | 0.12 | 0.29 |

17 | 0.11 | 0.49 | 0.11 | 0.15 | 0.11 | 0.47 | 0.13 | 0.19 | 0.13 | 0.19 | 0.10 | 0.20 | 0.13 | 0.50 | 0.14 | 0.41 | 0.11 | 0.41 | 0.12 | 0.43 | 0.18 | 0.55 | 0.14 | 0.21 |

18 | 0.12 | 0.28 | 0.14 | 0.12 | 0.11 | 0.39 | 0.13 | 0.33 | 0.11 | 0.53 | 0.10 | 0.43 | 0.11 | 0.21 | 0.12 | 0.29 | 0.12 | 0.43 | 0.13 | 0.26 | 0.15 | 0.24 | 0.13 | 0.16 |

19 | 0.11 | 0.46 | 0.10 | 0.33 | 0.11 | 0.12 | 0.14 | 0.18 | 0.15 | 0.53 | 0.13 | 0.34 | 0.12 | 0.50 | 0.14 | 0.12 | 0.11 | 0.48 | 0.14 | 0.24 | 0.10 | 0.26 | 0.13 | 0.29 |

20 | 0.11 | 0.47 | 0.13 | 0.24 | 0.17 | 0.33 | 0.12 | 0.40 | 0.11 | 0.37 | 0.11 | 0.21 | 0.13 | 0.50 | 0.13 | 0.40 | 0.14 | 0.49 | 0.14 | 0.36 | 0.18 | 0.37 | 0.12 | 0.16 |

21 | 0.16 | 0.53 | 0.11 | 0.27 | 0.12 | 0.48 | 0.11 | 0.29 | 0.17 | 0.29 | 0.13 | 0.34 | 0.18 | 0.44 | 0.11 | 0.36 | 0.12 | 0.22 | 0.11 | 0.35 | 0.19 | 0.16 | 0.13 | 0.41 |

22 | 0.14 | 0.20 | 0.12 | 0.24 | 0.13 | 0.31 | 0.13 | 0.42 | 0.11 | 0.32 | 0.13 | 0.36 | 0.18 | 0.42 | 0.11 | 0.40 | 0.17 | 0.31 | 0.12 | 0.36 | 0.17 | 0.35 | 0.10 | 0.42 |

23 | 0.15 | 0.49 | 0.10 | 0.43 | 0.14 | 0.19 | 0.10 | 0.28 | 0.18 | 0.19 | 0.12 | 0.17 | 0.13 | 0.25 | 0.10 | 0.12 | 0.11 | 0.38 | 0.14 | 0.32 | 0.12 | 0.50 | 0.11 | 0.33 |

24 | 0.17 | 0.37 | 0.14 | 0.33 | 0.11 | 0.18 | 0.10 | 0.16 | 0.12 | 0.43 | 0.11 | 0.32 | 0.17 | 0.20 | 0.10 | 0.23 | 0.13 | 0.32 | 0.12 | 0.24 | 0.16 | 0.12 | 0.11 | 0.30 |

25 | 0.17 | 0.20 | 0.11 | 0.12 | 0.17 | 0.19 | 0.11 | 0.24 | 0.19 | 0.21 | 0.10 | 0.33 | 0.11 | 0.45 | 0.13 | 0.30 | 0.16 | 0.49 | 0.12 | 0.37 | 0.16 | 0.53 | 0.13 | 0.16 |

26 | 0.11 | 0.48 | 0.10 | 0.29 | 0.18 | 0.28 | 0.11 | 0.12 | 0.11 | 0.31 | 0.12 | 0.38 | 0.11 | 0.18 | 0.14 | 0.41 | 0.14 | 0.50 | 0.12 | 0.28 | 0.16 | 0.26 | 0.14 | 0.18 |

27 | 0.15 | 0.15 | 0.11 | 0.42 | 0.16 | 0.33 | 0.13 | 0.42 | 0.15 | 0.41 | 0.13 | 0.25 | 0.15 | 0.48 | 0.13 | 0.23 | 0.13 | 0.12 | 0.10 | 0.39 | 0.11 | 0.26 | 0.13 | 0.20 |

28 | 0.12 | 0.27 | 0.12 | 0.40 | 0.14 | 0.23 | 0.12 | 0.28 | 0.13 | 0.18 | 0.14 | 0.23 | 0.13 | 0.19 | 0.12 | 0.40 | 0.13 | 0.39 | 0.12 | 0.26 | 0.11 | 0.46 | 0.13 | 0.26 |

29 | 0.16 | 0.37 | 0.10 | 0.35 | 0.10 | 0.24 | 0.12 | 0.12 | 0.12 | 0.24 | 0.12 | 0.19 | 0.18 | 0.27 | 0.13 | 0.42 | 0.13 | 0.21 | 0.12 | 0.29 | 0.19 | 0.38 | 0.11 | 0.23 |

30 | 0.11 | 0.40 | 0.11 | 0.19 | 0.18 | 0.33 | 0.12 | 0.41 | 0.19 | 0.11 | 0.12 | 0.37 | 0.13 | 0.55 | 0.10 | 0.29 | 0.18 | 0.41 | 0.12 | 0.36 | 0.19 | 0.19 | 0.13 | 0.16 |

31 | 0.11 | 0.12 | 0.12 | 0.39 | 0.12 | 0.12 | 0.11 | 0.30 | 0.14 | 0.33 | 0.13 | 0.35 | 0.16 | 0.29 | 0.11 | 0.29 | 0.14 | 0.40 | 0.13 | 0.37 | 0.11 | 0.22 | 0.10 | 0.40 |

32 | 0.39 | 0.16 | 0.18 | 0.43 | 0.35 | 0.26 | 0.23 | 0.35 | 0.23 | 0.52 | 0.23 | 0.36 | 0.32 | 0.28 | 0.33 | 0.16 | 0.39 | 0.17 | 0.22 | 0.41 | 0.26 | 0.41 | 0.24 | 0.34 |

33 | 0.30 | 0.40 | 0.24 | 0.22 | 0.27 | 0.43 | 0.29 | 0.18 | 0.26 | 0.41 | 0.29 | 0.17 | 0.31 | 0.31 | 0.28 | 0.24 | 0.36 | 0.23 | 0.18 | 0.58 | 0.41 | 0.16 | 0.24 | 0.36 |

34 | 0.37 | 0.18 | 0.21 | 0.27 | 0.19 | 0.74 | 0.25 | 0.32 | 0.25 | 0.47 | 0.24 | 0.32 | 0.33 | 0.30 | 0.26 | 0.39 | 0.37 | 0.27 | 0.25 | 0.29 | 0.25 | 0.65 | 0.30 | 0.22 |

35 | 0.33 | 0.22 | 0.18 | 0.47 | 0.30 | 0.28 | 0.25 | 0.30 | 0.31 | 0.24 | 0.29 | 0.18 | 0.31 | 0.26 | 0.31 | 0.21 | 0.18 | 0.53 | 0.21 | 0.37 | 0.25 | 0.58 | 0.24 | 0.32 |

36 | 0.18 | 0.54 | 0.15 | 0.40 | 0.17 | 0.37 | 0.21 | 0.25 | 0.21 | 0.28 | 0.17 | 0.34 | 0.32 | 0.33 | 0.37 | 0.17 | 0.34 | 0.32 | 0.30 | 0.24 | 0.43 | 0.15 | 0.22 | 0.42 |

**Table 10.**Performance improvement in terms of the overall operation time of relays obtained using the proposed HHO–LP.

Interval | Conventional Settings | PSO–LP [55] | Proposed Approach HHO–LP | ||
---|---|---|---|---|---|

Operation Time (s) $\sum}({\mathit{t}}_{\mathit{p}}+{\mathit{t}}_{\mathit{b}})$ | Operation Time (s) $\sum}({\mathit{t}}_{\mathit{p}}+{\mathit{t}}_{\mathit{b}})$ | Time Reduction (%) | Operation Time (s) $\sum}({\mathit{t}}_{\mathit{p}}+{\mathit{t}}_{\mathit{b}})$ | Time Reduction (%) | |

1 | 51.248 | 39.515 | 22.894 | 31.860 | 37.831 |

2 | 48.066 | 37.643 | 21.684 | 32.078 | 33.262 |

3 | 49.125 | 38.540 | 21.547 | 32.457 | 33.93 |

4 | 50.864 | 38.632 | 24.048 | 31.638 | 37.799 |

5 | 51.660 | 39.521 | 23.497 | 31.445 | 39.131 |

6 | 53.981 | 42.180 | 21.861 | 33.891 | 37.217 |

7 | 52.112 | 41.919 | 19.559 | 32.514 | 37.607 |

8 | 55.561 | 43.171 | 22.299 | 34.741 | 37.472 |

9 | 53.046 | 42.348 | 20.167 | 35.726 | 32.651 |

10 | 49.220 | 38.790 | 21.933 | 33.189 | 32.57 |

11 | 58.756 | 47.044 | 19.9332 | 35.210 | 40.074 |

12 | 52.550 | 41.651 | 20.740 | 32.374 | 38.394 |

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## Share and Cite

**MDPI and ACS Style**

Rizwan, M.; Hong, L.; Waseem, M.; Ahmad, S.; Sharaf, M.; Shafiq, M.
A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. *Appl. Sci.* **2020**, *10*, 6318.
https://doi.org/10.3390/app10186318

**AMA Style**

Rizwan M, Hong L, Waseem M, Ahmad S, Sharaf M, Shafiq M.
A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. *Applied Sciences*. 2020; 10(18):6318.
https://doi.org/10.3390/app10186318

**Chicago/Turabian Style**

Rizwan, Mian, Lucheng Hong, Muhammad Waseem, Shafiq Ahmad, Mohamed Sharaf, and Muhammad Shafiq.
2020. "A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems" *Applied Sciences* 10, no. 18: 6318.
https://doi.org/10.3390/app10186318