Article movement factors consist of two main components: an inevitable element and a random component. Each particle is attracted to the best current position G and the best X i * Site in history, while at the same time tends to transfer arbitrarily. For example, let be the vector location x_i and the particles velocity v_i of i, respectively, so the following formula calculates the new speed transducer [14]:

When required, the diversity in quality solutions increase, improving the standard PSO uses the best position individual X i * , and the global best g * [15]. The difference can be simulated by using randomization for using the best individual is mainly. Later, there is no convincingly motive to use the best individuals, except the problem optimization is multimodal and extremely nonlinear [16]. A basic model that can accelerate the meeting of the procedure is to use the globally better only. Thus, in this way of the acceleration of particle swarm optimization [17] [18], the velocity vector is generated by the Equation:

v i t + 1 = v i t + α ϵ n + β [ g * − X i t ] (1)

where: ϵ n is outgoing N(0, 1) to replace the next term. To increase the convergence more, we can also write the location update in one step:

x i t + 1 = ( 1 − β ) x i t + α ϵ n + β g * (2)

Usually, α = 0.1K to 0.5K, where K is the measure of each variable and β selects from 0.1 to 0.7 for suitable in most applications. There is no requirement to accord with the vectors of velocity formation, so be noted that speed does not appear in Equation (5); therefore, APSO is much simpler, and the mechanism is easy to understand. Compared with many standard PSO variables, the APSO uses two exclusive parameters; another improvement in the accelerated PSO system is to reduce randomization while duplicates persist. This means that we can use a monotonous decrease function like:

α = α 0 ⋅ exp [ − γ t ] (3)

or,

α = α 0 ⋅ γ t ,     ( 0 < γ < 1 ) (4)

where: α₀, approximately from 0.5 to 1, is the initial value of the random parameter. Here t is the number of duplicates or time steps, and γ is a control parameter [19]. For example, in this application, we will use:

α = 0.75 ⋅ t (5)

where: t ∈ [0 and t_max] and t_max is the maximum iteration [20].

In the power systems, the dispatch of reactive electricity power may have different goals, such as obtaining the best voltage quality and reducing real power losses. Considered the voltage magnitude is |V_i| and |V_j| at buses i and j, respectively so, the power loss as an objective function and described by the formula [21]:

f : P l o s s = ∑ K = 1 M     g i j ( | V i | 2 + | V j | 2 − 2 | V i | | V j | ⋅ cos θ i j ) (6)

M is the number of branches, while the angle θ_ij is the difference in the phase of voltage angle at bus i and bus j, respectively, and g_ij represents the branch conductance of impedance between the buses i andj [22].

There are two constraints dispatch issue of reactive power, equality, and inequality constraints as follows:

1) Equality Constraint: If we suppose the real and imaginary power supplying to bus i are P_gi and Q_gj respectively, and the real and imaginary demand load power at bus i are P_di and Q_di respectively, so The real and imagery power is given by the following formulas [23]:

y 1 : P g i − P d i − V i ∑ V j ( G i j cos θ i j + B i j sin θ i j ) = 0 (7)

y 2 : Q g i − Q d i − V i ∑ V j ( G i j sin θ i j − B i j cos θ i j ) = 0 (8)

2) Inequality Constraint: It’s the voltage magnitude range value, tap changing transformer positions value, and injection reactive power to the system. In this paper, and based upon the IEEE 30-bus system’s data, the voltage magnitude values are selected from 0.950 p.u to 1.10 p.u, and the tap changing transformer range is usually from 0.975 to 1.025. The compensator’s injection of reactive power to the system from 0 MVAR to 20 MVAR. The Inequality Constraint is given by the following relations [20] [24]:

g 1 : V i min < V i < V i max (9)

g 2 : t j min < t j < t j max (10)

g 3 : Q g i min < Q g i < Q g i max (11)

So, the objective function given in equation nine can be expressed by follows:

F = P l o s s + ∑ r g i ⋅ ( V i − V i lim ) 2 + ∑ r t i ⋅ ( T i − T i lim ) + ∑ r Q i ⋅ ( Q i − Q i lim ) 2 (12)

And the values of voltage magnitude, tap changing transformer, and injection reactive power described as follows [25] [26]:

V i lim = { V i max ; V i > V i max V i min ; V i < V i min (13)

T i lim = { T i max ; T i > T i max T i min ; T i < T i min (14)

Q i lim = { Q i max ; Q i > Q i max Q i min ; Q i < Q i min (15)

The Wind Power depends on [27]:

• amount of air (volume);

• speed of air (velocity);

• mass of air (density) A.

flowing through the area of interest (flux).

The Kinetic Energy definition as:

Kinetic   Energy   KE = 1 2 ⋅ m ⋅ v 2 (16)

The Power is K.E. per unit time [28]:

P = 1 2 ⋅ m ˙ ⋅ v 2 (17)

where m ˙ is the mass flux and given by:

m ˙ = d m d t (18)

Fluid mechanics gives the mass flow rate,

d m d t = ρ ⋅ A ⋅ v (19)

Thus,

P = 1 2 ⋅ ρ ⋅ A ⋅ v 3 (20)

where ρ is the air density.

A = π ⋅ r 2 (21)

• Therefore, the power proportional to air density, rotor swept area and the velocity cube.

Power Coefficient, C_p, is the ratio of power extracted by the turbine to the total contained in the wind resource [28],

C p = P T P W (22)

• Turbine power output,

P T = 1 2 ⋅ ρ ⋅ A ⋅ v 3 ⋅ C p (23)

The Betz Limit is the maximal possible C_p = 16/27. Thus, 59% efficiency is the BEST a conventional wind turbine can do in extracting power from the wind [29].

Capacity Factor (C.F.): The fraction of the year the turbine generator is operating at rated (peak) power [30].

CapacityFactor ( CF ) = AverageOutput PeakOutput ≈ 30 % (24)

C.F. is based on both the characteristics of the turbine and the site characteristics (typically 0.3 or above for a good site) ENERCON wind energy converters run with a particular storm control feature. An E-82 ENERCON wind energy converters as D.G. with 2.05 MW rated power. Figure 2 illustrates Power-Wind

speed characteristics for 2.05 MW/E-82 ENERCON wind energy converters, while Figure 3 shows the characteristics of Power Coefficient-Wind speed for its [29] [30] [31].

In this work, the particle selected in each technique as the voltage magnitude at slack bus and each P|V| generation bus also taps changing transformer ratio and the injected reactive power to the system. There are three particles: (|V1|, |V2|, and |V3|) no tap changing transformer ratio and no injected reactive power at buses. When the optimization development process begins, each particle position will continuously update until they reach the stop criteria. The minimum inertia weight is selected as 0.45, and the maximum weight limitations are chosen as 0.95. With continuous repetition, the weight value will drop from 0.95 to 0.45.

Before using the SCADA system for the first time, we should make a one-time hardware connection for mutual communication. In some cases, the various devices are connected via different interfaces to the P.C. The configuration for each type of connection is described in the corresponding section of this topic.

The following installation instructions explain how to connect devices to the SCADA system via Ethernet. This is important when using a SCADA control computer with network access, the double busbars CO3301-5R/CO3301-5S, and other supported equipment such as the power quality meter CO5127-1S the human-machine interface (HMI) CO3301-5L.

To establish a structured hierarchy of I.P. addresses assigned to each Tableor set of equipment. This makes subsequent setting up much easier.

For the I.P. address of the P.C. could use 192.168.168.1, for example, as in Figure 4, with a subnet mask of 255.255.255.0, in this network, then have the addresses 192.168.168.1 to 192.168.168.254 (excluding the P.C.’s I.P. address) available for other equipment.

The SCADA Viewer/Designer now searches the double busbars using all the ports on the computer. The computer and the device to be connected do not have to be configured for a common sub-network, as shown in Figure 5, control of double busbars via SCADA, details of busbars via SCADA.

Busbar 1;

Busbar 2;

Control panel for isolator Q1;

Control panel for isolator Q2;

Control panel for circuit breaker Q3;

Ethernet connections for measurement and control;

0 V control voltage connection (not a neutral conductor!);

400 V incoming/outgoing busbar feeder.

This work setup permits any required combination of energy sources and consumers to study smart grids’ basic relationships. The voltages and currents are evaluated using the SCADA for Power Lab software converter. Figure 6 shows a basic setup of the ESG 1.1 training station.

The manual calculation used Fast Decoupled Load Flow to calculate the voltage at each bus, power flow, and power losses in each line. The bus admittance is given as:

Y b u s = [ 0.388 − j 3.698 − 0.02342 + j 0.2821 − 0.365 + j 3.445 − 0.02342 + j 0.2821 0.04684 − j 0.5615 − 0.02342 + j 0.2821 − 0.365 + j 3.445 − 0.02342 + j 0.2821 0.3919 − j 3.7178 ]

The current flow to bus 2 is 0.25A, and bus 3 is 0.46A, respectively.

The power mismatch,

Δ P 2 = P 2 s p e c . − P 2 c a l c . = ( 0.0448 − 0.024 ) − 0.044444 = − 0.0236   p .u

Δ P 3 = P 3 s p e c . − P 3 c a l c = ( 0.0666 − 0.0453 ) − 0.08177 = − 0.06047   p .u

and,

Δ Q 2 = Q 2 s p e c . − Q 2 c a l c = ( 0 − 0.015 ) − 0 = − 0.015   p .u

[ Δ P 2 | V 2 | Δ P 3 | V 3 | ] = − [ B 22 B 23 B 32 B 33 ] [ Δ δ 2 Δ δ 3 ]

[ − 0.0236 1.0 − 0.06047 1.0 ] = − [ − 0.5615 0.28121 0.28121 − 3.7178 ] [ Δ δ 2 Δ δ 3 ]

[ Δ δ 2 Δ δ 3 ] = [ 0.5615 − 0.28121 − 0.28121 3.7178 ] − 1 [ − 0.0236 − 0.06047 ]

[ Δ δ 2 Δ δ 3 ] = [ − 0.052184 − 0.02 ] rad = [ − 2.9899 − 1.1459 ] degree

[ Δ Q 2 | V 2 | ] = − [ B 22 ] [ Δ | V 2 | ]

[ − 0.015 1.0 ] = − [ − 0.5615 ] [ Δ | V 2 | ]

[ Δ | V 2 | ] = − 0.0267   p .u

| V 2 | = 1.0 − 0.0267 = 0.9733   p .u

So, the voltage after the first iteration:

V 2 ( 1 ) = 1.0 ∠ − 2.9899   p .u And

V 3 ( 1 ) = 0.9733 ∠ − 1.1459   p .u

After three iterations, the final results were the convergence of the value given in Table 4 for power losses in each branch for 3-busbar test system, and Table 5 shows the voltage profile for 3-busbar test system. The results in the tables show that they are almost close. Figure 7 shows a three-bus emulation system representation by SCADA, and Figure 8 shows a three-bus system result by SCADA for Line voltage and injected current at each busbar.

The performance of the proposed method is verified on the three bus systems. In addition, the MATLAB code for the power dispatch without and with adding new D.G. has been run. The optimization process’s stopping criteria are set as the iteration number reaching 200 iterations to give the particles enough opportunities to reach the global minimum. The size of the swarm is 50. The initial weight inertia is set to 0.9, and the final weight inertia is set to 0.4. As the iterations go on, the weight value will drop from 0.9 to 0.4. When the optimization process begins, each particle’s position will be continuously updated until they reach the stopping criteria.

The results show that the minimum losses occurred when the D.G. was installed at bus no.3; it is given 15.944 MW when used the APSO technique. From this Table, we can learn that the real power loss can be reduced by 10.47%; after adding a new D.G. to the system and using the APSO algorithm to further adjust the control variables’ values, the real power loss can be reduced by as much as 14.42%. A comparison of the loss reduction when D.G. is installed, the results also show that the losses result in each line, and the total losses for the system are close for both techniques. The losses after adding D.G. are less than the losses before installed D.G., so the power losses are modified in each line and the system due to installing a wind generator at the optimum location of bus no. 3. The voltage magnitude and the angle are modified after installed the wind turbine at the optimum location.

Figure 9 shows the power losses when installing the wind generator located in a different bus position. Then it shows the minimum power, where when used the APSO technique, the minimum power accord when the D.G. located at bus three and the power losses of 15.944 MW. Finally, a comparison of the loss reduction when D.G. is installed on different buses is presented in Figure 10 using APSO techniques.

The voltage profile was evaluated using The Fast-Decoupled Load Flow (FDLF) method and then calculated the system’s real power loss. After that, put wind generators at each bus to evaluate the total corresponding real power losses to obtain the D.G.’s optimal location when the total loss accord. The proposed approach is based on the APSO technique and aims to identify the load’s position and compare the results with the applied standard PSO technique.

Figure 11 shows the magnitude voltage profile for different cases for APSO techniques without D.G. It compares it without optimization, and the figure illustrates the voltage magnitude improved when using optimization. As shown in Figure 12, the angle profile improved where the angle changed from −9˚ the −7.2˚ when optimization was used. In addition, the voltage magnitude and angle improved when a wind generator was installed, as shown in Figure 13 and Figure 14, respectively.