**Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm**

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**HIGHLIGHTS**

  - Modified Artificial Bee Colony Algorithm was used to determine the minimum power loss.

  - The standard IEEE-30 bus test system was tested in finding the minimum value of power loss.

  - The obtained results have been compared with Firefly Algorithm and Artificial Bee Colony Algorithm.

ABSTRACT

*Electrical energy losses are found in any part of the power system. In the power system, it is very essential to minimize the real power loss in transmission lines and the voltage deviation at the load buses through controlling the reactive power flow is a very important. This ensures for secured operation of power systems with regard to voltage stability and economics of operation owing to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the optimal reactive power flow problem in a power system. Generator bus voltages, transformer tap positions and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for the loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system and the results are compared with Firefly (FFA) algorithm and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The results of the power loss are quite productive and the algorithm is found to be the most efficient than the other method such as ABC algorithm and FFA algorithm. This results are produce by Matlab 2017b.*

*Keywords: Modified Artificial Bee Colony (MABC) algorithm, electrical energy, power loss, power system.*

# INTRODUCTION

Electricity is most critical to our daily existence. Almost the complete of the device at home, companies and industries are used because of electricity. So, we will see that the demand of electrical strength has boomed globally. Electric powered energy is typically produced via electric powered mills. However additionally it may be supplied through resources which include electric powered batteries. It also includes supply to groups and houses via electric strength enterprise through an electric strength grid. Electric strength grid is one of the examples of electric power gadget that offer the electricity to an extended place.

Electric power gadget is a community of electrical deployed to supply, transfer and use electric strength. Monti and Ponci (2015) said that electric power machine is a large infrastructure and it is a complicated operation on society and other infrastructures. The transmitted powers are misplaced all through the manner to the surroundings due to the fact over utilization. The significance of electrical energy cannot be overemphasized due to the fact that it holds the key for energy resources in business and home activities. The most reliable value of electrical energy is vital to the advancement of civilization.

Aside from that, in line with the Anumaka (2012), losses in electric energy gadget can be diagnosed as the loss as a result of inner elements and external elements, which the inner component called technical losses and the external element is known as non-technical losses. Consequently, reduction of system losses could be very significant to economic, financial and socioeconomic values of the software organization, customers and the host country. To reduce the losses in transmission machine, the generating station will be installed near the weight centres due to the fact power loss is very useful to enhance the voltage profile and to reduce the device power losses.

Sankaramoorthy and Veluchamy (2017) said that, the electrical strength distribution is the manner of distributing electric strength to the end customers. In the power system, there's a distribution gadget that be one a part of the power machine that's distributes the strength to stop customers for utilization. Dixit et al. (2014) state that in the power system, the current structures with loaded traces can be resulted. Its function is to give the better voltage deviation and the higher strength loss. It can be carry out about insecure operation of strength gadget. The especially due to continuous, uncertain increase and demand of electrical power is the reasons.

# METHODOLOGY

**Power Loss**

Deenadhayalan (2014) has proposed Firefly Algorithm (FA) to minimize power loss for achieving Optimal Reactive Power Dispatch (ORPD). Denenadhayalan also said that to easily implement for any optimization problems and less number of operators contain in FA. It has shown that FA bring a better optimal power loss and voltage deviation compared to Biogeography-based Optimization (BBO) algorithm since the result obtain is more efficiency and high quality solution. Meanwhile, Singh et al. (2017) have proposed Particle Swarm Optimization (PSO) to minimize the power loss in the power system. They stated that PSO algorithm has a better performance compared to others since this algorithm gives a good result in terms of reduction in higher order in losses compare to other algorithm.

The objective function of power loss is determined as in Eq. (1) (Deenadhayalan, 2012).

(1)

where, is total real power which can be defined as follows:

(2)

where, is the total number of lines in the system, is the conductance of the line *k*, and are the magnitudes of the sending end and receiving end voltages of the lines, and are angles of end voltages,

(3)

(4)

where, P is the real power, x is the line impedance.

**Artificial Bee Colony Algorithm**

**Rao et al. (2008) stated that Artificial Bee Colony (ABC) algorithm is a metaheuristic approach by honeybee swarm as a new population. They stated that there are some advantages of the ABC algorithm where this algorithm does not involve the external parameter for example mutation, cross over rate and differential evolution in genetic algorithm. In addition, the global search in ABC algorithm is to use the same neighbourhood resource mechanism with the mutation process.**

A few obligations are performing by using specialised individuals in an actual bee colony. Those specialised bees try and maximize the nectar amount in the hive the usage of efficient division of labour and self-agency. The selection of honey bee colony consists of 3 types of bees such as hired bees, onlooker bees and scout bees. Hired bees are answerable for exploiting the nectar resources explored earlier than and giving the statistics to the ready bees (onlooker bees) within the hive about the best of the food source websites which they're exploiting. Onlooker bees wait inside the hive and decide on meals source to take advantage of based on the records shared by the hired bees. Scout either randomly seek the surroundings with the intention to discover a new meals supply relying on an internal motivation (Akay and Karaboga 2012).

If the search is regarded as a nest environment that contains a food source site, this algorithm begins by randomly generating site feed resources that correspond to the solution in space search. Source of start up the feeds are generated randomly within the boundary parameter environment.

(5)

where , . *SN* is the number of food sources and *D* is the number of optimization parameters.

Each bee used is related to only one source of source food. Therefore, the number of food source sites is the same as the number of bees used. The bee used produced changes to the food source (solution) position in bee memory depending on the information (visual information) and find the source of the neighbour’s food and then assess its quality.

(6)

where, j is a random integer in the range \[1, D\] and k ϵ \[1, 2, …, SN\] is a randomly chosen index that has to be different from *i* and is a uniformly distributed real random number in the range \[-1,1\].

After all the working bees finish solving their quest, they share information related to the number of nectar and their source position with onlooker bee in the dance area. These are various characteristics of bee imagination. An onlooker will evaluate the information of nectar taken from all the bees performed and select the source of the food source with the probability associated with the amount of nectar. This probabilistic selection depends on the value fitness solution in the population. However, in this study roulette wheel selection is used as in Eq. (7).

(7)

In the ABC algorithm, a random real number within the range \[0,1\] is generated for each source. Once the source is assessed, greedy selections are used and bees thinking either memorize new positions by forgetting the old ones or keep the old ones where the trial stop counts are executed by 1, if cannot be fixed otherwise the counter is reset as 0. This process is repeated until all onlookers are distributed to the source of the food source.

**Modified Artificial Bee Colony Algorithm**

Sulaiman et al. (2013) stated the JA-ABC is to increase the capabilities of residents by deciding on a poor solution since ABC algorithm has only one “limit” parameter other than the standard parameter-based control algorithm such as population size or colony size (SN) and maximum generation number or maximum cycle number (MCN).

ABC algorithm are very effective for multimodal and multidimensional basic function. However, the convergence rate of the algorithm is worse when working with a controlled problem, the function page and some function cannot be separated. The issue was increased from the process of stochastic variation in which a new solution that derived from a competition. In this process, some research of parameters such as perturbation frequency or magnitude of the perturbation are important since they affect the division of new solutions. To improve rate convergence, some modifications have been introduced in the ABC algorithm perturbation process where this process will focus on fitting the diverse population. The flowchart of modified ABC algorithm is shown below.

![](60d186e136bd7_media/media/image20.png)

Figure 1: Flowchart of Modified ABC Algorithm

(Source: Akay and Karaboga, 2012)

**FINDINGS AND DISCUSSIONS**

A set of data are adopted from Deenadhayalan (2014) where the data contains from three control variables which are generator bus voltage magnitudes, transformer tap settings and volt-ampere reactive (VAR) outputs from shunt compensating devices. The value of upper bound and lower bounds of the different control variable are given in the Table 1.

**Table 1: Boundary of Each Dataset**

| **Control Variable**            | **Limit**              |
| ------------------------------- | ---------------------- |
| **Generator Voltage,**          | **(0.9-1.1) per unit** |
| **Tap Setting,**                | **(0.9-1.1) per unit** |
| **MVAR by Static Compensator,** | **(0-10) MVAR**        |

IEEE-30 bus system is used to find the minimum value of power loss. In this bus system, there have four of the tap changing transformers which is connected between the bus numbers 6-9, 6-10, 4-12 and 27-28. At the same time, two shunts of compensators are also connected in the bus numbers of 10 and 24. The illustration of IEEE-30 bus system are shown in Figure 2.

![](60d186e136bd7_media/media/image24.png)

Figure 2: Single Line of IEEE-30 Bus System

(Source: IEEE 30-Bus System, 2018)

The optimal values for the control variables used in real power loss are achieved in real power loss minimization are shown in Table 2.

**Table 2: Optimal Parameter Values**

| **Parameter** | **Optimal value (MABC)** |
| ------------- | ------------------------ |
| **VG1**       | **0.9804**               |
| **VG2**       | **0.9152**               |
| **VG5**       | **0.9480**               |
| **VG8**       | **0.9247**               |
| **VG11**      | **0.9368**               |
| **VG13**      | **0.9480**               |
| **T6-9**      | **0.9835**               |
| **76-10**     | **0.9099**               |
| **T4-12**     | **1.0805**               |
| **T27-28**    | **1.0890**               |
| **Q10**       | **4.9086**               |
| **Q24**       | **0.8925**               |

Based on the results obtained in Table 2 above, the minimum value of power loss is determined which the value is 4.4333.

**CONCLUSION AND RECOMMENDATIONS**

As a conclusion, the performance of the Modified Artificial Bee Colony (MABC) algorithm for solving optimal real power flow problems is explained using IEEE-30 bus system to get the minimum value of the power loss. The results had been proven. This results are compared with other algorithm like ABC and FFA algorithm. The results shown that MABC are more effective compared to basic ABC and FFA algorithm approach and high quality solution that this algorithm where the value of where the value of power loss for MABC algorithm is 4.4333, for ABC algorithm 4.5184 and for ABC algorithm is 4.7196. Since the power loss obtained gives a better performance, then the electrical supplier can use the optimal value of each parameter found.

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