Improvement of energy supply configuration for telecommunication system in remote area s based on improved chaotic world cup optimization algorithm

Journal Pre-proof Improvement of Energy Supply Configuration for Telecommunication System in Remote Area s Based on Improved Chaotic World Cup Optimization Algorithm

Huan Li, Kun Li, Nicholas Zafetti, Jianfeng Gu PII:

S0360-5442(19)32309-6

DOI:

https://doi.org/10.1016/j.energy.2019.116614

Reference:

EGY 116614

To appear in:

Energy

Received Date:

13 April 2019

Accepted Date:

23 November 2019

Please cite this article as: Huan Li, Kun Li, Nicholas Zafetti, Jianfeng Gu, Improvement of Energy Supply Configuration for Telecommunication System in Remote Area s Based on Improved Chaotic World Cup Optimization Algorithm, Energy (2019), https://doi.org/10.1016/j.energy.2019.116614

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof

Improvement of Energy Supply Configuration for Telecommunication System in Remote Area s Based on Improved Chaotic World Cup Optimization Algorithm

Huan Li1, Kun Li2, *, Nicholas Zafetti3, Jianfeng Gu4 of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China 2School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300130, China 3Clemson University, North Charleston, South Carolina United States 4School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan, 523808, China Corresponding Author: Kun Li ([email protected] ) 1School

Abstract- Generally, using the telecommunication industry in remote rural areas which can’t use the grid is difficult. This issue makes this industry to be extremely dependent on the diesel generators and battery banks for having backup resources. Despite, the unreliability and the environment impacts of these power supplies, using diesel generators is still popular. In this paper, a new clean and optimized configuration based on the proton exchange membrane fuel cell (PEMFC) along with an optimized boost converter unit is proposed for obtaining a reliable source to supply the telecom towers. Using the proposed optimized method makes the cost of telecom companies decreasing. In this study, a feedback controller based on a newly introduced optimization algorithm called improved chaos world cup optimization algorithm is proposed for optimal regulation of the converter unit. The proposed technique provides reliable and high-efficiency performance for the base transceiver station under oscillations on the telecom load and the output voltage of the PEMFC. Simulation results of the proposed optimization method are compared by the traditional Ziegler-Nichols based PI controller and final results showed the superiority of the proposed system.

Keywords: PEMFC; Telecom Tower; Base Transceiver Station; Boost Converter; Improved Chaotic World Cup Optimization Algorithm; Voltage Regulation.

1.

Introduction

Journal Pre-proof

The telecommunication industry is one of the key industries and can be the driving force behind many other advanced technologies in the world. Therefore, strengthening domestic capacity in this field and attracting more credits in this area will have a significant effect on the realization of the predicted economic development [1]. In the meantime, and before anything else, the systematic and structured recognition of this industry determines its demarcation with other industries and identifies its components and their internal relations with each other, in general, its interaction with macroeconomics and its contribution to the production of the total value of the world, etc. is preceded by any other task [2]. In other words, the telecommunication industry not only fills the vacancy of the basic requirements of communication but also has a high effect on the development of different parts of the economy. On the path to progress and modernization of telecommunication industry, the significant issue is to establish the supply to base transceiver station (BTS) of telecommunication tower, especially for the distant rural zones. Therefore, the telecommunication industry needs a nonstop and robust energy source to provide guaranteed high-quality customer services without interruption [3]. Fig. 1 shows the average annual power supply interruption in minutes for Europe in 2016 based on System “Average Interruption Duration Index” (SAIDI) [4].

Fig.1. Average annual power supply interruption in minutes for Europe in 2016 [4].

Generally, diesel generators (DGs) have been utilized as the power supply of the BTS while the required power is not provided by the network. One of the disadvantages of the DGs is the rate of their startup failure which is about 15%. To cover this issue, we need to employ a battery bank as the backup power supply. By increasing the use of battery banks as the power supply of the telecommunication industry, the total cost of them is increasing [5].

Journal Pre-proof

By considering the environmental hazard of DGs, department of telecommunication is working on upgrading these power supplies collection based on the green technologies. Therefore, improving the application of renewable energy resources along with the network has been decreased the amount of carbon in the telecommunication industry in the year 2019 [6]. Recently, the use of renewable energy resources like wind energy and photovoltaic energy conversion system has been improved as the backup power supplies of the DGs [7-9]. For achieving a reliable power supply backup, we need a fuel cell with the features including the low cost of maintenance, the higher efficiency for conversion, and the lower pollutant to the environment [10, 11]. Based on the literature [12, 13], among the applications of renewable resources, fuel cells have been known as interesting technologies for the long term backup for the telecommunication industry. In 2009, Gomez et al. presented a photovoltaic-hydrogen based on different simulation of variations for introducing hydrogen technologies to improve the independence of the remote telecommunication systems [14]. Final results showed that the best structure is to consider the applications of the hydrogen storage, the excess of radiation, and the number of photovoltaic panels can be changed by the management system for feeding the different parts. In 2014, Karami et al. proposed a hybrid PEMFC/PV system by considering the PV as the primary energy resource of the network [15]. In this paper, the type 3 controller was applied to the buck converter for regulating the output voltage. Final results showed a low-cost system with a simple management strategy. In 2015, Brizon et al. presented a high-performance controller for a hybrid Fuel Cell-based source energy [16]. They employed a standalone control system for tracking and save hydrogen fuel. They worked on a comparative study for four control strategies. Four different control topologies of the RES/FC HPS were performed based on the performances indicators and the results showed an increase of 3 to 5% of fuel efficiency. In 2016, Serincan et al. proposed a reliable topology for a power system with the fuel cell backup in the telecommunication applications [11]. They tested the method in the practical position in a station with GSM. About 98.5% robustness was achieved for the fuel cell system following 260 cycles. Final results declared the importance of the system components sizing for an isolated operation during the fault. Varkaraki et al. presented a hydrogen-based back-up system for telecommunication applications [17]. The system was a combination of a hydrogen buffer tank, a water electrolysis unit, a polymer electrolyte membrane fuel cell (PEMFC),

Journal Pre-proof

and a metal hydride tank. Special attention was spent on the integration of the hydrogen storage and the fuel cell in accordance with optimizing heat exchange. In 2016, Nguyen et al. proposed a CCHP system including PEMFC for telecommunication applications [18]. The PEMFC-HR was then compared with the PEMFC coupled with an external electric heater (PEMFC-EH). Final results showed that 30% of the generated electrical energy can be saved annually by the PEMFC by replacing the PEMFCEH arrangement with a PEMFC-HR system. The method has good efficiency but a genetic algorithm can be improved for this work. Based on the literature, BTS is sensitive to the voltage oscillations which need compressed adjusted DC voltage at distribution bus for various conditions which contain source as well as load side disturbances. In contrast, the output voltage (VO) for the PEMFC decreases linearly by increasing load current and fluctuations in the operating pressure, humidity, and temperature [19]. In most of these works, PEMFC, due to its needs to lower operating temperature was utilized; because this characteristic causes a fewer need to thermal stress on components and warm-up time [20, 21]. In addition, because of the absence of mechanical parts like rotating parts, it has less mechanical wear and so it has more reliability. Finally, because of the modularity characteristic, it can be so flexible in operation designs. The required hydrogen for feeding the PEMFC to backup the telecom site can be supplied using different processes such as thermochemical processes that employ heat and chemical reactions to extract hydrogen from organic materials like biomass and fossil fuels [22]. Water (H2O) can be split into hydrogen (H2) and oxygen (O2) based on solar energy or electrolysis [23]. Microorganisms like algae and bacteria can generate hydrogen through biological processes [24]. These resources can be supplied from the nearby gas pipelines. In this situation, Hydrogen can be made by electrolyzing operation powered by PV or other similar resources or the combination of some of these resources [25]. Although, the main problem with a fuel cell is that its output differs by load variations and in the other hand, telecom tower has frequently a wide range of load variations due to the variations of communication signal traffic. However, the combination of the PEMFC with the base transceiver station in the telecom tower can guarantee a tuned energy source for the base transceiver station distribution bus for different operations. Based on the aforesaid cases, we need a reliable interface for regulating the oscillations in the output voltage (VO) of the fuel cell.

Journal Pre-proof

This paper presents a new optimized boost converter interfacing unit to cover this shortcoming. The main purpose of a boost converter is to step up the level of the voltage to a considered voltage level in DC bus. Here, a new improve optimization algorithm, called improved chaotic world cup optimization algorithm, is designed and applied on the boost controller to adjust DC distribution voltage and to provide a quick response for the dynamic of the interfacing unit and to guarantee the reliability of the system under output oscillations of the PEMFC during load variation of telecom tower. The principal highlights of the presented method are: 1.

Designing a new optimization algorithm to control the boost converter

2.

Adopting chaos theory for improving the ability of the optimization algorithm.

3.

Proposing a new clean configuration based on the PEMFC.

4.

Using the PEMFC as a power supply for BTS in telecom tower.

5.

Applying a new optimization algorithm to regulate the PEMFC output.

2.

The architecture of the BTS Feeding System

The general architecture of the BTS for telecom tower has been shown in Fig. 2. As it is clear from the figure, the distribution system of the BTS adopts 48 V DC by considering the system security and high DC voltage. Point of loads (POLs) converters is employed for feeding the load end electronic equipment through the DC distribution bus. The network grid is intermediated to the distribution bus of the base transceiver station based on SMPC. The main objective of this unit with SMPC is to operate as a filter for the input source and the telecom load oscillations to achieve a compressed regulated voltage at DC bus. It is important to note that telecommunication load is sensitive to the voltage oscillations.

Journal Pre-proof

DC/AC

~

PMU

POL Converter

SMPC

= =

48 v

=

DC BUS

= =

Network Grid

PWM Controller

Diesel Generator

Battery P a c k

BTS AMERICAN POWER CONVERSION

Batteries

Fig.2. A general configuration of the BTS feeding.

The power needed for a telecom tower site in different BTS architectures is illustrated in table 1.

Table 1. The required power for the telecom tower location in various BTS architectures [3]. Architecture 2×2×2 4×4×4 6×6×6

Load DC (kW) AC (kW) 1.24 2.04 2.82

1.8 2.4 3.6

Total Power (kW) 3.04 4.44 6.42

2.1. Proposed configuration for power supplying the PEMFC The proposed configuration of the BTS feeding for the proposed PEMFC has been shown in Fig.3. Here, 2.5 kW PEMFC is employed as the considered clean energy to feed the BTS that receives oxygen and hydrogen from a side to electricity generation based on chemical reaction and generates water vapor as a by-product. An important advantage of PEMFC is that doesn’t need continuous maintenance checking. The output voltage (VO) of the PEMFC depends on different cases from load variations to an environmental condition like atmospheric pressure, temperature, and humidity. This variation is in the interval [15%, 30%] below or above the output voltage and hence makes interconnecting the interfacing unit to keep in 48 V at the DC bus.

Journal Pre-proof

POL Converter

=

48 v

=

PEMFC

Proposed ICWCO based Controller

DC BUS

= =

Battery P a c k

AMERICAN POWER CONVERSION

Optimized Regulator

Batteries

BTS

Fig.3. The proposed configuration of the BTS feeding for the proposed PEMFC.

The proposed optimized regulator here forms a DC/DC boost converter and utilized the controller to adjust the output voltage of the converter. The proposed optimized converter increases the PEMFC output voltage to 48 V for the BTS distribution bus. The proposed optimized regulator in here holds the DC bus regulated under PEMFC voltage oscillations and load variations in the BTS placed on the telecom tower due to the communication signal traffic. In addition, the proposed regulator results in a high-speed response which increases the speed of the overall system. 2.2. A mathematical model of a fuel cell A fuel cell is an electrochemical element that converts the chemical reaction between oxygen and hydrogen into electrical energy and water. By considering the thermodynamic principles, the potential energy across the cells can be achieved by the following formula [26]:

E npsc  1.229  0.85  0.01(cell  298.15)  4.3085  105 cell ln( p H 2 pO 2 )

(1)

where cell describes the cell temperature based on Kelvin, p H 2 is the pressure of the hydrogen and pO 2 is the pressure of the oxygen. The partial pressure of the oxygen and the hydrogen are given below:

Journal Pre-proof

(2)

PO 2  0.5RH a  p Hst 2O

    1    1  RH p st  (1.635( i / A ))    a H 2O  exp     pa  1.334    

(3)

PH 2  0.5RH a  p Hst 2O

    1   1  RH p st (4.192(i / A ))    c H 2O  exp     pc  1.334    

We can also evaluate the saturation pressure of the water vapor by the following formula:

log10 ( p Hst 2O )  2.18  0.0295  (cell  273.15)  0.919  104 (cell  273.15) 2  0.144  106 (cell  273.15)3

(4)

The output voltage of each cell can be obtained by the equation below:

V cell  E npsc   ma  al  O

(5)

where, E npsc is the terminal output voltage of the cells  ma is the mass transfer loss, al is the activation loss, and O is the Ohmic loss. As it is clear, the terminal output voltage of the cells is achieved after subtracting their values from the internal losses inside the fuel cell, including mass transfer loss, activation loss, and Ohmic loss. Such that the activation loss can be achieved as follows:

al   Y 1 Y 2cell Y 3cell ln(C O ) Y 4cell ln(i ) 

(6)

2

where, (Y1, Y2, Y3, Y4) are unknown parameters which could be evaluated using global optimization [27], and:

C O2 

(7)

PO 2 508  10 exp(498 / cell ) 4

Due to the existence of electrical resistance in electrodes and polymer membrane, there are Ohmic losses which can be achieved as follows: 

O  i  RC  

M l  A

 

(8)

where, A is cell area,  is the specific resistivity of membrane for electron flow, RC is an unknown parameters that could be evaluated using global optimization and:

Journal Pre-proof

 i 181.6 1  0.03  A 

2.5  cell   i     0.062   303   A         M   cell  303      i    0.634  3     exp 4.18   A      cell  

(9)

And the mass transfer loss can be achieved as follows:

  

ma    ln 1 

i den i lim it , den

  

(10)

where, i den is current density, i lim it , den is limiting current density, and  is concentration loss constant.

2.3. A mathematical model of the Boost converter The boost converter is a kind of DC to DC converter that is, the DC voltage increases its efficiency when connecting the load. The basis of this type of converter is based on switching. To eliminate the existing ripple at the output voltage, a capacitor is used in parallel. On the other hand, for a reactive load to feed without problems, a diode is used to recuperate current. As the key is closed, the source of the voltage and the inductor are a closed circuit. At this time, energy will be stored in the predecessor. When the circuit is switched off, this energy will be evacuated. As a result, the output voltage converter is stable and will have a greater value than the input voltage. Here, in the feedback part, after sensing the voltage level and sending it to the comparator, the error value is achieved. Afterward, the error signal (e(t)) has been sent to the PI controller for correcting the output voltage, and finally, the modulator is utilized for generating the optimal PWM signal for igniting the MOSFET. The optimal values for PI controller in here will be achieved by the proposed optimized method. In the following, the state-space model of the system dynamics is described. In here, two states have been considered for two active elements: the current of the inductor and the voltage of the capacitor.

a b  e  x    x  f V in c d     y  g h x where, by considering the “on” state for the model in the interval DTS, it can be modeled as follows: a

rL 1 1 R , b  c  f  g  0, d  ,e  , h  L C (R  rC ) L R  rC

And, by considering the “off” state for the model in the interval DTS, it can be modeled as follows:

(11)

Journal Pre-proof

rL  a e

RrC R  rC R 1 , b  c  ,d  , L C (R  rC ) C (R  rC )

RrC 1 R , f  0, g  ,h  L R  rC R  rC

A total model of the system is achieved by the averaged large-signal model based on the circuit averaging when it is in “ON” state and “OFF” state of the switch over a cycle as:

r a L D L e

rL 

RrC R  rC R 1 (1  D ), b  c  (1  D ), d  , L C (R  rC ) C (R  rC )

RrC 1 R , f  0, g  (1  D ) ,h  L R  rC R  rC

where, DTS and (1-D) TS describe the ON and the OFF states of the switch. Table 2 illustrates the employed values for simulating the load distribution of the BTS and the boost converter. Table 2. The utilized values for simulating the load distribution of the BTS [28] Load distribution Parameters in the BTS Number of BTS at the site

1

Battery voltage

48 V

Load/Transceiver

12

Microwave equipment load

100 W

Total BTS load (Transceiver + microwave)

2380 w, 50 A

Heat exchanger load

360 W

Transceiver

160 W Parameters of Boost Converter

Input Voltage

26-39 V

Output Voltage

48 V

Inductance

0.01 mH

Conductance

21.6 mF

Resistance

1.2 Ω

Journal Pre-proof

fs

3.

10 kHz

Optimized Regulating Unit

This unit is composed of a boost converter and a new optimization algorithm, ICWCO as an optimized controller. By considering the optimized boost converter, it tunes the source and the load voltages by incrementing the level of the voltage. In addition, the distribution DC bus for BTS needs a robust, adjusted, and uninterrupted 48 V DC power source. Therefore; using boost converter gives us a proper and tuned voltage by low oscillation at the output. It is important to consider that after any sudden disturbances in the system, the voltage should be settled in a steadystate value with minimum time, undershoot, and overshoot. So, it is usually designed in feedback (see Fig.4). In the feedback based configuration, voltage sensors sense the output voltage of the distribution bus and they compare its value by the desired voltage (Vd) using a comparator to obtain the error signal (e(t)). Afterward, the error signal is sent to a proportional-integral (PI) controller as the input for achieving the desired control signal (u(t)). To apply the control signal to the modulator, we need to fire the gate driver of the semiconductor (here, we used MOSFET) based on error signal and applying by PWM signal. To obtain a proper and adjusted terminal voltage, the optimal value for the MOSFET should be achieved.

4.

Materials and methods

4.1. Basic World Cup Optimization algorithm The main purpose of the optimization is to find the best acceptable solution of the problem by considering the system dynamic and its constraints. Meta-heuristic algorithms are a kind of optimization algorithms which are inspired by nature, physics, and human social reactions and are used to solve many of the optimization problems. In most cases, these algorithms are used in combination with other algorithms to reach the optimal solution or exit from the local optimum. Some of these algorithms are Genetic algorithm [21, 29-32], particle swarm optimization [33, 34], quantum invasive weed optimization [35], and world cup optimization algorithm [36] which have been designed for solving different complicated problems.

Journal Pre-proof

In recent years, Razmjooy et al. presented the basic version of the world cup optimization algorithm (WCO) and employed on different problems [36-41] which inspired from the FIFA world cup competitions to reach the championship up as an optimal solution. The process of teams’ competitions is mathematically modeled to employ as an optimization tool to find the global optimum solution. The main purpose in WCO is to compete with each other, to beat the competitors and to achieve the Medal of Honor. Each team that gets this medal has indeed the best solution for the considered problem. WCO algorithm, like most of the other Meta-heuristic algorithms, has two important parts of exploration (like random surprising teams which there is no expectation to have a good solution) and exploitation (like team’s ranking and playoff). In the basic WCO algorithm, it starts by a random number of populations which is called teams to reach the global optimum point. By considering N as the number of variable dimensions ( N var ) and M number of continents for an optimization problem,  x c 1,1  x c 1,2 Continent       x c 1, N var

x c 2,1 x c 2,2  x c 2, N var

x cM ,1  x cM ,2       x cM , N var   

(12)

where, x i , j shows the ith team of the jth country. The scoring of the teams for ranking has been performed by the score function ( f r ) as follows:

f r (continent i )  f r (x ci ,1 , x ci ,2 ,  , x i ,N ) var

(13)

An important parameter in the WCO algorithm is “Rank”. Rank has a great impact on the solution. After obtaining the rank scores, the first n potent teams are included in the first seed, the other n weaker teams are included in the second seed, and the others have been classified like the first and second seeds hierarchically. The rank scoring in WCO is modeled as follows:

Rank 

(    X ) 2

where,  is a parameter for increasing or decreasing the impact of standard deviation in the interval [0, 1] and,

(14)

Journal Pre-proof

X



1 n 1

1 n

n

X

i

(15)

 X )2

(16)

i 1

n

(X

i

i 1

where, n, X and  are the number population, the mean value and the standard deviation of the continent X, respectively. In the WCO algorithm, after raising the first seed directly to the second stage of competitions, the challenge has been started. The competition commences among each team separately in their seeds and the winner of the competition improves its score (rank) and move to the next stage of the competition. Another important parameter in the WCO algorithm is the “Play-Off”. After getting the two strong teams in each team to the next level of competition, the rest teams should be discarded. In the meantime, the third team of each seed can have a second chance to return to the competition (Play-Off). For updating the solution for the next iteration (the next competition), it uses previous information of the competition scores and the teams ranking. To do so, WCO uses two-part vector: Pop  [X Best , X Rand ]

(17)

where, Pop is an N  M matrix which describes the new population, X Rand describes a random value in the problem interval constraints, and X Best is: 1 1  ac  (Ub  Lb )  X Best   ac  (Ub  Lb ) 2 2

(18)

where, Ub and Lb describe the higher and the lower bounds of the problem constraints and ac is the accuracy parameter between Lb and Ub.

4.2. Chaotic theory and its conception

Chaos theory is the science of studying on random and unpredictable systems to control them by definite rules. There are some complicated dynamical and nonlinear systems which chaotic nature with randomly and unpredictable behavior.

Journal Pre-proof

The main idea behind this theory is to analysis the highly sensitive dynamic systems which have been affected by even very small variations. i.e., a small change in the condition, make big changes on the system behavior. By considering this definition, we can generate a large diversity for the population for the global optimum improvement and for escaping from the local optimum [42, 43]. A simple definition for the chaos behavior can be described as follows:

CM ij1  f (CM ij ) j  1, 2,..., k

(19)

where, k is the map dimension, f (CM ij ) is the chaotic model generator function. Finally, the last competition is held between the two strongest teams to find the best team (best solution).

4.3. Improved chaotic based world cup optimization algorithm In this subsection, an improvement version of the WCO algorithm based on Sinusoidal chaotic map is proposed. The proposed method is called Improved Chaotic World Cup Optimization (ICWCO) algorithm. The main advantage of the proposed technique toward the classic WCO algorithm is that can escape from sticking in the local optimum point following by high speed in convergence. In the proposed ICWCO algorithm, the parameter X Rand is modeled based on the Sinusoidal chaotic map as follows:

X Rand, k  apk2 sin( pk )

(20)

pk  [0, 1], a  ( 0, 4] where, k describes the number of iteration. The improvement makes the handling of the spiral model handling selection easy for updating the teams ranking. Fig. (4) shows the diagram flowchart of the proposed ICWCO algorithm.

Journal Pre-proof

Start Initialize Improved Chaos World Cup Optimization (ICWCO) algorithm: Generate some groups of population as continents and their countries (teams) randomly based on WCO initial values; evaluate the objective function of each solution vector

Initialize an optimization function and the algorithm parameters

Ranking Stage Rank objective function values based on their mean value and standard deviation values among their components.

Calculate the objective function of each solution vector

Give records (points) of each team based on their fitness and classify all the continents into two groups of high record and low record continents.

Preliminary competition: Initial worth holding and finding the minimum valued teams in the continents

The last competition: find the minimum/maximum values

Termination satisfied?

No

Generate new population based on high value record countries (XRand+Xbest)

Yes

Stop

Apply Chaos Theory

Fig.4. The flowchart diagram of the proposed ICWCO Algorithm.

5.

Proposed Optimized Method Scheme for the Telecom Supply

Fig. (5) shows the general schematic of the utilized boost converter is shown. The boost converter here is tuned based on MOSFET for switching on and off. The circuit includes a boost converter, a comparator, a controller, and a modulator. As described before, for the proposed controlled PEMFC powered BTS system, after sensing the feedback signal of the output voltage (Vo) by the sensor, it is compared by the desired voltage (Vd) for obtaining the value of the error signal:

Journal Pre-proof

e(t )  Vd  Vo (t )

(21)

Where,Vd and Vo (t ) are the reference voltage and the output voltage of the boost converter, After that, the error signal is fed into an op-amp as the PI controller where the parameters for the proportional (KP) and the integral (KI) coefficients are achieved from the ICWCO algorithm such that:

u(t )  K P  K I  e(t )

(22)

In the following, the optimized signal generated from the controller is applied to the modulator. Afterward, the modulator performs an optimal PWM signal control on the MOSFET for igniting. By this way, the regulated signal is utilized for use in the boost converter part for the DC distribution bus of the BTS. In this study, for illustrating the strength of the introduced optimization technique, it is compared by the Ziegler–Nichols (ZN) adjusting technique.

Fig. 5. The configuration of the boost converter by feedback method and PI controller. Ziegler–Nichols Tuning Method in here is employed for adjusting the PI controller parameters to regulate the proposed boost converter in the PEMFC supplied BTS of telecom tower. In the ZN method, the proportional coefficient steps up until the feedback system becomes critically stable. The KP and KI in here are evaluated as 0.45 and 0.83, respectively. The main purpose of the proposed chaotic based WCO algorithm here is to improve the ability of exploration and the convergence speed in the algorithm. It needs no derivation which decreases the complexity and the required run time for obtaining the global optima. The fitness function in this study is based on minimizing error signal, e(t), as follows:

Journal Pre-proof

MSE 

1

(23)



te (t )dt t 2

0

min F ( )  MSE Subject to,

(24)

 min     max And the final fitness function is:

FitnessFunction 

(25)

1 1  F ( )

where,   f (K P , K I ) . Table 3 shows the selected parameters for the proposed ICWCO algorithm.

Table 3. Selected parameters for the proposed ICWCO based controller.

6.

No. of population

50

No. of iteration

100

Playoff

4%

ac

0.3

Simulation Results

National telecom policy (NTP-2012) of India provides a roadmap in which, remote rural areas that have not supported completely by the grid, should be penetrated deeply by the telecom industry. These telecom companies should guarantee to provide a high-quality service such that they supply uninterruptible and robust power to the BTS. To do so, in this paper, a new hybrid supply configuration based on PEMFC is proposed for BTS. This configuration, in addition to providing green energy (by removing the DG package), decreases the size of the battery bank that is an expensive part of the renewable energy resources. An optimized boost converter based on a new algorithm, called ICWCO, is proposed to generate an interface between PEMFC and BTS load to achieve a fast dynamic response regulator. PEMFC here generates a 30 V direct current to 1.8 kW in the standard condition. However, load and environment variations like temperature and humidity limited the PEMFC output voltage in the range [26 V, 39 V]. In the other

Journal Pre-proof

side, the changes in the number of the transceivers effect on the telecom load and vary its power in the range [500 W, 2.2 kW]. Hence, the main purpose of this regulator is to keep the distribution bus of the BTS in 48 V, in the presence of the oscillations. Here, the performance of the proposed ICWCO based controller is compared with the traditional PI controller based on the Ziegler-Nichols method, PSO-based method, and ordinary WCO-based method to optimize the boost converter along with PEMFC for supplying the BTS. To validate and to testify the proposed improved world cup optimization-based regulator to achieve a reliable 48 V DC in different conditions, three states have been considered.

1) Tracking the desired signal: for analyzing the tracking ability of the proposed optimized regulator along with the desired voltage (Vd) by considering ±10% for the voltage of the distribution bus (43 V and 53 V). 2) Disturbance rejection in the input: for analyzing the proposed regulator ability by considering the decrease and increase of the input voltage (Vin) of the boost converter from 26 V to 39 V and conversely. 3) Disturbance rejection in the output: for analyzing the proposed regulator ability by considering the controller efficiency for 22% of overloading and 72% of under loading condition when the peak traffic of the communication signals and when only a transceiver works, respectively.

The simulation and the programming of the system are performed by Matlab R2017b. The proportional (Kp) and the integral (KI) parameters of the PI controller achieved by the traditional Ziegler-Nichols (ZN) method, PSO-based method, and ordinary WCO-based, and the proposed ICWCO algorithm are illustrated in Table 4.

Table 4. The parameters of the PI regulator for the boost converter Fitness function MSE

Algorithm

KP

KI

ZN [44]

0.04

8.12

ICWCO

6.35

62.81

WCO

5.84

71.23

2.90

52.9

[36] PSO [45]

Journal Pre-proof

For tracking the desired voltage (Vd=49 V) of 1.8 kW load by the input signal, a step up increasing from 0 to 30 V is performed on the voltage. Fig. (6) shows the dynamic response of the studied system based on the aforementioned regulators by the aforesaid condition. 50

Voltage (V)

40

30

20 ZN ICWCO PSO WCO

10

0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (Seconds)

0.07

0.08

0.09

0.1

Fig.6. The output voltage of the studied system based on the controllers during an increasing input voltage from 0 to 30 V

Results showed that the converter response based on the ZN method has about 20% undershoot and it decreases to about 29.5 V at that time. The converter settles again to the steady-state in 96.37 ms. We can also see that based on the introduced ICWCO regulator, there is no undershoot or overshoots in this condition. Settling time for this method is about 10 ms. Table 5 illustrates a detailed analysis of the system dynamic response. In this condition, the settling time for the WCO method is 99.57 ms and PSO responses to the system with 99.12 ms of settling time and 4% undershoot. Table 5. A detailed study on the system dynamic response. Dynamic response

ZN based

PSO based

WCO based

ICWCO based

characteristic

controller [44]

controller [45]

controller

controller

Settling time (ms)

96.37

99.12

99.57

10

Rise time (ms)

1.21

1.15

1.22

1.11

Overshoot (%)

-

-

-

-

20

4.0

-

-

Undershoot (%)

Journal Pre-proof

Peak time (ms)

0.5

0.5

3

0.5

By considering a step increase in the input voltage from the 26 V to 39 V, at time= 0.5 s, the settling time of the introduced ICWCO based regulator is about 39.7 ms by an overshoot less than 0.68% (see Fig. (7)). As can be seen, WCO has better results than the PSO in terms of overshoot, settling time and other characteristics but adding the mechanisms for improving the algorithm makes it so better than the default mode. In Fig. (8), an inverse condition is shown; by considering a step decreasing in the input voltage from 39 V to 26 V, at time= 0.5 s, the settling time for the introduced ICWCO regulator has achieved about 64.7 ms with less overshoot of 0.9%. it is also clear that WCO gives better results than the PSO in terms of overshoot, settling time and other characteristics but adding the mechanisms for improving the algorithm makes it so better than the default mode.

Fig.7. The output voltage of the studied system by ZN and the proposed ICWCO based controller during an increasing input voltage from 0 to 30 V

Journal Pre-proof

Fig.8. The output voltage of the studied system by ZN and the proposed ICWCO based controller during a decreasing input voltage from 30 V 0.

In the following, the reliability of the proposed ICWCO method by considering the load variations has been studied. The dynamic response of the system based on the proposed method and the compared regulators for the load variations from the minimum load to the rated load is shown in Fig. (9). Consider Fig. (10); from the dynamic response of the converter, it is clear that the classic ZN regulator indicates an extra undershoot about 37.9% with 28.7 V in 24.9 ms and its settling time is about 112 ms; instead, other methods based on optimization algorithms show acceptable results for the system, especially ICWCO based regulator that has its settling time about 71.34 ms with the minimum overshoot about 0.42%.

Fig.9. Load variations for the studied system in about 4.6 Ω. 50

Voltage (V)

45

40

35 ICWCO ZN PSO WCO

30 0.5

0.55Time (Seconds)

0.6

0.65

Fig.10. The output voltage of the system by ZN and ICWCO based regulators for load variation at time = 0.5 s.

Journal Pre-proof

Fig. (12) shows the dynamic response of the ZN based and ICWCO based regulators applied to the converter. By considering other load variations in about 1.2 Ω, the settling time for the ICWCO based regulator is the minimum of the methods and is about 34 ms which illustrates again the superiority of the introduced method toward the other methods.

Fig.11. Load variations for the studied system in about 1.2 Ω. 48 47

Voltage (V)

46 45 44 43

ZN ICWCO PSO WCO

42 41 0.49

0.5

0.51

0.52 0.53 Time (Seconds)

0.54

0.55

0.56

Fig.12. The output voltage of the system based on ZN and ICWCO regulators for load variation at time = 0.5 s.

In another condition, a value of ±10% variation than the desired voltage is applied on the time= 0.5 s. From Fig. (13) and Fig (14), it is clear that the proposed ICWCO has the fastest response toward others. From Fig. (13), it is clear that the proposed ICWCO based regulator takes 2.4 ms to track the reference variations from 53 V to 48 V, whereas the ordinary ZN based regulator as the slowest of them takes about 71.1 ms to respond the reference variations.

Journal Pre-proof

From Fig. (14), it can be seen that a delay of 27 ms was taken to track the reference variations from 48 V to 43 V, with no overshoots and undershoot. We can see from Fig. (14) that the introduced ICWCO based regulator follows the variations by about 2.8 ms.

Fig.13. Step response for +10% variations of the output voltage in the system using ZN and the ICWCO based regulators (53-48) V

Fig.14. Step response for -10% variations of the output voltage in the system using ZN and the ICWCO based regulators (48-53) V

7.

Conclusion

In this study, a new optimized method is presented for PEMFC based energy conversion system to provide a guaranteed power to the telecom system. The main purpose is to improve system performance and to reduce its

Journal Pre-proof

operating cost. Using the PEMFC sharing by DC distribution bus along with output voltage regulation, increases the robustness, the quality of the power supply, and makes it interruptible to the loads. Here, the PI controller is utilized for keeping the boost converter output voltage in the 48 V to achieve a proper power source for the distribution system of the BTS. For improving the PI controller, we introduced a new version of the world cup optimization algorithm based on chaos theory. Final results of the presented method have been compared with the traditional Ziegler-Nichols PI controller to show the system efficiency which shows the superiority of this method as an optimal regulator for the output voltage with no overshoot and undershoot within a fast response to different load variations toward the traditional PI controller. In future work, we will study on improving the performance of the system by considering more reliable controllers like model predictive controllers and sliding mode controllers. We also analyze the proposed improved world cup optimization algorithm to improve it in terms of running time and accuracy.

Acknowledgment: This work is supported by the project Research on Accurate Recommendation Model for Transformation of Scientific and Technological Achievements Based on Machine Learning Algorithm” (No. 2018KTSCX222), and The Research Center of Guangdong E-commerce Big Data Engineering Technology”.

References

[1] [2] [3] [4] [5] [6]

C. Suryagayathri and J. S. Krishnan, "A Study on the Evolution of Distribution channels of Telecom Industry in India," International Journal of Pure and Applied Mathematics, vol. 119, pp. 1393-1401, 2018. Y. Cao, Y. Wu, L. Fu, K. Jermsittiparsert, and N. Razmjooy, "Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics," Energy Reports, vol. 5, pp. 1551-1559, 2019. R. Kaur, V. Krishnasamy, K. Muthusamy, and P. Chinnamuthan, "A novel proton exchange membrane fuel cell based power conversion system for telecom supply with genetic algorithm assisted intelligent interfacing converter," Energy conversion and management, vol. 136, pp. 173-183, 2017. M. Kornatka, A. Gawlak, and G. Dudek, "Determination of reliability indices of the distribution network based on data from AMI," in E3S Web of Conferences, 2019, p. 02004. D. Yu, Y. Wang, H. Liu, K. Jermsittiparsert, and N. Razmjooy, "System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm," Energy Reports, vol. 5, pp. 1365-1374, 2019. G. G. Hallur and V. S. Sane, "Indian telecom regulatory framework in comparison with five countries: structure, role description and funding," Digital Policy, Regulation and Governance, vol. 20, pp. 62-77, 2018.

Journal Pre-proof

[7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

[26] [27] [28]

F. Mirzapour, M. Lakzaei, G. Varamini, M. Teimourian, and N. Ghadimi, "A new prediction model of battery and wind-solar output in hybrid power system," Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 77-87, 2019. M. Dideban, N. Ghadimi, M. B. Ahmadi, and M. Karimi, "Optimal location and sizing of shunt capacitors in distribution systems by considering different load scenarios," Journal of Electrical Engineering and Technology, vol. 8, pp. 1012-1020, 2013. H. Hosseini, B. Tousi, N. Razmjooy, and M. Khalilpour, "Design robust controller for automatic generation control in restructured power system by imperialist competitive algorithm," IETE Journal of Research, vol. 59, pp. 745-752, 2013. Z. Ma, J. Eichman, and J. Kurtz, "Fuel Cell Backup Power System for Grid Service and Microgrid in Telecommunication Applications," Journal of Energy Resources Technology, vol. 141, p. 062002, 2019. M. F. Serincan, "Reliability considerations of a fuel cell backup power system for telecom applications," Journal of Power Sources, vol. 309, pp. 66-75, 2016. F. Zhang, P. Zhao, M. Niu, and J. Maddy, "The survey of key technologies in hydrogen energy storage," International Journal of Hydrogen Energy, vol. 41, pp. 14535-14552, 2016. F. Ramadhani, M. Hussain, H. Mokhlis, and S. Hajimolana, "Optimization strategies for Solid Oxide Fuel Cell (SOFC) application: A literature survey," Renewable and Sustainable Energy Reviews, vol. 76, pp. 460-484, 2017. G. Gómez, G. Martínez, J. L. Gálvez, R. Gila, R. Cuevas, J. Maellas, et al., "Optimization of the photovoltaic-hydrogen supply system of a stand-alone remote-telecom application," International journal of hydrogen energy, vol. 34, pp. 5304-5310, 2009. N. Karami, N. Moubayed, and R. Outbib, "Energy management for a PEMFC–PV hybrid system," Energy conversion and management, vol. 82, pp. 154-168, 2014. N. Bizon, M. Oproescu, and M. Raceanu, "Efficient energy control strategies for a standalone renewable/fuel cell hybrid power source," Energy Conversion and Management, vol. 90, pp. 93-110, 2015. E. Varkaraki, N. Lymberopoulos, and A. Zachariou, "Hydrogen based emergency back-up system for telecommunication applications," Journal of Power Sources, vol. 118, pp. 14-22, 2003. H. Q. Nguyen, A. M. Aris, and B. Shabani, "PEM fuel cell heat recovery for preheating inlet air in standalone solar-hydrogen systems for telecommunication applications: An exergy analysis," International journal of hydrogen energy, vol. 41, pp. 2987-3003, 2016. M. Hossain and N. Rahim, "Recent progress and development on power DC-DC converter topology, control, design and applications: A review," Renewable and Sustainable Energy Reviews, vol. 81, pp. 205230, 2018. M. Karimi, M. Imanzadeh, P. Farhadi, and N. Ghadimi, "Voltage control of PEMFC using a new controller based on reinforcement learning," International Journal of Information and Electronics Engineering, vol. 2, 2012. N. Ghadimi, "Genetically tuning of lead-lag controller in order to control of fuel cell voltage," Scientific Research and Essays, vol. 7, pp. 3695-3701, 2012. I. K. Kapdan and F. Kargi, "Bio-hydrogen production from waste materials," Enzyme and microbial technology, vol. 38, pp. 569-582, 2006. X. Wang, K. Maeda, A. Thomas, K. Takanabe, G. Xin, J. M. Carlsson, et al., "A metal-free polymeric photocatalyst for hydrogen production from water under visible light," Nature materials, vol. 8, p. 76, 2009. A. Melis and T. Happe, "Hydrogen production. Green algae as a source of energy," Plant physiology, vol. 127, pp. 740-748, 2001. H. Hosseini, M. Farsadi, A. Lak, H. Ghahramani, and N. Razmjooy, "A Novel Method Using Imperialist Competitive Algorithm (ICA) for Controlling Pitch Angle in Hybrid Wind and PV Array Energy Production System," International Journal on Technical and Physical Problems of Engineering (IJTPE), pp. 145-152, 2012. Z. Sun, N. Wang, Y. Bi, and D. Srinivasan, "Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm," Energy, vol. 90, pp. 1334-1341, 2015. U. K. Chakraborty, T. E. Abbott, and S. K. Das, "PEM fuel cell modeling using differential evolution," Energy, vol. 40, pp. 387-399, 2012. H. T. CO. (2016). HUAWEI Telecom Energy Solutions. Available: http://astana-com.kz/www/wpcontent/uploads/2016/05/HUAWEI-Telecom-Energy-Solutions-Catalog-1.pdf

Journal Pre-proof

[29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45]

H. N. Aghdam, N. Ghadimi, and A. Ataei, "Adjusting PID controllers coefficients to control fuel cell using genetic algorithm," in 2011 10th International Conference on Environment and Electrical Engineering, 2011, pp. 1-5. J. H. Holland, "Genetic algorithms," Scientific american, vol. 267, pp. 66-73, 1992. S. Mirjalili, "Genetic Algorithm," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 43-55. B. S. Mousavi and F. Soleymani, "Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments," Signal, Image and Video Processing, vol. 8, pp. 831-842, 2014. P. Moallem and N. Razmjooy, "Optimal threshold computing in automatic image thresholding using adaptive particle swarm optimization," Journal of applied research and technology, vol. 10, pp. 703-712, 2012. N. Razmjooy and M. Ramezani, "Training Wavelet Neural Networks Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for System Identification." N. Razmjooy and M. Ramezani, "An Improved Quantum Evolutionary Algorithm Based on Invasive Weed Optimization," Indian J. Sci. Res, vol. 4, pp. 413-422, 2014. Bagal, Hamid Asadi, et al. "Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory." Solar Energy 169 (2018): 343-352. Gheydi, Milad, Alireza Nouri, and Noradin Ghadimi. "Planning in microgrids with conservation of voltage reduction." IEEE Systems Journal 12.3 (2016): 2782-2790. Firouz, Mansour Hosseini, and Noradin Ghadimi. "Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system." Journal of Intelligent & Fuzzy Systems 30.2 (2016): 845-859. Eslami, Mahdiyeh, et al. "A new formulation to reduce the number of variables and constraints to expedite SCUC in bulky power systems." Proceedings of the national academy of sciences, india section a: physical sciences 89.2 (2019): 311-321. Saeedi, Mohammadhossein, et al. "Robust optimization based optimal chiller loading under cooling demand uncertainty." Applied Thermal Engineering 148 (2019): 1081-1091. Nejad, Hadi Chahkandi, et al. "Reliability based optimal allocation of distributed generations in transmission systems under demand response program." Electric Power Systems Research 176 (2019): 105952. D. Yang, G. Li, and G. Cheng, "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, vol. 34, pp. 1366-1375, 2007. C. Rim, S. Piao, G. Li, and U. Pak, "A niching chaos optimization algorithm for multimodal optimization," Soft Computing, vol. 22, pp. 621-633, 2018. P. Swain and D. Jena, "PID control design for the pressure regulation of PEM fuel cell," in 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015, pp. 286-291. N. Khanduja and B. Bhushan, "CSTR Control Using IMC-PID, PSO-PID, and Hybrid BBO-FF-PID Controller," in Applications of Artificial Intelligence Techniques in Engineering, ed: Springer, 2019, pp. 519-526.

Journal Pre-proof

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Journal Pre-proof

o

New optimization algorithm is applied to control the boost converter

o

Chaos theory is employed for improving the ability of the optimization algorithm.

o

New clean configuration based on the PEMFC is introduced.

o

The PEMFC is utilized as a power supply of BTS of telecom tower

o

New optimization algorithm is applied to regulate the PEMFC output