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\title{Improving Profile Parameters of the Power System Network Using Krill Herd Algorithm with Facts Device: UPFC}
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\begin{document}

             \author[1]{Sunil Kumar  Jilledi}

             \affil[1]{  OPJS University}

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\date{\small \em Received: 13 December 2016 Accepted: 31 December 2016 Published: 15 January 2017}

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\begin{abstract}
        


In the electric power system the optimal power flow plays a key role in optimal location, steady state operation point, minimization of generation cost, losses etc.  Large Power transmission networks are facing lot of problems to improve the power profiles, improvement of losses, security constraints, and stability issues. The advent of Flexible AC Transmission System (FACTS) devices has led the controlling capabilities to improve the power profiles. Out of these Unified Power Flow Controller (UPFC) has the capability to improve all the three system variables namely line reactance, magnitude and phase angle difference of voltage across the line simultaneously or individually. Many natural inspired algorithms have implemented to solve the optimal power flow with incorporation of UPFC. The main scope of this paper is to implement the KRILL HEARD ALGORITHM (KHA) a novel meta-heuristic approach which is influenced from the herding actions of the krill swarms search for food or communication with each other. Considering the generator buses play down the real power losses, generator bus voltages and reactive power injection. The proposed KRILL HEARD ALGORITHM based optimal power flow has been tested for the IEEE 14- 30 bus systems. The results have been presented clearly for the proposed algorithm with and without incorporation of UPFC. Proposed algorithm results have been compared with  GA, PSO, FF and ABC algorithms.

\end{abstract}


\keywords{krill heard algorithm (kha), U P FC facts - optimal power flow.}

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\let\tabcellsep& 	 	 		 \par
Improving Profile Parameters of the Power System Network Using Krill Herd Algorithm with Facts Device: UPFC 
\section[{Sunil Kumar Jilledi}]{Sunil Kumar Jilledi}\par
Abstract-In the electric power system the optimal power flow plays a key role in optimal location, steady state operation point, minimization of generation cost, losses etc. Large Power transmission networks are facing lot of problems to improve the power profiles, improvement of losses, security constraints, and stability issues. The advent of Flexible AC Transmission System (FACTS) devices has led the controlling capabilities to improve the power profiles. Out of these Unified Power Flow Controller (UPFC) has the capability to improve all the three system variables namely line reactance, magnitude and phase angle difference of voltage across the line simultaneously or individually. Many natural inspired algorithms have implemented to solve the optimal power flow with incorporation of UPFC. The main scope of this paper is to implement the KRILL HERD ALGORITHM (KHA) a novel meta-heuristic approach which is influenced from the herding actions of the krill swarms search for food or communication with each other. Considering the generator buses play down the real power losses, generator bus voltages and reactive power injection. The proposed KRILL HERD ALGORITHM based optimal power flow has been tested for the IEEE 14-30 bus systems. The results have been presented clearly for the proposed algorithm with and without incorporation of UPFC. Proposed algorithm results have been compared with GA, PSO, FF and ABC algorithms.\par
Keywords: krill herd algorithm (kha), UPFC, factsoptimal power flow.\par
Nomenclature  
\section[{Introduction}]{Introduction}\par
he optimal power flow problem is becoming a peculiar topic in the power systems. Due to increasing of load demand the power systems are becoming large by interconnecting with different regional systems. Interconnected systems are facing more failures \hyperref[b0]{[1]}. It is becoming a tedious task for the power system engineers to utilize the existing transmission lines efficiently. Optimal power solution is the best process to get better output with the existing systems, by generation relocation. For efficient utilization of the existing system the shunt capacitors and shunt reactors are incorporating to improve the voltage profile and transmission line reactance as well as power transfer capability. To improve the phase shift between receiving and sending voltages phase shifting transformers are using. Moreover the faster expansion and interconnection of the regional systems voltage stability, power system securities are facing in the deregulated market. In the literature different authors described about the voltage collapse \hyperref[b1]{[2,}\hyperref[b2]{3]}. The power electronic devices are playing a key role in the recent era. The advanced development in the power electronics controllers leads to develop the Flexible AC Transmission System (FACTS) to supply flexible power in the system. Optimize the utilization of the existing system by incorporating the FACTS devices. The FACTS devices technology was presented by Electric Power Research Institute (EPRI) in the year 1980s. These devices has the capability to control the different parameters of the transmission line such as shunt/ series impedance, phase angle, real and reactive power compensation, etc The FACTS family include number of devices such as Stastic VAR Compensator(SVC), thyristor controlled reactor (TCRs), are Shunt FACTS devices, later the series FACTS devices \hyperref[b3]{[4]}.UPFC powerful FACTS device, combination of Static Synchronous Compensator(STATCOM) and Static Synchronous Series Compensator(SSSC) coupled by DC link \hyperref[b4]{[5]}. Optimal power flow problem is solved by adjusting several variables in the objective function considering generations cost, loss function.etc. Over the decades many researchers presented different solutions to optimal power flow by using different methods Newton Method, Genetic algorithm \hyperref[b5]{[6]}, Differential Evolution and Evolutionary programming, BAT Algorithm \hyperref[b6]{[7,}\hyperref[b7]{8]}. Researchers are showing interest on meta-heuristic techniques which includes Genetic Algorithm (GA), Practical Swarm optimization(PSO),Ant colony Algorithm. In the literature authors \hyperref[b8]{[9]} proposed optimal power flow using GA other \hyperref[b9]{[10]}, gravitational search algorithm (GSA) \hyperref[b10]{[11]}, artificial bee colony (ABC) optimization \hyperref[b11]{[12]} using swarm intelligence for the optimal power flow. Researchers proposed these algorithms to overcome the failures of the conventional implemented to solve the optimal power flow problem. In this paper the Krill Herd (KH) a Meta heuristic algorithm is proposed it is one of the bio-based swarm intelligence algorithms. The Krill Herd is developed based on the behaviour of Krill Swarms \hyperref[b12]{[13]} i.e., distance between food and highest density of swarms simulates the objective function of individual krill. Comparing with other optimization techniques in the KH the controlling variables are very few. The Krill Herd already using in some research areas like optimization problems \hyperref[b13]{[14]}. This paper solves the optimal power flow without and with UPFC using the Krill Herd algorithm for different IEEE standard bus systems. The main objective function considered is minimization of the real power losses, voltage deviation, incorporation of UPFC is considered based on the real power losses. The results obtained are presented clearly. KH algorithm optimal power flow results with UPFC is compared with GA and BAT algorithm. The paper organization is follows in the coming section about the KHA, Formulation of UPFC model, optimal power flow using conventional method and proposed method using KHA. Problem solving using the Matlab simulation results and discussion finally the conclusion of the paper and the future workII. 
\section[{Power Flow Model of UPFC}]{Power Flow Model of UPFC}\par
Gyugyi proposed the Unified Power Flow Controller (UPFC), for real time control and dynamic compensation of AC transmission systems. UPFC consists of Static Synchronous series Compensator (SSSC) and STATCOM connected by a DC link capacitor. UPFC is capable to control the active and reactive power and voltage magnitudes simultaneously at the terminals of UPFC \hyperref[b14]{[15]}. UPFC consists of two converters, Converter 2 controls the power flow of the device by infuse of an AC voltage V\textunderscore pq in controllable magnitude and phase angle in series to the transmission line. Similarly the converter 1 can absorb or supply the real power demand by the converter 2 at the DC link. Each converter can supply or absorb the real and reactive power demanded by the system independently \hyperref[b15]{[16]}. Finding the load flows of any power system is the initial stage to evaluate the power system. Many iterative solutions are there for finding the load flow like Gauss, Newton Raphson method, decouple, fast decouple, Ranga-Kutta methods are available. In this paper the load flows are performed by Newton Rahson Method by using the polar coordinates.Fig. \hyperref[fig_1]{01} shows the clear model of UPFC connected between the bus i and j. and power flow directions of real and reactive power at the shunt and series elements where UPFC is connected. For each bus the real and reactive powers are computed by Eqs (  {\ref 1}) and (  {\ref 2})?? ?? = ? ?? ?? ?? ?? (?? ???? ??ð??"ð??"?? ?? ???? + ?? ???? ?????? ?? ???? ) ?? ?? =1\par
(1)Q i = ? V i V j (G ij sin ? ij ? B ij cos ? ij ) N j=1 (2)\par
After finding the load flows by the conventional method, the UPFC is incorporated in the system, compute the power flows with UPFC. The UPFC voltage sources are given in Eqs. ( \hyperref[formula_3]{3}) and ( \hyperref[formula_4]{4})?? ??? = ?? ??? (??ð??"ð??"?? ?? ??? + ?????? ?? ???\textbf{(3)}?? ???? = ?? ???? (cos ?? ???? + sin ?? ???? )\textbf{(4)}\par
The active and reactive power equations are given in Eqs. (  {\ref 5}) -( \hyperref[formula_7]{8} \par
At bus -j?? ?? = [?? ?? ?? ?? ?? ???? sin??? ?? ? ?? ?? ? + ?? ?? ?? ???? ?? ???? sin??? ?? ? ?? ???? ?]\textbf{(7)}?? ?? = ??? ?? 2 ?? ???? ? ??? ?? ?? ?? ?? ???? cos??? ?? ? ?? ?? ?? ? [ ?? ?? ?? ???? ?? ???? cos??? ?? ? ?? ???? ?]\textbf{(8)}\par
Power flow equations at the converter terminals of UPFC Eqs. ( \hyperref[formula_8]{9})-( \hyperref[formula_12]{12}) At the series converter?? ???? = [?? ???? ?? ?? ?? ???? sin(?? ???? ? ?? ?? )] + [?? ?? ?? ??? ?? ???? sin??? ???? ? ?? ?? ?]\textbf{(9)}?? ???? = ??? ???? 2 ?? ???? ? ??? ?? ?? ???? ?? ???? cos??? ?? ? ?? ?? ?? ? [ ?? ?? ?? ???? ?? ???? cos??? ?? ? ?? ???? ?] (\textbf{10})\par
At the shunt converter?? ??? = ?? ??? ?? ?? ?? ??? sin(?? ???? ? ?? ?? )\textbf{(11)}?? ??? = ?? ??? 2 ?? ??? ? [?? ?? ?? ??? ?? ??? cos(?? ???? ? ?? ?? )]\textbf{(12)}\par
For the analysis in this paper the source reactance are considered as X\textunderscore sr=X\textunderscore sh=0.1 p.u. The UPFC Source voltage and phase angles are considered as V\textunderscore sr=0.02 p.u,V\textunderscore sh=1 p.u,?\textunderscore sr=85 ?\textunderscore sh=0.\par
When UPFC is connected between bus-i and j in the power system? ?? ?? ?? ?? ? = ? ?? ???? + ?? ??? ??? ???? ??? ???? ??? ??? ??? ???? ?? ???? ?? ???? 0 ? ? ?? ?? ?? ?? ?? ???? ?? ??? ? (\textbf{13})\par
Where ?? ???? = The imaginary distances between the krill herd and food give the best fitness value .The main two characteristics considered in the engineering optimization problems are exploration and random search are needed for better performance. The main objective function of the KHA is from the Lagrangian model \hyperref[b16]{[17]}\hyperref[b17]{[18]}\hyperref[b18]{[19]}. In the two dimensional problems the above mentioned factors are sufficient, for the ndimensional problem analysis for the i\textasciicircum th krill individual is given by???? ?? ???? = ?? ?? + ?? ?? + ?? ??\textbf{(14)}\par
Motion Induced by other Krill Individuals:\par
The fitness function mainly depends on the concreteness of the krill's in the searching space. The main significant and essential thing to obtain the optimum solution is to maintain the krill density. The motion of the individual krill is promptly dependent on the adjacent individual krill and the effects between them. The direction of individual krill movement is designed on different swarm densities \hyperref[b18]{[19]}. a) Local effect provided by local krill density b) Target effect provided by target krill density c) Repulsive effect provided by repulsive swarm density?? ?? ?????? = ?? ?????? ?? ?? + ð??"ð??" ?? ?? ?? ð??"ð??"???? (\textbf{15})\par
Where ?? ?? = ?? ?? ??ð??"ð??"?????? + ?? ?? 
\section[{??????ð??"ð??"????}]{??????ð??"ð??"????}\par
The effect of krill individual on the nearest krill is calculated by?? ?? ??ð??"ð??"?????? = ? ?? ???? ?? ???? ???? ?? =1\textbf{(16)}\par
Where ?? ???? =?? ?? ??? ?? ???? ?? ??? ?? ??+?? ; ?? ???? = ?? ?? ??? ?? ?? ??ð??"ð??"?????? ??? ???????? ;\par
To know the distance between each individual is given by?? ???? = ? ||?? ?? ? ?? ?? || ???? ?? =1\textbf{(17)}\par
Foraging activity: Foraging activity is computed based on two main factors, First factor is current food location, second is prior food location information. The foraging velocity of the ith krill individual is given by the formula (18) \hyperref[b12]{[13]} ?? ?? = ?? ð??"ð??" ?? ?? + ð??"ð??" ð??"ð??" ?? ?? ð??"ð??"???? \hyperref[b17]{(18)} Where ?? ?? = ?? ?? ð??"ð??"ð??"ð??"ð??"ð??"?? + ?? ?? ???????? Food attraction is calculated by Eqs. ( \hyperref[formula_21]{19})?? ?? ð??"ð??"ð??"ð??"ð??"ð??"?? = ?? ð??"ð??"ð??"ð??"ð??"ð??"?? ?? ??,ð??"ð??"ð??"ð??"ð??"ð??"?? ?? ??,ð??"ð??"ð??"ð??"ð??"ð??"??\textbf{(19)}\par
Where?? ð??"ð??"ð??"ð??"ð??"ð??"?? = 2(1 ? ?? ?? ?????? ) 
\section[{Physical Diffusion:}]{Physical Diffusion:}\par
In the diffusion process mainly considered to increase density of population. This motion is a based on maximum diffusion speed and random directional vector, It is given by Eqs. \hyperref[b19]{(20)}  \hyperref[b12]{[13]} ?? ?? = ?? ?????? ?? \hyperref[b19]{(20)} Motion process in KHA Depending up on the local effect, global effect, presence of food, best fitness position, the presence of the i\textasciicircum th krill stays in the time interval [t,t+Î?"t] given by Eqs.  {\ref (} ???? ?? = ??(??) + ??(??) + ??(??)\textbf{(22)}\par
The scaling factor Î?"t is formulated in Eqs.( \hyperref[formula_24]{23})Î?"?? = ?? ?? ? (???? ?? ? ???? ?? ) ???? ?? =1\textbf{(23)}\par
Step  \par
In equality constrains are Generator active and reactive powers, voltage magnitudes, Transformer tap settings, UPFC settings. The limits of the generator real and reactive powers limits at P\textasciicircum th bus should lie between maximum and minimum limits Eqs. \hyperref[b25]{(26)}\hyperref[b26]{(27)}. The voltage magnitude at the each load bus is given in Eqs. \hyperref[b27]{(28)}. Transformer tap setting minimum and maximum conditions is given in Eqs. \hyperref[b28]{(29)}. Transmission line loading should not violate the loading limits is Eqs (30) \hyperref[b19]{[20]}\hyperref[b20]{[21]}\hyperref[b21]{[22]}\hyperref[b22]{[23]}\hyperref[b23]{[24]}\hyperref[b24]{[25]} ?? ???? ?????? ? ?? ???? ? ?? ???? ?????? ????????? ?? = 1,2,3, ? ? ?? ????\textbf{(26)}\par
?? ???? ?????? ? ?? ???? ? ?? ???? ?????? ????????? ?? = 1,2,3, ? ? ?? ???? \hyperref[b26]{(27)} where ?? ???? ?????? , ?? ???? ?????? , ?? ???? ?????? , ?? ???? ?????? are minimum and maximum limits of real and reactive powers at ?? ??? bus.\par
?? ???? ?????? ? ?? ???? ? ?? ???? ?????? ????????? ?? = 1,2,3, ? ? ?? ???? \hyperref[b27]{(28)} where ?? ???? ?????? , ?? ???? ?????? are minimum and maximum limits of voltage at ?? ??? bus .?? ?? ?????? ? ?? ?? ? ?? ?? ?????? ????????? ?? = 1,2,3, ? ? ?? ??\textbf{(29)}\par
where ?? ?? ?????? , ?? ?? ?????? are minimum and maximum tap setting limits of transformer .?? ???? ? ?? ???? ?????? ????????? ?? = 1,2,3, ? ? ?? ??\textbf{(30)}\par
Where ?? ???? , ?? ???? ?????? are the total power flow in the ?? ??? ???????????. 
\section[{Objective function}]{Objective function}\par
The objective functions considered in this article are based on the fuel cost \hyperref[b24]{[25]} ???????? ??ð??"ð??"???? (?? ?? ) = ? (?? ?? + ?? ?? ?? ???? + ?? ?? ?? ???? 2 )?? ???? ??=1\textbf{(31)}\par
For minimization of transmission losses, the mathematical formula is given asMin P Loss = ? ?? ?? ?? ?? ??=1 [?? ?? 2 + ?? ?? 2 ? 2|?? ?? ||?? ?? |cos(?? ?? ? ?? ?? )]\textbf{(32)}\par
Line identification is very essential to locate the UPFC in the proposed system. Optimal location of UPFC is calculated by using the Performance Index (PI) given Eqs.( \hyperref[formula_31]{33}) By satisfying the equality and inequality constants minimize the objective function is the main objective of optimal power flow (OPF). OPF is used for the incorporation of UPFC in the system, considering four different objective functions by fulfilling equality and inequality constraints.PI = ?? ?? 2?? ( ?? ?? ??? ?? ?? ??? ?????? ) 2??\textbf{(33)}\par
The general optimization problem constraints are as follows Objective function to be minimised is Min(u, v), and subjected to g(u, v)=0; h(u, v)?0, the g(u, v) is equality constraints, h(u, v) is inequality constraints, u is dependent variable , v is independent variable. The dependent variables considered in the problem formulation are generator active power (slack bus) P\textunderscore G1, load voltages (V\textunderscore (L1,) V\textunderscore L2, ??.V\textunderscore (?LN\textunderscore PQ )), reactive power at the generators (Q\textunderscore (G1,) Q\textunderscore G2,?? Q\textunderscore (GN?\textunderscore PV)), Line loading of the transmission system (S\textunderscore L1,S\textunderscore L2,??.S\textunderscore (LN?\textunderscore L)).\par
The independent variables are active power of the generators apart from slack bus (P\textunderscore G1,P\textunderscore G2??.P\textunderscore (?GN?\textunderscore PV )), generator voltages (V\textunderscore (G1,) V\textunderscore G2, ??.V\textunderscore (?GN?\textunderscore PV)), Transformer tap settings (T\textunderscore (1,) T\textunderscore 2,??.. T\textunderscore (N\textunderscore T)), active power injections (P\textunderscore c1,P\textunderscore c2,??.P\textunderscore (CN\textunderscore U)), reactive power injection (Q\textunderscore c1,Q\textunderscore c2,??.Q\textunderscore (CN\textunderscore U)) \hyperref[b19]{[20]}\hyperref[b20]{[21]}\hyperref[b21]{[22]}\hyperref[b22]{[23]}\hyperref[b23]{[24]}\hyperref[b24]{[25]}. 
\section[{Equality and Inequality constraints:}]{Equality and Inequality constraints:}\par
Mentioned above g is the set of equality constraint and h is inequality constraint. With the help of load flow equations the equality constraints are represented by Eqs. \hyperref[b23]{(24)}\hyperref[b24]{(25)}. The inequality constraint h is the operating limits represented by Eqs. \hyperref[b25]{(26)}\hyperref[b26]{(27)}\hyperref[b27]{(28)}\hyperref[b28]{(29)}\hyperref[b29]{(30)} Voltage Deviation should be very minimum at all the bus formulated as Eqs.( \hyperref[formula_32]{34})?? ?????? = min(??????) = min ? |?? ?? ? ?? ?? ????ð??"ð??" | ?? ??=1\textbf{(34)} 
\section[{V. Simulation Results and Discussion}]{V. Simulation Results and Discussion}\par
For better understanding analysis of the proposed KHA is simulated by using IEEE 14 and 30 bus standard systems. At the initial state IEEE 14 and 30 bus system load flows are run by Newton Raphson Method using the polar coordinates in the MATLAB environment. IEEE 14 bus system is included by 5 generation units which are located at the Bus No. 1, 2, 3, 6, 8 and 20 transmission lines are used to interconnect the system and tap changing transformers are connected between the buses(4-7,4-9 and 5-6) and for the Bus-9 and 14 shunt VAR compensators are connected . The total demand by the system is 2.98p.u. at 100MVA base. Control variables and line data is considered \hyperref[b25]{[26]} The data of the Modified IEEE 30 bus system is having six generators located at the buses -1, 2, 5, 8, 11, 13 and remaining 24 are the load buses, 41 transmission lines are used to interconnect the system. The slack bus is considered as bus -1. Total demand by IEEE 30 bus system is 2.83 p.u at 100 MVA base. In the system load bus, voltages are considered in the range of 0.95 to 1.1p.u. IEEE-14 bus system minimum and maximum constraints is shown in Table  {\ref .}01 \hyperref[b25]{[26]}    \hyperref[b27]{[28]}, GA \hyperref[b29]{[30]}, ABC \hyperref[b30]{[31]}, PSO \hyperref[b32]{[33]}. The total power generated by using KHA is decreased by 1.4\% as compared with ABC with the incorporation of UPFC between bus 24 and 25. The results of ABC are mentioned in \hyperref[b30]{[31]}. Similarly there is a decrease of 1.06\% for GA is reported in \hyperref[b29]{[30]}, and 1.248\% decrease for FF reported in \hyperref[b27]{[28]}. By using the fuel cost optimization for the KHA method, the power losses have reduced to 4.6986\% as compared with the other optimization techniques.       
\section[{Conclusion and Future Scope}]{Conclusion and Future Scope}\par
A novel Meta heuristic algorithm KHA is used to solve the Optimal power flow problem of the proposed power system networks IEEE-14 and 30 bus systems. Two main objective functions has considered (i) cost function (ii) Active power transmission losses due to high impact of equality and inequality constraint each objective function is studied individually. For the analysis of the KHA, FACTS device UPFC is incorporated in the system. Results obtained using KHA is compared with Genetic Algorithm, Practical Swarm Algorithm, Fire Fly and ABC algorithms and compared with the other popular optimization techniques for the optimal power problem, The results obtained from the KHA are better and robustness, stability and the convergence rate is faster than the other methods. By this article the new algorithm KHA may be extended for other optimization methods for the further research. In future the KHA can be extended to OKHA, and can be implemented for the other FACTS devices like IPFC, UPQC etc,. for the better analysis.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{01}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 01 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
       \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: ) At bus-i]} 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.6375\textwidth}P{0.2125\textwidth}}
???? ?? ????\tabcellsep (21)\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.009409594095940959\textwidth}P{0.06586715867158671\textwidth}P{0.10977859778597786\textwidth}P{0.12859778597785979\textwidth}P{0.0470479704797048\textwidth}P{0.235239852398524\textwidth}P{0.012546125461254613\textwidth}P{0.24151291512915127\textwidth}}
? ?\tabcellsep ?? ?? ??=1 ?? ?? ??=1\tabcellsep ?? ???? ? ?? ???? ?? ???? ? ?? ????\tabcellsep + ? + ? ?? ???????? ??=1 ?? ???????? ??=1\tabcellsep ?? ???? ?? ????\tabcellsep = ? = ? ? ?? ?? ??=1 ?? ?? ? ??=1 ?? ?? ??=1 ? ?? ?? |?? ?? | |?? ?? | ??=1\tabcellsep (24)\tabcellsep ( ) Volume XVII Issue III Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Journal of Researches in Engineering\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Global\end{longtable} \par
  {\small\itshape [Note: ??? ???]} 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.26079545454545455\textwidth}P{0.30265151515151517\textwidth}P{0.28655303030303025\textwidth}}
Generating Unit\tabcellsep ?? ???????? (p.u)\tabcellsep ?? ???????? (p.u)\\
Pg1\tabcellsep 0.00\tabcellsep 0.1\\
Pg2\tabcellsep -0.4\tabcellsep 0.5\\
Pg3\tabcellsep 0.00\tabcellsep 0.4\\
Pg6\tabcellsep -0.06\tabcellsep 0.24\\
Pg8\tabcellsep -0.06\tabcellsep 0.24\\
Voltage Limits Transformer tap changer Line voltage\tabcellsep ?? ?? ?????? = 0.95 ?? ?????? = 0.9 ?? ?? ?????? = 0.95\tabcellsep ?????? = 1.05 ?? ?? ?? ?????? = 1.1 ?? ?? ?????? = 1.05\end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.06538461538461539\textwidth}P{0.6944011684518013\textwidth}P{0.02317429406037001\textwidth}P{0.001655306718597858\textwidth}P{0.06538461538461539\textwidth}}
\tabcellsep Fig. 04:\tabcellsep \\
Global Journal of Researches in Engineering ( ) Volume XVII Issue III Version I\tabcellsep \multicolumn{3}{l}{Table-02: Cost constraints and maximum and minimum power limits of the generator units [27] Generating unit P(min) P (Max) ?? ?? ?? ??  *  ???? ??? ?? ??  *  ???? ??? MW MW \$/? \$/????? \$/???? 2 ? Pg1 50 200 0.00 200 Pg2 20 80 0.00 175 175.0 Pg5 15 50 0.00 100 625.0 Pg8 10 35 0.00 325 83.0 Pg11 10 30 0.00 300 250.0 Pg13 12 40 0.00 300 250.0 Case study-i: smoothly reduced as compared with the GA and PSO. From the convergence results, it is clearly observed that reduced by 80\% with respective to other algorithms. 12 13 14 15 16 17 18 19 20 Active power Transmission Losses(p.u) Genetic Algorithm PSO Krill Herd Algorithm 37.5 Total APTL are presented clearly in the Fig.03, from which it is clearly observed that the APTL are APTL are reduced by 0.8p.u in contrast with GA and PSO. By implementing the KHA almost APTL are}\tabcellsep Global Journal of Researches in Engineering ( ) Volume XVII Issue III Version I\\
\tabcellsep 11\tabcellsep \\
\tabcellsep 0\tabcellsep 40\tabcellsep 80\\
\tabcellsep \tabcellsep Number of Fitness Function\end{longtable} \par
  {\small\itshape [Note: Fig. 03: Comparative Active power transmission losses (APTL) of GA,PSO and Krill Herd for IEEE 14 bus sytem. © 2017 Global Journals Inc. (US)]} 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.12871428571428573\textwidth}P{0.15785714285714286\textwidth}P{0.16028571428571428\textwidth}P{0.153\textwidth}P{0.11171428571428571\textwidth}P{0.13842857142857143\textwidth}}
\multicolumn{2}{l}{Generator ABC}\tabcellsep GA\tabcellsep FF OPF\tabcellsep PSO\tabcellsep KHA OPF\\
PG1\tabcellsep 180.5218\tabcellsep 176.7307\tabcellsep 176.7311\tabcellsep 174.26\tabcellsep 174.16\\
PG2\tabcellsep 48.7845\tabcellsep 48.8488\tabcellsep 48.8454\tabcellsep 49.77\tabcellsep 48.754\\
PG5\tabcellsep 21.2598\tabcellsep 21.4941\tabcellsep \tabcellsep \tabcellsep 21.82\\
PG8\tabcellsep 18.6469\tabcellsep 21.6881\tabcellsep 21.6923\tabcellsep 21.4\tabcellsep 20.61\\
PG11\tabcellsep 11.8145\tabcellsep 12.1530\tabcellsep 12.1535\tabcellsep 11.93\tabcellsep 11.95\\
PG13\tabcellsep 12.1011\tabcellsep 12.0009\tabcellsep 12.000\tabcellsep 12.00\tabcellsep 12.01\\
TOTAL\tabcellsep 293.3805\tabcellsep 292.3805\tabcellsep 292.9154\tabcellsep 290.41\tabcellsep 289.304\\
COST\tabcellsep 802.1649\tabcellsep 802.717\tabcellsep 802.3646\tabcellsep 802.36\tabcellsep 795.41\\
?? ????????\tabcellsep 9.7286\tabcellsep 9.5156\tabcellsep 9.5155\tabcellsep 9.3064\tabcellsep 9.292\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.15454545454545454\textwidth}P{0.04636363636363636\textwidth}P{0.06954545454545455\textwidth}P{0.06954545454545455\textwidth}P{0.06954545454545455\textwidth}P{0.06954545454545455\textwidth}P{0.07727272727272727\textwidth}P{0.09272727272727271\textwidth}P{0.07727272727272727\textwidth}P{0.12363636363636363\textwidth}}
\multicolumn{2}{l}{Generator PG1}\tabcellsep PG2\tabcellsep PG5\tabcellsep PG8\tabcellsep PG11\tabcellsep PG13\tabcellsep TOTAL\tabcellsep COST\tabcellsep ?? ????????\\
KHA OPF\tabcellsep 74.356\tabcellsep 63.509\tabcellsep 49.999\tabcellsep 33.431\tabcellsep 28.77\tabcellsep 34.251\tabcellsep 284.316\tabcellsep 952.56\tabcellsep 2.753\end{longtable} \par
 
\caption{\label{tab_6}}\end{figure}
 			\footnote{Year 2017 F © 2017 Global Journals Inc. (US)} 			\footnote{© 2017 Global Journals Inc. (US)} 			\footnote{Year 2017 F Improving Profile parameters of the Power System Network using Krill Herd Algorithm with FACTS device: UPFC} 			\footnote{Year 2017 F © 2017 Global Journals Inc. (US)The IEEE 30 bus system active power generating limits and unit cost of generators are presented in table-2.Improving Profile parameters of the Power System Network using Krill Herd Algorithm with FACTS device: UPFC} 			\footnote{Year 2017 F Improving Profile parameters of the Power System Network using Krill Herd Algorithm with FACTS device: UPFC} 		 		\backmatter  			 \par
Case study-ii IEEE 30 bus system is considered for the enhanced analysis. Voltage profiles, real power at generating units, APTL, Cost analysis is evaluated in the MATLAB environment. The bus system is simulated with and without incorporation of UPFC. Based on TVD the UPFC is incorporated. The UPFC is installed between bus 24 and 25. The bus data, line data, generation data are considered from \hyperref[b27]{[28]}.\par
The voltage profiles of the IEEE 30 bus system is obtained by simulating in MATLAB,and the results are presented in Table  {\ref .}05 is incorporating the UPFC in line 33 between and 25 buses. As compared with NR, GA, FF, ABC \hyperref[b27]{[28]}\hyperref[b28]{[29]}\hyperref[b29]{[30]}\hyperref[b30]{[31]}\hyperref[b31]{[32]} with KHA the voltage profiles are smoothly and drastically increased in the system. Voltage profile is improved almost 0.06\% compared with the conventional and remaining algorithms. 			 			  				\begin{bibitemlist}{1}
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\end{document}
