enhancement of decentralized load frequecy based pid ... · 4 cuckoo search algorithm more and more...

Enhancement of decentralized loadFrequecy based PID Controller by

Cuckoo Search Agorithm

R. Ganesh1, K. S. V. Phani Kumar2,B. Ravinder3

1,3Assistant Professor, Dept of EEE,B.V.Raju Institution of Technology,

Narsapur Medak(Dist),Telangana, India2Assistant Professor, Dept of EEE,

C V R Engineering, Telangana,India

April 19, 2018

AbstractIn this Paper, a novel approach based on Cuckoo search

optimization, with dominant eigen value shift for designingrobust decentralized load frequency control system for in-terconnected power system is presented. A proportional -integral derivative (PID) controller is considered to exem-plify the optimum parameter search. To demonstrate therobustness of the obtained controller, a two-area non reheatthermal system, equipped with such optimized tuned con-troller, is tested under different operating conditions andparameter Changes. The optimal PID parameters searchis formulated as an optimization problem with a standardeigen value objective function. Its effectiveness is shownthrough comparison with the well-known conventional inte-gral controller. The eigen value based performance indexis considered. The simulation results demonstrate the en-hancement in the dynamic response of the two area powersystem.

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International Journal of Pure and Applied MathematicsVolume 118 No. 24 2018ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

Key Words:Decentralized control, load frequency con-trol, Power system, proportional-integral-derivative controller,stochastic particle swarm optimizer. Cuckoo search algo-rithm

1 INTRODUCTION

Large scale power systems are normally managed by viewing themas being made up of control areas with interconnections betweenthem. Each control area must meet its own demand and its sched-uled interchange power. Any mismatch between the generation andload can be observed by means of a deviation in frequency [1]. Thisbalancing between load and generation can be achieved by usingAutomatic Generation Control (AGC).

The engineering aspects of planning and operation have been re-formulated in a restructured power system in recent years althoughessential ideas remain the same. To improve the efficiency in the op-eration of the power system some major changes into the structureof electric power utilities have been introduced by means of dereg-ulating the industry and opening it up to private competition. Theutilities no longer own generation, transmission, and distribution;instead, there are three different entities, viz., GENCO (GenerationCompanies), TRANSCOs (Transmission Companies) and DISCOs(Distribution Companies).

As there are several GENCOs and DISCOs in the deregulatedstructure, a DISCO has the freedom to have a contract with anyGENCO for transaction of power. A DISCO may have a contractwith a GENCO in another control area. Such transactions arecalled bilateral transactions. All the transactions have to be clearedthrough an impartial entity called an Independent System Opera-tor (ISO). The ISO has to control a number of so-called ancillaryservices, one of which is AGC. One of the most profitable ancil-lary services is the load frequency control. The main goal of theLFC is to maintain zero steady state errors for frequency deviationand minimize unscheduled tie-line power flows between neighboringcontrol areas.

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International Journal of Pure and Applied Mathematics Special Issue

2 LOAD FREQUENCY CONTROL

In an interconnected power system, as the load varies, the frequencyand tie-line power interchange also vary. To accomplish the ob-jective of regulating system electrical frequency error and tie-linepower flow deviation to zero, a supplementary control action, thatadjusts the load reference set points of selected generating units, isutilized. This control process is referred to as Automatic Genera-tion Control (AGC).

The role of AGC is to divide the loads among the system, stationand generator to achieve maximum economy and accurate controlof the scheduled interchanges of tie-line power while maintaining areasonability uniform frequency.

An interconnected power system can be considered as beingdivided into control areas which are connected by tie lines. Ineach control area, all generator sets are assumed to form a coherentgroup. The power system is subjected to local variations of randommagnitudes and durations. A control signal made up of tie lineflow deviation added to frequency deviation weighted by a biasfactor would accomplish the desired objective. This control signalis known as area control error (ACE). ACE serves to indicate whentotal generation must be raised or lowered in a control area.

A. Power System Frequency Control:Frequency deviation is a direct result of the imbalance between

the electrical load and the active power supplied by the connectedgenerators. A permanent off-normal frequency deviation directlyaffects power system operation, security, reliability, and efficiencyby damaging equipment, degrading load performance, overloadingtransmission lines, and triggering the protection devices. Since thefrequency generated in the electric network is proportional to therotation speed of the generator, the problem of frequency controlmay be directly translated into a speed control problem of the tur-bine generator unit. This is initially overcome by adding a gov-erning mechanism that senses the machine speed, and adjusts theinput valve to change the mechanical power output to track theload change and to restore frequency to a nominal value.

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3 PARTICLE SWARM OPTIMIZA-

TION

Particle Swarm Optimization (PSO) is an optimization techniquewhich provides an evolutionary based search. This search algorithmwas introduced by Dr. Russ Eberhart and Dr. James Kennedy in1995. James is a social psychologist and is from the Bureau ofLaborStats, Washington Dr. Russ is an electrical engineer fromPurdue School of engineering and technology, Indianapolis. PSO isan evolutionary computation technique developed by Eberhart andKennedy in 1995, and was inspired by the social behaviour of birdflocking and fish schooling. PSO has its roots in artificial life andsocial psychology as well as in engineering and computer science.It utilizes a population of individuals, called particles, which flythrough the problem hyperspace with some given initial velocities.In each iteration, the velocities of the particles are stochasticallyadjusted considering the historical best position of the particlesand their neighbourhood best position; where these positions aredetermined according to some predefined fitness function. Then,the movement of each particle naturally evolves to an optimal ornear optimal solution.

V k+1i = V k

i +C1×rand1× (pbesti−Ski )+C2×rand2× (gbest−Sk

i )(1)

Sk+1i = Sk

i + V k+1i (2)

WhereV k+1i = Velocity of particle i at iteration k+1

V ki = Velocity of particle i at iteration k

Sk+1i = Position of particle i at iteration k+1

Ski = Position of particle i at iteration k

C1= Constant weighting factor related to pbestC2= Constant weighting factor related to gbest

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4 CUCKOO SEARCH ALGORITHM

More and more modern metaheuristic algorithms inspired by natureare emerging and they become increasingly popular. For example,particles swarm optimization (PSO) was inspired by fish and birdswarm intelligence, while the Firefly Algorithm was inspired by theflashing pattern of tropical fireflies. These nature-inspired meta-heuristic algorithms have been used in a wide range of optimiza-tion problems, including NP-hard problems such as the travellingsalesman problem.

The power of almost all modern metaheuristic comes from thefact that they imitate the best feature in nature, especially biolog-ical systems evolved from natural selection over millions of years.Two important characteristics are selection of the fittest and adap-tation to the environment. Numerically speaking, these can betranslated into two crucial characteristics of the modern meta-heuristic: intensification and diversification. Intensification intendsto search around the current best solutions and select the best can-didates or solutions, while diversification makes sure the algorithmcan explore the search space efficiently.

A. Cuckoo Bredding Behavior Cuckoo are fascinating birds,not only because of the beautiful sounds they can make, but alsobecause of their aggressive reproduction strategy. Some speciessuch as the ani and Guira cuckoos lay their eggs in communalnests, though they may remove others eggs to increase the hatch-ing probability of their own eggs [12]. Quite a number of speciesengage the obligate brood parasitism by laying their eggs in thenests of other host birds (often other species). There are threebasic types of brood parasitism: intraspecific brood parasitism, co-operative breeding, and nest takeover. Some host birds can engagedirect conflict with the intruding cuckoos. If a host bird discoversthe eggs are not their owns, they will either throw these alien eggsaway or simply abandon its nest and build a new nest elsewhere.Some cuckoo species such as the New World brood-parasitic Taperahave evolved in such a way that female parasitic cuckoos are oftenvery specialized in the mimicry in colour and pattern of the eggsof a few chosen host species. This reduces the probability of theireggs being abandoned and thus increases their reproductivity.

In addition, the timing of egg-laying of some species is also

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amazing. Parasitic cuckoos often choose a nest where the host birdjust laid its own eggs. In general, the cuckoo eggs hatch slightlyearlier than their host eggs. Once the first cuckoo chick is hatched,the first instinct action it will take is to evict the host eggs byblindly propelling the eggs out of the nest, which increases thecuckoo chicks share of food provided by its host bird. Studies alsoshow that a cuckoo chick can also mimic the call of host chicks togain access to more feeding opportunity.

B. Levy Flights:On the other hand, various studies have shown that flight be-

haviour of many animals and insects has demonstrated the typ-ical characteristics of Levy flights. A recent study by Reynoldsand Frye shows that fruit flies or Drosophila melanogaster, exploretheir landscape using a series of straight flight paths punctuatedby a sudden 90o turn, leading to a Levy-flight-style intermittentscale free search pattern. Studies on human behaviour such as theJu/hoansi hunter-gatherer foraging patterns also show the typicalfeature of Levy flights. Even light can be related to Levy flights.Subsequently, such behaviour has been applied to optimization andoptimal search, and preliminary results show its promising capabil-ity.

5 SIMULATION RESULTS

A. By Using Particle Swarm Optimizationa. Design of Set:1Kp1=.554590Ki1=.107291kp2=.000367ki2=.412490

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Fig: 1 Minimization of Lamda using PSO Algorithm

Fig: 2 THD Minimization using PSO Algorithm

b. Design of set2:kp1=.429048ki1=.297018kp2=.258816ki2=.202928

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c. Design of set3:kp1=.638047ki1=.072057kp2=.108859ki2=.241228

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B. By Using Cuckoo Search Algorithm:Design of Set: 1kp1=0.158167,kp2=0.133653,ki1=0.143256,ki2=0.180232

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Fig: 7 Minimization of Lamda using Cuckoo Algorithm


Design of Set: 3kp1=0.006494,kp2=0.025145,

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ki1=0.666121,ki2=0.170488


6 CONCLUSION

A Cuckoo based PID type controller for the AGC problem in dereg-ulated power systems is proposed using the modified AGC schemein this paper. This control strategy was chosen because of the in-creasing complexity and changing structure of deregulated powersystems. This newly developed control strategy combines the ad-vantages of the Cuckoo based PID and integral controllers for achiev-ing the desired level of robust performance, such as precise referencefrequency tracking and disturbance attenuation under a wide rangeof area load changes and disturbances. Moreover, it has a simplestructure and is easy to implement, which makes it ideally usefulfor the real world power systems. The Cuckoo-PID controller wastested on a three area deregulated power system to demonstrateits robust performance for the three possible contracted scenariosunder different operating conditions. Simulation results show thatthe proposed strategy is very effective and guarantees good robust

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performance against parametric uncertainties, load changes. Thesystem performance characteristics in terms of ITAE indices revealthat the proposed Cuckoo-PID is a promising control scheme for theAGC problem. Thus, it is recommended to generate good qualityand reliable electric energy in deregulated power systems

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