optimal power flow to manage voltage profiles in interconnected networks using expert systems

1622 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 4, NOVEMBER 2007

Optimal Power Flow to Manage Voltage Profiles inInterconnected Networks Using Expert Systems

Ahmed M. Azmy

Abstract—The optimal power flow issue is one of the most im-portant problems faced by dispatching engineers regarding largescale power systems. It is a particular mathematical approach ofthe global power system optimization problem that aims at deter-mining the least control movements to keep power system at themost desired state. Thus, it represents a flexible and powerful tool,which can address a wide range of planning and operation studies.However, the complexity of optimal power flow increases dramat-ically with large-scale networks, which often discourages the uti-lization of this powerful tool in many applications. This paper pro-poses a new intelligent approach to facilitate the implementation ofthe optimal power flow calculations to be utilized in various con-trol centres. A main advantage of the proposed intelligent systemswith real control centres is the possibility of controlling the systemvoltage profile in a tracking mode. The simulation results using thisintelligent system when applied to the IEEE 30-bus power networkemphasize the validity and effectiveness of the proposed technique.

Index Terms—Expert systems, heuristics, intelligent systems,knowledge base, linear programming, optimal power flow.

I. INTRODUCTION

THE optimal power flow (OPF) problem in interconnectedpower systems has acquired an increasing interest since it

results in significant improvements in the power system opera-tion [1]–[3]. This is of a major concern for dispatching engineersto handle large-scale power systems in an effective and efficientmanner [4]–[6]. The OPF provides a useful support to the op-erator to overcome many difficulties in the planning, operationand control of power systems [3], [4]. Therefore, it is widelyused in many applications, such as constrained economic dis-patch and voltage control problems [5], [6].

The problems associated with the OPF have been discussed inmuch of the literature [2], [7], [8]. Among these problems arethe voltage control and the VAR compensation. The methodsused to handle these problems are based mainly on optimiza-tion techniques and are directed to minimize the total systemlosses and to improve the voltage profiles. However, most ofthese techniques are complex in nature and have the capabilityto provide analytical solutions without recommending appro-priate control actions required to avoid the violation of voltageconstraints [8], [9]. Furthermore, most conventional methods as-sume continuous-variable control means, simplified substationtopologies, or both. Therefore, they are not relevant for someapplications in real systems, where the abovementioned simpli-

Manuscript received February 16, 2007; revised August 11, 2007. Paper no.TPWRS-00132-2007.

The author is with the Electrical Power and Machines Engineering Depart-ment, Faculty of Engineering, Tanta University, Tanta, El-Gharbia 33311, Egypt(e-mail: [email protected], [email protected]).

Digital Object Identifier 10.1109/TPWRS.2007.907961

fications can affect the accuracy of the results. In other situa-tions, the obtained results will not be reliable due to the conflictbetween the assumed and the real circumstances.

Intelligent systems, on the other hand, can provide a flex-ible tool that has the capability to handle different logical prob-lems simply and effectively [10]–[15]. Moreover, they can pro-vide the required topological decisions and recommendationsthat are necessary in the operation of power systems [11], [13],[15]. This can be easily accomplished by supporting the solu-tion tool by suitable topology-evaluation programs. Therefore,intelligent systems are favoured to handle the OPF problem es-pecially when concerning large interconnected power systems.

Among the intelligent techniques, expert systems can providea robust tool to manage the OPF problem and to give suitableproposals to improve the overall performance of power systems[6], [11]–[15]. In addition, expert systems are advantageous dueto the simplicity of the control structure of rules, the compactformulation of complex problems and the use of natural lan-guages in the form of questions as an interface link with theuser [11]. However, the idea of connecting an expert systemto the power flow algorithm to recommend a suitable actionwithin definite roles to overcome the convergence problem isnot clearly discussed.

The main focus of this paper is to develop an expert systemthat facilitates the utilization of OPF methods in power sys-tems to observe and mange the voltage profiles at different loadcentres. This is accomplished by regulating the processing ofthe optimization program and the direct linear programming toachieve the required conversion. The proposed technique solvesthe OPF problem in two phases, where the first phase is a con-ventional OPF based on linear programming technique. Thesecond phase is an expert-system based optimization techniqueto alleviate the constraints violation problem. The later step min-imizes the total number of switching processes associated withthe control action. Thus, the technique has the capability to rec-ommend solutions within definite roles to solve the voltage vi-olation problem. To evaluate the usefulness of the presentedmethodology, it is applied to the IEEE 30-bus power networkunder different loading conditions. The simulation results showthat the proposed technique can achieve a considerable reduc-tion in the total transmission losses in addition to satisfying dif-ferent technical and operating constraints. This encourages thedevelopment of expert systems to manage the OPF in large-scaleinterconnected power systems.

II. DESCRIBING THE OPTIMAL POWER FLOW PROBLEM

To describe the OPF problem, all variables have to be definedthrough the following distinct four categories: Objectives, Con-

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AZMY: OPTIMAL POWER FLOW TO MANAGE VOLTAGE PROFILES IN INTERCONNECTED NETWORKS USING EXPERT SYSTEMS 1623

straints, Network and Controls. Then, they are used in the de-velopment of the mathematical representation of the problem[1]–[4].

a) Objectives: the desirable solution attributes that are notspecified as constraints. The main task of the OPF is tooptimize the objective function while meeting all con-straints. Many common objective functions, such as min-imizing fuel cost, or finding a feasible solution with min-imum control movement, can be directly expressed as costfunctions of the control. However, some other objectivefunctions, such as minimizing the total active power lossesas a common objective function, cannot be directly ex-pressed as a cost function of the control.

b) Constraints: the limits defined by operating procedurethat keep the power system within a safe and sustainableoperating region. These are the equipment operating andsystem security limits, such as bus voltage magnitudeand line flow limits. Useful by-products of solving theOPF problem are the sensitivities of enforcing theseconstraints, relative to the objective function. This givesthe user an idea about the cost caused by holding each ofthe constraints at its present value.

c) Network: the model common in all functions that requirea network definition. The OPF must satisfy the physicalconstraints implied by the network definition. Similar toconventional power flow, primary network constraints arethe bus real and reactive power mismatch equations.

d) Controls: the set of power system controls that can be ad-justed to meet the constraints and to optimize the objec-tives. Examples of such category are the adjustment oftransformer tabs, changing the voltages of the generatorsand switching the reactive power compensators.

It is important for the operator to precisely convert the real-world operation problems into the domain of describing the OPFproblem. In addition, the interpretation of the OPF results has asignificant importance to maximize the benefits behind the op-timization process. The complete and accurate description ofthe problem may require several modifications in the model de-pending on the obtained solution [1]. For instance, if the resultsobtained from the OPF indicate unfeasible control recommen-dations, it will be necessary to adapt the problem definition andrepeat the solution.

III. OVERVIEW OF CONVENTIONAL OPTIMAL POWER FLOW

OPF can be set up using several methodologies and can helpin solving many problems. In the following, some typical sce-narios will be introduced to highlight the contribution of OPFin the analysis of power systems with a brief description of theway by which OPF is introduced [1]–[4].

• In the standard description of the OPF problem, if an emptyset is specified for the controls, the algorithm reduces di-rectly to a typical power flow problem. The procedures inthis case depend on the bus mismatch equations and pro-vide the same state solution like the classic power flow,including bus voltages and branch flows.

• In addition, OPF may be associated with the constrainedeconomic dispatch to define the optimal allocation of loads

Fig. 1. Structure of the expert system.

among the generators [4]–[6]. By specifying the generationcost characteristics, the network model and the load profile,it is straightforward for the OPF solution to realize the mosteconomic operation of the power system [4]. Thus, the OPFcan define the lowest cost solution without causing networksecurity problems.

• OPF can also be used to minimize the total real power lossthrough reactive power dispatch [2], [4], [6], [7]. In thiscase, only reactive controls such as transformer tap posi-tions, shunt capacitors and reactors, and excitation systemsare used to minimize the total losses in the entire network,or in a subset of the network.

• Furthermore, OPF can be used to define feasible solutionsor indicates if one exists using the so-called “minimum ofcontrol movements” strategy [4], [13]. According to thisstrategy, the objective of the optimization process is to min-imize the cost function based on control deviations fromthe base case [13]. It is common to develop the cost func-tion in the form of a parabolic cost curve with its minimumpoint centred on the base-case control setting. Thus, onlythose controls that must be shifted to alleviate constraintsviolations will be modified. Otherwise, the resultant costwill increase considerably.

IV. EXPERT-SYSTEM-BASED OPTIMAL POWER FLOW

The main barrier of handling the OPF problem using the con-ventional methods is the complexity of the optimization model.This is true regarding interconnected networks especially whenall constraints are considered in details [3], [8].Therefore, theapplications of these methods are limited to simple networks.Expert systems can provide a solution for such situations takinginto account their flexibility and simplicity [11]–[15]. In the fol-lowing, a short description about expert system components andoperation is given [11]–[15].

A simplified block diagram of the expert system is shownin Fig. 1 [11], [14]. The system includes seven fundamentalcomponents: Database, Knowledge Base, Inference Engine,Man–Machine Interface (MMI), Linear Programming Routine(LPR), Sensitivity Factor Routine (SFR), and Actual TopologyEvaluation Routine (ATER).


A. Man–Machine Interface (MMI)

The MMI is the connecting environment through which therequired inputs to solve the problem are introduced and the finaloutputs are displayed [14].

In the present case, the final results are the voltage profile thatcan help to specify the changes in the network topology and/oroperating conditions to avoid voltage problems in the network

B. Database

The database contains the following data [13], [14]:• Transmission line data: the resistance , reactance , and

susceptance of a transmission line connecting betweenBus and Bus are stored in the form: line_data

• Sensitivity matrix: the sensitivity factor between anytwo buses, Bus and Bus is stored in the form:

• Bus data: bus data for bus is stored as: bus_statedWhere: is the voltage, is the ca-

pacity in use and is the installed capacity of the inductoror capacitor at Bus .

• The inductor/capacitor data: the status of each capacitorbank or inductor at Bus is described by two lists ,and as follows: . Where: is alist of all capacitor banks or inductors at Bus that arein use, while is a list of those capacitors and inductorsthat are not in use.

• Transformer tapping data: the present tap setting (TAP),maximum tap setting , minimum tap setting

, and step size (STEP) of the transformer tapbetween Bus and Bus are stored in the form:tap_data

C. Knowledge Base

A successful intelligent system relies on a high-qualityknowledge base. The knowledge base needs to incorporatethe sufficient knowledge (e.g., facts and heuristics) required toperform the proposed task accurately [13], [14]. Some exam-ples of the knowledge base used in the paper are given in theAppendix.

1) General Optimal Power Flow Heuristics: In the fol-lowing, the general heuristics that are independent of the OPFformulation are identified [11], [14], [15]. Thus, they can beused with any OPF approach.

• Based on the current real-time data, the operating statusof the power system is identified. The power system statuscan be identified through the following three states: normaloperating state, disturbance state, or emergency state. Therules that handle these categories are unique for each indi-vidual power system.

• The system is operating under normal conditions if all rele-vant power system indexes such as voltage, current, trans-mitted power, etc. are within normal operating limits de-fined by the operator. In this case, economic dispatch andproduction costs objective functions have to be applied tooptimize the performance of the power system.

• If one or more of the transmission lines are overloaded,only the economic dispatch objective function is applied.

The problem in this case necessitates that some inequalityconstraints are transferred to equality constraints. In fact,the transmitted power of each overloaded line is main-tained fixed at its extreme value to avoid the violation ofthis constraint. This process is accomplished as a part ofthe normal optimization procedure. The heuristic is essen-tial to ensure feasible operation of the power system.

• When the voltage at one or more bus is found to be lowerthan the acceptable limit, minimum control movement ob-jective function is used to regulate the voltage magnitudes.Thus, only reactive control devices that are electricallyclose to the buses with low voltage are controlled using theresults of the OPF.

• Handling the over voltage problem at one or more busis similar to the previous heuristic. The minimum controlmovement objective function is used to regulate the reac-tive control devices that are electrically close to these busesuntil all voltages are within the normal limits.

D. Inference Engine

The inference engine is the core of any intelligent systemthat processes the developed rules to attain a predefined specifictarget [11]. An inference engine was earlier developed and useddepending on the AI language “PROLOG”. Therefore, it is usedin this research to achieve the desired voltage control action.

E. Actual Topology Evaluation Routine (ATER)

The term “Actual Topology” is intended to cover the nodesand impedance bearing branches necessary for the physical de-scription of the system with reference to the switching state ata certain point in time. On the other hand, the term “PotentialTopology” is meant to cover all existing switchable and fixedconnecting elements of a network between and within substa-tions, fields, etc.

The topology evaluation routine has three main tasks: readinginformation from the database, defining the actual topology ofthe investigated system and generating special lists of the net-work elements. Examples of the generated lists of network el-ements include: nodes, branches (lines and transformers) andloads.

The actual topology specifies all nodes and branches with im-pedances, which are connected with a synchronized power unit.After evaluating the actual topology of the investigated powersystem, the following data has to be obtained from the listing:

• R [Ohm], X [Ohm], G [Siemens] and B [Siemens].• Maximum apparent power [MVA].• Connectivity.

F. Sensitivity Factors Routine

The sensitivity factor routine assists the intelligent systemduring the solution of the OPF problem. It is required to esti-mate the expected changes in the load bus voltages and also inthe generator reactive power outputs for a given increment inthe control variables. This is important especially when the min-imum control movement is considered as an objective function.In addition, the utilization of the sensitivity factors as by-prod-ucts of solving the OPF problem can give the user a global ideaabout the cost caused by holding any constraint at its presentvalue.


G. Linear Programming Routine

The linear programming approach is used to compute the re-quired control actions in an optimal manner [1], [9]. It is wellknown that the mathematical formulation of the problem re-quires an objective function, or multi-objective functions, thathave to be minimized in addition to a set of constraints. Thecommonly-used objective functions are the minimization of thetotal number of adjustments and the minimization of the totaltransmission losses.

To decide for the most suitable objective function to be mini-mized, the problem is discussed with a number of specialists andoperators in the Egyptian unified power network. The generalimpression was to favour the minimization of the total numberof switching processes associated with the control action. Inother words, they prefer to minimize the total number of ca-pacitors/inductors switching processes and/or transformer-tapadjustments.

The main motivation of this tendency is attributed to the factthat the continuous capacitor/inductor switching process and thetransformer-tap adjustments result in two main drawbacks: thefirst is the significant reduction in the operating life of these de-vices and the second is the increases of the maintenance cost.Based on the abovementioned practical considerations, it is pro-posed in this research to set the objective function to minimizethe total number of required switching/adjustments to controlthe voltage at different nodes of the network.

It is not intended from concerning the voltage violationproblem to develop a voltage control algorithm. Rather, thetask of alleviating the voltage violation is accomplished withinthe OPF solution as a main request in power systems Themathematical description of the objective function is given asfollows:

(1)

where is the total number of capacitors/inductors that haveto be switched on or off in addition to the number of steps of thetransformer tap at bus .

It is important to mention here that the solution algorithm willremain the same if the objective function is set to minimize theoverall transmission losses in the system. It is also possible touse a multi-objective function by combining the two objectivefunctions in an augmented one using suitable weights for eachfunction [9].

The constraints in the linear programming formulation can besummarized in the following:

• Constraints related to control variables:

(2)

(3)

• Constraints related to bus voltages:

(4)

where:is the reactive power increment of switchable Capac-

itors/Inductors at bus ;

and are the lower and upper limits of the re-active power increment of switchable Capacitors/Inductorsat bus , respectively;

is the increment of the transformer tap between busesand ;

and are the lower and upper limits of thetransformer tap between buses and , respectively

The algorithm starts with reading the data base and thetopology of the network, which are required to accomplish thepower flow calculations. Then, a linear-programming-basedOPF is accomplished and the voltage profiles are examined forviolating their constraints. For correcting the voltage problemif exists, the knowledge-base roles are applied (see appendix),and the minimum control movement objective function is usedto regulate the voltage magnitudes. Thus, the voltage profileis tracked during solving the OPF problem to be corrected inan optimal manner. For severe problems in the voltage profile,the linear program is controlled, where a re-despatch study isaccomplished.

V. CASE STUDIES

To demonstrate the effectiveness of the developed intelligentsystem, it is used to accomplish the calculations of the OPF forthe IEEE 30-bus network [16]. The layout of the investigatednetwork is illustrated in Fig. 2. The calculations are carriedout under different situations and loading conditions. Amongthe various investigated cases, only three examples will bedemonstrated.

A. First Case

The resultant voltage profile after applying the expert system-based OPF on the IEEE-30 bus network is illustrated in Fig. 3by the solid line. The obtained results are obtained based on thesimulation of the network under the standard loading conditions.For comparison reasons, classical optimal power flow calcula-tions are carried out under the same conditions and the resultantvoltage profile is illustrated by the dashed line in the same figure.The linear Programming algorithm is used as a classical powerflow routine. According to the classical optimal power flow cal-culations, it is clear that the network has over voltages at twobuses: bus 11 and bus 13.

Furthermore, under voltage is observed at five buses, i.e.,buses: 25, 26, 27, 29, and 30. The values of the unacceptablevoltage levels at the mentioned seven buses are summarized inTable I.

When the proposed intelligent approach is used to carry outthe OPF calculations, the voltage-violations at all buses are alle-viated, where the voltage magnitudes are maintained within thedefined 5% margin. This can be regarded clearly in Fig. 3. Thevoltage values at the seven buses after applying the proposedOPF technique are summarized also in Table I. The total reac-tive power required to reach the optimal solution without vio-lating the constraints is about 0.5 p.u. The number of switchingoperations to alleviate the voltage-violations at all buses is onlyfour operations. In addition, the transmission losses are reducedfrom about 0.076 p.u. to about 0.072 p.u. This represents a re-duction of about 5.3% in the total power losses. The reduction


Fig. 2. The layout of the IEEE 30-bus power system.

Fig. 3. Voltage profiles of the IEEE 30-bus system using the classical and theproposed OPF techniques: case 1.

of the power loss is a result of the improvement in the voltageprofile.

TABLE ISUMMARY OF VOLTAGE VIOLATION FOR CASE 1


TABLE IISUMMARY OF VOLTAGE VIOLATION FOR CASE 2

B. Second Case

In the second case, some loads are removed from the IEEE30-bus system. The starting operating state is the OPF conditiondescribed in the previous section after voltage correction. Then,the loads at buses 19 and 30 in the IEEE 30-bus system areremoved and a new classical power flow calculations are carriedout. The new voltage profile after removing the loads is shownby the dashed line in Fig. 4.

As can be expected, the voltage is generally increased at allbuses as a result of the load reduction. Over-voltage can be ob-served at six buses in the network, i.e., buses 2, 11, 13, 27, 29,and 30. The voltage values at these buses are summarized inTable II.

After applying the proposed expert-system-based approachto carry out the OPF calculations, the transmission losses arereduced from 0.045 p.u. to 0.039 p.u. Thus, more than 13%reduction in the power loss is achieved compared to conven-tional OPF technique. Furthermore, the voltage magnitudes are



TABLE IIISUMMARY OF VOLTAGE VIOLATION FOR CASE 3

all within the acceptable level. In this case, the amount of re-active power required to reach the optimal solution without vi-olating the voltage constraints is equal to 0.16 p.u. The totalnumber of switching operations in this case is two operations.The voltage values at the six buses after applying the OPF tech-nique are summarized in Table II.

VI. THIRD CASE

In the third case, a new loading condition is investigated. Sim-ilar to the previous case, the starting state is the OPF conditionin case 1 after voltage correction. Then, the loads at buses 19,20, and 21 are increased by 20% and the load at but 30 is re-moved. As a result of the classical power flow, the voltages atdifferent buses take a new profile as illustrated by the dashedline in Fig. 5. The voltages are increased at some nodes as a re-sult of the load removal compared to the base case. This can beobserved at buses 25, 27, 28, 29, and 30. On the other hand, thevoltage is decreased at some other buses, i.e., 10, 17, 19, 20, 21,and 22, due to the increase of some existing loads. Important isto notice that the system has under-voltages at buses 19 and 21,while over-voltage is observed at buses 28 and 30. A summaryof these abnormal voltages is given in Table III.

Once again, voltage-violations are alleviated at all busesand the voltage magnitudes are maintained within the defined5% margin when the proposed intelligent approach is usedas an alternative to the classical power flow. This is achievedthrough switching operations. Similar to the previous cases,

the transmission losses are also reduced by more than 12%(from 0.068 p.u. to 0.0598 p.u.). About 0.36 p.u. reactive poweris required to reach the optimal solution. A summary of thevoltage values after applying the OPF technique is also shownin Table III.

VII. CONCLUSION

This paper introduces a new intelligent optimal power flowapproach based on expert systems. The expert system can be in-tegrated in an open SCADA/EMS environment. The simplicityof the proposed techniques assist the implementation of the op-timal power flow calculations to be utilized in various controlcenters. Thus, using this technique with actual control centersenables the control of the voltage profile in a tracking mode. Theproposed methodology is evaluated by performing OPF calcu-lations in the IEEE 30-bus network under different loading con-ditions. The results obtained from all investigated cases demon-strate the robustness of the proposed technique and its capa-bility to alleviate the voltage-violations at all buses while min-imizing the control movements. This has also the advantage ofreducing the total transmission losses in the network. The re-sults encourage the implementation of this approach in differentapplications that need regular and efficient optimal power flowcalculations.

APPENDIX

Examples of the knowledge base used in the paper follow.• IF it is required to solve the voltage/reactive power problem

using the expert system for voltage and reactive power con-trol, THEN check the voltage violation;

• IF the system has no nodes with voltage violations, THENthe voltage profile of the system is acceptable;

• IF the system has nodes with voltage violations, THEN thevoltage profile of the system is not acceptable;

• IF the voltage profile of the system is acceptable (i.e.,within the desired limits), THEN the run can be terminated;

• IF the voltage profile of the system is not acceptable (i.e.,not within the desired limits), THEN construct low andhigh violated lists;

• IF the bus voltage is less than the minimum voltage limit,THEN identify that bus as having low voltage;

• IF the bus voltage is greater than the maximum voltagelimit, THEN identify that bus as having high voltage;

• IF two or more buses in the network have a voltageproblem, THEN identify the bus with severest violation;

• IF the severest violated bus has a high voltage problemAND has available controller, THEN find out amount ofreactive power required to avoid the violation;

• IF the available controller at severest violated bus has reac-tive power sufficient to alleviate the high voltage problem,THEN command necessary compensation (i.e., switch offcapacitors and /or switch on inductors to avoid the highvoltage problem) AND check the voltage violation;

• IF the available controller at severest violated bus is notat limit AND its amount of reactive power is insufficientto alleviate the high voltage violation, THEN move thecontroller to limit AND check the voltage violation;


• IF the available controller at severest violated bus is at fulloutput, AND is insufficient to alleviate the high voltageproblem, THEN find out the most sensitive controllerwith respect to the severest violated bus AND find out theamount of reactive power required to avoid the violation;

• IF the most sensitive controller with respect to severest vio-lated bus has reactive power sufficient to alleviate the highvoltage problem, THEN command necessary compensa-tion (i.e., switch off capacitors and/or switch on inductorsto avoid the high voltage problem) AND check the voltageviolation;

• IF the most sensitive controller with respect to severestviolated bus is not at limit AND its amount of reactivepower insufficient to alleviate the high voltage violation,THEN move the controller to limit AND check the voltageviolation;

• IF the severest violated bus has a low voltage problemAND has available controller, THEN find out amount ofreactive power required to avoid the violation;

• IF the available controller at severest violated bus has reac-tive power sufficient to alleviate the low voltage problem,THEN command necessary compensation (i.e., switch oncapacitors and/or switch off inductors to avoid the lowvoltage problem) AND check the voltage violation;

• IF the available controller at severest violated bus is not atlimit AND its amount of reactive power insufficient to alle-viate the low voltage violation, THEN move the controllerto limit AND check the voltage violation;

• IF the available controller at severest violated bus is atfull output AND is insufficient to alleviate the low voltageproblem, THEN find out the most sensitive controllerwith respect to the severest violated bus AND find out theamount of reactive power required to avoid the violation;

• IF the most sensitive controller with respect to severest vi-olated bus has reactive power sufficient to alleviate the lowvoltage problem, THEN command necessary compensa-tion (i.e., switch on capacitors and/or switch off inductorsto avoid the low voltage problem) AND check the voltageviolation;

• IF the most sensitive controller with respect to severestviolated bus is not at limit AND its amount of reactivepower insufficient to alleviate the low voltage violation,THEN move the controller to limit AND check the voltageviolation;

••

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Ahmed M. Azmy was born in El-Menoufya, Egypt,in 1968. He received the B.Sc. and M.Sc. degrees inelectrical engineering from the El-Menoufya Univer-sity, Egypt, in 1991 and 1996, respectively. He re-ceived the Ph.D. degree in electrical engineering fromUniversity Duisburg, Essen, Germany, in 2005.

Since 1992, he has been with the Electrical Powerand Machines Engineering Department, Faculty ofEngineering, University of Tanta, Egypt. His majorscientific interests are focused on modelling, simu-lation and control of power system dynamics using

intelligent techniques. He has a special interest in modelling, control and man-agement of Interconnected Distributed Generating Units, especially Fuel Cells.

optimal power flow to manage voltage profiles in interconnected networks using expert systems

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