pervision and control of regional voltage profile and security using fuzzy logic. The control strategies are defined by system operators based on their experience and on off-line studies, which are trans- lated into rules of a hierarchical fuzzy inference system (FIS). Two hierarchical levels, namely, task-oriented control level (high level) and set-point control level (low level) compose the control struc- ture. The high-level control is comprised of a continuous FIS that updates high-side voltage set points at power plants, and a discrete FIS that switches capacitor/reactor banks at the transmission net- work. The low level control is comprised of automatic voltage reg- ulators and joint var controllers at the power plants. It is presented a simulation study in the Rio de Janeiro (Rio) Area, an energy im- porting region part of the South/Southeastern Brazilian system.
Index Terms—Coordinated voltage control, fuzzy logic, hierar- chical control, knowledge-based systems, voltage security.
I. INTRODUCTION
L
ONG-TERM power system voltage instability has been object of increasing concern of various electric utilities around the world. The coordinated, maybe automatic, utiliza- tion of the various available reactive power sources can be an effective way of mitigating this problem.Electric utilities in some European countries [1]–[6] have adopted strategies that maintain an adequate voltage profile at key regions of the system on different loading scenarios.
Since the 1980s, the French [1]–[3] are utilizing an automatic coordinated voltage control, which is based on three hierar- chical levels, namely primary, secondary and tertiary. Control actuation time constants in each level differ one from the other by one order of magnitude.
In Italy [4], [5], the coordinated voltage control has some sim- ilarities to the French approach. It has been satisfactorily em- ployed nationwide for some years. Coordinated voltage control has also been employed in Belgium since 1998 [6], as a tool to support decisions made by the system operators. In this applica- tion, the secondary hierarchical level, as defined by the French proposition, is not utilized. In Brazil, a research project was car- ried out with the purpose of quantifying the benefits and lim- itations of applying secondary voltage control schemes using PI-based control structure, as defined by the Europeans, to the Rio Area [7].
Manuscript received October 16, 2003. This work was supported in part by CNPq, CAPES, and FAPERJ. Paper no. TPWRS-00007-2002.
A. B. Marques is with Petrobras, Rio de Janeiro, RJ, Brazil (e-mail: abul- hoes@br-petrobras.com.br).
G. N. Taranto and D. M. Falcão are with Federal University of Rio de Janeiro/COPPE, Rio de Janeiro, RJ, Brazil (e-mail: tarang@coep.ufrj.br;
falcao@nacad.ufrj.br).
Digital Object Identifier 10.1109/TPWRS.2004.831672
within secure operating limits, and keep the dynamic reactive power reserves maximized over different loading scenarios and network configurations, require an efficient coordination among the reactive power sources [8]. Control actions in opposite di- rections in a short period of time, inevitably mean unnecessary actions and should be avoided.
This paper presents a knowledge-based system for supervi- sion and control of regional voltage profile using fuzzy logic.
Control strategies are defined by the system operators based on their experience and on off-line studies, which are translated into rules of a hierarchical fuzzy inference s ystem (FIS). The hierarchical FIS presented in this paper is built upon the work in [9], which dealt only with a FIS for continuous variables.
While modern control theory has made modest progress in practice, fuzzy logic control has been rapidly gaining accep- tance among practicing engineers [10]. This can be attributed to the fact that fuzzy logic provides a powerful vehicle that al- lows engineers to incorporate human reasoning in the control algorithm. As opposed to the modern control theory, fuzzy logic design is not based on the mathematical model of the process.
The controller designed using fuzzy logic implements human reasoning that has been programmed into fuzzy logic language (membership functions, rules, inference engine, fuzzification and defuzzification) [11].
The use of conventional control methods in coordinated voltage/reactive power control is rather difficult due to highly nonlinear relationship between voltage magnitude and reactive power, and the existence of discrete voltage control equipment.
Moreover, the system-dependent transmission voltage control calls for customized solutions. Fuzzy logic control is quite ade- quate to this application since nonlinear control laws naturally arise from the operational experience coded into fuzzy rules.
A number of expert systems have been proposed for reac- tive power and voltage control [12], [13]. A fuzzy linear pro- gramming-based tool to help operators in reactive power and voltage control is described in [14]. More recently, closed-loop fuzzy voltage/var control systems for on-line application have also been presented [10], [15].
The study system analyzed is the Rio de Janeiro (Rio) Area, an energy importing area with a peak load of 6000 MW, which is part of the South/Southeastern Brazilian system. The simulation studies are performed through the use of a fast simulation tool, where the fast transients are assumed to be stable and neglected.
Slow-acting voltage mechanisms, such as LTC actuation and overexcitation limiters, are thoroughly modeled. The simulator [16] was developed following the ideas described in [17].
0885-8950/$20.00 © 2005 IEEE
Fig. 1. FIS.
II. BASICS OFA FIS
FISs are methods for information processing dealing with un- certainty and vagueness based on the theory of fuzzy sets and fuzzy logic [11].
A FIS is based on rules of the following type:
where x and y are numerical variables and A and B are linguistic variables, i.e., variables that assume linguistic values such as HIGH, LOW, NOT TOO LOW, etc., which are defined by fuzzy sets and their membership functions.
Fig. 1 shows the main elements of a FIS that is used in fuzzy logic controllers and signal processing applications. The FIS maps crisp inputs into crisp outputs. The four main elements are the rules, the inference engine, the fuzzifier, and the defuzzifier.
Description of the main elements is as follows.
Rules: a set of rules as defined before.
Fuzzifier: determines the degree of membership of each input in the rule antecedent. If the antecedent has more than one com- ponent (proposition), the fuzzy operators AND (min) and OR
(max) are used to combined the effects.
Inference Engine: determines the degree of validity of the rule consequent and combines the results into the output fuzzy set.
The principle assumed is that “rules with a low membership de- gree in the antecedent must have small validity in the conse- quent”.
Defuzzifier: determines a crisp output from the fuzzy set pro- duced by the inference engine.
Once the rules have been established, a FIS can be viewed as a nonlinear mapping from a crisp input vector to a crisp output
vector .
III. CONTROLSYSTEMDESCRIPTION
Fig. 2 shows a general overview of the proposed two-level su- pervision and control structure for coordination of task-oriented and set-point controls for regional voltage profile and security.
The low-level feedback control is comprised by the gener- ating unit’s AVR and by the power plant’s JVC. The high-level
“feedforward” control is comprised by a hierarchy of two FIS:
there is a FIS that updates the set-points of the high-side voltages at power plants, namely, continuous FIS (CFIS), and another FIS that decides the switching of capacitor and reactor banks at the transmission network, namely, discrete FIS (DFIS). The
Fig. 2. General overview.
high-level controller may receive data from the SCADA system and/or from dedicated remote measurements.
Depending on the physical characteristics of the power system, a priority list of actions for the continuous and the discrete controls must be chosen. Considering that priority is given to the continuous control1, this resource is used to ex- haustion or used until some limits are reached before any action from the discrete controls is taken. Those limits could be the capability of the machines or some variable on the transmission network, e.g., voltage in a bus other than the generator bus. In such cases, a defensive layer detects the problems and calls for the help of the discrete controls, whenever available. This strategy is conducive to:
• minimize the number of capacitor/reactor bank switching operations;
• maximize the generation of reactive power from the line charging of the network in heavy load conditions and to minimize it in light/minimum load conditions; this strategy is especially useful for systems where the loads are fed through very long EHV transmission lines, which is the case of the Rio Area system;
• hold the voltage at regulated buses smooth most of the time, avoiding excessive spikes caused by the switching of discrete devices.
The CFIS and the DFIS are built with rules that come from the experience of system operators and from off-line studies. Fig. 3 shows that the CFIS is subdivided in a FIS for heavy/median load operating conditions and another one for light/minimum load operating conditions. Qualitatively the rules are the same on both FIS, although they can be made different if necessary.
The main difference is the voltage set-point values for some busses on the system. The decision, in which FIS information should be processed, may be given by the value of the system demand, the time of day, etc.
Fig. 4 shows the internal hierarchical structure of the DFIS subdivided in three layers on which information is processed
1This control strategy was chosen in order to comply with actual operational practices currently adopted in the studied system (Rio Area).
Fig. 3. CFIS.
before the final decision is made. Besides the information pro- vided by the SCADA system and/or by the remote measurement system that indicates the status of the switching devices, the DFIS also receives information from the defensive layer. The information from the defensive layer indicates whether static re- active power must be switched on/off.
The first and second layers are further subdivided according to the substations where there are capacitor and reactor banks that participate in the coordinated regional voltage control. For each substation there is a corresponding FIS that decides which shunt bank should be switched. The first layer receives infor- mation from the defensive layer and outputs vague information for the amount of reactive power to be switched (few, much, too much, etc). The output of the first layer, together with the status of the switching devices in each substation, are the inputs to the second layer. The output of the second layer is a decision of which shunt bank should be switched on/off in each substa- tion. The third layer deliberates the only one device, among the outputs of the second layer that should be switched on/off. The DFIS has underlying rules that checks for switching inconsis- tencies, i.e., no capacitor bank must be switched on if there are any reactor banks turned on.
IV. APPLICATION TOCOORDINATEDVOLTAGECONTROL
The system studied is an equivalent of the Brazilian South/
Southeastern system modeled with 730 buses, 1146 branches, and 104 generators. The region of interest for the studies is the so-called Rio Area, which consists of the electric utilities Light, Cerj, Escelsa, and part of the Furnas system. It mainly repre- sents the Rio de Janeiro metropolitan area, with a peak load of approximately 6000 MW in the summer time (from January to March).
Fig. 5 depicts the bulk power transfer corridors that lead to the Rio Area and the main dynamic reactive power re- sources for voltage control in the area. In the 500-kV corridor, Marimbondo hydro plant and Angra nuclear plant are the main dynamic reactive sources for voltage control, whereas in the 345-kV corridor, Furnas hydro plant is the main reactive source. Although L.C.Barreto and V.Grande hydro plants are located at the 345-kV corridor, their influence in the Rio Area voltage profile is of less importance. The main sources of
Fig. 4. DFIS.
dynamic reactive power support within the Rio Area are two Mvar synchronous condensers (SCs) at Grajau station, and the Santa Cruz thermal power plant.
The application of a FIS to the coordinated voltage control in the Rio Area is accomplished by a set of rules, which are based on the experience of system operators and on off-line studies done for the area.
The voltages at the Adrianopolis 138-kV bus and at the Jacarepagua 138-kV bus, together with the reactive power output of the Grajau SC are the input variables (regulated variables) for the CFIS. The first two variables were chosen as practical operator experience indicates that the voltages in these busses are representative of the voltage profile in the Rio Area.
The output of the Grajau SC, on the other hand, was chosen for security reasons, as it is the main dynamic reactive power source in the area. The output variables (control variables) for the CFIS are the high-side voltages at the Marimbondo, Furnas, Santa Cruz, and Angra power plants.
Fig. 6 shows the integration of the CFIS with the power system. The CFIS can be viewed as a fuzzy controller.
Depending on the values of the monitored variables, some of the rules will be activated and weighted automatically by the fuzzy logic. When the CFIS operates as a decision-aid tool, con- trol actions like increasing/decreasing the high-side voltage at power plants will be displayed to the system operator, who de- cides to do the action.
Two objectives were kept in mind in the process of building the rules.
• Keep the output voltage of Jacarepagua and Adrianopolis around the desired values established by the electric utili- ties of the Rio Area. This corresponds to assume a high degree (close to 1) of membership of these voltages to
Fig. 5. Main transmission corridors leading to the Rio Area.
Fig. 6. Closed-loop system.
the fuzzy set represented by the term OK. Fig. 7 shows the set of membership functions of the linguistic vari- able Jacarepagua 138 kV Voltage. The membership func- tions shape and overlapping characteristics were chosen by trial-and-error.
• Keep the Grajau SC reactive power output between zero and Mvar. This corresponds to assume high values for the membership function of the term OK of the lin- guistic variable Grajau\_Reactive\_Output.
Based on the experience in controlling transmission network voltage in the Rio Area from Furnas control room, and on the knowledge of off-line studies for voltage control in the Rio Area, 29 operating rules were built. Extra knowledge-based experi- ence is incorporated into the rules by fine adjustments of the membership functions. Table I shows only seven of these rules.
Taking Rule 1 as an example, it translates to the following sit- uation: If Adrianopolis 138-kV voltage is OK (see Fig. 7 for definition), Jacarepagua 138 kV is OK and Grajau SC reactive power output is OK, Then the high-side voltage at Marimbondo, Furnas, Santa Cruz, and Angra power plants must remain at the present values. The fuzzification and the defuzzication methods utilized were the min-max and the centroid, respectively.
Fig. 8 shows a typical nonlinear characteristic of the rules for a particular case when the input variables are the voltage at Adri- anopolis (kV) and the reactive power output of the Grajau SC
Fig. 7. Membership functions for the linguistic variable Jacarepagua 138 kV\_Voltage.
TABLE I KNOWLEDGE-BASEDRULES
(Mvar), and the output variable is the high-side voltage varia- tion (pu) at the Marimbondo power plant.
Due to the existence of very long bulk transmission corri- dors in the system, in some situations, voltage limit violations are observed in intermediate busses along these corridors. This problem is detected by the Defensive Layer that checks the voltage at all busses along the transmission corridors against op- erating limits. If any limit violation occurs, an alarm may sound to call the attention of the operators. In this way, a control hi- erarchy is accomplished, where priority is given to the control actions that eliminate limit violations, even if those are opposed to the control action given by the CFIS.
In a actual open-loop implementation of the FIS in the control room, it could be setup to refresh a computer screen every 5 min, displaying to the system operator the actions to be taken.
In automatic mode of operation, the FIS could be setup to read information from the SCADA system and send control signals every 20 s using a communication system similar to the one used by the automatic generation control (AGC).
V. KNOWLEDGEACQUISITION ANDCODING
The knowledge incorporated into the FISs used in the con- trol system described above was obtained from interviews with power system operators actually involved in real-time voltage control of the Rio Area system, the study of the operational in- structions prepared by operational planning engineers, and com- plemented with off-line simulation studies, when it became nec- essary. A power system engineer, with reasonable knowledge of
Fig. 8. Nonlinear control surface.
FIS development, translated this information into if then else fuzzy rules, with appropriate definitions of the fuzzy sets cor- responding to the term set of the respective linguistic variables.
These rules were coded into a FIS development system soft- ware and tuned (membership function adjustments) by a trial- and-error process guided by simulation studies.
It was noted that only in a few number of operating conditions was there no agreement among the operators on how to take actions. In these cases, off-line simulation studies were used to decide which set of rules should be included in the FIS. The actual implementation of the control system and extensive use in the field will allow a refinement in the rules database, correcting imperfect control actions that may be detected later on.
Rules used for the CFIS and for the DFIS are of different na- ture. The CFIS rules are based on system-wide information and produces general control actions to set values to network voltage controlled busses. Examples of such rules are given in Table I.
The DFIS receives information from the CFIS output and from the defensive layer and takes actions based on the availability of equipment in each substation. These rules are specific for each control layer and substation. Examples of such rules, for the Jacarepagua substation, are given in the following.
Example of first layer rule:
If Voltage is low and Adrianopolis voltage is not too low and Grajau output is low capacitive , Then switch on large amount of shunt capacitors
Example of second layer rule:
If Switching on is large amount of shunt capacitors and Number of reactors switched on is zero , Then switch on large capacitor
VI. SIMULATIONRESULTS
The system base case corresponds to a heavy load condition taken from the summer of 1991. Santa Cruz thermal power plant was assumed not participating in the hierarchical control. The unavailability of this power plant did not alter the rules of the CFIS, since there is some inherit degree of redundancy in the rule set. Table II shows a sequence of events considered during the simulation.
TABLE III
AVAILABLECAPACITOR ANDREACTORBANKS
Fig. 9. Marimbondo 500-kV voltage.
Table III shows the capacitor and reactor banks that are used for voltage control in the Rio Area and being considered in the DFIS. The size of each bank is established according to its rela- tive size compared to the existing banks in the same substation.
For example, a 50-Mvar reactor bank is considered small in the Adrianopolis substation, whereas a 40-Mvar capacitor bank is considered median in the same substation. Table III also shows the number of capacitor and reactor banks that are in operation at the beginning of the simulations.
Results shown in Figs. 9–13 are obtained from the fast dy- namic mid-, long-term simulation program presented in [16]. In
Fig. 10. Angra 500-kV voltage.
Fig. 11. Jacarepagua 138-kV voltage.
the simulations, the FIS (both CFIS and DFIS) was considered as operating in the automatic control mode with an actuation cycle of 40 s. The figures also show the upper and lower ac- cepted operating limits for the monitored variables. It can be noted that the generating busses have wider ranges (Figs. 9 and 10) than the load busses (Figs. 11 and 12). The load busses have, in addition, a narrower range of desired voltage, not shown in the figures.
Based on the simulation results, the following observations can be made.
• Marimbondo 500 kV (Fig. 9) At the beginning of the simulation, the voltage is over the limit of 550 kV (1.05 pu at the 525-kV base voltage). At the same moment, the voltages within the Rio Area are under their lower limits, Jacarepagua voltage is equal to 132.5 kV and Adrianop- olis voltage is equal to 127.5 kV (Figs. 11 and 12, re- spectively). This scenario forces the CFIS to increase the voltage at Marimbondo plant. In this situation, the de- fensive layer triggers the DFIS. Having the information
Fig. 12. Adrianopolis 138-kV voltage.
Fig. 13. Grajau SC reactive power output.
given in Table III together with the current operating con- dition, the DFIS order to switch on a capacitor bank at Jacarepagua. Since the area load is being reduced, Marim- bondo high-side voltage is also reduced in order to keep the voltage at the load busses within the desired range.
At 700 s, the terminal voltage (not shown in the figure) at Marimbondo reaches its lower operating limit. At this time, the defensive layer triggers the DFIS again, which orders to switch a small capacitor bank at Adrianopolis.
After 3000 s, the area load starts to increase, so as Marim- bondo voltage. At 3900 s, the high-side voltage reaches its upper limit and the defensive process orders that a small reactor bank in Adrianopolis be switched off. Table IV shows the switching sequence as ordered by the DFIS.
• Angra 500 kV (Fig. 10) The behavior of the high-side voltage in Angra is similar to Marimbondo’s high-side voltage. It is important to remark that at 1800 s, Angra’s underexcitation limiter is reached (not shown in the
figure). The behavior of the voltage at Furnas power plant was similar to Angra’s voltage behavior.
• Jacarepagua 138 kV (Fig. 11) The arrows in Fig. 11 rep- resent some of the switchings described in Table IV. At 40 s, a capacitor bank in Jacarepagua is switched on because the voltages in the Rio Area are below the desired values.
Marimbondo voltage being a little bit above its upper limit corroborates the switching action of a capacitor bank in the Jacarepagua substation.
• Adrianopolis 138 kV (Fig. 12) The desired range at Adrianopolis substation in heavy load conditions is be- tween 143 and 145 kV. The voltage behavior at Adria- nopolis is similar to Jacarepagua’s voltage behavior. It is interesting to observe the voltage behavior after 3000 s, when starts the positive load ramp. Note that along the first 1000 s of the positive ramp, the hierarchy prioritizes the voltage control by the generating plants. When Marim- bondo reaches its upper limit around 4000 s (see Fig. 9), the control system has no alternative other than to start switching the shunt banks.
• Grajau SCs (Fig. 13) The reactive power outputs of the SCs remain, most of the time, within the recommended value set by the operating rules, which state that the SCs must operate within the range [ Mvar, zero Mvar] in order to keep a safe dynamic reactive reserve for voltage security. The fast response of the SCs can be observed at 300 s when one of the two circuits of the 500-kV Adriano./C.Paulista trips off. The reactive power output reaches Mvar, and it is reduced to approximately Mvar by the action of the FIS. After 4850 s, the system remain in steady state.
VII. CONCLUSION
This paper proposes a tool for supporting decisions of a system operator in the coordinated voltage control of trans- mission networks based on fuzzy logic. This work intends to
tasks and be used for more noble tasks.
ACKNOWLEDGMENT
The authors gratefully acknowledge Mr. C. Braga Gomes for the help with the use of the simulation tool.
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Alessandro B. Marques was born in Rio de Janeiro, Brazil, in 1973. He re- ceived the B.Sc. degree in 1998 from CEFET-RJ, Rio de Janeiro, He is presently pursuing the M.Sc. degree at the Federal University of Rio de Janeiro/COPPE.
From 1993 to 2001, he was with FURNAS, a major Brazilian electric utility, as a System Operator. Since 2002, he has been with PETROBRAS-BR.
Glauco N. Taranto (S’92–M’96–SM’04) was born in Rio de Janeiro, Brazil, in 1965. He received the B.Sc. degree in 1988 from the State University of Rio de Janeiro, Rio de Janeiro, the M.Sc. degree in 1991 from the Catholic University of Rio de Janeiro, and the Ph.D. degree in 1994 from Rensselaer Polytechnic Institute, Troy, NY.
He is currently an Associate Professor of Electrical Engineering at the Fed- eral University of Rio de Janeiro/COPPE, Brazil. His research interests include power system dynamics and control, intelligent control, and robust control de- sign.
Djalma M. Falcão (M’75–SM’96–F’04) received the B.Sc. degree in 1971 from the Federal University of Paraná, Paraná, Brazil, the M.Sc. degree in 1974 from the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, and the Ph.D.
degree in 1981 from UMIST, Manchester, U.K.
He is currently a Professor of Electrical Engineering at the Federal University of Rio de Janeiro/COPPE. From 1991 to 1993, he was on sabbatical leave at the University of California, Berkeley. His general research interest is in the area of computer methods for power system simulation, optimization, and control.
