Power System Contingencies seminar or presentation report
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21-01-2010, 12:27 AM



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ABSTRACT
In order to achieve more efficient voltage regulation in a power system, coordinated secondary voltage control has been proposed, bringing in the extra benefit of enhancement of power system voltage stability margin. The study is presented by the e.g. with two SVCs and two STATCOMs in order to eliminate voltage violation in systems contingencies. In the paper, it is proposed that the secondary voltage control is implemented by a learning fuzzy logic controller. A key parameter of the controller is trained by P-type learning algorithm via offline simulation with the assistance of injection of artificial loads in controllerâ„¢s adjacent locations. A multiagent collaboration protocol, which is graphically represented as a finite state machine, is proposed in the paper for the coordination among multiple SVCs and STATCOMs. As an agent, each SVC or STATCOM can provide multilocation coverage to eliminate voltage violation at its adjacent nodes in the power system. Agents can provide collaborative support to each other which is coordinated according to the proposed collaboration protocol.

INTRODUCTION
Power system voltage control has a hierarchy structure with three levels: the primary, secondary, and the tertiary voltage control. Over the past 20 yrs, one of the most successful measures proposed to improve power system voltage regulation has been the application of secondary voltage control, initiated by the French electricity company, EDF, and followed by some other electricity utilities in European countries. The secondary voltage control closes the control loop of the references value setting of controllers at the primary level. The primary objective of secondary voltage control is to achieve better voltage regulation in power systems. In addition, it brings in the extra benefit of improvement of power system voltage stability, for this application, several methods to design secondary voltage controllers have been proposed.

The useful concept of secondary voltage control is explored for a new application-the elimination of the voltage violations in power system contingencies. For this particular application, the coordination of various secondary voltage controllers is proposed to be based on a multi agent request “and- answer type of protocol to between any two agents. The resulted secondary voltage control can only cover the location where voltage controllers are installed
This paper presents results of significant progresses in investigating this new application to eliminate voltage violations in power system contingencies via secondary voltage control. A collaboration protocol, expressed graphically as
finite state machine, is proposed for the coordination among multiple FACTS voltage controllers. The coordinated secondary voltage control is suggested to cover multiple locations to eliminate voltage violations in the adjacent locations to a voltage controller. A novel scheme of a learning fuzzy logic control is proposed for the design of the secondary voltage controller. A key parameter of the learning fuzzy logic controller is proposed to be trained through off-line simulation with the injection of artificial loads in the controllerâ„¢s adjacent locations.

FACTS (Flexible AC Transmission Systems)
Sudden changes in the power demands or changes in the system conditions in the power system are often followed by prolonged electromechanical oscillations leading to power system instability. AC transmission lines are dominantly reactive networks characterized by their per mile series inductance and shunt capacitances. Suitably changing the line impedance and thus the real and reactive power flow through the transmission line is an effective measure for controlling the power system oscillations and thereby improving the system stability.

Advances in high power semiconductors and sophisticated electronic control technologies have led to the development of FACTS. Through FACTS the effective line impedance can be controlled within a few milliseconds time. Damping of the power system oscillation is possible through effective changes in the line impedance by employing FACTS members (SVC, STATCOM, UPFC etc).

MULTILOCATION COVERAGE AND MULTI-AGENT COLLABORATION PROTOCOL.

Figure shows the configuration of a ten machine 39-node power system installed with two SVCs at node 4 and 13 and two STATCOMs at 8 and 14.The objective of setting up this e.g. power system is to demonstrate ideas and principles to be presented and to test methods to be presented and to test methods to be proposed for the violation in system contingencies.

In the power system, as we know, the criteria for locating SVCs and STATCOMs usually should be at either key or weak positions as far as system voltage profile is concerned. Based on extensive simulation, authors have found that (1) area around node 3,4,5,7, and 8 is weak is getting reactive power support and prone to voltage violations in system contingent operation; (2) transmission line connecting node 4 and 14 is a key position in the power system, because it often behaves like a two-area system splitting on the left and right side of the line connecting 4 and 14. Hence, an SVC is assigned at node 4 and two STATCOMs are installed at node 8 and 14, respectively. However, a full process of technical justification of this installing location is not performed as far as the primary functions of SVCs and STATCOMs in the system.
SVCs at node 4 and 13 is defined to be agent 1(AG1) and 3(AG3) and STATCOM at 8 and 14 to be AG2 and AG4 respectively. Scheme of coordinated secondary voltage control violation at node 4, 8, 13, and 14 in the power system contingencies. In a complex power system, this scheme of the secondary voltage support only to the agentâ„¢s location may not be appropriate, as it can be illustrated by figure.

VOLTAGE PROFILE
Figure shows the voltage profile the power system in a contingent operation mode.

The contingent scenario is a 25% increase of load at node 8 and the loss of transmission lines between node5, 6 and 7.in figure, horizontal plane of the graph represents electric connection of the system and the height is the magnitude of voltage at each node. The voltage profile along transmission lines is also demonstrated and the voltage at the horizontal level is 0.95 p.u. from figure, we can see that in this system contingents operations mode, voltage violation appeared not only at the node where a voltage controller is installed (node 8, AG2), but also at the adjacent node to the agent location (node7). Since the agentâ„¢s stations and associated intelligence are ready in plane, an extra function can be added for an agent to monitor the voltage level at its adjacent nodes, which are reachable from the agent via estimation from local measurement(hence, feasible).This extents the coverage of each agent in this system from a single location to the multiple ones.
Table 1 shows the arrangement of multi-location coverage of secondary voltage control for each agent in the power system. Obviously this multi-location coverage extends the influence of the agent in the power system. It will enhance the capability of multi agent system to eliminate voltage violation and achieve better voltage management in the power system contingencies.

For the coordination of multiple FACTS voltage controllers in the power system to eliminate the voltage violation in system contingencies, a collaboration protocol is proposed for the coordinated secondary voltage control. The protocol is shown in graphical form of a finite state machine by figure. The finite state machine is an effective technique used to specify and verify complex computer communication protocols. With this technique, each protocol agent is always at a specific state at every instant of time. The state of the complete is the combination of all state of protocol agents and channels, which are carriers of the transistors of the state. By doing reachability analysis of complex protocols, it can be determined if a protocol is correct or not. However, the protocol shown in the figure is simple and easy to be checked. No informal reachability analysis is necessary.

For simplicity, the fig. only the agent being supported and supporting agent are presented. From fig., we can see that the collaboration protocol can be illustrated further as follows:-
1) Each agent constantly monitors the voltage profile of its node and adjacent nodes and the initial state of the agent is listen;
2) Once an agent detects a voltage violation, it checks its capacity limit ant acts on its own via the secondary voltage control if the limit permits. If the voltage violation disappears, the agent returns to its initial state. This is the loop of local action in figure. Otherwise, it sets up a timer and enters the status of multicast are predetermined by examining the electric connections and locations of agents. Only agents who can support each other are put into a same group;
3) When an agent in listen receives a message of multicast for voltage support, it check its capacity and the voltage profile of its own and adjacent nodes. This gives it an estimation of the amount of support it can offer. Then, it can send back an indication to the source of multicast to tell its offer of support;
4) After the agent in multicast receives several offers of indication; it makes decision to choose the support from one agent. Then it sends request to the agent chosen for support;
5) After the agent intending to support sends our indication, it sets up a timer. If the timer is out while the agent receives no request, it goes back to LISTEN;
6) Upon receiving request, the support agent acts to provide the amount of reactive power support it can and enters the status of reply by sending the agent being supported a reply to confirm the action;
7) After finishing the action, the supporting agent returns to the status of listen;
8) After receiving the reply, the agent being supported goes back to listen if the voltage violation disappears or enters multicast if not to seek further support;
9) If the agent in multicast receives no indication after the timer is out, it goes back to the status of listen. This is the case that the voltage violation in system contingents is not able to be overcome via the coordinated secondary voltage control by the collaboration among agents.
In the collaboration protocol, two timers are assigned to prevent the deadlock where there is no progress that can be made from a state.

DESIGN OF THE LEARNING FUZZY LOGIC SECONDARY VOLTAGE CONTROLLER.
The figure shows the arrangements of a learning fuzzy logic secondary voltage controller for a FACTS voltage controller, which could be an SVC or a STATCOM. The FACTS voltage controller, is installed at node c where node voltage is denoted by Vc.Va is the voltage at the node a which is adjacent and of direct electric connection to node c. Vs is the supplementary signal of secondary voltage control for the elimination of voltage violation at node c and a in power system contingencies.

In the secondary voltage controller, online operation units are represented by bold lines and blocks. The voltage estimator is to calculate voltage at the adjacent nodes to node c, such as VA, through local measurement at node c. This estimation is feasible because these adjacent nodes are directly connected to node c by transmission lines. The output signal of voltage violation identifier x is given by the following identifying function:
x = 0, if |Va-1.0|=0.05
x = sign (Va-1.0) (1- 1 )
1+ (Va-1.0)²
0.05
If|Va-1.0|> 0.05
The learning fuzzy logic secondary voltage controller in figure is the standard two-input single-output fuzzy controller as shown by figure. The fuzzy controls rules are:

If x is +ve and x is +ve, then Vs is “ve big;
If x is “ve and x is “ve, then Vs is +ve big;
If x is +ve and x is “ve, then Vs is zero;
If x is “ve and x is +ve, then Vs is zero;
Therefore
Vs = DF*[fa(x,x)- fb (x,x)]
Where
fa(x,x) = µp(x) µp(x)
h(x,x)
fb (x,x)= µN(x) µN(x)
h(x,x)
h(x,x)= µp(x) µp(x)+ µp(x) µN(x)+ µN(x) µp(x) + µN(x) µN(x)
DF is the parameter of the output membership functions µp and µN are input membership functions. We can see that the correct setting of the parameter, DF, determines the output control signal, which is crucially important for the effective operation of the secondary voltage violation in power system contingencies. Therefore, to set the parameter, DF, an offline P learning algorithm is adopted. The learning algorithm is assisted by the injection of an artificial load in the adjacent nodes to cause voltage violation. Learning objective is to set correct value of the parameter DF to eliminate voltage violation effectively. The effectiveness of the learning fuzzy controller is ensured by comparing learning result with learning reference signal Videal provided by a learning reference provider. The learning is preceded by offline simulation and its relevant units are shown in the figure by dashed lines and blocks.
Iterative learning control is a modeling-free synthesis approach for control system design. Given a reference a trajectory and a system, the basis formulation of learning control it is to find the control law such that the system output follows the reference trajectory as closely as possible. In the following, it is proposed that the P type of learning control is used to set DF. The iterative learning algorithm is:
DF (k+1) = DF (k) + n (k) |Videal (k-1)-VA (k-1)|
Where k is the kth learning step in system simulation,nk is the P learning rate at the k step, Videal (k-1) is the value of learning reference at the (k-1)th step, and Va(k-1) is the voltage variation at the adjacent node a.

FUZZY LOGIC CONTROLLER
A fuzzy system is any system whose variables range over states that are fuzzy sets. The fuzzy logic starts with the concept of fuzzy sets. Elements included can have partial degree of membership. Fuzzy logic is capable of dealing with complex phenomenon which cannot be analyzed by classical approaches. Fuzzy controllers are capable of utilizing human operators. The knowledge of a human operator may be used as an alternative to the precise model of controlled process. A general fuzzy controller consists of four modules.
(1) A fuzzy rule base
(2) A fuzzy inference engine
(3) A fuzzification module
(4) A defuzzification module
FUZZY SYSTEMS AS FUNCTION APPROXIMATORS
Majority of the engineering problems involves only numerical variables, such as in the case of control and modeling. To apply the fuzzy system to those numerical environments, two procedures namely fuzzification and defuzzification, are normally employed. The measured value x are converted into fuzzy values X compatible wit the form of the fuzzy system. The inferred values Y has to be converted into crisp values y compatible with the numerical environment. The fuzzy system is a function approximator due to its interpolative nature as discussed previously. Thus, viewing the fuzzy system as black-box with corresponding representation and reasoning schemes, we can use the constructed fuzzy system to represent a known or unknown function. Notice that the function to be represented can be nonlinear. This point will be developed further in the discussion which follows.
NUMERICAL LEVEL VERSUS LINGUISTIC LEVEL.
From practical and implementational standpoints, it is worth while to distinguish two different environments in which the fuzzy system may be involved: developing an environment in which the fuzzy system is designed off-line, and a working environment in which the designed fuzzy system is actually operated. Traditionally a fuzzy system is build by using linguistic rules derived normally from human experts and fuzzy set theory, and thus it can readily work in a linguistic environment as an inference engine at a set level shown in figure. However, when Appling such a developed system to a numerical environment, two interfaces as indicated in figure have to be added. Here, we implicitly employ a philosophy of making the working environment which is numerical, adapt to the environment which is linguistic. Membership functions play a central role in converting qualitative graded values. Considerable computational difficulties arise in adopting the above philosophy, particularly when dealing with multivariable system.
CONCLUSION
The major contributions of the paper are:
1) A multiagent collaboration protocol for the multiple FACTS voltage controller is proposed.
2) Multilocation coverage of power system voltage profile around a voltage control agent is suggested
3) A novel scheme to design a learning fuzzy logic secondary voltage controller.
BIBLIOGRAPHY
1) IEEE transactions on power systems, vol.18, No.2, May 2003, Pg.588-595
2) Computer Networking , A.S.Tanenbaum
3) Fuzzy Engineering , Bart Kosko
4) Fuzzy Neural Control , Junhong Nie & Derek Linkens

ACKNOWLEDGEMENT
I express my sincere gratitude to Dr.Nambissan, Prof. & Head, Department of Electrical and Electronics Engineering, MES College of Engineering, Kuttippuram, for his cooperation and encouragement.
I would also like to thank my seminar and presentation guide Mrs. Priya N. (Lecturer, Department of EEE), Asst. Prof. Gylson Thomas. (Staff in-charge, Department of EEE) for their invaluable advice and wholehearted cooperation without which this seminar and presentation would not have seen the light of day.
Gracious gratitude to all the faculty of the department of EEE & friends for their valuable advice and encouragement.

CONTENTS
¢ INTRODUCTION
¢ FACTS (Flexible AC Transmission Systems)
¢ MULTILOCATION COVERAGE AND MULTI-AGENT COLLABORATION PROTOCOL.
¢ VOLTAGE PROFILE
¢ DESIGN OF THE LEARNING FUZZY LOGIC SECONDARY VOLTAGE CONTROLLER.
¢ FUZZY LOGIC CONTROLLER
¢ CONCLUSION
¢ BIBLIOGRAPHY
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.doc   Power System Contingencies..doc (Size: 56.5 KB / Downloads: 82)
INTRODUCTION


Power system voltage control has a hierarchy structure with three levels: the primary, secondary, and the tertiary voltage control. Over the past 20 yrs, one of the most successful measures proposed to improve power system voltage regulation has been the application of secondary voltage control, initiated by the French electricity company, EDF, and followed by some other electricity utilities in European countries. The secondary voltage control closes the control loop of the references value setting of controllers at the primary level. The primary objective of secondary voltage control is to achieve better voltage regulation in power systems. In addition, it brings in the extra benefit of improvement of power system voltage stability, for this application, several methods to design secondary voltage controllers have been proposed.

The useful concept of secondary voltage control is explored for a new application-the elimination of the voltage violations in power system contingencies. For this particular application, the coordination of various secondary voltage controllers is proposed to be based on a multi agent request –and- answer type of protocol to between any two agents. The resulted secondary voltage control can only cover the location where voltage controllers are installed
This paper presents results of significant progresses in investigating this new application to eliminate voltage violations in power system contingencies via secondary voltage control. A collaboration protocol, expressed graphically as

finite state machine, is proposed for the coordination among multiple FACTS voltage controllers. The coordinated secondary voltage control is suggested to cover multiple locations to eliminate voltage violations in the adjacent locations to a voltage controller. A novel scheme of a learning fuzzy logic control is proposed for the design of the secondary voltage controller. A key parameter of the learning fuzzy logic controller is proposed to be trained through off-line simulation with the injection of artificial loads in the controller’s adjacent locations.

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04-04-2012, 12:41 PM

Power System Contingencies


.ppt   power contingencies.ppt (Size: 38.5 KB / Downloads: 49)

Action for restoration of normalcy in the plant



FEEDER TO 2Pn, 6Pn, 15Pn TO BE GOT S/OFF FROM BPSCL END.
RUNNING BF EXHAUSTERS & CAI TO BE S/OFF.

ASK WMD TO S/OFF 1 VERTICAL PUMP OUT OF 3 RUNNING PUMPS AT PH-2.

BPSCL TO CO-ORDINATE WITH TBS TO GET THE TC’S STOPPED AND FOR PARALLELING AT TBS (paralleling of TB’s to be done in consultation with BF dispatcher).

11 KV LOAD AT BF TO BE REDUCED BY PRESSING EMERGENCY PUSH BUTTONS AT 3Pn, 4Pn AND 5Pn S/STATION.


CONTINGENCY ACTION PLAN – 2


Confirm that AVR has taken place in 2RP, 6RP, 8RP, 9RP, 15RP, 8RP-1 S/S.

TPP to isolate the faulty 132 KV BUS and restore power in 132 KV healthy BUS through line 3C OR 5C.

TPP 11KV to be charged through 1T/2T thus restoring power to 3RP, 4RP, 5RP, 7RP, 1RP and 1RP-1.

TPP to S/ON Bay 14(Feeder to BOO Oxygen plant) and line 1C, 2C.

TPP to roll generators and synchronise.


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