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Designing a controller based on fuzzy logic to improve the performance of synchronous static compensator

Number of pages: 110 File Format: word File Code: 32258
Year: 2013 University Degree: Master's degree Category: Electrical Engineering
Tags/Keywords: Compensator function - Controller design - Fuzzy logic - Oscillation damping - Synchronous static compensator - transient stability - Transmission networks - Uncertainty
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  • Summary of Designing a controller based on fuzzy logic to improve the performance of synchronous static compensator

    Dissertation for M.Sc.

    Power Orientation

    Abstract

    Transmission networks of modern power systems are increasingly being transformed due to increasing demand and limitations in building new lines. One of the consequences of such a stressed system is the risk of losing stability after a disturbance. Flexible alternating current transmission systems (facts) are very effective equipment in a transmission network for better use of available capacities without losing the desired stability margin. FACTS devices, such as synchronous static compensator (statcom) and static VAR compensator (svc), are the latest technology of electronic power switching equipment in electric power transmission systems to control voltage and power factor. Synchronous static compensator is a parallel controller from the family of facts devices. The synchronous static compensator controls the voltage at its terminal by controlling the reactive power absorbed from; or injected into the power system. When the system voltage is low, the synchronous static compensator produces reactive power, and when the system voltage is high, this compensator absorbs the reactive power from the power system.

    In this thesis, different controllers of the synchronous static compensator, i.e. based on fuzzy logic and Fuzzy-pi, are designed to improve the transient stability of two-machine systems. The proposed controllers are implemented under matlab/simulink software environment. The results of Fuzzy-pi and Fuzzy-based controllers installed with a two-machine system are compared with the conventional pi-based synchronous static compensator.

    Key words: transient stability, synchronous static compensator, fuzzy logic controller, uncertainty, oscillation damping. rtl;"> 

     

    Introduction

    In recent years, due to the restructuring of power systems, many economic and technical features of the electricity industry have been affected in various sectors, including production, transmission, distribution and consumption. This issue is raised especially in transmission networks whose line loading increases to the thermal limit and exceeds its current stability margin. Therefore, in order to achieve an acceptable level of reliability, special control strategies must be used that not only in the normal operation of the system, but also after significant structural changes, such as the removal of production units, transmission lines, or changes in load conditions, the continuity of supplying the consumer's needs will not be lost. Planning is the long-term adjustment of voltages and power to establish the desired working conditions of the system (steady state). Stabilization should be done continuously and in all working conditions of the system to prevent the system from becoming unstable.

    The stability of power systems is examined in two ways: voltage stability and angular stability. Static instability or voltage instability occurs due to slow and continuous load changes in the power system. Angular stability in power systems is studied according to the range of disturbances and their frequency in two small signal (dynamic) and transient modes. If the amplitude of the introduced disturbances is large, the stability of the system is called transient stability. Transient stability depends on the structure of the power system, the operating point of the equilibrium state before and after the disturbance, as well as the amplitude and duration of the disturbance. A power system is transient stable if the system can reach an acceptable equilibrium state after the fault is cleared. To evaluate the transient stability of the power system, the stability limit or critical fault clearing time (cct[1]) is determined. The greater the distance between this time and the duration of the error, the greater the margin of system stability. Therefore, transient stability is an important security criterion in the design of power systems. The theoretical foundations of transient stability have been examined in different books and references [1-4].

    The main focus in this research is on transient stability.

    The main focus in this research is on transient stability. Various methods have been introduced to analyze transient stability in power systems. One of the first and most widely used methods for investigating transient stability is the use of equal levels index, which is based on the single machine-infinite bus system and uses the simplified model of the system. In this method, it is necessary to solve the nonlinear dynamic equations of the system to check the margin of transient stability or the critical time of solving the error. Rangkota and Euler's method is one of the methods used to solve these equations [1]. The second method is a direct method that does not need to solve the dynamic equations of the system and is faster than the first method. This method is mainly based on Lyapunov stability analysis and requires the form of system energy function as Lyapunov function. If the energy function is obtained, the stability of the power system can be checked by monitoring this function and its rate of change. The model based on the specifications and measurement of several parameters is one of the main weaknesses of this method [5].

    Many factors affect the transient stability of power systems, some of which are: generator inertia constant, generator output during the fault and transmission system impedance after the fault is fixed. Transient stability can be improved through system reconfiguration. For example, reducing the reactance of the lines or using tools such as the power system stabilizer that is placed as a supplementary tool in the generator excitation system, or facts tools that are installed in the transmission network. In this research, the application of static synchronous compensator and controller design for it will be investigated to improve the transient stability of power systems.

    The advances made in the field of power electronics in the late 80s led to the construction of flexible alternating current transmission systems (facts[2]). These devices increase voltage and power controllability to increase the efficiency and stability of existing systems [6]. So far, many controllers have been designed for facts tools. Among these controllers, we can mention the examples designed for the linearized model in special working points [7]. Others include advanced controllers designed to account for system operating point changes. Resistive and adaptive control is one of the methods used for this purpose [8-11].

    Synchronous static compensator (statcom[3]) is one of the parallel controllers of FACTS devices, which is used to adjust the voltage and also improve the stability of the system by injecting or absorbing reactive power. The power produced or absorbed by the synchronous static compensator depends on the capacitor capacity of the voltage source converter. For the operation of the synchronous static compensator, it is necessary to control the input and output signals. Several controllers, including pi controller and controller based on energy function, have been used to implement the control strategy.

    In reference [12], the energy function approach has been used to design the synchronous static compensator controller. The performance of this controller is such that the derivative of the system energy function is negative in the presence of the synchronous static compensator, that is, the system energy is depreciated. In this approach, a simplified model of the system is used and the system is a single machine-infinite bus. Also, this method is applied to multi-machine system and the results are presented without mathematical proof. One of the most important problems of the direct method of transient stability analysis (controller design to improve transient stability) is the dependence of this method on the power system model to define the energy function, especially in the case of a multi-machine system. Assuming that the dynamic equations of the system are correctly modeled and the appropriate energy function is also defined, the design of the controller with this method and its practical implementation is very difficult due to the need to measure at different points of the system.

    Uncertainty exists in almost every physical system and this can be caused by various phenomena according to the nature of the system, system information or measurement. Large-scale power systems are highly nonlinear, so there can be significant uncertainty in any part of it. In 1973, fuzzy logic was introduced by Professor Lotfizadeh as a powerful tool to deal with this uncertainty and to consider human experiences.

  • Contents & References of Designing a controller based on fuzzy logic to improve the performance of synchronous static compensator

    List:

    1- First chapter: review of sources. 1

    1-1- Introduction. 2

    1-2- parallel controllers. 5

    1-2-1- Static VAR compensator (SVC) 6

    1-2-2- Synchronous static compensator (STATCOM) 7

    1-2-2-1- Basis of STATCOM operation. 10

    1-2-3- Comparison of STATCOM and SVC. 12

    1-3- An overview of the work done 13

    1-4- Summary. 18

    2- Chapter Two: Stability of power systems. 19

    2-1- Introduction. 20

    2-2- Rotor angle stability 22

    2-3- Power-angle curve. 23

    2-4- Power-angle equation. 23

    2-5- Oscillation equation. 24

    2-6- Criterion of equal levels. 27

    2-7- Oscillation curve. 29

    2-8- General strategies for improving transient stability 30

    2-8-1- Improving transient stability by STATCOM. 31

    2-9- Summary. 33

    3- The third chapter: materials and methods. 34

    3-1- Introduction. 35

    3-2- Fuzzy logic controller. 35

    3-2-1- Reasons for using fuzzy logic in power systems. 36

    3-2-2- How to use fuzzy logic. 37

    3-2-3- Linguistic variables. 38

    3-2-4- membership function. 39

    3-2-5- Fuzzy logic controller design. 40

    3-2-5-1- fuzzifier or fuzzifier 41

    3-2-5-2- base of rules or knowledge. 42

    3-2-5-3- fuzzy inference engine. 43

    3-2-5-3-1- Mamdani method. 44

    3-2-5-4- Non-fuzzifier 45

    3-2-5-4-1- Center of gravity method. 46

    3-2-6- The steps of forming a fuzzy logic controller. 46

    3-2-7- Designing a controller based on fuzzy logic for STATCOM. 47

    3-3- Fuzzy-PI controller 50

    3-3-1- Necessity of using Fuzzy-PI controller 51

    3-3-2- Fuzzy-PI controller design for STATCOM. 51

    4- The fourth chapter: results and discussion. 57

    4-1- Introduction. 58

    4-2- Two-machine system simulation. 59

    4-3- Simulation results. 64

    4-3-1- System without STATCOM (under fault) 64

    4-3-2- System installed with STATCOM based on PI controller (under fault) 64

    4-3-3- System installed with STATCOM based on Fuzzy controller (under fault) 67

    4-3-4- System installed with STATCOM based on controller Fuzzy-PI (under error) 70

    4-3-5- Comparison between STATCOM based on Fuzzy controller and PI controller (under error) 72

    4-3-6- Comparison between STATCOM based on Fuzzy-PI controller and PI controller (under error) 74

    5- Chapter Five: Conclusion and suggestions. 78

    5-1- Conclusion. 79

    5-2- Suggestions. 80

    Attachments. 81

    Sources and sources. 86

     

    Source:

    1-

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