Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-machine System Considering Detailed Model

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Damping of power system oscillations with the help of proposed optimal Proportional Integral Derivative Power System Stabilizer (PID-PSS) and Static Var Compensator (SVC)-based controllers are thoroughly investigated in this paper. This study presents robust tuning of PID-PSS and SVC-based controllers using Genetic Algorithms (GA) in multi machine power systems by considering detailed model of the generators (model 1.1). The effectiveness of FACTSbased controllers in general and SVC-based controller in particular depends upon their proper location. Modal controllability and observability are used to locate SVC–based controller. The performance of the proposed controllers is compared with conventional lead-lag power system stabilizer (CPSS) and demonstrated on 10 machines, 39 bus New England test system. Simulation studies show that the proposed genetic based PID-PSS with SVC based controller provides better performance.
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  • 1. Long Paper ACEEE Int. J. on Electrical and Power Engineering , Vol. 5, No. 1, February 2014 Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-machine System Considering Detailed Model Hitesh Rameshchandra Jariwala1 and Anandita Chowdhury 2 1 Electrical Engineering Department, S. V. National Institute of Technology, Surat, 395007, India. Email: hrj@svnit.ac.in 2 Electrical Engineering Department, S. V. National Institute of Technology, Surat, 395007, India. Email: ac@svnit.ac.in line. These oscillations may enforce limitations on power transfer through tie line and/or may be responsible for loss of synchronism. The damping of electromechanical oscillations can be achieved by means of a proper control strategy. The use of a supplementary control added to the Automatic Voltage Regulator (AVR) is a practical and economic way to supply additional damping to electromechanical oscillations and it is achieved with the help of Power System Stabilizer (PSS). The widely used conventional power system stabilizer (CPSS), basically a lead-lag compensator, is designed using the theory of phase compensation in the frequency domain considering deviation of speed as control signal. It is important to note that the parameters of the machine change with loading. The dynamic behavior of the machine is quite different with different operating conditions. A set of PSS parameters which give good system performance under a certain operating condition may not give equally good result under different operating conditions. Sometimes CPSS may cause great variation in the voltage profile and may even result in leading power factor operation under severe disturbances [3]. In order to improve the performance of CPSS, quite good numbers of tuning techniques such as optimal control, adaptive control, variable structure control etc. have been proposed [4]. Apart from the techniques mentioned above, methods based on artificial intelligence techniques such as GA, simulated annealing, tabu search, fuzzy logic, partical swam optimization etc. are also used for obtaining parameters of CPSS [3-6]. Apart from PSSs, Flexible AC Transmission Systems (FACTS) devices are also used to improve system stability. Particularly, in multi-machine systems, use of CPSS may not provide sufficient damping for inter-area oscillations. In such cases, FACTS devices can provide effective damping [1-2], [5-7]. Although, the damping duty of FACTS devices is not their primary function, the capability of FACTS based controller to increase electromechanical damping characteristics has been recognized. The FACTS devices are capable enough to control network conditions in a very fast manner and this feature of FACTS can be exploited to improve system stability [8]. FACTS devices such as Static VAr Compensators (SVC), Static Synchronous Compensators (STATCOM), Thyristor Control Series Compensators (TCSC) and Unified Power Flow Controller (UPFC) have led their use Abstract— Damping of power system oscillations with the help of proposed optimal Proportional Integral Derivative Power System Stabilizer (PID-PSS) and Static Var Compensator (SVC)-based controllers are thoroughly investigated in this paper. This study presents robust tuning of PID-PSS and SVC-based controllers using Genetic Algorithms (GA) in multi machine power systems by considering detailed model of the generators (model 1.1). The effectiveness of FACTSbased controllers in general and SVC-based controller in particular depends upon their proper location. M odal controllability and observability are used to locate SVC–based controller. The performance of the proposed controllers is compared with conventional lead-lag power system stabilizer (CPSS) and demonstrated on 10 machines, 39 bus New England test system. Simulation studies show that the proposed genetic based PID-PSS with SVC based controller provides better performance. Index Terms— Controllability, Genetic algorithms, Observability, Power system modeling, Static VAr compensators. NOMENCLATURE δ ω M  Generator rotor angle Generator rotor speed Generator moment of Inertia Angle of bus voltage I IR V Psh Qsh Bsh Magnitude of line current Shunt injected reactive current Magnitude of bus voltage Shunt injected real power Shunt injected reactive power Shunt injected susceptance I. INTRODUCTION Damping of power system oscillations between interconnected areas is very important for the reliable and secured system operation. Such oscillations are of low frequency (0.5 Hz -3 Hz) and occur when there is a fault in the system or sudden change in load [1-2] or when existing generation/load areas are connected to other similar areas through relatively weak transmission lines. With the increase in the demand it is very common that power is transferred from one area to another through a tie © 2014 ACEEE DOI: 01.IJEPE.5.1.6 1
  • 2. Long Paper ACEEE Int. J. on Electrical and Power Engineering , Vol. 5, No. 1, February 2014 available signals. Controllability of a signal depends on location and nature of the FACTS device under consideration. A higher controllability of a signal means smaller variable compensation requirement of a controller. The modal observability based ranking of candidate locations can be more convenient than modal controllability or residue based ranking as it can be done a priori on a system without explicitly representing the FACTS damping controller at each candidate location. to damp inter-area oscillations [7-10]. Unlike PSS control at a generator location, the speed deviations of the machines of interest are not readily available to FACTS devices sited in the transmission path. Furthermore, speed signals themselves may not be the best choice for an input signal for FACTS devices. Because of these reasons, it is desired to extract an input signal from the locally measurable quantities at the controller location [1112]. An SVC is usually located at mid-point of a transmission line or near the loads if voltage control is required. The location is not so obvious when SVC is required for damping purpose. Most of the studies suggest the midpoint of a transmission line or load buses as the location for SVCs as the voltage swings are maximum at these locations. Others however, propose the generator bus bar for placing SVCs to damp oscillations. In some studies, the location of SVC is found using residue method. There obviously exists difference of opinion for the best location of a SVC for damping of low frequency oscillations. Various techniques were applied to improve the performance of the controllers. M. A. Abido et al. [7], [13-14] have discussed the enhancement of stability by designing PSS and SVC using real coded genetic algorithms considering change in rotor speed as input for both controllers and Single Machine Infinite Bus (SMIB) system was considered. Hu Guo-qiang et al. [15] have used GA to design PSS for a four machine system and verified its performance under different operating conditions. Khodabakhshian et al. have [5] applied same concept for a multi-machine system. Y. L. Abdel-Magid et al. [16] applied TABU search method to tune parameters of PSS for multi-machine system in which synchronous machines are represented by model 1.0. Li-Jun Cai et al. [1] applied Sequential Quadratic Programming (SQP) algorithm for simultaneous tuning of PSS and FACTS damping controllers in large power systems. X. Y. Bian et al. [17] have employed probabilistic eigenvalue-based objective function for design of PSS and SVC. Ahad Kazemi et al. [18 ] applied fuzzy logic to tune parameters of PSS and FACTS based controllers. A. Andreoiu et al. [19] have applied a Lyapunov method based GA to tune the parameters of PSS and applied it to SMIB system. In most of the studies, change in rotor speed is considered as input signal to SVC controller and generators were represented by model 1.0. The objective of this study is to present use of proposed PID-PSS controller and SVC based controller for damping of power system oscillations with locally available signals in multi-machine systems in which all generators are represented by their detailed model (model 1.1). The optimal setting of parameters of both proposed PIDPSS and SVC controller is obtained using Genetic algorithms. The location of PID-PSS can be determined with the help of Participation Factors [2]. However, in this study, it is considered that all machines are equipped with proposed PID-PSS. To locate shunt FACTS devices like SVC, modal analysis is carried out as discussed in the section II [11]. Modal controllability and observability are determined for locally © 2014 ACEEE DOI: 01.IJEPE.5.1.6 II. MODAL ANALYSIS OF POWER SYSTEMS In order to identify the best location to site SVC and to determine control signals for SVC based controller, linearized analysis is carried out in which synchronous machine is represented by its classical model. The controllabilityobservability relationship for various locally available signals is determined in this section. A. Modal Analysis For a multi-machine interconnected power system, the complex non-linear model can be described in a set of differential-algebraic equations by assembling the models for each generator, load and other devices such as controls in the system, and connecting them appropriately via the network algebraic equations. These differential-algebraic equations of a non-linear power system are given as,  X  AX  Bu Y  CX  Du (1) Classical techniques for the analysis and design of controllers are mostly based on the linearized representation of the power system at a representative operating point. While carrying out linearized analysis of power system, following assumptions are made: 1. Loads are considered as constant impedance type. 2. Synchronous machines are represented by classical model. 3. Mechanical input to the generator is considered as constant. 4. FACTS devices are considered as fast acting devices as compared to the period associated with power swings. The linearized swing equations along with the real and reactive power balance equations for all nodes and the lines in which shunt and series compensation is present can be represented as follows [2], [11].         M       P   J    sh      V   I R      where, matrix J is represented as follows: 2 (2)
  • 3. Long Paper ACEEE Int. J. on Electrical and Power Engineering , Vol. 5, No. 1, February 2014 I [0]  [0]  A [0] A12 J   11 A [0] A22  21 A31 [0] A32   [0]   A13  A23   A33   Aij  AT ji  [0 ] B  M 1   A12 (4) i , j  1 3 A J r   22  A32 d 2 k  Pmk  Pek k  1,2, ng (5) dt 2 where, ng is number of generators in the system. At any bus j (except generator internal bus), the injected power Pshj by a FACTS controller is given by;  V jVi in j xij   V j 'Ek sin j  k  xdk  sin  j  i  E kV j ' xdk  sin  k   j  Vj x in j   Vi cos  j   i x ij ij   ' km j x dk   V j cos  j   k ' x dk    (8)       Bu   T (23)  i  Cei (24) From (13) to (24), it can be shown that,  i  ki  i (25) Above relation suggests that modal controllability and observability vectors are parallel. This relation shows that relative modal controllability using various inputs mentioned in u can be inferred from the relative modal observability in (12)  [0 ] A 1  M Ar © 2014 ACEEE DOI: 01.IJEPE.5.1.6    i  f iT B  BT f i (11)    y  C    Du    (13) I   [0] (21)  Me  f i  ki  i i  (22)  Mei  Here, k i is a constant whose value can be chosen arbitrarily. The modal controllability and observability vectors are defined by (23) and (24) respectively. (9) T A (20) Due to symmetry of the state matrix A and Ar , the left and right eigenvectors are related with each other as follows: (10) y   T V T Equation (2) can be written in state space form as follows;            f   f  f i   i    i i   f i   f i    T (17) D e   e  ei   i    i  ei  i ei  where, bshj is the fixed shunt susceptance at jth bus. The controllable inputs of the system are: T T u  Psh I R while observable quantities of the system are: 1  (7) Vj (16) T (19) f iT i  f iT A  fT fi A i For the system under consideration, it can be verified that the right and left eigenvectors of state matrix A corresponding to ith eigenvalue λi have the following form: The reactive current balance equation at the jth node is given by: I Rj  V j bshj  (15) e  i ei  Aei  A i  (18) ei  Each right eigenvector is a column vector with length equal to number of states. The left eigenvector fi satisfies the equation where, nj is set of buses connected to bus j by a transmission line, mj is the set of generator (internal) buses connected to bus j. PLj is the power consumed by the load connected at bus j, xij is the reactance of the transmission line connecting bus i and j and x’dk is the transient reactance of generator k. At the kth generator internal bus, injected power is given by, Pek  A23   A33  (14) B. Modal Controllability and Observability There is one eigenvector associated with each eigenvalue of the state matrix A. For the ith­ eigenvalue ëi, the eigenvector ei satisfies the equation. (6) km j [0 ]  J A13  r  [0] A C   J r  21   A31 [0] The elements of various sub-matrices shown in (3) are the partial differentiations of (5) to (8) with respect to variables mentioned in (2). For the kth generator, the swing equation can be expressed as; Pshj  PLj   (3) Here Aij are block matrices, M is a diagonal matrix and following relationship holds true for the block matrices of J. Mk A  A13 J r  21  A31   Ar  A11  A12 3
  • 4. Long Paper ACEEE Int. J. on Electrical and Power Engineering , Vol. 5, No. 1, February 2014 the output signals mentioned in y. Hence, the location of series or shunt FACTS devices can be determined using modal observability of the mode in the appropriate local signals. The structure and symmetry of the sub-matrices Aij unchanged if the inputs and corresponding outputs are  T u '  Psh or y '    T   V T  T T Qsh  Vo    T Bsh  Vo V T T u "  Psh y"   T B. Proposed PID-PSS (26) T  T (27) (28)  T Figure 2. Structure of PID-PSS with excitation system (29) The structure of PID-PSS along with excitation system is as shown in Fig.2. The input to the PID-PSS controller is change in rotor speed (Δω) of the machine on which it is installed. The system can be described. Same results can be obtained for these set of inputs and outputs. In this study, input-output pairs corresponding to shunt FACTS devices are only considered. Here, Vo is initial operating voltage. as. III. CONTROLLERS dE fd dt  1 K A Vref  Vt  Vs TA    (30) where, KA and TA are the gain and time constant of the excitation system respectively; Vref is the reference voltage; KP, KI and KD are the Proportional, Integral and Derivative gain for the machine on which it is installed. The supplementary stabilizing signal Vs is generated with the help of PID-PSS installed in the feedback loop is as shown in Fig. 2. The low frequency oscillations can be damped with the help of controllers. In this study, a comparison is made on the performance of CPSS, PID-PSS and SVC based controllers. The structure of these controllers is as discussed below. A. Conventional PSS PSS introduces a component of additional damping torque proportional to speed change through the excitation system. The standard IEEE type-1 excitation system is as shown in Fig.1(a). The structure of CPSS is illustrated in Fig 1(b). It consists of an amplification block, a wash out block and a lead/lag block. The lead-lag blocks provide required phase compensation. The input signal to the CPSS can be change in speed, change in power or can be the combination of both. In this study, change in speed of a machine is considered as input to the CPSS. C. SVC Based Controller Figure 3 represents the block diagram of a SVC with leadlag compensator [3]. The susceptance B of an SVC can be expressed as: dBsh 1  K s Bref  u svc  Bsh dt Ts     (31) where, Bref is the reference susceptance of SVC, Ks and Ts are gain and time constant of SVC respectively. The parameters of CPSS are obtained using phase compensation techniques and those of PID-PSS and SVC based controller are obtained with the help of GA, which is discussed in next section. (a) Figure 3. SVC based controller (b) IV. OBJECTIVE FUNCTION AND GA Figure 1. (a) Excitation system (b) Conventional PSS GA have recently found extensive applications in solving global optimization searching problems. They are useful when © 2014 ACEEE DOI: 01.IJEPE.5.1.6 4
  • 5. Long Paper ACEEE Int. J. on Electrical and Power Engineering , Vol. 5, No. 1, February 2014 the closed form optimization technique cannot be applied. GA simultaneously evaluates many points in the parameter space, so it is more likely to converge towards global minimum solution. GA is used to search for optimal PID controller parameters. GA are powerful search algorithms based on the mechanics of natural selection and natural genetics.  These algorithms work with a population of strings, searching many peaks in parallel, as opposed to a single point.  GA work directly on strings of characters representing the parameter set, not the parameter themselves.  GA use probabilistic transition rules instead of deterministic rules.  GA use objective function information instead of derivatives or other auxiliary knowledge. To determine the optimal controller settings, GA method is applied. GA optimal technique is used to minimize performance index which is of Integral Square Error (ISE) type. Objective function considered is, input-output pair mentioned above. These results suggest that modal controllability and modal observability vectors are parallel. The normalized values of above quantities show that susceptance of bus no. 4 has the highest controllability. The voltage of same bus number has the highest observability. So, the observability vector alone can be used to locate shunt FACTS device like SVC.  2 Minimize J e  e (t )dt  (32) Figure 5. 10 machines, 39 bus test system 0 TABLE I. OSCILLATORY MODES - C LASSICAL MODEL To get the optimal setting of KP, KI and KD, the error function is minimized. Sr. no. 1 2 3 4 5 6 7 8 9 Figure 4. Transfer function The values of controllability and observability considering Bsh as an input and bus voltage V as an output of the system are also obtained considering detailed model of the generators (model 1.1). In this case also the loads are modeled as constant impedance. There are nine swing modes for the system under consideration and the eigenvalues corresponding to these modes are as listed in Table III (eigenvalues without controllers). The real parts of these eigenvalues suggest that the damping is very poor and system may become unstable if a disturbance is applied as two of the eigenvalues have positive real part. The magnitudes of controllability and observability are shown in Table IV for a swing mode (l = -0.085428 ± 4.3617i) having frequency of 0.6943 Hz and damping ratio of 0.0196. In this case the ratio of controllability to observability does not remain constant but remains in a narrow range. A closer scrutiny of Table II and Table IV show that ranking of locations by observability vector for the classical and detailed model matches closely. The bus with highest controllability and highest observability is the same and it agrees with the result obtained with classical model of the generators. Hence, observability vector alone can be used to screen out locations with poor controllability. Here, e(t) is the error signal generated by taking a difference between reference input and the feedback signal as shown in Fig 4. In this study, speed d
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