Decentralized Robust Adaptive Control Based On Dynamic Programming for SVC Complement Controller Design

Subject Areas : International Journal of Smart Electrical Engineering

Ameneh Barani ¹ , Majid Moazzami ² , Mohammad Amin Honarvar ³ , S.Mohammadali Zanjani ⁴

1 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
2 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
3 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
4 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

Received: 2021-09-15 Accepted : 2021-10-30 Published : 2022-03-01

Keywords: PSO Algorithm, Dynamic programming, static VAR compensator, PSS,

Abstract :

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Full-Text:



1                                International Journal of Smart Electrical Engineering, Vol.10, No.1, Winter 2021                      ISSN: 2251-9246        

EISSN: 2345-6221

 

pp. 1:6

 

Decentralized Robust Adaptive Control Based on Dynamic Programming for SVC Complement Controller Design

Ameneh Barani1,2, Majid Moazzami*1,2, Mohammad Amin Honarvar1,3, Sayed Mohammad Ali Zanjani1,2

1Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

*2Intelligent Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

3Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

[e-mail], [e-mail], [e-mail], [e-mail]

  

Abstract

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Keywords: PSO algorithm, dynamic programming, PSS, static var compensator

Article history: Received 15-Sept-2021; Revised 01-May-2021; Accepted 15-May-2021.

© 2021 IAUCTB-IJSEE Science. All rights reserved

 

1.       Introduction

 

The power system is a large interconnected net­work whose important components include pow­er generating station, transmission lines, distrib­ution system and utilization [1-5].

The occurrence of disturbances in the power system causes fluctuations in the frequency, load angle and voltage of the units, which if the power system is stable, these oscillations will disappear within a few seconds and the system will continue to operate in new conditions [6,7].

These oscillations in the power system are called low frequency oscillations (LFO) and are usually in the fraction of one Hz (a few tenths of a Hz) to a few Hz [8,9]. They are divided into two types: local (low frequency fluctuations of a power plant unit) and interregional (oscillations of units of one area comp­ar­ed to units of another area). These modes are pres­ent in all interconnected power systems, and tie line strength and unit load factors affect damping [10,11].

Low frequency oscillations are the same as the mechanical mode fluctuations of the system, which can be determined using the linearized model of the system due to small periodic fluctuations [12,13].

Due to the operating point conditions of the system and the values of its parameters, fluctuations are able to continue for a long time and in the worst conditions increase the amplitude [14,15].

There are usually two types of power oscillati­on damper (POD) controllers, which include power system stabilizer (per generator unit) and FACTS controllers (in transmission system).

In terms of connection type, FACTS contro­llers (as shown in fig. 1) are divided into four types: series controller, parallel controller, series-series controller and series-parallel controller [16,17].

 

 

Fig. 1. Classification of FACTS devices by connection type

 

 

The application and design of power oscillat­ion damping controllers have been mention­ed in various studies [18,19].

To increase damping and stability of the power system in [20], thyristor controlled series capacitor (TCSC) and SVC are used, which adaptive neuro-fuzzy inference system- based TCSC and SVC controllers applies for damping oscillations.

To improve the attenuation of electrom­echa­ni­c­al oscillations of power grids, using intentional time delays in [21] is proposed and the control parameter settings for delays in power system stabilizers (PSS) are systematically specif­ied. Num­erical analysis on the standard 14-bus IEEE system shows that a dual-channel PSS can be designed to achieve the best damping characteristics for a wide range of latencies. 

The problems of wind farm induction genera­tors of induction dual power supply with variable wind speed in strong and weak networks have been studied in [22], which the impact of power PSS and static series synchronous compensator on the stability of wind power system is analysed by utilizi­ng modified IEEE 14-bus test system.

Researchers have pro­po­sed various studies on the application of adaptive control methods in PSS to improve the stability of power systems [23,24].

Theoretical formulation and implementation of intelligent approach for the development of a power system stabilizer in [25] is proposed, which a hybrid controller with two algorithms is designed including a neural network based controller and an adaptive controller.

Decentralized control design of complex syst­ems with unknown parameters and dynamic unce­rtainties is presented in [26], where strong dy­na­­mic and comparative programming theory and iteration technique are used. The effectiveness of the propo­sed computational control algorithm with online learning control of multi-machine power systems with governor controllers is also shown.

An adaptive neural-fuzzy inference system POD controller built into the energy storage system controller is proposed to damp the LFOs induced by induction motors in ac/dc hybrid microgrids in [27].

Particle swarm optimization (PSO) is a computational method. In this method, by repe­atedly trying to improve the solution, the candidate optimizes the problem according to a certain quality measurement [28].

In this paper, the aim is to improve the transient characteristics of the system and the damping of mechanical oscillations. The decentralized robust adaptive control method based on dynamic prog­ram­ming has been used to design a stabilizing power system and a complementary SVC controller. In order to compare the results, the stabilization design of the power system and the controller of the classic SVC have been performed using the PSO optimiz­ation method. The performance of the control method in the IEEE 16-machine, 68-bus, 5-area is demonstrated by performing simulations in MATLAB software.

2.       Power oscillation damping controllers

Complementary control measures are nece­ss­ary to improve the damping of oscillations in power systems. The damping control of power syst­em osc­illations is done by the PSS and FACTS devices.  

A.       PSS structure:

PSS control contributes to the stability of the power system by dampening the oscillations of the generator rotor angle [29,30].

Fig. 2 shows the structure of a PSS with a compensator, which includes a phase compe­nsating block, a torsion filter, a limiting block, a gain block (KP), and a washout block (with time constant TW) [31]. The input signal is considered as speed dev­iation and the output signal is considered as voltage changes [32].

 

 

Fig. 2. Structure of the power system stabilizer

B.       SVC structure:

Facts devices such as STATCOM [33] and SVC [34] can be used to improve the damping of fluctuations in power systems. Static VAR compe­nsator is a set of electrical devices to provide fast reactive power in high voltage transmis­sion netwo­rks. The circuit structure of the SVC, which is parallel to the transmission line, is shown in Fig. 3, which consists of a capacitor parallel to a TCR.

 

 

Fig. 3. Structure of the static var compensator

 

The purposes of using SVC include the follow­i­ng:

(A) Improves stability by quickly adjusting the voltage.

(B) Increases power transmission in long lines.

(C) Reduces the oscillation created due to oscillation modes (rotor).

(D) Oscillation reduces the following frequency simultaneously due to tensile modes.

(E) Controls voltage dynamics.

3.       Adaptive dynamic programming (ADP)

There are many methods of stable controller design for nonlinear systems. Adaptive dynamic programming (ADP) is an approximate optimal control program used in the field of optimal control [35]. In this method, an approx­imate function structure is used to approxi­mate the solution of the Hamilton-Jacobi-Belman equation [36,37].

The main characteristic of ADP is shown in fig. 4 [38]. To implement the ADP scheme, two basic structures are usually considered: heuristic dynamic programming and dual heuristic progra­mming, the structures of which are shown in figs. 5 and 6, respectively [39].

Generator 9 angle variations are considered as input signal for PSS. The active power of line 26-29 is intended as the input signal for the complementary SVC controller.

 

 

Fig. 4. Schematic representation of the ADP controller

 

 

Fig. 5. Heuristic dynamic programming structure

 

 

Fig. 6. Dual heuristic programming structure

4.       Power system under study

Fig. 7 shows the power system under study, which is a 5-zone, 16-machine power system consis­ting of 68 basses and 86 lines. The 16 generators of this system are located on buses 53 to 68, and bus 63 is considered as the reference bus. In this paper, the underpass model is used for mach­ines. Complete information on the small system signal model is given in [40,41].

5.       Stability of power system without PSS and SVC

To evaluate and evaluate the stability, a three-phase fault with ground at bus 26 and line 26-29 is applied in 1.1 sec in the studied system. This error separates from bus 26 in 1.15 sec. The system is simulated for 14 sec. 

The 26 bus voltage magnet is shown in fig. 8. The relative angles of machines 1, 3 and 9 to the reference machine (machine 13) are shown in figs. 9, 10 and 11, respectively.

As can be seen from the simulation results, the studied power system has a highly oscillating and unstable behavior after the error occurs.

To improve the stability of this system, a power system stabilizer is designed and mounted on the 9 machine.

An SVC with a capacity of 546 MVAR is also placed in bus 1 and a complementary controller is designed for it. The location of SVC is determined by the PSO algorithm.

 

 

Fig. 7. Power system under study (IEEE 68-bus, 16-machine, 5-area system)

 

 

Fig. 8. Voltage magnitude after three-phase to ground fault for system without controller

 

Fig. 9. Generator angle 1 after three-phase to ground fault for system without controller

 

 

Fig. 10. Generator angle 3 after three-phase to ground fault for system without controller

 

 

 

Fig. 11. Generator angle 9 after three-phase to ground fault for system without controller

6.       Simulation results

In this section, the performance of the power system for two types of connections is examined: three-phase to ground fault and single-line to ground fault. The simulation results are shown for four modes which are:

- without PSS and complementary controller

- design PSS and complementary cont­roller SVC using classic method

- design PSS using ADP and without and comp­lementary controller SVC

- design PSS and complementary controller SVC using ADP method.

Table (1) shows the PSS parameters and the complementary SVC controller determined using the PSO algorithm.

 

Table 1. System controller parameters for simulation

Controller Parameter	PSS	SVC
K	63.598	1.682
TW	1.832	5.665
T1	1.012	0.955
T2	1.399	0.284
T3	1.994	0.400
T4	0.536	1.899

A.       Three-phase to ground fault

First, a three-phase to ground fault at bus 26 is considered. The voltage magnitude is shown in fig. 12. The load angles of generators 1, 3 and 9 are shown in figs. 13, 14 and 15, respectively.

B.       Single line to ground fault

To evaluate the robustness of the system with the designed controllers, the performance of the system for the single-line to ground fault at bus 26 on line 26-29 was examined in this section. The voltage magnitude is shown in fig. 16. The load angles of generators 1, 3 and 9 are shown in figs. 17, 18 and 19, respectively.

 

 

Fig. 12. Voltage magnitude after three-phase to ground fault

 

 

Fig. 13. Generator angle 1 after three-phase to ground fault

 

 

Fig. 14. Generator angle 3 after three-phase to ground fault

 

 

Fig. 15. Generator angle 9 after three-phase to ground fault

 

 

Fig. 16. Voltage magnitude after single-line to ground fault

 

 

Fig. 17. Generator angle 1 after single-line to ground fault

 

 

Fig. 18. Generator angle 3 after single-line to ground fault

 

 

Fig. 19. Generator angle 9 after single-line to ground fault

7.       Conclusions and suggestions

There are electromechanical oscillations in interc­onnected power systems.

In this paper, to increase the stability of the power system, a power system stabilizer and a complementary controller have been used. For design, two control methods including non-centrali­z­ed and classical resistive adaptation were used. The simulation results show the robustness of the dece­ntr­alized adaptive control method against perturb­ations.

Suggestions for further study include the plac­em­ent of other FACTS devices with similar algorit­hms and the solution of the optimal placement prob­lem by considering the economic indicators and the reliability of the power system.

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