Power System Control And Stability Anderson Fouad Pdf

Calls the json file case39.json which data origin is PYPOWER.This network was published the first time in G. Bills et al., On-line stability analysis study, RP 90-1, E. P. R. I. North American Rockwell Corporation, Edison Electric Institute, Ed. IEEE Press, Oct. 1970,. Some more information about this network are given by Illinois University case 39.Because the Pypower data origin proposes vn_kv=345 for all nodes the transformers connect node of the same voltage level.

Calls the json file case89pegase.json which data is provided by MATPOWER.The data origin are the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

This grid represents a part of the European high voltage transmission network. The data is provided by MATPOWER.The data origin are the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

This grid represents a part of the European high voltage transmission network. The data is provided by MATPOWER.The data origin i the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

Flexible ac transmission systems (FACTS) utilize power electronic based fast switching devices which can control power flow in the lines and improve stability (Padiyar 2007). FACTS devices are considered as the prominent ones among many effective means to improve operation of power system, increase power transfer capacity etc. Series capacitive compensation method has been employed to remove significant portion of the reactive line impedance and hence improve the amount of transmittable power under dynamic conditions. Static Synchronous Series Compensator is a voltage source converter based FACTS device which is connected in series with the transmission line (Gyugi et al. 1997).

The attractive features and effectiveness of SSSC has made its use widespread in very short period. The SSSC based damping controller design has been handled differently in recent literatures. Time optimal control theory has been employed to design a SSSC based damping controller in (Pandey & Singh 2008). A simplified two area system is considered and the linearized power system model is used. The drawback of this work is that the solution of the Riccati equation is time consuming and large matrix manipulations are required to obtain the desired result. Three different operating modes of SSSC are identified in (David & Venkataramanan 2007) and the controller design problem is handled by frequency domain based loop shaping technique. The results presented shows that the response time taken by the proposed controllers is quite large. Tuning of the SSSC damping controller is performed by real coded genetic algorithm (RCGA) in (Swain et al. 2011). An integral time absolute error based objective function is selected and deviation in the rotor speed from the synchronous speed is referred as the error. Like genetic algorithm, RCGA, too, has a tendency to get stuck at a local minimum of the solution space. A differential evolution (DE) based approach is proposed in (Swain et al. 2013). Built-in Simulink blocks are used for the analysis which may not allow much freedom for the user to work with. No information is provided regarding the convergence scenario of the proposed algorithm which is one of the major criteria to evaluate an optimization algorithm. Similar drawbacks are observed in (Swain et al. 2012). Adaptive Neuro-fuzzy inference system (ANFIS) is employed to design the damping controller of SSSC in a multi-machine power system network (Murali & Rajaram 2010). The deviation in line power is taken as the error signal for the controller and the output is the SSSC injected voltage magnitude. Self- tuning PID controller is utilized in (Therattil & Panda 2011) to damp out electromechanical oscillations. The responses show that it takes around 5 seconds to completely suppress the oscillations. Nonlinear adaptive control technique is proposed for the SSSC damping controller design (Gu et al. 2010). Like any adaptive control algorithm, it takes a healthy computation time to get the required control effort. Design of SSSC damping controller is modeled as a multi-objective optimization problem and solved using Particle Swarm Optimization (PSO) algorithm in (Ajami & Armaghan 2010). Different loading scenarios are considered to show the effectiveness of the proposed method. Nonlinear feedback linearization control technique is employed in (Ghaisari & Bakhshai 2005) where the study system dynamic model is represented as a multi-input multi-output system. Although the paper describes the need of zero-dynamic study for the stability of overall system, it is not explained for the study system.

Invasive Weed Optimization (IWO) is a recent meta-heuristic search algorithm which is inspired from the weed colonizing technique (Mehrabian & Lucas 2006). IWO was tested for different multi-dimensional benchmark systems and the performance is compared with other efficient search algorithms. Performance of IWO was found superior to Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, Memetic Algorithm and Shuffled Frog Leaping algorithm. From then on IWO has found numerous applications in diversified field of engineering and science. IWO is efficiently applied for optimizing and tuning of a robust controller (Mehrabian & Lucas 2006), designing an E-shaped MIMO antenna (Mallahzadeh et al. 2009), optimal positioning of piezoelectric actuators (Mehrabian & Yousefi-Koma 2007), studying the electricity market dynamics (Ardakani et al. 2008), designing the encoding sequences for DNA computing (Zhang et al. 2009), and developing a recommender system (Rad & Lucas 2007).

This paper utilizes the superior performance of IWO to find out the optimal parameter of SSSC damping controller in an SMIB system. A time-domain based objective function is chosen which considers deviation in the rotor speed as error signal and the job of the optimizer is to minimize the error in the quickest possible time. The performance of IWO is then compared to that of PSO based controller.

The SSSC comprising of a voltage source converter, a series injection transformer and a dc-storage capacitor is indicated in a dotted block. The SSSC injects into the transmission line which can be modulated for control of power flow in the line. The control variables are the modulation index mb and phase angle δb of the voltage source converter.

The rotor speed deviation Δω is achieved by introducing different dynamic scenarios for different loading conditions. The system is initialized each time there is any change in operating condition. The rotor speed deviation is taken as the input to the PI controller through a wash-out block. The same speed deviation signal is passed to the IWO optimizer which calculates an objective function and tries to obtain an optimal solution to the problem at hand by minimizing the deviation in the speed signal. The IWO optimizer then sends the optimized parameter set (in this case Kp and Ki) to the controller block and the controller output gives the desired magnitude of modulation index (mb) in order to improve the system damping. The optimizer is applied for a certain fault case and loading condition.

For any power system, changes in operating conditions of system are common phenomenon. For a good design of damping controller, besides the maximum effectiveness of the controller, the robustness of damping controller to the variations of power system operating conditions is an equally important factor to be taken under consideration. Hence it is desirable for SSSC optimal POD controller to be able to tolerate changes in operating point and the performance should be satisfactory over a range of change of operating conditions. Therefore, it is particularly important to study the effect of load variations on the performance of designed controller. Keeping these in mind, to exhibit the effectiveness of designed POD controller, simulation studies has been carried out for three different operating conditions of the system which are listed in Table 1.

Citation: Alashqar M, Yang C, Xue Y, Liu Z, Zheng W and Zhang X-P (2023) Enhancing transient stability of power systems using a resistive superconducting fault current limiter. Front. Energy Res. 10:1106836. doi: 10.3389/fenrg.2022.1106836

Dr. Geng is offering the course of Power System Operation Analysis in Fall 2018. This course is offered in English (incl. lectures, assignments and projects) to graduate students with the research focus on power systems, at College of Electrical Engineering, Zhejiang University. 2b1af7f3a8