Li Xingyuan

Interaction Analysis and Coordination Control between SSSC and SVC

With the wide application of flexible AC transmission system (FACTS) controllers in power system, the researches have been interested in on interaction among FACTS controllers, on interaction between FACTS controller and power system. This interaction can bring negative influence on the reliability of system. With technical development, power electronics devices, such as GTO, IGCT and IGBT, have been applied widely in power system. A typical application is the static synchronous series compensator (SSSC) , a series reactive power compensation device. In this paper, the relative gain array (RGA) theory is proposed to analyze the interaction between SSSC and static var compensator (SVC); And the negative interaction is solved through direct feedback linearization (DFL) based nonlinear control. Finally, the effect of the proposed methods are confirmed on a single machine infinite bus system with a SSSC and a SVC, and the simulation results agree with that from RGA theory described in this paper.

A robust adaptive control with unmodeled dynamic for HVDC transmission systems

Utilizing the feature of quick response of HVDC to improve the performance of AC/DC system has become the emphasis to be researched. This paper introduces firstly the principle of the robust adaptive control of nonlinear systems with unmodeled dynamics, then developed the robust adaptive additional control of HVDC with unmodeled dynamics of generator in order to improve stability of power system. The additional control of HVDC with unmodeled dynamics only uses the local signals and its design is simple, furthermore it can obviously improve the stability of power system in different operational conditions. Experimental results using the presented concepts obtained on single machine infinite bus model are also included. These results prove the efficiency of the control scheme. The design process of controller provided a new idea to design controller by use of simplified model.

Robust adaptive control of nonlinearly parameterized systems with unmodeled dynamics

Many physical systems such as biochemical processes and machines with friction are of nonlinearly parameterized systems with uncertainties. How to control such systems effectively is one of the most challenging problems. This paper presents a robust adaptive controller for a significant class of nonlinearly parameterized systems. The controller can be used in cases where there exist parameter and nonlinear uncertainties, unmodeled dynamics and unknown bounded disturbances. The design of the controller is based on the control Lyapunov function method. A dynamic signal is introduced and adaptive nonlinear damping terms are used to restrain the effects of unmodeled dynamics, nonlinear uncertainties and unknown bounded disturbances. The backstepping procedure is employed to overcome the complexity in the design. With the proposed method, the estimation of the unknown parameters of the system is not required and there is only one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. It is proved theoretically that the proposed robust adaptive control scheme guarantees the stability of nonlinearly parameterized system. Furthermore, all the states approach the equilibrium in arbitrary precision by choosing some design constants appropriately. Simulation results illustrate the effectiveness of the proposed robust adaptive controller.

A novel evolving scale-free model with tunable attractiveness

In this paper, a new evolving model with tunable attractiveness is presented. Based on the Barabasi–Albert (BA) model, we introduce the attractiveness of node which can change with node degree. Using the mean-field theory, we obtain the analytical expression of power-law degree distribution with the exponent γ (3, ∞). The new model is more homogeneous and has a lower clustering coefficient and bigger average path length than the BA model.

A fuzzy clustering method for coherent generator groups identification based on A-K

A coherent groups recognition method using fuzzy clustering method based on A-K networks is proposed. Firstly, a fuzzy similarity matrix is formed by applying maximum-min-mum algorithm. Then the A-K networks are trained with each row of the fuzzy similarity matrix as inputs. The nerves of output layer which win ultimately represent different dynamic styles. Finally, it is tested on the EPRI-36 bus model of PSASP. The results based on A-K fuzzy method are more similar to the results based on time simulation compared to A-K method. Moreover, A-K fuzzy method can identify coherent generator groups in greater time range.

Comprehensive decision-making of power grid DC de-icing plan based on equivalent net-ability method

When the power grid suffers a wide range of freezing disasters, how to make a reasonable DC de-icing plan of transmission line became a urgent problem for the dispatchers. For the reason the DC de-icing ranking evaluating indicator of transmission line and the comprehensive index on net-ability drop are proposed to develop the power grid DC de-icing plan. The DC de-icing ranking evaluating indicator of transmission line is proposed to distinguish DC de-icing urgency of transmission lines. The importance of transmission line, the current rate of icing-coating and the predicted rate of icing-coating are the three attribute factors of the evaluating indicator, the weights of which are determined by the method of weighted average combinative evaluation. The comprehensive index on net-ability drop is used to reflect the system security. The transmission line DC de-icing group which would pose a serious threat to system security will not appear if planning the power grid DC de-icing plan based on the proposed equivalent net-ability method. DC de-icing comprehensive decision-making method of power grid is proposed with overall consideration of the emergence of transmission line DC de-icing and the power system security, the effectiveness and feasibility of which are demonstrated by the example of Guizhou power grid. The DC de-icing comprehensive decision-making method of power grid would be a good reference for the dispatcher to make DC de-icing plan g.

The Simulation of Doubly Fed Induction Generators Accessing Gansu Jiajiu Power Grid Based on PSASP

Due to the asynchronous generator has many negative factors such as large power fluctuation, poor transient performance, low energy efficiency, etc. The doubly fed induction generator(DFIG) is becoming a popular concept and thus the modeling of DFIG becomes an interesting research topic. This paper develops the DFIG model of power flow and third-order transient in which the power converter is simulated as a controlled voltage source, regulating the rotor current to meet the command of real and reactive power production, so as to achieve decoupling control. This model is specially integrated into the power system simulation tool as Power System Analysis Software Package (PSASP), which is a software invented by china electric power research institute for the study of power systems. As an example, the interaction between three wind farms respectively named San Shili Jingzi, Di Wopu, Yu Men and the Gan Su grid is simulated using the proposed model. The model performance and accuracy is also compared with the actual operation data. The result shows that: the model is reasonable accurate and effective, and it has better function for improving the utilization of wind energy and increasing the system transient stability.

Observational linearization and tracking objective excitation control strategy based on phasor measurement unit

To improve the transient stability of multimachine power systems, observational linearization and tracking objective excitation control laws were derived from the phasor measurement unit (PMU), observational linearization, and tracking objective control theory based on synchronized coordinates and reference generator coordinates. The control strategies utilized real-time state variables obtained by PMU to linearize the state equations of the system, and then the linear optimal control strategy was used to design excitation controllers. The inaccuracy of the local linearization method and the complexity of the system models designed in the exact linearization method for nonlinear systems were avoided. Therefore, the control strategies were applied in real time. Simulation results show that the proposed method can improve the transient stability of power systems more efficiently than nonlinear optimal excitation control.

Robust Adaptive Output Feedback Control of Nonlinearly Parameterized Systems

The ideas of adaptive nonlinear damping and changing supply functions were used to counteract the effects of parameter and nonlinear uncertainties, unmodeled dynamics and unknown bounded disturbances. The high-gain observer was used to estimate the state of the system. A robust adaptive output feedback control scheme was proposed for nonlinearly parameterized systems represented by input-output models. The scheme does not need to estimate the unknown parameters nor add a dynamical signal to dominate the effects of unmodeled dynamics. It is proven that the proposed control scheme guarantees that all the variables in the closed-loop system are bounded and the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately. Simulation results have illustrated the effectiveness of the proposed robust adaptive control scheme.