Hee-Sang Ko

Power Quality Control of Wind-Hybrid Power Generation System Using Fuzzy-LQR Controller

This paper presents modeling and control design of a wind-hybrid power system that includes a battery storage and a dumpload. The proposed control scheme is based on the Takagi-Sugeno (TS) fuzzy model and the linear quadratic regulator. The TS fuzzy model expresses the local dynamics of a nonlinear system partitioned into sub systems by linguistic rules. A possibility auto-regression model is presented that provides optimally partitioned sub systems based on the observed time series. The controllers for each sub system are designed by the linear quadratic regulator. In the simulation study, the proposed controller is compared with the conventional proportional-integral controller and shown to be more effective against disturbances caused by the wind speed and the load variations. Thus, a better power quality is achieved on the given site.

Active Use of DFIG-Based Variable-Speed Wind-Turbine for Voltage Regulation at a Remote Location

Renewable energy sources are presently exempted from control functions. This, in turn, simplifies the requirements for the renewable energy source interconnection as well as the project developer, allowing connection to the system without having to take part in the overall stabilization effort. Thus, in order to maintain the power generation and consumption balance, necessary for stable functioning of the power system, the traditional power plants always exert necessary control actions. However, the present practice requires the independent power producers and/or generators who want to connect to the grid to meet the so-called connection requirements of the local electric utility (the grid company). Therefore, this paper proposes an innovative reactive power control scheme for voltage regulation at a remote location that manipulates dynamically the reactive power from the voltage source converter (VSC) with taking into account its operating state and limits.

A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market

Presents a fuzzy regression model to estimate uncertain electricity market prices in a deregulated industry environment. The price of electricity in a deregulated market is very volatile in time. Therefore, it is difficult to estimate an accurate market price using historically observed data. In the proposed method, uncertain market prices are estimated by an autoregressive model using a neural network, and the time series model is extended to a fuzzy model to consider the possible ranges of market prices. The neural network finds the crisp value for the AR model and then the low and high ranges of the fuzzy model are found by linear programming. Therefore, the proposed model can represent the possible ranges of a day-ahead market price. For a numerical example, the model is applied to California Power Exchange market data.

A PI Control of DFIG-Based Wind Farm for Voltage Regulation at Remote Location

This paper presents a voltage control methodology that provides a doubly-fed induction generator (DFIG)-based wind farm with the capability to provide voltage regulation. The controller manipulates dynamically the reactive power from the voltage source converter (VSC) to control the voltage at a specified location. Since a proportional-plus-integral (PI) controller is popularly used in power systems, it is designed in this paper. In wind power generation systems, since operating conditions are changing continually by wind speed fluctuations and load changes, a robust control mechanism is necessary. Thus, a PI controller is designed by the Nyquist constraint design technique for the robustness. The proposed voltage control methodology is demonstrated on a candidate DFIG-based wind farm site on Vancouver Island, British Columbia, Canada.

Power quality control of hybrid wind power generation with battery storage using fuzzy-LQR controller

This work presents a modeling and control design for a wind-hybrid power system with a battery storage. The proposed control scheme is based on the Takagi-Sugeno fuzzy model and the linear quadratic regulator. The Takagi-Sugeno fuzzy model expresses the local dynamics of a nonlinear system through subsystems partitioned by linguistic rules. The controllers for each subsystem are designed by the linear quadratic regulator. In the simulation study, the proposed controller is compared with the proportional-integral (PI) controller. The simulation results show that the proposed controller is more effective than the PI controller against disturbances caused by wind speed and load variation. Thus, better quality of the wind-hybrid power system is achieved.

Active Use of DFIG-Based Variable-Speed Wind-Turbine for Voltage Control in Power System Operation

This paper presents an active use of doubly-fed induction-generator (DFIG)-based variable-speed wind-turbine for voltage control in power system operation. For reasonable simulation studies, a detail dynamic model of a DFIG-based wind-turbine grid-connected system is presented. For the research objective, an innovative reactive power control scheme is proposed that manipulates dynamically the reactive power from the voltage source converter (VSC) with taking into account its operating state and limits.

An intelligent controller for a remote wind-diesel power system - design and dynamic performance analysis

This paper presents an intelligent controller based on a neural network for a wind-diesel power system equipped with a stall regulated wind turbine run an induction generator. The goal for the wind-diesel power system is to design an intelligent controller to maintain a good power quality under varying wind and load conditions. On the other hand, the controller has to show acceptable closed-loop performance including stability, robustness, optimal energy, steady state, and transient performance at a permissible level of control effort. Moreover, such a controller has to be highly adaptive to various operating conditions and independent of model parameters that can be uncertain. This paper presents an inverse dynamic neural controller, which consists of an inverse dynamic neural model and generalized minimum variance control. This proposed controller is applied to design an integrated nonlinear controller to provide control input from excitation system and governor system at the same time.

Increase of fault ride-through capability for the grid-connected wind farms

In many countries, high level of penetration of wind energy in power systems requires revisiting the grid connection standards in terms of impact on transient voltage stability. While the presently used common practice is to disconnected the wind turbines from the grid immediately when a fault occurs somewhere in the grid, in the future the wind turbines may be required to stay connected longer and ride through the part or the whole fault transient(s). To achieve easier grid integration and reliable voltage control, active control of wind turbines is becoming an area of increasing importance. This paper presents a computer model of a multi-turbine wind energy system that is based on the candidate wind farm site on Vancouver Island, Canada. A new voltage control scheme is proposed and compared to the traditional modes of the wind turbine operation. The simulated studies demonstrate an enhancement of the proposed controller during a fault-ride-through transient

Machine learning approach to power system dynamic security analysis

In this paper, the authors present a pattern-learning/recognition approach for dynamic security classification using neural networks with a limited number of input data. The input is a set of data representing the precontingency power system state (voltages, angles, etc.), and the output is the possible system status (stable/unstable) after contingency. Data clustering is applied to reduce the number of input representing the cases. The reduced input data are then used to train the neural network that learns the input patterns for a possible post-contingency status. The overall accuracy of the classification is considered to be reasonable for a practical-scale power system application.

Power system stabilization using fuzzy-neural hybrid intelligent control

This paper presents fuzzy-neural hybrid control for power system stabilization. The main idea of hybrid control is that the dynamic feedforward compensator can be used for improving the ability to track the reference rather than changing the dynamics, while feedback is used for stabilizing the system and for suppressing disturbances. In this paper, fuzzy logic is applied to design a feedback controller and then a neural network inverse model is obtained for a feedforward compensator. The controller is tested for a one-machine infinite-bus power system under various operating conditions.