Shenglong Yu

A Novel Quasi-Decentralized Functional Observer Approach to LFC of Interconnected Power Systems

This paper presents a novel functional observer based quasi-decentralized load frequency control scheme for power systems. Based on functional observers theory, quasi-decentralized functional observers are designed to implement any given state feedback controller. The designed functional observers are decoupled from each other and have a simpler structure in comparison to the state observer based schemes. The proposed functional observer scheme is applied to a complex nonlinear power system and the proposed design method is based on the entire network topology.

State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems

This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is performed using the Unscented Kalman Filter (UKF) with a bad data detection scheme. Such a state estimation scheme for a DFIG is important because not all dynamic states of a DFIG are easily measurable. Furthermore, the proposed state estimation technique is decentralized and the network topology of the entire power system is taken into consideration in the estimation process. In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed.

Realization of State-Estimation-Based DFIG Wind Turbine Control Design in Hybrid Power Systems Using Stochastic Filtering Approaches

This paper uses three popular stochastic filtering techniques to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable-stator flux. Filtering methods to be discussed in this paper include particle filter, unscented Kalman filter, and extended Kalman filter, where their mathematical algorithms are presented, their implementations in the DFIG wind farm connected to complex power systems are studied, and their performances are compared. The whole power system network topology is taken into consideration for the state estimation, but only local phasor measurement unit measurement data are required. The purpose of using different stochastic filtering techniques to estimate dynamic states of DFIG in power systems is to resolve the long-lasting issue of the unavailability of DFIG internal states used in the DFIG controller design.

A Comparison Study for the Estimation of SOFC Internal Dynamic States in Complex Power Systems Using Filtering Algorithms

This paper enumerates three commonly used filtering algorithms and shows the detailed steps of their incorporation with general nonlinear systems for dynamic state estimation. The mathematical model of a stand-alone solid oxide fuel cell (SOFC) is briefly discussed and derived, which is then mathematically connected to a multiarea, diverse-generator, interconnected complex test system. The mathematical representation of the entire power system is tailored into a certain compact form to provide suitability for the implementation of filtering algorithms for the design of dynamic state estimators. With the utilization of phasor measurement units, the state estimators are able to work in a decentralized manner with the mere knowledge of local noisy voltage and current measurements. Successfully estimating the internal dynamic states of SOFC connected to complex power systems offers a novel methodology for the acquisition of the internal unmeasurable states of SOFC, which will facilitate future controller designs that may require the otherwise inaccessible states.

Dynamic State Estimation Based Control Strategy for DFIG Wind Turbine Connected to Complex Power Systems

This paper proposes a viable solution to the long-lasting issue of using flux-involved control scheme to regulate the behavior of doubly fed induction generator (DFIG) during faults. Instead of trying to design a complicated method to measure flux, which cannot be directly measured with contemporary technology, the solution utilizes unscented Kalman filter-based dynamic state estimation of DFIG connected to a complex power system to estimate the wanted variables. The decentralized estimation scheme takes into consideration the overall power system network and uses only local noisy PMU measurement data. DFIG control schemes are also investigated to a fair extent where three control methods are discussed with comparison results presented. The improved control scheme displays a better fault recovery response and system compatibility. A number of considerations are taken into account in the design of DFIG control schemes, including reactive power supports and dc-link current compensation.

Voltage Control Strategies for Solid Oxide Fuel Cell Energy System Connected to Complex Power Grids Using Dynamic State Estimation and STATCOM

In this paper, a novel Dynamic State Estimation–current feedback with STATCOM control scheme is proposed for the mitigation of voltage fluctuation of Solid Oxide Fuel Cell (SOFC) power station connected to the complex power grids during electrical faults. The proposed control scheme is compared to two existing control strategies and shows its superiority in alleviating voltage flickers and deviations as well as protecting the internal membranes of the fuel cells. Since SOFC internal dynamic states are able to closely reflect the transience and dynamic behavior of SOFC, using them in controller designs can generate better regulations of the state-related internal voltage of SOFC than other methods. STATCOM is also utilized in this study to mitigate the voltage oscillations induced by unavoidable voltage fluctuations during electrical faults. The power system with proposed control strategy is proven to be stable through linear analysis. The acquisition of the useful internal dynamic states is realized using unscented Kalman filter algorithm based state estimator. The success of incorporating estimated states into the development of control strategies is conducive to the designs and implementations of new control schemes for power systems and also the applications of interdisciplinary control theories.

A DSE-Based Power System Frequency Restoration Strategy for PV-Integrated Power Systems Considering Solar Irradiance Variations

With power networks undergoing an unprecedented transition from traditional power systems to modern electric grids integrated with renewable energy sources, maintaining frequency stability of generators in modern power systems has become one of the major concerns. Targeting this issue, in this paper, we propose a novel frequency restoration strategy in photovoltaics (PV)-connected power systems using decentralized dynamic state estimation technique and PV power plant as a contingency power source. When a sudden increase in load demand occurs, the output power of PV panels is increased in order to compensate for the shortage of real power capacity of the generator, in order to restore the frequency of a certain generator bus bar. An unscented Kalman filter-based decentralized dynamic estimation is utilized in this study to estimate the frequency of a selected generator bus bar with local noisy voltage and current measurement data acquired by using phasor measurement units. Solar luminous intensity may vary over a period of time in different seasons, weather conditions, etc., which causes the variations in the output power of PV power plants. This irradiance uncertainty is also considered in this study. The proposed control strategy not only incorporates the frequency deviations of a generator bus-bar, but also takes into account the tie-line power deviations under disturbances. Simulation results demonstrate the capacity of proposed control schemes in restoring the frequency of generator bus-bars and also maintaining the tie-line power flowing between adjoining areas at it scheduled value.

Dynamic Behavior Study and State Estimator Design for Solid Oxide Fuel Cells in Hybrid Power Systems

In this paper, an application-oriented tubular Solid Oxide Fuel Cell (SOFC) mathematical model is presented. Its dynamic behaviors in complex power systems which consist of synchronous generators of different types are then investigated where SOFC transience is studied on a small (second) timescale. Based on the mathematical model of the entire power system, SOFC dynamic states estimator is designed to track and predict the behaviors of unmeasurable states inside SOFC during normal operating and electrically faulty conditions, using stochastic filtering algorithms. The proposed estimator operates on a holistic level with all generators, transmission lines and loads taken into consideration, whereas only SOFC local electrical data are needed for the dynamic state estimation. The success of estimating SOFC internal states will lead to a higher possibility of designing state-related controllers so as to regulate the behaviors of SOFC during electric faults.

Novel Quasi-Decentralized SMC-Based Frequency and Voltage Stability Enhancement Strategies Using Valve Position Control and FACTS Device

This paper proposes a novel sliding mode control (SMC) strategy for load frequency and voltage control in a complex power system under electrically faulty conditions. The load frequency regulation is achieved by changing the valve position of generators, while the bus voltage regulation is realized with the utilization of a popular FACTS device, Static Var Controller. Inter-area tie-line power is taken into consideration in the load frequency control so as to maintain the obligations of importing/exporting active power from/to connecting areas. The proposed control method thus operates in a quasi-decentralized manner, utilizing local frequency and voltage signals, as well as inter-area tie-line power information. An improvement is then made to the originally proposed SMC scheme to suppress its inherent fluctuations. The proposed enhanced SMC is easy to implement, and compared with conventional PI controllers, it produces superior performances in regulating frequencies and magnitudes of bus-bar voltages.

A dynamic state estimation based sliding mode controller for wind energy generation system connected to multimachine grids

This paper proposes a sliding mode control scheme suitable for designing controllers in general nonlinear systems. To improve its performance, a chattering mitigation strategy is also proposed, which is proven to be capable of effectively alleviating the oscillations generated by the original sliding mode controller. For the validation of the functionality of the proposed control scheme in a nonlinear system, a sensorless doubly fed induction generator wind energy generation system connected to a multimachine system is used in the case study. Dynamic state estimation is required for sensorless doubly fed induction generator wind turbine as the internal state, rotor speed, which is used to construct the control scheme is not directly available. In order to do that, a unscented Kalman filter algorithm based dynamic state estimator is designed. The success of using proposed dynamic state estimation-based sliding mode control strategy to regulate the dynamical behavior of a wind energy system broadens the ken of the field of control methodology as well as the field of power systems.