Sun Qiuye

Generating Li-Yorke chaos in a stable continuous-time T-S fuzzy model via time-delay feedback control

This paper investigates the chaotification problem of a stable continuous-time T–S fuzzy system. A simple nonlinear state time-delay feedback controller is designed by parallel distributed compensation technique. Then, the asymptotically approximate relationship between the controlled continuous-time T–S fuzzy system with time-delay and a discrete-time T–S fuzzy system is established. Based on the discrete-time T–S fuzzy system, it proves that the chaos in the discrete-time T–S fuzzy system satisfies the Li–Yorke definition by choosing appropriate controller parameters via the revised Marotto theorem. Finally, the effectiveness of the proposed chaotic anticontrol method is verified by a practical example.

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.

Load Modeling of Power System Based on Rough Cloud Generator

Based on the knowledge representation of cloud theory and rough sets, a rough-cloud model is put forward, which bridges the gap between quantitative knowledge and qualitative knowledge. In relation to classical rough sets, the rough-cloud model can deal with the uncertainty of the attribute and make a soft discretization for continuous ones. A novel approach, including discretization, attribute reduction, value reduction and data complement, is presented. With the origin data rough reduction, a combination cloud generator is put forward, which combines forward cloud generator and backward cloud generator to a close-loop structure. The generator is used to load model for power system to solve the load origin data shortage of distribution system. Considering of the distribution system load data characteristic, the restriction equations and system data complement unit are joined to combination cloud generator, which ensure that the created load data cover most of the system situation without impossible data. The cloud drop reflects the fuzziness and randomness of the load data. The loads are identified by T-S fuzzy model based on the generation cloud drop. The identification result implies the effectiveness and usefulness of the approach by the contrast with some kinds of universal load model.

A Neural Network Current predictive control of STATCOM for non-linear loads

This paper presents a novel current predictive control scheme based on Artificial Neural Network of STATCOM for non-linear loads which have sudden changing current. The current harmonics and power of non-linear loads, especially, uncontrolled rectifier is introduced by the instantaneous power theory. The track issue of the traditional hysteresis current control strategy of STATCOM is analyzed. In order to resolve this issue, a novel hysteresis current control scheme using the Artificial Neural Network Predictive Current Pulse Compensator is designed to predict compensating reference current waveform of the STATCOM. The method could change the STATCOM output current in advance, make the output current quickly track reference current, and then , minimize harmonics of grid current , suppress the sag of micro-grid voltage. Simulation results verified that the proposed control scheme can effectively compensate reactive power of the grid, and suppress grid current harmonics, even in sudden changing current of non-linear load.

A multi-agent technology based predictive control strategy in cascading failures of large power grids

A real-time predictive control strategy based on multi-agent technology is presented to prevent cascading trips which can trigger a blackout accident in bulk power systems. The optimum principle of load shedding and generator tripping is also given. Every node in power grid is regarded as an agent differing from traditional distributed computation that used the subarea or substratum method. Each agent calculates independently and communicates with others simultaneously though its action module, data acquisition module, computation module and communication module. Then the optimal load shedding and generator tripping can be obtained by the proposed rolling optimization of predictive control method. The measurement data for the optimization algorithm can be acquired from WAMS.

Storage control strategy for energy hub of We-Energy in the energy internet

This paper consider the role of energy hub in We-Energy (WE). WE is a novel energy accessing mode in order to match up Energy Internet. This new mode is proposed for the convenience of energy regulation, energy trading and information interaction. A new power flow expression of energy hub which contain storage is given in order to overcome the disadvantage of traditional power flow expression. The comparison between new power flow expression and traditional expression is also given in this paper. This study also investigates the optimal operation of using storage systems. To avoid the influence of forecast accuracy, a new control strategy of storages without the forecast based on historical data has been proposed in which economic problem has been given for an energy hub include both electricity, natural gas and heating storage. This new control strategy determines the optimal storage operation to minimize the total energy cost of the hub contained by WE. The proposed control strategy has been applied to two simple case studies which are used to compare the economic cost between the energy hub contain storage or not. The obtained simulation results demonstrate that the new control strategy can make great economic benefit for energy hub contain storage system.

Power Flow Calculation in the Voltage Output Stage

The distribution system represents the final stage in the transfer of power to the individual customers. In the distribution system analysis, the power flow calculation is the most basic and important calculation and is the foundation of the operation, plan, security, reliability analysis and optimization of the power system and is the foundation and springboard of the various electromagnetic transient analysis. A lot of uncertain factors exist in the distribution system. Uncertainty in the distribution system must be addressed in any analysis. In this situation, traditional certain power flow methods have limitation. The flow calculation in the fault condition is studied. The forward-backward sweep method is adopted to calculate fault power flow. The simulation research shows that the calculation results with the polynomial load model are proper in the practical application when the fault occurs.

Novel pinning control strategies for synchronisation of complex networks with nonlinear coupling dynamics

of the uncoupled system by a local pinning control strategy. Several sufficient conditions are derived to guarantee the network synchronisation by investigating the relationship among pinning synchronisation, network topology, and coupling strength. Also, some fundamental and yet challenging problems in the pinning control of complex networks are discussed: (1) what nodes should be selected as pinned candidates? (2) How many nodes are needed to be pinned for a fixed coupling strength? Furthermore, an adaptive pinning control scheme is developed. In order to achieve synchronisation of an uncertain complex network, the adaptive tuning strategy of either the coupling strength or the control gain is utilised. As an illustrative example, a network with the Lorenz system as node self-dynamics is simulated to verify the efficacy of theoretical results.

The Application of Iterative Metacognitive Theory in Electrical Engineering Experiment Teaching

Graduates not only need to learn, but needed to learn how to learn efficiently and effectively, specially in experiment course. Therefore, the well-known and widely accepted concept, metacognition, is revisited for use in a power systems experiment course. For conflict between continuous studying proceed and isolated cognitive way, the iterative metacognition is put forward. The new metacognition may accelerate the studying efficiency, especially in experiment couse. A combination of improved metacognition and the appropriate use of innovated experiment learning model may be one solution for addressing this issue. The study examined the current use of iterative metacognitive strategies in a innovated experiment unit at Northeastern University and how these strategies could be adopted to increase learning effectiveness. The study investigates how iterative way can support the metacognitive aspects of learning.