No abstract

No abstract

No abstract

SUMMARYIn this paper, a novel inertial control method for Doubly Fed Induction Generator (DFIG) is proposed, namely mode conversion. An excellent inertial response performance is obtained by adopting the mode conversion method. DFIG operating mode can be switched between normal operating mode and inertial response mode by setting logic judgment conditions to start and block inertial control. Constant additional electromagnetic torque is set in this method, and the effect of inertial response does not recede with decrease of rotor speed. Rotor speed stability constraints for DFIG are considered in the method; the minimum rotor speed limit is determined through stability analysis of DFIG in its inertial response process. A simulation model is built to demonstrate the advantages of the proposed method. Simulation result shows that the mode conversion method can more effectively restrain the system frequency decline than existing inertial control methods within the stability operation constraints of the DFIG.

Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs) are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network's dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs' charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators' charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs' charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

The uncertainty of the wind power generation and complex constraints of the hydropower pose challenges for the short-term scheduling of coordinated wind power, thermal power, and cascaded hydroelectric system (WTHS). In this paper, a robust security-constrained unit commitment model is established for a WTHS. The proposed model ensures the utilization of wind power and economic return from the scheduling. Conservative adjustable uncertainty sets are used to characterize the uncertainty of wind power over temporal and spatial dimensions. In this model, pumped hydro energy storage (PHES) is incorporated to cope with the wind power fluctuations. A simplified affine policy is developed for the decision making of the adjustable variables. Based on a series of linearization techniques, the proposed model is formulated as a single-level mixed-integer linear programming (MILP) problem, where the numerical tests performed on the modified IEEE 30-bus, IEEE 118-bus, and Polish 2736-bus systems verify the effectiveness of the model. The comparative analyses quantitatively evaluate the contributions of the PHES in terms of economic performance and wind power accommodation. The test results reveal that the robustness of scheduling plans is enhanced by the use of the PHES, and the proposed approach is applicable to the largescale power systems. INDEX TERMS Wind-thermal-hydro power system, pumped hydro energy storage, robust securityconstrained unit commitment, mixed integer linear programming.

No abstract

Class temporal speci¯cation is a kind of important program speci¯cations especially for objectoriented programs, which speci¯es that interface methods of a class should be called in a particular sequence. Currently, most existing approaches mine this kind of speci¯cations based on¯nite state automaton. Observed that¯nite state automaton is a kind of deterministic models with inability to tolerate noise. In this paper, we propose to mine class temporal speci¯cations relying on a probabilistic model extending from Markov chain. To the best of our knowledge, this is the¯rst work of learning speci¯cations from object-oriented programs dynamically based on probabilistic models. Di®erent from similar works, our technique does not require annotating programs. Additionally, it learns speci¯cations in an online mode, which can re¯ne existing models continuously. Above all, we talk about problems regarding noise and connectivity of mined models and a strategy of computing thresholds is proposed to resolve them. To investigate our technique's feasibility and e®ectiveness, we implemented our technique in a prototype tool ISpecMiner and used it to conduct several experiments. Results of the experiments show that our technique can deal with noise e®ectively and useful speci¯cations can be learned. Furthermore, our method of computing thresholds provides a strong assurance for mined models to be connected.