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This paper investigates the dynamic interactions of current controllers for multi-paralleled, grid-connected inverters. The consequent harmonics instability phenomena, which features with oscillations above the fundamental frequency, are evaluated by the impedance-based stability criterion. The frequency range of effective impedance-based stability analysis is first identified. The effect of each inverter on the system harmonic instability is then identified by case studies on different groups of inverters. Lastly, the PSCAD/EMTDC simulations on a system with five passivelydamped, LCL-filtered inverters are performed to verify theoretical analysis. It shows that the impedance-based stability analysis results agree with the time-domain simualtions provided that the frequency of concerns are around the half of the Nyquist sampling frequency.

Abstract-Participation factor analysis is an interesting feature of the eigenvalue-based stability analysis in a power system, which enables the developers to identify the problematic elements in a multi-vendor project like in an offshore wind power plant. However, this method needs a full state space model of the elements that is not always possible to have in a competitive world due to confidentiality. In this paper, by using an identification method, the state space models for power converters are extracted from the provided data by the suppliers. Some uncertainties in the identification process are also discussed and solutions are proposed, and in the end the results are verified by time domain simulations for linear and nonlinear cases with different complexities, no matter which domain (phase or dq) is used.

Abstract. In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of Green IT to save energy. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches user requirements. The RS recommends a service based on the network QoS and Virtual Machine (VM) platform factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.