To deal with the problem that uncertain parameter in Affinity Propagation (AP) clustering algorithm affects the clustering results, this paper proposes a clustering method based on AP clustering algorithm and Genetic Algorithm (GA) for Photovoltaic (PV) power stations. Firstly, the modeling of PV power stations is established and clustering index are determined according to the dynamic characteristics of PV power stations. Then the proposed method takes the silhouette index, which is used to evaluate the quality of the clustering result, as the fitness function of GA. The bias parameter placed on the main diagonal of the similarity matrix in the AP clustering algorithm is constantly iterated and updated until obtaining the optimization result. Finally, the studied case verifies the better clustering performance of the proposed method. The 20 PV power stations in the Taoling Substation can be simplified into three groups, reducing the complexity and calculation time for equivalent modeling.

Droop-controlled dc microgrids are widely used in the field of renewable energy generation. In order to endow fast transient response to the source converters, current-mode droop control approaches are preferred rather than the voltage-mode counterparts. However, a small droop gain in current-mode droop-control will bring instability challenges to the system. In this paper, a novel impedance-shaping based active damping technique is proposed to facilitate better stability performance of the current-mode droop-controlled system. A detailed impedance model is established to analyze the system stability under different operating conditions. The theoretical results obtained in this paper are validated by simulation results with switching models.

Static voltage stability of power grids will become more sensitive to the coordinated operation of renewable energy resources (RESs) and energy storage systems (ESSes) due to their different output characteristics. This paper presents a generalized approach for static voltage stability evaluation under coordinated operations of wind power, PV and energy storage stations. First, a combined model for coordinated operation of stations which describes the output characteristics of active and reactive power is established. Then, the integration point of stations is optimized by modal analysis such that the most sensitive bus in the system can be located. Afterwards, the static voltage stability margin can be evaluated for load buses via impedance modulus margin index (IMMI). Furthermore, impact of penetration rates and penetration rate ratio among wind power, PV and energy storage stations on static voltage stability margin is investigated. To validate the effectiveness of proposed method, a standard IEEE 14 bus test system is studied and numerical results are presented.

Combing advanced measurement infrastructure (AMI) and information and communications technology (ICT), the Internet of Things (IoT) has been widely used in the energy system. The continuous state modeling method cannot effectively deal with the various operation scenarios in the park multiple energy system. This article proposes a hybrid state system modeling method based on the discrete hybrid automaton (DHA). The DHA-based model judges the equipment mode through the logical discriminant and describes the energy transmission process of the equipment with a switched affine system. And a double-layer optimization structure for the multiple energy system is proposed, which combine the long-term forecasting data and short-term forecasting data. Based on the AMI, the forecasting system predicts the system state and determines the scheduling plan of each equipment. This article uses a typical industrial-park case with multiple energy system to verify advantages of the DHA-based modeling method, calculates the economic benefits brought by the optimization and evaluates the maximum carbon emission reduction capacity of the industrial park.

Multilevel converters play an important role in power electronic systems. In recent years, data-driven fault diagnosis technologies for multilevel converters have rapidly developed and are generally based on feature engineering and intelligent classification algorithms. However, the fault detection rate, speed, and stability still need to be improved, especially for complex converter systems. In this article, based on a probabilistic autoencoder architecture, an affine-invariant Riemannian metric autoencoder (AIRMAE) is proposed for feature extraction, and combined with classifiers, a supervised learning fault diagnosis method for multilevel converters is constructed. The proposed AIRMAE is able to investigate low-dimensional representations from high-dimensional data manifolds while preserving sufficient valid information. Based on a five-level nested neutral-point piloted converter, the effectiveness of the proposed method is verified by experiments. The performance is superior to that of existing state-of-the-art fault diagnosis methods, with high detection accuracy, good stability, and strong waveform reconstruction ability.

In an AC microgrid, the grid-supporting converter can operate either as a current source or a voltage source. By establishing the state-space models of parallel inverters for different operating modes, this paper conducts the stability assessment of droop-controlled grid-supporting inverters, and compares parallel inverters operating as voltage sources and current sources in AC MGs. Detailed analyses of participation factors, damping ratios, and oscillation frequencies are also performed for the given inverter operation. Furthermore, the effect of key control parameters on the system stability are analyzed. Finally, analytical results are further verified through simulation studies.

In this paper, power losses of a multiport DC – DC converter are analysed. The converter has four bidirectional ports and consists of a triple active bridge and four half bridge converters. As a result, sixteen different operating modes, defined by all combinations of power flow direction, are possible. The converter power losses and resulting efficiencies vary widely with operating conditions. This paper presents methods to accurately calculate the efficiency of the four-port converter in each operating mode using component datasheet information, taking into account losses in the transformer of the triple active bridge converter, passive components and semiconductors. A 200 W converter prototype is used to experimentally validate the loss model for one specific operating mode.

Droop control methods are widely used in dc microgrids due to its simplicity and easy implementation. However, DC bus voltage deviation increases as the droop gain increases although accuracy of power sharing is improved. In order to overcome this paper proposes an improved voltage regulation method under high droop gain circumstance with consideration of cable impedance. It allows accurate power sharing and the low voltage-regulation simultaneously. The reduced-order model is developed neglecting fast converter dynamics. The effect of this proposed method on stability is also studied by eigenvalues method. Time domain simulation in Matlab/Simulink validates all the analysis.

This paper deals with the control design for an aircraft electric starter-generator system that utilizes recent advances in modern power electronics allowing the use of novel machine types together with the introduction of controlled power electronics into the main path of energy flow. The paper describes the developed system and focuses on control design including flux weakening control of high-speed permanent magnet machine and droop control of the system output dc-link current. The analytical design results and the expected system performance are confirmed by time-domain simulations.