Partial discharge (PD) signals generated within electrical power equipment can be used to assess the condition of the insulation. In practice, testing often results in multiple PD sources. In order to assess the impact of individual PD sources, signals must first be discriminated from one another. This paper presents a procedure for the identification of PD signals generated by multiple sources. Starting with the assumption that different PD sources will display unique signal profiles this will be manifested in the distribution of energies with respect to frequency and time. Therefore the technique presented is based on the comparison of signal energies associated with particular wavelet-decomposition levels. Principal component analysis is adopted to reduce the dimensionality of the data, whilst minimizing lost information in the data concentration step. Physical parameters are extracted from individual PD pulses and projected into 3-dimensional space to allow clustering of data from specific PD sources. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is chosen for its ability to discover clusters of arbitrary shape in n-dimension space. PD data from individual clusters can then be further analyzed by projecting the clustered data with respect to the original phase relationship. Results and analysis of the technique are compared for experimentally measured PD data from a range of sources commonly found in three different types of high voltage (HV) equipment; ac synchronous generators, induction motors and distribution cables. These experiments collect data using varied test arrangements including sensors with different bandwidths to demonstrate the robustness and indicate the potential for wide applicability of the technique to PD analysis for a range of insulation systems.

This paper proposes a current sensorless control strategy for the dual active bridge (DAB) converter with nonlinear disturbance observer (NDO)-based load current feedforward. The proposed method can regulate both the dc-bus voltage and the current of the DAB converter without current sensors. Several performance indexes are established for the parameter design and a detailed design method is presented in this paper. When a single-phase H-bridge inverter is connected, the double-frequency oscillation from the feedback path is suppressed by proper parameter design, while the transient response of dc-bus voltage can be further improved by the NDO-based load current feedforward with a notch filter in the feedforward path. The performance of the proposed control method and the effectiveness of the parameter design are verified in a prototype by experimental results.

This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to capture the interactions between the energy provider (leader) and energy consumers (follower). A distributed algorithm was proposed to derive the Stackelberg equilibrium, then, the best strategies for the energy provider and each energy hub operator were explored in order to maximize their benefits. Simulation results showed that the proposed method can balance the energy supply and demand, improve the payoffs for all players, as well as smooth the aggregated load profiles of all energy consumers.

Electric propulsion is widely used in ship propulsion systems, and multiphase machines are gaining increasing popularity because of their fault‐tolerant ability, high power density, and reduced torque ripple. This study concentrates on the tandem ship propulsion configuration with two 15‐phase induction machines (IMs) fixed on the same shaft. Based on field‐oriented control and reconfiguration fault‐tolerant control for the single 15‐phase IM, the master–slave coordinated control strategy is designed for the tandem motors, which can not only achieve power allocation between two motors during normal operation, but also minimise the total stator copper loss of the two motors and maximise the propulsion output power ability during fault‐tolerant operation. Experiment results from a ship propulsion experimental platform verify the effectiveness of the proposed control strategies.

A sawtooth carrier-based sinusoidal pulse width modulation (SC-SPWM) technique for asymmetrical six-phase two-level inverters is proposed in this paper, which could reduce both the peak-to-peak amplitude and the changing frequency of the common-mode voltage (CMV). The intersection-plotting method reveals that the mirror symmetrical sawtooth carriers for two three-phase groups can help optimize the distribution of overlapping areas, which is the CMV reduction mechanism of SC-SPWM. Additionally, the Fourier analysis is employed to quantitatively evaluate the modulation index and harmonic performance of SC-SPWM compared with the conventional SPWM and two phase-shifted SPWM methods. Also, further analysis demonstrates that dead time does not affect the reduction of CMV in a noticeable way. Finally, both simulation and experimental results coincide with the theoretical analysis that the proposed SC-SPWM shows the best performance in reducing the CMV amplitude and changing frequency among these four SPWM methods, though it leads to a higher phase current distortion.

To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM) models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter–wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS), which are validated by receiver operating characteristic (ROC) analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologies, and the transient stability predicting capability of the SVM model based on the GTCFS is extensively tested. The experimental results show that the selected GTCFS features improve the prediction accuracy with high computational efficiency. The proposed method has distinct advantages for transient stability prediction when faced with incomplete Wide Area Measurement System (WAMS) information, unknown operating conditions and unknown topologies and significantly improves the robustness of the transient stability prediction system.