• Energy Research
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The training process of learning-based distribution system state estimation (DSSE) methods relies on accurate state variables, which typically contain unknown noise and outliers in practice. To this end, this paper proposes an adaptive noise-resistant graphical learning-based DSSE method considering the impact of inaccurate state variables. Specifically, two global-scanning graph jumping connection networks are first developed to capture the regression rules between measurements and state variables considering the structure constraints. To mitigate the negative impact caused by inaccurate labels, a collaborative learning framework is further developed, within which Gaussian mixture model-based discriminators are employed to adaptively select clean samples in each mini-batch. These allow the method to obtain robustness against noisy state labels in historical data, as well as anomalous measurements during online operations. Comparative tests show the superiority of the proposed method in tackling abnormal data in both the training and test phases.

The training process of learning-based distribution system state estimation (DSSE) methods relies on accurate state variables, which typically contain unknown noise and outliers in practice. To this end, this paper proposes an adaptive noise-resistant graphical learning-based DSSE method considering the impact of inaccurate state variables. Specifically, two global-scanning graph jumping connection networks are first developed to capture the regression rules between measurements and state variables considering the structure constraints. To mitigate the negative impact caused by inaccurate labels, a collaborative learning framework is further developed, within which Gaussian mixture model-based discriminators are employed to adaptively select clean samples in each mini-batch. These allow the method to obtain robustness against noisy state labels in historical data, as well as anomalous measurements during online operations. Comparative tests show the superiority of the proposed method in tackling abnormal data in both the training and test phases.

In a hybrid AC/DC microgrid (MG), power quality issues arise when an unbalanced load connects to the AC subgrid, which are not confined to the AC subsystem but extend to affect the DC subsystem as well. This paper investigates the potential power quality issues caused by AC imbalance, including DC voltage fluctuation and AC current harmonics. Multiple control objectives are developed, aiming to eliminate DC fluctuation, reduce AC distortion and imbalance, and achieve negative sequence current sharing among distributed generations in the AC subgrid. To realize these control objectives, a two-layer coordinated control strategy is proposed. The first layer involves local interlinking converter (IC) control to improve the power quality of the DC subgrid, while the second layer focuses on distributed unbalance compensation control to improve the power quality of the AC subgrid. Finally, several experiments are conducted to verify the effectiveness of the proposed control strategy.

In a hybrid AC/DC microgrid (MG), power quality issues arise when an unbalanced load connects to the AC subgrid, which are not confined to the AC subsystem but extend to affect the DC subsystem as well. This paper investigates the potential power quality issues caused by AC imbalance, including DC voltage fluctuation and AC current harmonics. Multiple control objectives are developed, aiming to eliminate DC fluctuation, reduce AC distortion and imbalance, and achieve negative sequence current sharing among distributed generations in the AC subgrid. To realize these control objectives, a two-layer coordinated control strategy is proposed. The first layer involves local interlinking converter (IC) control to improve the power quality of the DC subgrid, while the second layer focuses on distributed unbalance compensation control to improve the power quality of the AC subgrid. Finally, several experiments are conducted to verify the effectiveness of the proposed control strategy.

Direct-driven generation is considered one of the most reliable selections in wave energy conversion systems. Therefore, a direct-driven linear-rotary wave generator (LRWG), which can transfer the low-speed linear wave motion to the high-speed rotating motion of the rotor and then generate electrical power simultaneously, is proposed in this paper. The proposed LRWG comprises three parts: translator, common rotor, and stator. From the energy view, the proposed LRWG can be divided into two sections: the energy transmission section to increase the velocity of the wave and the energy conversion section to generate power. The initial design method is given to determine the main dimensions of the proposed LRWG. To meet the maximum power tracking control requirements, a multi-mode and multi-objective optimization method, which can consider both generator and motor mode, is proposed to improve the performance of the proposed LRWG by sensitivity analysis, response surface methodology, and non-dominated sorting genetic algorithm II method. Finally, a prototype and its experimental platform are developed based on one of the optimization schemes from the possible solutions to verify the proposed approach and the performance of the proposed LRWG.

Direct-driven generation is considered one of the most reliable selections in wave energy conversion systems. Therefore, a direct-driven linear-rotary wave generator (LRWG), which can transfer the low-speed linear wave motion to the high-speed rotating motion of the rotor and then generate electrical power simultaneously, is proposed in this paper. The proposed LRWG comprises three parts: translator, common rotor, and stator. From the energy view, the proposed LRWG can be divided into two sections: the energy transmission section to increase the velocity of the wave and the energy conversion section to generate power. The initial design method is given to determine the main dimensions of the proposed LRWG. To meet the maximum power tracking control requirements, a multi-mode and multi-objective optimization method, which can consider both generator and motor mode, is proposed to improve the performance of the proposed LRWG by sensitivity analysis, response surface methodology, and non-dominated sorting genetic algorithm II method. Finally, a prototype and its experimental platform are developed based on one of the optimization schemes from the possible solutions to verify the proposed approach and the performance of the proposed LRWG.

The distributed frequency control system of microgrids, which relies on classical communication networks between distributed generations (DGs) for frequency regulation and restoration, is vulnerable to cyber-attacks. Quantum distributed controllers offer a secure quantum communication scheme but are less efficient because of continuous communication in quantum systems. This paper proposes a quantum distributed event-triggered secondary frequency control strategy for the islanded AC microgrid. The suggested event-triggered control significantly lessens the communication load and is Zeno-free. Furthermore, a novel false data injection attack (FDIA) scenario is introduced for the quantum-microgrid system. The non-periodic nature of communication can be exploited to directly identify and isolate compromised communication links, thereby enhancing the resilience of the quantum-microgrid system. Finally, simulation results on an AC microgrid with four DGs validate the effectiveness of the suggested control scheme.