Construction of a Digital and Physical Hybrid Simulation Platform for MMC-HVDC Grid With Fault Current Suppression Equipment

In order to fully study the working characteristics of large-scale power electronic devices in the field of renewable energy delivery, it is imperative to build digital and physical hybrid simulation platforms. A power interface algorithm based on damping impedance is proposed to improve the stability of DC power grid hybrid platforms. Firstly, according to the characteristics of the open-loop transfer function of the damping impedance method, the matching principle between damping impedance at the power interface and equivalent impedance of the physical simulation system is obtained. Secondly, the calculation method of the equivalent impedance of multi-type equipment on the physical side is proposed, and the impedance real-time matching under different working conditions is realized. In order to reduce the simulation error caused by interface delay, a DC voltage interface delay compensation method based on slope prediction is proposed, and a prediction compensation model is established. A digital and physical hybrid platform for a four-terminal flexible DC power grid with DC circuit breakers is built to verify the proposed interface algorithm. The simulation results show that the proposed interface algorithm can effectively compensate for the interface delay and ensure the stable operation of the platform under different conditions.

EEGG: An analytic brain-computer interface algorithm

<div><b>Objective.</b> A black box called brain-computer interface (BCI) model is used to identify another black box, the brain. However, one black box cannot explain another black box. This paper presents the first analytic "white box" brain-computer interface algorithm named EEGG. </div><div><b>Approach. </b>Independent and interactive effects of neurons or brain regions can fully describe the brain. This paper constructed a relationship model that extracted the independent and interactive features of EEG for intention recognition and analysis using ResDD, a novel dendrite module of Gang neuron. A total of 4,906 EEG data about motor imagery (MI) of left-hand movements and right-hand movements from 26 subjects were obtained from GigaDB. Firstly, we explored EEGG's generalization ability according to cross-subject accuracy. Secondly, we transformed the EEGG model into a relationship spectrum expressing independent and interactive effects of brain regions. Then, the relationship spectrum was verified through the known ERD/ERS phenomenon. Finally, we explored the previously unreachable further analysis based on a BCI model.</div><div><b>Main results.</b> (1) EEGG was more robust than typical "CSP+" algorithms for the poor quality EEG data [AUC:0.825±0.074(EEGG)>0.745±0.094(CSP+LDA)/0.591±0.104(CSP+Bayes)/0.750±0.091(CSP+SVM), p<0.001]. (2) The transformed EEGG model showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that the interactive effects of brain regions put a brake on ERD/ERS effects for classification (p<0.001). This means that generating fine hand intention needs more centralized activation of the brain.</div><div><b>Significance.</b> EEGG implies that, henceforth, not only can BCI be used for recognition but also analysis.</div>

EEGG: An analytic brain-computer interface algorithm

<div><b>Objective.</b> A black box called brain-computer interface (BCI) model is used to identify another black box, the brain. However, one black box cannot explain another black box. This paper presents the first analytic "white box" brain-computer interface algorithm named EEGG. </div><div><b>Approach. </b>Independent and interactive effects of neurons or brain regions can fully describe the brain. This paper constructed a relationship model that extracted the independent and interactive features of EEG for intention recognition and analysis using ResDD, a novel dendrite module of Gang neuron. A total of 4,906 EEG data about motor imagery (MI) of left-hand movements and right-hand movements from 26 subjects were obtained from GigaDB. Firstly, we explored EEGG's generalization ability according to cross-subject accuracy. Secondly, we transformed the EEGG model into a relationship spectrum expressing independent and interactive effects of brain regions. Then, the relationship spectrum was verified through the known ERD/ERS phenomenon. Finally, we explored the previously unreachable further analysis based on a BCI model.</div><div><b>Main results.</b> (1) EEGG was more robust than typical "CSP+" algorithms for the poor quality EEG data [AUC:0.825±0.074(EEGG)>0.745±0.094(CSP+LDA)/0.591±0.104(CSP+Bayes)/0.750±0.091(CSP+SVM), p<0.001]. (2) The transformed EEGG model showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that the interactive effects of brain regions put a brake on ERD/ERS effects for classification (p<0.001). This means that generating fine hand intention needs more centralized activation of the brain.</div><div><b>Significance.</b> EEGG implies that, henceforth, not only can BCI be used for recognition but also analysis.</div>

Design and Analysis on an Optimized Interface Algorithm for Real-Time Integrated Simulation System of Renewable Energy and the Power Grid

The interface algorithm is critical for accuracy of the real-time integration simulation system of renewable energy and the power grid. To improve the overall performance of the existing interface algorithms, this paper proposes an optimized interface algorithm based on the auxiliary damping impedance method interface current feedback. We explain in detail the implementation principle of the new interface algorithm and the calculation method of impedance matching and also provide a parallel timing control logic. Using the new interface algorithm, we derive equations for voltage and current of the digital simulation system side and the device under test side and also compare it with the naturally coupled system without interface delay. Finally, we verify the accuracy of the new interface algorithm via establishing a complete model of the real-time integration simulation system with a wind turbine and the power grid. The results show that the accuracy can be improved 95% in the digital simulation system side and 17% in the device under test side by using the proposed interface algorithm in this paper.