Search for: All records

In this paper, we propose a droop-free distributed frequency control for the hybrid photovoltaic and battery energy storage (PV-BES) based microgrid. A distributed state of charge (SOC) balancing regulator achieves balanced SOC among the distributed generators (DGs) with BES utilizing a distributed average SOC estimator and the power sharing regulator ensures proportional power sharing among the PV-BES based DGs. These regulators generate two frequency correction terms which are then added to the microgrid rated frequency to generate references for the lower level controllers. The performance of the proposed distributed control is validated through real-time simulations in OPAL-RT, which demonstrates the effectiveness of the proposed control in achieving frequency regulation, SOC balancing, and active power sharing in the hybrid PV-BES units under both islanded and grid-connected operation modes. 

This paper proposes a unified distributed secondary control for the grid-forming (GFM) and grid-feeding (GFE) converters in DC microgrids. An optimization problem is formulated for the secondary control and the objective function considers regulating the global average of the GFM and GFE converter output voltages and proportional current sharing among all GFM and GFE converters. A unified distributed control is then designed to generate voltage and current references respectively for GFM and GFE converters based on the formulated optimization problem. The dynamic model of the DC microgrid under the proposed control is also developed, and steady-state analysis is performed to show that the proposed distributed control can achieve the control objectives in steady state. The performance of the proposed control is validated through real-time simulations in OPAL-RT on an 8-DG DC microgrid system. 

This paper presents a deep learning based multi-label attack detection approach for the distributed control in AC microgrids. The secondary control of AC microgrids is formulated as a constrained optimization problem with voltage and frequency as control variables which is then solved using a distributed primal-dual gradient algorithm. The normally distributed false data injection (FDI) attacks against the proposed distributed control are then designed for the distributed gener-ator's output voltage and active/reactive power measurements. In order to detect the presence of false measurements, a deep learning based attack detection strategy is further developed. The proposed attack detection is formulated as a multi-label classification problem to capture the inconsistency and co-occurrence dependencies in the power flow measurements due to the presence of FDI attacks. With this multi-label classification scheme, a single model is able to identify the presence of different attacks and load change simultaneously. Two different deep learning techniques are compared to design the attack detector, and the performance of the proposed distributed control and the attack detector is demonstrated through simulations on the modified IEEE 34-bus distribution test system. 

This paper presents a droop-free distributed secondary control for DC microgrids with admissible voltage profile guarantees. The control objectives are achieved through an average voltage regulator, voltage variance regulator, and a relaxed current sharing regulator. Regulations of the global average voltage to the microgrid rated voltage is ensured by the average voltage regulator and regulations of the global voltage variance to a predetermined reference is enabled by the voltage variance regulator. In order to achieve the objectives of voltage regulation, the current sharing from one of the DGs which may be owned by the microgrid community is relaxed. The global dynamic model of the DC microgrid with the proposed control is derived. Besides, steady-state analysis is performed to show that all objectives can be achieved. Finally, simulations on a 4-DG DC microgrid test system are performed to validate the efficacy of the proposed control.