Modern distribution grids are shifting toward resilient and sustainable energy networks through the use of unprecedented number of distributed generation (DG) units and renewable energy sources. However, unlike conventional grids, they are also highly integrative in the sense that, centralized control applications must support critical protective actions to reliably provide power to consumers. To achieve this goal, this paper presents a novel mechanism for coordinated protection and control of DG systems under permanent line faults in distribution networks. First, a centralized fault detector employs voltage phasors and frequency data to identify and isolate the fault within a tolerance time. Upon fault detection, a secondary control algorithm retrieves archived synchrophasor datasets to calculate real and reactive power disturbances caused by the fault isolation. In this mechanism, the secondary controller facilitates voltage/frequency recovery by adapting reference points of local controllers to the post-fault conditions. Coordination is carried out based on the knowledge of the response time of protective devices and communication delays in control links. The proposed approach is a promising paradigm for reliable networked protection and improved situational awareness in smart distribution networks.

Faults are extreme events that canadversely affect the voltages in islanded microgrids. This paper provides a new data-driven methodology for timely prediction of the post-fault voltage stability in hybrid AC/DC microgrids. The proposed method performs a binary classification with delay constraints by processing sequences of the short-time mean squared deviations using a deep learning system. The deep learning system consists of a bidirectional long short-term memory network whose output is a probabilistic voltage instability indicator. When the value of the indicator is non-zero, persistent voltage disturbances are most likely to occur even after the fault clearance. The proposed method enables the microgrid to carry out remedial or preventive actions, such as event-triggered protection and control of distributed energy resources (DERs), which are advantageous to the resilient operation of the microgrids. Extensive and detailed electromagnetic transient (EMT) simulations of a low-voltage hybrid AC/DC microgrid benchmark are analyzed, and the results confirm the effectiveness of the proposed method for online prediction and fast voltage regulation.

The output power of distributed energy resources (DERs) may experience irregular fluctuations due to variations of renewable sources, which need to be monitored in order to reliably control the grid. This paper proposes a novel approach for centralized detection of such irregularities based on the time-series analysis of the data reported by phasor measurement units (PMUs). In this approach, a network controller constructs datasets of time-aligned real/reactive powers for different zones. The datasets are transformed into sequences of short-time local outlier probability (ST-LOP) which are analyzed to identify the DER events. The network controller estimates features such as the average duration and the similarity degree which is a measure of spatio-temporal correlation between the DER events. As a use case, event-triggered control of solar photovoltaic (PV) systems with energy storage devices is investigated. The simulation results for the IEEE 123-bus network corroborate the effectiveness of the developed analytics for detection and mitigation of ramp-rate solar power fluctuations. Smart microgrids and active distribution networks can employ the developed analytics to improve a range of diagnostic and control functionalities.

In this article, the resilience of a coordinated control system for a set of PV-based distributed energy resources (DERs) against false data injection (FDI) attacks is evaluated. The evaluation is performed using a functional mock-up interface (FMI)-compatible cosimulation platform which enables the interaction of multi-domain simulators (EMTP, MATLAB/Simulink, and NS-3). The cosimulation platform permits rigorous analysis of cybersecurity through detailed modeling of all system components. The DER coordinated control and communication systems implemented on the IEEE-34 bus benchmark consist of measurement, control and monitoring components including substation central controller, DER local controllers, synchrophasor network and advanced metering infrastructure (AMI). Some DERs are equipped with an energy storage system (ESS) and coordinated by the central control unit in order to correct voltage disturbances resulting from the intermittent solar photovoltaic (PV) generation. The FDI attack targets the AMI system and aims at manipulating the load profile messages reported by the smart meter collector, thus yielding a central control failure. To detect the attacks and mitigate their impacts, a neural network-based algorithm is proposed and incorporated in the central control unit. The effectiveness of the proposed detection and mitigation algorithm is confirmed through simulations using the proposed FMI-compatible cosimulation platform.