Fault Detection Under Uncertainty in Active Hybrid Distribution Networks

Contemporary distribution networks incorporate Distributed Energy Resources (DER) and encompass both AC and DC systems interfaced with various types of power electronic converters. These active hybrid networks are the new norm in generating, distributing, and consuming electric energy in a sustainable manner, hence being integral to the holistic concept of the Smart Grid that aims for goals like self-awareness, high resiliency, self-healing, and bidirectionality of energy flow. Achieving these objectives, however, requires collecting and communicating real-time information to estimate the system state with high accuracy and low latency, and quickly discover potential faults or malfunctions. Even though partial observability has always been a prevalent challenge in distribution grids, active hybrid distribution networks are wreaked by further sources of uncertainty including inherent stochasticity of DER generation, inclusion of non-linear power electronic converters that interface DER with the grid, heterogeneity of AC and DC systems, and finally receiving noisy or potentially corrupt data. Therefore, improved fault detection schemes must be capable to handle uncertainty, as conventional zonal protection schemes are less reliable once applied to active hybrid distribution networks (AHDN). The proposed novel methodology in this research relies on retaining a Bayesian Belief Network (BBN) paradigm for decision making under uncertainty which enhances the performance of existing relaying and protection schemes. AHDN is a multivariate dynamic system; thus, it can be efficiently analyzed by resorting to probabilistic graphical model formalism. A factor graph representation (FG) has been used to pass messages among cluster nodes. Furthermore, to collect causal data, a distributed state estimation (DSE) has been employed, where deviation of state variables within their probability distribution function (PDF) signals a likely fault. To feed the graph with correlational evidence, the data collected from IoT sensors are exploited. Since phasor measurement units (PMU) and DSE algorithm are used to compute state variables, the proposed methodology spans over the inherent heterogeneity of AC and DC agents. Additionally, because both the magnitude of anomalies or abrupt changes and their trajectory are used to detect faults, thus shifts towards PDF mean are eliminated to reduce false positive rate (FPR). The proposed methodology is intended to accompany and enhance conventional protection schemes as outlined in IEEE standards 1547 and 2030 for DER interconnection. The method is mainly a decision-making process for detecting and locating faults, and it can be successfully applied to both passive and active networks. It functions well without a priori knowledge about the bonding and grounding scheme and whether the neutral is earthed or not; the latter has been a source of difficulty in islanded microgrids. Furthermore, the proposed technique can identify low and high impedance faults within the range of 0.1 pu to 10 pu, and it discerns open circuit faults whenever possible. Due to its scalability, the proposed method can be applied to larger systems with various measurement sources. The purported fault detection scheme has been simulated in MATLAB / SIMULINK environment. After verifying the concept, the model has been applied to an experimental test bench which has been developed and validated by the Ecole Polytechnique Fédérale de Lausanne (EPFL) to compare and establish the results. Then it is implemented in an augmented version of IEEE 13- bus to corroborate its functionality and illustrate the outcome.

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