Distributed Fault Isolation Research Articles

Enhancing the distribution grid resilience using cyber‐physical oriented islanding strategy

The increasing penetration of distributed generations enables an innovative operation paradigm that allows islanded operation to enhance the resilience of the distribution grid. In this study, a cyber-physical oriented islanding strategy is proposed by coordinating centralised and distributed control to achieve seamless islanding transition and operational flexibility in emergency conditions. A cyber-physical control structure is developed to mitigate various disturbances (e.g. emergencies or fluctuations) according to different operation conditions. Specifically, the distributed fault isolation and seamless islanding transition are coordinated to mitigate the outage caused by unplanned islanding, while a secondary control is proposed to support primary control by reducing the power fluctuations during islanded operation. With a rapid response speed, the local cyber-physical devices are coordinated to accomplish islanding separation by selecting a feasible islanded area even under an unplanned islanding situation. A field test was conducted on a practical distribution network in China, and the results demonstrated the effectiveness and feasibility of the proposed islanding strategy.

Distributed sensor fault diagnosis for a formation system with unknown constant time delays

In this paper, a distributed velocity sensor fault diagnosis scheme is presented for a formation of a second-order multi-agent system with unknown constant communication time delays. An existing distributed proportion-derivation (DPD) formation control law is adopted and a delay-independent condition is proposed to guarantee the asymptotical formation stability of the formation system based on the Nyquist stability criterion. Then a distributed fault diagnosis scheme is developed. In each agent, a distributed fault detection residual generator (DFDRG) and a bank of distributed fault isolation residual generators (DFIRGs) are designed based on the closed-loop model of the whole system. Each DFIRG is built up on the basis of a reduced-order unknown input observer (UIO) which is robust to the fault of one neighboring agent. According to the robust relationship between DFIRGs and faults, distributed fault isolation can be achieved. Conditions are presented to guarantee that each agent is able to diagnose faults of itself and its neighbors despite the disturbance of time delays. Finally, outdoor experimental results illustrate the effectiveness of the proposed schemes.

Linguistic OWA and two time-windows based fault identification in wide plants

Fault detection and diagnosis in industrial processes are challenging tasks that demand effective and timely decision making procedures. The multivariate statistical approaches for fault detection based on data have been very useful. However, they are known to be less powerful for fault diagnosis because they normally require prior knowledge of the problem involved. In this context, this proposal is based on an on-line, distributed fault isolation approach to provide a scored rank of variables considered as responsible for the faults in a more robust and earlier way than usual approaches. The fault isolation is carried out considering some top Fault Isolation (FI) methods, without prior knowledge regarding faults, in a distributed and collaborative way by a linguistic based decision making. The isolation of faulty variables provided by each FI approach is aggregated to provide a fault identification based on a scored ranking at two time points: after the fault detection and when the plant has recovered. In both cases, the final fault isolation is provided as a scored ranking obtained by Ordered Weighted Average operators (OWA) and Regular Increasing Monotone (RIM) aggregation functions, which permit the implementation of linguistic aggregation functions. The risk aversion during this multicriteria isolation is tuned by the user and can provide several strategies or policies. The fault isolation at two key times searches for the origin of faults and evaluates the evolution of the system after the fault’s occurrence in the new working position of the plant. This is because faults in an industrial plant are propagated to different variables due to the actions of the process controllers. This method has been applied to two complex benchmark plants obtaining an earlier and more robust isolation.

A New Local-Model-Based Distributed Fault Diagnosis Scheme for Multi-Agent Systems with Actuator Faults

In the paper, a new local-model-based distributed fault diagnosis (LMDFD) scheme is proposed for multi-agent systems subject to actuator faults. The proposed LMDFD scheme consists of a distributed fault detection (DFD) unit and a distributed fault isolation (DFI) unit. A distributed fault detection residual generator (DFDRG) and a bank of distributed fault isolation residual generators (DFIRGs) are developed in each agent based on the closed-loop model of both the agent and neighbors of the agent. Each agent updates states of its DFDRG and DFIRGs by using information of itself, its neighbors, and the delayed information of its neighbors’ neighbors. Moreover, adaptive fault detection and isolation thresholds are designed to deal with bounded disturbances induced by the delayed information. Based on the adaptive thresholds and the residuals of DFDRG and DFIRGs, the DFD and DFI logic are designed for each agent, respectively. Simulation results demonstrate the effectiveness of the proposed LMDFD scheme.

Hyperdimensional Computing in Industrial Systems: The Use-Case of Distributed Fault Isolation in a Power Plant

This paper presents an approach for distributed fault isolation in a generic system of systems. The proposed approach is based on the principles of hyperdimensional computing. In particular, the recently proposed method called Holographic Graph Neuron is used. We present a distributed version of Holographic Graph Neuron and evaluate its performance on the problem of fault isolation in a complex power plant model. Compared to conventional machine learning methods applied in the context of the same scenario the proposed approach shows comparable performance while being distributed and requiring simple binary operations, which allow for a fast and efficient implementation in hardware.

Distributed sensor fault diagnosis for a formation of multi-vehicle systems

In the paper, a distributed sensor fault detection and isolation scheme is presented for a network of second-order integrators. A new distributed control law is developed to achieve formation of the system. By using the integration information of distributed formation errors, the control law improves the robustness of the formation. A distributed observer is then designed in each vehicle based on the closed-loop dynamic model of the vehicle. Each vehicle updates the states of the distributed observer by employing the measurements of itself and the transmitted state estimations from its neighbors. Based on the distributed observer, a distributed fault detection observer and a distributed fault isolation observer are designed. The presented distributed fault detection observer in each vehicle is able to be sensitive to the faults of all vehicles in the system. By using the distributed fault isolation observers, each vehicle is able to be sensitive to the faults of itself, its neighbors and its neighbors’ neighbors and to be robust to the faults of other vehicles. Although the fault isolation of the proposed scheme is simple, computation loads of the scheme are lower than the current ones since only the model of the individual vehicle is used. Finally, the effectiveness of the control law and the fault diagnosis scheme is demonstrated by simulations and real-time experiments carried out based on a formation of three quadrotors.

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems

This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and air-conditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology.