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Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach

Erfan Mehdipour Abadi, Masoud H. Nazari

TL;DR

Addresses distributed anomaly detection and mitigation in prosumer-based modern power systems with DERs through a dynamic probing framework built on $DOPF$ and $ADOPF$. Utilizes neighbor-watch-based detection with energy mismatch $d_{ij}(z)$ derived from two-hop measurements $p^{probe}$ and $p^{ref}$, enabling decentralized estimation of anomalies. Introduces an anomaly factor $F_{ij}$ and an exponential penalty mechanism using $P_i(t)$ and threshold $C_{th}$ to deter misbehavior and enable isolation by a utility while preserving distributed operation. Demonstrates effectiveness via simulations on a modified IEEE 5-Bus system, showing timely detection and isolation of persistent anomalies under varying conditions.

Abstract

The evolving landscape of electric power networks, influenced by the integration of distributed energy resources require the development of novel power system monitoring and control architectures. This paper develops algorithm to monitor and detect anomalies of different parts of a power system that cannot be measured directly, by applying neighboring measurements and a dynamic probing technique in a distributed fashion. Additionally, the proposed method accurately assesses the severity of the anomaly. A decision-making algorithm is introduced to effectively penalize anomalous agents, ensuring vigilant oversight of the entire power system's functioning. Simulation results show the efficacy of algorithms in distributed anomaly detection and mitigation.

Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach

TL;DR

Addresses distributed anomaly detection and mitigation in prosumer-based modern power systems with DERs through a dynamic probing framework built on and . Utilizes neighbor-watch-based detection with energy mismatch derived from two-hop measurements and , enabling decentralized estimation of anomalies. Introduces an anomaly factor and an exponential penalty mechanism using and threshold to deter misbehavior and enable isolation by a utility while preserving distributed operation. Demonstrates effectiveness via simulations on a modified IEEE 5-Bus system, showing timely detection and isolation of persistent anomalies under varying conditions.

Abstract

The evolving landscape of electric power networks, influenced by the integration of distributed energy resources require the development of novel power system monitoring and control architectures. This paper develops algorithm to monitor and detect anomalies of different parts of a power system that cannot be measured directly, by applying neighboring measurements and a dynamic probing technique in a distributed fashion. Additionally, the proposed method accurately assesses the severity of the anomaly. A decision-making algorithm is introduced to effectively penalize anomalous agents, ensuring vigilant oversight of the entire power system's functioning. Simulation results show the efficacy of algorithms in distributed anomaly detection and mitigation.
Paper Structure (5 sections, 6 equations, 4 figures)

This paper contains 5 sections, 6 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Distributed Prosumer Monitoring
  3. Automated Detection and Resolution of anomaly
  4. Simulation
  5. Conclusion

Figures (4)

  • Figure 1: (a) Schematic of modern power systems with multiple prosumers. (b) Separation of neighboring prosumers.
  • Figure 2: Flowchart of anomaly detection and mitigation algorithm.
  • Figure 3: Schematic of the modified IEEE 5-Bus System and initial power flow values saadat1999power.
  • Figure 4: (a) Generated power, (b) anomalous factor values of node 2 for $k \in \{1,2,\dots,24\}$, (c)Penalty value of node 2, and (d) isolation signal.