Hany F. Habib

A multi-agent based technique for fault location, isolation and service restoration

This paper proposes a communication-assisted fault localization, isolation, and restoration method for microgrids based on a multi-agent system. The proposed system comprises distributed agents, located in the middle and at the two ends of a protection section, which will detect a fault through phase angle comparison of current signals at both sides of a given distribution line. The agents then send trips signal to corresponding circuit breakers accordingly. The importance of the proposed protection technique is twofold. First, it eliminates the use of voltage transformers and thus reduces costs. Second, it does not require transfer of data along long distances, which decreases the delay time for fault isolation. Power restoration processes following the fault clearance considering voltage, frequency, and power flow constraints in the microgrid under study were also performed. Simulation of the proposed protection methodology was presented followed by experimental verification. The experimental results showed excellent agreement with the simulated protection scheme.

Multi-Agent-Based Technique for Fault Location, Isolation, and Service Restoration

This paper proposes a communication-assisted fault localization, isolation, and restoration method for microgrids based on a multi-agent system. The proposed system comprises distributed agents, located in the middle and at the two ends of a protection section, which will detect a fault through phase angle comparison of current signals at both sides of a given distribution line. The agents then send trips signal to corresponding circuit breakers accordingly. The importance of the proposed protection technique is twofold. First, it eliminates the use of voltage transformers and thus reduces costs. Second, it does not require transfer of data along long distances, which decreases the delay time for fault isolation. Power restoration processes following the fault clearance considering voltage, frequency, and power flow constraints in the microgrid under study were also performed. Simulation of the proposed protection methodology was presented followed by experimental verification. The experimental results showed excellent agreement with the simulated protection scheme.

Online false data detection and lost packet forecasting system using time series neural networks for IEC 61850 sampled measured values

Migrating to a smart grid requires a paradigm shift in the implementation of power system applications. With the advent of IEC 61850, contemporary Substation Automation Systems (SAS) are utilizing electronic instrument transformers and merging units to transmit current and voltage measurements over Ethernet as Sampled Measured Values (SMV). However, if a substations network resources are not properly managed, the high transmission rate of SMV would make them prone to packet loss. Also, the strict 4ms time constraint imposed on SMVs makes encrypting these messages nearly impossible. As such, this paper presents an online device level fake data detection system for detecting fake SMV messages without violating the 4ms time constraint set forth by IEC 61850. In order to ensure a reliable SAS operation, this paper also presents a coupled neural network — time series method for forecasting lost SMV packets. The proposed algorithm was implemented in a system composed of merging units and intelligent electronic devices developed for this purpose. Real-time experimental results of the proposed algorithms over a real IEC 61850 network showed excellent results in terms of detecting fake messages and increasing the robustness of protection schemes by accurately forecasting dropped SMV packets.

On the adaptive protection of microgrids: A review on how to mitigate cyber attacks and communication failures

One main challenge in the practical implementation of a microgrid is the design of an adequate protection scheme in both grid connected and islanded modes. Conventional overcurrent protection schemes face selectivity and sensitivity issues during grid and microgrid faults since the fault current level is different in both cases for the same relay. Various approaches have been implemented in the past to deal with this problem, yet the most promising ones are the implementation of adaptive protection techniques abiding by the IEC 61850 communication standard. This paper presents a critical review of existing adaptive protection schemes, the technical challenges for the use of classical protection techniques and the need for an adaptive, smart protection system. However, the risk of communication link failures and cyber security threats still remain a challenge in implementing a reliable adaptive protection scheme. A contingency is needed where a communication issue prevents the relay from adjusting to a lower current level during islanded mode. An adaptive protection scheme is proposed that utilizes energy storage (ES) and hybrid ES (HESS) already available in the network as a mechanism to source the higher fault current. Four common grid ES and HESS are reviewed for their suitability in feeding the fault while some solutions are proposed.

A targeted attack for enhancing resiliency of intelligent intrusion detection modules in energy cyber physical systems

Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time-critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware to trigger false positives in the neural networks response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural networks ability to learn and adapt to these attacks.