Abstract
This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators’ awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models.
Highlights
M ODERN power networks are undergoing considerable operational and structural modifications, resulting from significant technology advances with respect to forms of electric power generation and communications infrastructure, let alone the energy market liberalisation [1], [2]
It is widely recognised that wide area monitoring systems (WAMS) contribute to operators’ knowledge of system’s operational status, and dynamic state estimation (DSE) may prove to be a very useful tool [3]
In the context of the analysis conducted here, the process disturbance vector w lies within a range of values corresponding to nominal operation, and, as far as the measured input and output measurement noises (i.e. ν and υ, respectively) are concerned, they are characterized by maximum errors, as specified by standards, with which the measurement devices used have to comply, and in this case the IEEE Standard C37.118.1-2011 for phasor measurement units (PMUs) [42]
Summary
M ODERN power networks are undergoing considerable operational and structural modifications, resulting from significant technology advances with respect to forms of electric power generation and communications infrastructure, let alone the energy market liberalisation [1], [2]. There have been several observer based research studies, involving power system models requiring many assumptions and simplifications [21], [22]; in cases where more advanced power system models are utilized, they are linearised with respect to one specific operating point, with the observer’s filtering matrix term being optimized with reference to this particular point [23]–[26], restricting the applicability of the observer in a continuously changing environment like operation of a power network To address these issues, the work conducted in [27] is extended to consider nonlinear output equations, leading to the development of a discrete-time observer, based on time-varying linearisation with respect to the estimate at every time step, in a similar approach as in Extended Kalman filtering (EKF).
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