Description
Over last decade, electrical distribution networks have experienced significant changes caused by dispatchable load integration and growing demand for green but uncertain renewable energy. This requires increased level of situational awareness in distribution networks in order to maintain the power balance in real time. On the other hand, distribution networks still face limited number of sensors installed in the field making an accurate insight into the whole system state hardly achievable. More advanced state estimation tools considering new algorithms, new measurement and new information and communication technologies are needed to respond successfully to emerging challenges. In order to improve the estimation accuracy and to increase the level of situational awareness, lots of effort are being made in recent years in developing new and improving existing Dynamic State Estimation (DSE) algorithms instead of relying on conventional Static State Estimation (SSE). Forecasting-Aided State Estimation (FASE), as a particular application of DSE concept, is more applicable to real distribution networks due to lack of Phasor Measurement Units able to track the system dynamics. FASE usually deploys Kalman-type filters of which the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are commonly used. Although Kalman-type filters provide satisfactorily results in steady-state, uncertain loads and uncertain output powers of distributed generators can easily move the system out of the steady-state, thus potentially deteriorate the filter accuracy. To prevent this scenario, it is essential to assess appropriately inevitable uncertainties of the process model. In this paper we present a simplified adaptive method for online assessment of parameterized process noise covariance matrix in EKF based distribution system state estimation. Parameters of time-variant process noise covariance matrix are derived from characteristics of the EKF algorithm and characteristics of the existing metering infrastructure of distribution networks with the aim to improve the accuracy in steady-state and to avoid inaccurate results in the presence of sudden load changes. Analysis is performed on modified IEEE 37 distribution test system to verify the advantages of the proposed method.| Period | 20 Jun 2022 |
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| Event title | ENERGYMEET2022: INTERNATIONAL MEET ON POWER AND ENERGY ENGINEERING |
| Event type | Conference |
| Location | Copenhagen, DenmarkShow on map |