State estimation with Neural Networks and PMU voltage measurements

Abstract

State estimation is used currently in wide area electrical systems for real time analysis. Studies have shown that in HV transmission networks, where system-wide synchronized phasor measurement units are installed, voltage angle measurements can be included in the input measurements data set, with the result of improving the estimation precision. The authors developed in previous papers a SE algorithm based on Multilayer Perceptron Artificial Neural Networks. This paper extends this research by using PMU voltage magnitude and angle measurements in the input data for the ANN estimator, and shows in a case study that the estimation precision is improved. The development of advanced electricity markets in Europe and North America changed the operation and dispatch rules of power systems. In the United States and Canada, the ISO/RTO model is used to manage grid operation for approximately a half of North American Continent (1), while in Europe continuous efforts are made for coupling the national and regional electricity markets into a common market, markets supplying 75% of the demand using now a common day-ahead price mechanism and cross-border capacity management (2). High-performance tools are required for real time monitoring and control of such large systems. Enhanced state estimation (SE) algorithms are robust tools used today by system operators. Phasor measurement units (PMU) are used for synchronizing measurements used in state estimation analyses for systems covering wide geographical territories (3), and the trend is to generalize their deployment also at distribution level (4,5). Other approaches use distributed estimation, by splitting one large system in several areas estimated locally (6). The two approaches can be also combined (7). Recent research also use artificial neural networks (ANN) for state estimation (8,9). The authors proposed in (10) a SE approach which uses Multilayer Perceptron (MLP) artificial neural networks. A case study performed on a section of the Romanian HV distribution system was carried out, and the estimation results have shown a good precision. This paper extends the study by including PMU measurements into the measurement input set to improve the previous estimation results. The expected results are the improvement of estimation, a behaviour similar to classic algorithms.