Linear state estimation method for distribution grids

Abstract

The recent trend for monitoring and control of the distribution networks requires methods capable to calculate the network’s operating conditions. One way to monitor the distribution network is by state estimation. Although previously this method was established only for the transmission networks, its use has begun to also make sense for the distribution networks, due to the increased availability of smart meters. Typically, state estimation is a non-linear iterative procedure. Research aims to overcome this problem, by creating a non-iterative procedure. To achieve this, linearity of measurement function is a necessity. Although linearity is always desired, the measurement inputs or the procedure renders the state estimation problem difficult to formulate it in a linear fashion. The scope of this thesis is to create a linear state estimation algorithm for the distribution network. To achieve linearity, a reformulation of the states for the state estimation was used at this thesis. This reformulation created a linear optimization problem, which is easier and faster to be solved (Weighted Least Squared method). Since the measurements are not enough to provide full observability, based on these methods, additional assumptions were made. As an alternative to the primary method used for the state estimation, another optimization method was used, which is called Least Absolute Value. This method will be based on the same inputs as the previous method, but different optimization goal. The goal was to compare the two methods and find each one’s limitations. Measurements used for this thesis were provided tracked and provided by DEPsys S.A., by their main module, called GridEye. GridEye devices are synchronized SCADA-type devices, installed on cabinets of the distribution grid, based on the DSO needs. All measurements provided were from real networks within Switzerland. The smart meter device data were provided by collaborative projects of DEPsys S.A.. Smart meter devices were installed in different low-voltage consumers. Both methods were implemented correctly and were compared. Based on the results, it was obvious that the Weighted Least Square outperformed the Least Absolute Value method, in the absence of bad data. Moreover, the effect of the number of GridEye metering devices on the State Estimation was compared. While numerically and visually the outputs are comparable, the effect of having multiple devices is still important for different scenarios. Finally, bad data cases were examined for both methods under different types of errors for the smart meter devices. The smart meter devices were manually introduced to bad data, to understand the algorithm’s capabilities and limitations. An additional algorithm, in addition to the state estimation one, was implemented to not only detect and identify the location of the bad data occurrence but to correct the bad data inserted.