Integrated Approach for Network Observability and State Estimation in Active Distribution Grid

Abstract

This paper presents a unique integrated approach to meter placement and state estimation to ensure the network observability of active distribution systems. It includes observability checking, minimum measurement utilization, network state estimation, and trade-off evaluation between the number of real measurements used and the accuracy of the estimated state. In network parameter estimation, observability assessment is a preliminary task. It is handled by data analysis and filtering followed by calculation of the triangular factors of the singular, symmetric gain matrix using an algebraic method. Usually, to cover the deficiency of essential real measurements in distribution systems, huge numbers of virtual measurements are used. These pseudo measurements are calculated values, which are based on the network parameters, real measurements, and forecasted load/generation. Due to the application of a huge number of pseudo-measurements, large margins of error exists in the calculation phase. Therefore, there is still a high possibility of having large errors in estimated states, even though the network is classified as being observable. Hence, an integrated approach supported by forecasting is introduced in this work to overcome this critical issue. Finally, estimation of the trade-off in accuracy with respect to the number of real measurements used has been evaluated in order to justify the method’s practical application. The proposed method is applied to a Danish network, and the results are discussed.

A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks

“…The comparative study among DSSE's based on accuracy, computational burden, complexity, mean absolute percentage error (MAPE), absolute value error etc. are summarised in Table 2 from available literatures [62,67,[76][77][78][79][80] for proper selection and modification of DSSE algorithm.…”

Incremental Heuristic Approach for Meter Placement in Radial Distribution Systems

“…Typically, these two features conflict with each other: to obtain the lowest uncertainties at a measurement point, the best solution would be to use high-accuracy measurement instruments (typically PMUs) installed at MV level, thus coping with the costs of measurement instruments, MV transducers, and related installation costs. Thus, many distributed measurement system solutions proposed for MV power grids usually involve few MV metering points, whose information is integrated with that obtained by estimation algorithms [17][18][19][20]. This allows the reduction of total costs, but entails some drawbacks especially in terms of accuracy in the estimated quantities and also algorithm complexity.…”

“…For the measurement of the SE input data, different kinds of measurement equipment can be used, i.e., phasor measurement units (PMUs), smart meters (SMs), power quality analyzers (PQAs) and so on, with different accuracy features and costs [15,16]. Missing data are integrated with pseudo-measurements, exploiting historical information or an a priori estimate of the relative power magnitude of each load [17][18][19][20]. Since pseudo-measurements are load estimates with high variance, the quality of the estimated state variables is dependent on the number of pseudo-measurements.…”

A Virtual Tool for Load Flow Analysis in a Micro-Grid

“…However, such solutions can be unsuitable for small island micro-grids, because power lines are short and/or the intrinsic costs of such instrumentation are high. To reduce the installation costs, some authors propose to use a few measurement points and to integrate them with load estimations [20][21][22][23][24][25][26][27]; however, when dealing with load estimations (or pseudo-measurements), higher uncertainty levels are generally expected and more sophisticated algorithms can be needed for the distribution system state's estimation, which also may entail higher computational costs. The integration of differently distributed measurement solutions have also been investigated, for example, considering the possibility of smart meter and power quality meter exploitation or SCADA-and PMU-enhanced integration, for a number of applications (load forecasting, optimization, demand side management, fault detection and so on) [28][29][30][31][32][33][34][35][36][37][38][39][40].…”