Dyfyniad

Al-Wakeel A (2016). State estimation of medium voltage distribution networks using smart meter measurements. Cardiff University. http://doi.org/10.17035/d.2016.0011172323

Manylion y Set Ddata

Disgrifiad

Distributed generation and low carbon loads are already leading to some restrictions in the operation of distribution networks and higher penetrations of e.g. PV generation, heat pumps and electric vehicles will exacerbate such problems. In order to manage the distribution network effectively in this new situation, increased real-time monitoring and control will become necessary. In the future, distribution network operators will have smart meter measurements available to them to facilitate safe and cost-effective operation of distribution networks. This paper investigates the application of smart meter measurements to extend the observability of distribution networks. An integrated load and state estimation algorithm was developed and tested using residential smart metering measurements and an 11 kV residential distribution network. Simulation results show that smart meter measurements, both real-time and pseudo measurements derived from them, can be used together with state estimation to extend the observability of a distribution network. The integrated load and state estimation algorithm was shown to produce accurate voltage magnitudes and angles at each busbar of the network. As a result, the algorithm can be used to enhance distribution network monitoring and control.

The dataset includes the following entries:

1) The actual daily load profiles based on the active power demand (of aggregated smart meters) at Busbar 11 of the test residential network measured in kW. Each daily profile consists of 48 half-hourly measurements. The profiles are stored in row 5 of tabs “Fig. 5(a) and Fig. 5(b)”.

2) The estimated daily load profiles based on the active power demand (of aggregated smart meters) at Busbar 11 of the test residential network measured in kW. Each daily profile consists of 48 half-hourly measurements. The profiles are stored in rows 12 – 35 of tabs “Fig. 5(a) and Fig. 5(b)”. The rows represent the estimated measurements for 1-24 hours of measurement loss.

3) The mean, maximum and minimum values of the estimated measurements in (2) are given in row 40, 47 and 48 in a respective order.

4) Busbar voltage magnitudes (in volts) for a representative weekday “tab Fig. 6(a)” and weekend “tab Fig. 6(b)” estimated at half-hourly time steps. The estimated voltage magnitudes are given for 1-24 hours of pseudo measurements (estimated active and reactive power demand). For example, rows 5-16 are the estimated busbar voltage magnitudes obtained using 1 hour of pseudo measurements.

5) Busbar voltage magnitudes (in volts) for a representative weekday “tabs Fig. 6(a) and Fig. 8(a)” and weekend “tabs Fig. 6(b) and Fig. 8(b)” obtained from the load flow solution of the network at half-hourly time steps. The voltages are stored in rows 321-332.

6) Busbar voltage magnitudes (in per unit) for a representative weekday “tabs Fig. 6(a) and Fig. 8(a)” and weekend “tabs Fig. 6(b) and Fig. 8(b)” obtained by dividing the voltages in (b) by the voltage base which is equal to 11000 volts. The per unit voltages are stored in rows 338-349.

7) The mean, maximum and minimum (load flow) voltage magnitudes (in volts) of each busbar calculated over the 48 half-hours during a weekday “tabs Fig. 6(a) and Fig. 8(a)” and weekend “tabs Fig. 6(b) and Fig. 8(b)”. The voltages are stored in rows 355-366 and columns C, D and E of the aforementioned tabs.

8) The mean, maximum and minimum (load flow) voltage magnitudes (in per unit) of each busbar calculated over the 48 half-hours during a weekday “tabs Fig. 6(a) and Fig. 8(a)” and weekend “tabs Fig. 6(b) and Fig. 8(b)”. The voltage magnitudes are stored in rows 355-366 and columns G, H and I of the aforementioned tabs.

9) Busbar voltage angles (in degrees) for a representative weekday “tabs Fig. 7(a) and Fig. 10(a)” and weekend “tabs Fig. 7(b) and Fig. 10(b)” estimated at half-hourly time steps. The estimated voltage angles are given for 1-24 hours of pseudo measurements (estimated active and reactive power demand). For example, rows 5-16 are the estimated busbar voltage angles obtained using 1 hour of pseudo measurements.

10) Busbar voltage angles (in degrees) for a representative weekday “tabs Fig. 7(a) and Fig. 10(a)” and weekend “tabs Fig. 7(b) and Fig. 10(b)” obtained from the load flow solution of the network at half-hourly time steps. The voltage angles are stored in rows 321-332.

11) The mean, maximum and minimum (load flow) voltage angles (in degrees) of each busbar calculated over the 48 half-hours during a weekday “tabs Fig. 7(a) and Fig. 10(a)” and weekend “tabs Fig. 7(b) and Fig. 10(b)”. The voltage angles are stored in rows 339-350 and columns C, D and E of the aforementioned tabs.

12) The mean absolute percentage errors (MAPE) of the estimated busbar voltage magnitudes over the test period (7 days) were obtained using 1-24 hours of pseudo measurements. The errors are given in “tab Fig. 9”. For each busbar, there are 24 rows (which correspond to the durations of pseudo measurements) and 7 columns (representing each day of the test period).

13) The MAPE of the estimated busbar voltage angles over the test period (7 days) were obtained using 1-24 hours of pseudo measurements. The errors are given in “tab Fig. 11”. For each busbar, there are 24 rows (which correspond to the durations of pseudo measurements) and 7 columns (representing each day of the test period).

14) Busbar active power demands (in kilo Watt “kW”) for a representative weekday “tab Fig. 12(a)” and weekend “tab Fig. 12(b)” estimated at half-hourly time steps. The estimated active power demands are given for 1-24 hours of pseudo measurements. For example, rows 5-16 are the estimated active power demands obtained using 1 hour of pseudo measurements.

15) Busbar active power demands (in kilo Watt “kW”) for a representative weekday “tab Fig. 12(a)” and weekend “tab Fig. 12(b)” obtained from the load flow solution of the network at half-hourly time steps. The active power demands are stored in rows 321-332.

16) The mean (load flow) active power demands (in kilo Watt “kW”) of each busbar calculated over the 48 half-hours during a weekday “tab Fig. 12(a)” and weekend “tab Fig. 12(b)”. The active power demands are stored in rows 339-350 and the merged columns C, D and E of the aforementioned tabs.

17) The MAPE of the estimated active power demands over the test period (7 days) were obtained using 1-24 hours of pseudo measurements. The errors are given in “tab Fig. 13”. For each busbar, there are 24 rows (which correspond to the durations of pseudo measurements) and 7 columns (representing each day of the test period).

18) The mean values of the MAPE and overall maximum values of the APE in the estimated busbar voltage magnitudes for 0-100% uncertainty in the low-voltage network losses. Rows 4-9 are the percentage uncertainty in network losses while columns B-E are the corresponding values of errors in estimated busbar voltage magnitudes. Columns L-O are the errors in the estimated busbar voltage angles.

Research results based upon these data have been published at http://dx.doi.org/10.1016/j.apenergy.2016.10.010

Allweddeiriau

k-means cluster analysis, Load estimation, Smart meters, State estimation

Prosiectau Cysylltiedig

Increasing the observability of electrical distribution systems using smart meters (30.09.2012 - 29.03.2014) |