Reactive Power Compensation with PV Inverters for System Loss Reduction

Photovoltaic (PV) system inverters usually operate at unitary power factor, injecting only active power into the system. Recently, many studies have been done analyzing potential benefits of reactive power provisioning, such as voltage regulation, congestion mitigation and loss reduction. This article analyzes possibilities for loss reduction in a typical medium voltage distribution system. Losses in the system are compared to the losses in the PV inverters. Different load conditions and PV penetration levels are considered and for each scenario various active power generation by PV inverters are taken into account, together with allowable levels of reactive power provisioning. As far as loss reduction is considered, there is very small number of PV inverters operating conditions for which positive energy balance exists. For low and medium load levels, there is no practical possibility for loss reduction. For high loading levels and higher PV penetration specific reactive savings, due to reactive power provisioning, increase and become bigger than additional losses in PV inverters, but for a very limited range of power factors.

An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering.

Is Correlation Ranking Really Reliable for the Performance Counter Selection Conducted for Power Estimation?

Power estimation is of great value to power-aware adaptation and power provisioning in computing platforms. Performance counter measurements are widely used as main input of the power estimation. However, the number of hardware counters that can be simultaneously measured is relatively small, and the number of OS-level counters is usually far from endurable. As a result, how to pick up the most significant counters becomes an important pre-step of performance counters based power consumption modeling. As the most important criteria of counter selection, correlation between counter measurement and power dissipation has been widely used for several decades, and thus correlation ranking becomes a typical technique for counter selection. However, few works discuss its accuracy and the question why it should be the general first priority. This paper investigates the weightiness of the correlation ranking in counter selection for power estimation. Through comparing correlation ranking with several popular feature selection approaches on tremendous workloads on single and multiple core platforms, we obtain that correlation ranking is not optimal on a large number of benchmarks. Therefore, it can be concluded that correlation ranking should not be generally considered as the first priority to select performance counters. This paper provides an indication for potential researchers to be aware of these issues when estimating power dissipation.