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AMI Data Quality and Collection Method Considerations for Improving the Accuracy of Distribution Models

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Spectral clustering is applied to the problem of phase identification of electric customers to investigate the data needs (resolution and accuracy) of advanced metering infrastructure (AMI). More accurate models are required to accurately interconnect high penetrations of PV/DER and for optimal electric grid operations. This paper demonstrates the effects of different data collection implementations and common errors in AMI datasets on the phase identification task. This includes measurement intervals, data resolution, collection periods, time synchronization issues, noisy measurements, biased meters, and mislabeled phases. High quality AMI data is a critical consideration to model correction and accurate hosting capacity analyses.
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... Distribution system models, and the low-voltage networks, in particular, have historically contained more inaccuracies than other sections of the grid. The possible errors in the distribution system model cover a wide range of error types and possible consequences [1]. The distribution grid is a legacy system that has seen continuous usage for decades. ...
... Using this type of data ensures that a detailed analysis of the results using ground truth transformer labeling can be accomplished. Further details about the dataset can be found in [1], [5]. ...
... Measurement noise and missing data issues were also injected into the dataset for a subset of the results shown below. The measurement noise and missing data injections were done uniformly at random, and further discussion of the methodology for injecting those errors can be found in [1]. ...
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Distribution system model accuracy is increasingly important and using advanced metering infrastructure (AMI) data to algorithmically identify and correct errors can dramatically reduce the time required to correct errors in the models. This work proposes a data-driven, physics-based approach for grouping residential meters downstream of the same service transformer. The proposed method involves a two-stage approach that first uses correlation coefficient analysis to identify transformers with errors in their customer grouping then applies a second stage, using a linear regression formulation, to correct the errors. This method achieved >99% accuracy in transformer groupings, demonstrated using EPRI's Ckt 5 model containing 1379 customers and 591 transformers.
... The increase in the amount of data enables the application of data science and machine learning approaches for model calibration without the expense of manual verification. The authors of [21,22] discuss AMI and smart metering deployment, and [16,23,24] discuss some of the specific data requirements for model calibration tasks and the array of data considerations for AMI data collection and analysis in general. ...
... The synthetic dataset used for these simulations is described in detail in [16] and is also used in the AMI data analysis in [23]. The dataset consists of 12 months of AMI data for 1369 customers simulated on the Electric Power Research Institute's (EPRI) Ckt5, Fig. 7, [47]. ...
... The AMI data collection methods used were 15-min average measurement interval, 0.1 V resolution measurements, 6 months of available data, and AMI meter penetration of 100%. Further details of the specific implementation of the data manipulations used can be found in [23]. The values used in the results shown in Figs. ...
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Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.
... For ensemble methods (ESC-GIS and ESC-SCADA), a confidence score CS can be computed to get a feel of the prediction accuracy. For a given customer, CS represents the percentage of windows whose predicted phase equals the final predicted phase (see Section IV-C of [51] for more detail). ...
... The accuracy decreases drastically afterwards. This perfectly aligns with the results presented in [51]. Fig. 4 presents the impact of the number of measurement samples on Acc % on the CKT5 and South networks. ...
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The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.
... The synthetic dataset was originally based on work in [22] and modified to include synthetic events. There are 1379 residential customers and one year of 15-minute AMI data. ...
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The installation of digital sensors, such as advanced meter infrastructure (AMI) meters, has provided the means to implement a wide variety of techniques to increase visibility into the distribution system, including the ability to calibrate the utility models using data-driven algorithms. One challenge in maintaining accurate and up-to-date distribution system models is identifying changes and event occurrences that happen during the year, such as customers who have changed phases due to maintenance or other events. This work proposes a method for the detection of phase change events that utilizes techniques from an existing phase identification algorithm. This work utilizes an ensemble step to obtain predicted phases for windows of data, therefore allowing the predicted phase of customers to be observed over time. The proposed algorithm was tested on four utility datasets as well as a synthetic dataset. The synthetic tests showed the algorithm was capable of accurately detecting true phase change events while limiting the number of false-positive events flagged. In addition, the algorithm was able to identify possible phase change events on two real datasets.
... All meters that monitor the substation, loads, and solar PV nodes are assumed to be class 0.5. Meter class defines the maximum percent error of each measurement, and this meter class error drives the ability to accurately model the distribution system [15] [16]. We have modeled measurement errors as an additive Gaussian random variable with a standard deviation equal to one-third of the meter class, as shown in Fig. 3. ...
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High penetration of distributed energy resources presents challenges for monitoring and control of power distribution systems. Some of these problems might be solved through accurate monitoring of distribution systems, such as what can be achieved with distribution system state estimation (DSSE). With the recent large-scale deployment of advanced metering infrastructure associated with existing SCADA measurements, DSSE may become a reality in many utilities. In this paper, we present a sensitivity analysis of DSSE with respect to phase mislabeling of single-phase service transformers, another class of errors distribution system operators are faced with regularly. The results show DSSE is more robust to phase label errors than a power flow-based technique, which would allow distribution engineers to more accurately capture the impacts and benefits of distributed PV. Keywords-bad data processing, distribution system calibration, distribution system state estimation, phase label error.
... Actual real power profiles from customers were simulated using OpenDSS to produced AMI time series data for voltage and reactive power in addition to the existing real power time-series. More details on the dataset can be found in [27]. ...
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The recent growth of sensing devices on the distribution system, such as smart meter deployment, has enabled a wide variety of data-driven distribution system model calibration algorithms. A challenge associated with developing algorithms for model calibration tasks is the determination of parameters for a particular algorithm. This work proposes a method for parameter selection utilizing silhouette score analysis that allows these parameters to be tuned on a per-feeder basis. This method leverages cluster analysis and the distance matrices often produced by phase identification methods. The proposed method was tested on 5 feeders from 2 different utilities to select the number of clusters used in a spectral clustering phase identification algorithm. A synthetic dataset was then used to validate the method with the phase identification algorithm performing with 100% accuracy.
... Figure 9 (red histogram) shows a similar plot for the substation method and the mean for the Window Votes is around 0.5, signifying that only 50% agreed on the predicted phase on average. This metric was modeled on the confidence score demonstrated in [18]. ...
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On the Path to SunShot: Emerging Issues and Challenges in Integrating Solar with the Distribution System
  • B Palmintier
B. Palmintier et al., "On the Path to SunShot: Emerging Issues and Challenges in Integrating Solar with the Distribution System," Natl. Renew. Energy Lab., vol. NREL/TP-5D00-65331, 2016.