Extended Capabilities of the Power Quality Management System of the Power Distribution Grid for Data Exchange

The actual operation of the power distribution systems asks for high power quality (PQ). This fully justifies the investments in improving the metrics of power grid performances. But maintaining a PQ data infrastructure in a numerous locations is time-consuming and prohibitive. Moreover, each PQ monitor releases its own data format. These considerations justified using of a central PQ management system able to manipulate elliptical and time discontinuous information. The paper presents the characteristics of a webbased application designed to collect the data measured with different technologies of monitors and translate them into a common PQ data interchange format allowing comprehensive and long-duration grid operation assessment. The unitary formatted data generated by this customized software tool can be further processed by a proprietary software platform for PQ management owned by the network operator. The present version of this conversion tool is applicable only for one product family operating in the local power distribution grid. Further development is planned for integrating other two monitor vendors/families.


Introduction
The nature of the actual power quality (PQ) issues fully justifies the investments for improving the metrics of the power grid operator performances as a key measure in increasing: the reliability, efficiency, as well as the satisfaction of the power grid's users.In accordance with the national Performance Standard for Power Distribution Service [1], the power distribution grids' operator (DGO) must assume the obligations regarding keeping within standards of the power reliability and quality.
The performance assessment and reporting operation, as well as the understanding of the operational problems, is improved by processing high-precision and comprehensive measurement data.Consequently, a utility or customer can be tempted to monitor more points than they can justify financially.Maintaining a PQ data collection infrastructure in a very large number of locations over a very long time is time-consuming and prohibitive (the cost of an equipment with required performance amounts to around 10000 Euros).Moreover, this leads the power utilities to cope with GB's of real data.This data "avalanche" is supplied by a diversity of equipment: monitors, analyzers, loggers, power meters, derived from various vendors and technologies.So that, the management of PQ database becomes critical [2,3] and the high amount of collected data will ask for advanced analysis tools.This greatly increases the cost and difficulty of managing the PQ.
Moreover, most of the monitoring equipments have own software that can be used as platforms for database management.The development and use of a standard power quality data interchange format will make this data accessible for analysis, so that the utility or customer can increase the number of monitored points without large capital outlays for equipment.The standard exchange format will also enable the user to change equipment vendors without concern about incompatibilities with existing equipment.The data exchange format as COMTRADE (Common format for Transient Data Exchange for power systems) or PQDIF (Power Quality Data Interchange Format -IEEE® Std 1159.3-2003standard) can provide vendors with a common format for the exchange of data, allowing the end user maximum flexibility in choice of tool and vendor [4,5].
Therefore, specialized analysis can be performed with an integrated platform as PQView® (PQ management and analysis software system) [6].Such kind of system is currently used by the local DGO.An extension of its functionality is a project (SYMMPQI) in which the local DGO is currently involved in a partnership with INCESA Research Hub for Applied Sciences (University of Craiova).
The paper presents the characteristics and applications of one of the SYMMPQI project's outputs [7].SYMMPQI_PQDM is a customized software module that basically collects the output data measured with different technologies of PQ monitors and translate them into a unitary format -PQDIF standard.With clear procedures for data acquisition and storage, as well as methods of these data rebuilding and administration, these data in PQDIF format can be unitarily processed by the platform PQView.
The present development of SYMMPQI_PQDM web-based application is applicable only for one class family product operating in the local DGO installation.Further development of the application is intending to address to other equipment technologies.

National regulation framework for PQ in the power distribution grids
Presently, a considerable number of European countries use the European standard EN 50160 as the basis for their national quality of power supply regulations.According to EN 50160 ("Voltage characteristics of electricity supplied by public distribution systems") [8], accommodated by the Romanian Performance Standard for Power Distribution Service (PSPDS), the recommended levels of different PQ parameters are specified in the time-based percentage.
According to PSPDS, four PQ disturbances should be guaranteed by DGO at the interface with its grids users: slow voltage variations, voltage fluctuations, rapid voltage changes (normal operation), unbalances voltage, and waveform distortions.PSPDS considers the following objectives for PQ parameters: 1) Slow voltage variations During one week, under normal operating conditions: (1) 95% of the 10-min RMS voltage should be within U n  10% and 100% of the 10-min RMS voltage should be within +10% / -15% for LV system; (2) 99% of the 10min RMS voltage should be within U c  10% and 100% of the 10-min RMS voltage should be within Uc  15% for MV and HV system, where U n is the nominal voltage of the system and U c is the declared supply voltage.

2) Voltage fluctuations
The long-term flicker severity, P lt is calculated from a sequence of 12 P st values (short-term 10 min flicker severity) over a 2-hours interval, according to (1): (1) The 95th percentile value of P lt should not exceed the unity over one week.

3) Supply voltage unbalance
According to IEC definition, the negative sequence voltage unbalance factor is: For LV and MV systems, PSPDS regulates the 95% probability weekly value of negative-sequence voltage unbalance factor within 2%, with 1% in HV systems.

4) Harmonic distortion
In accordance with PSPDS, the 95% value of the total voltage harmonic distortion factor THD (defined as in (3)) shall not exceed 8% for the LV and MV system in any period of a week, under normal operating conditions.In the case of HV grid, the objective is 3%. (3) The DGO's are obliged to submit to the Romanian Energy Regulatory Authority the detailed annual PQ assessment report for their operated networks, which includes states of compliance with the PQ limits referred to the PSPDS and in accordance with Std.EN 50160.

Power Quality monitoring systems
In the Romanian power networks, there are voltage quality monitoring systems at both transmission and distribution level.The transmission and distribution operators are responsible for the network voltage waveform quality.They have the duty to look out for the levels of each characteristic.
Following this trend, the local DGO has placed PQ recorders presently organized within three fixed monitoring systems: (1) the monitoring system for 110 kV and 20 kV grids (i.e.equipped with monitors MAVOSYS for almost 100 sites) connected to a central server and managed by PQView multi-component system; (2) the monitoring system at the interface between the distribution network and its users, without a data management center; (3) the power monitors system (i.e.MEG) connected on the LV side of 20/0.4 kV substations.
Usually, each PQ monitor produces its own data file format.The DGO decision regarding one or other type of meter is associated with interoperation ability and related standards.These considerations have justified the using of a central PQ management system in compliance with the National Performance Standard for Power Distribution Service (PSPDS) and able to manipulate rather an elliptical and time discontinuous information for assessing the network's observability.
Since the PQView accepts formats from large categories/types of PQ monitors, it looks like a convincing solution to the need of DGO for transferring the output of different vendors' monitors and simulation programs to a common database and analysis program.Mainly it is using a standard data interchange format, e.g.PQDIF.For the case of other vendors' equipment, PQView should be assisted by a data handler for these specific formats.
In the present configuration of the PQ system of the local DGO only 22.4% of the permanent monitoring equipment can provide PQ data in PQDIF format.The rest of them export data in proprietary formats, as the case of the MEG analyzers placed in LV buses of the network -see Fig. 1.Though less than 14% of the equipments (MAVOSYS type) are currently integrated with the PQView software platform, the perspective is in the favor of a major inclusion.Additional tools, as SYMMPQI_PQDM are developed for PQDIF conversion of the data achieved from MEG equipmentbased monitoring system.

Data handling for PQ analysis
By using a centralized system for the PQ management and analysis in its networks, the DGO benefits of the evaluation of all of the PQ parameters for individual sites and their zonal global assessment; establishing references for the system's expected performances regarding all the PQ categories; integrated technical-economic evaluation of the PQ problems and their solutions; web-based access to the PQ information; useful tool for training in the PQ field.
The data asked for analysis is provided by the socalled Points of Interest System (PoI).Actually, these are the metering system components of DGO including a high number of heterogeneous families, types and classes of equipment: A or S class, PQ monitors, digital fault recorders, microprocessor relays, SCADA systems, demand and energy meters, operating logs.These devices differ in their data format and the method of measured quantities aggregation.In the case of the general power/energy meters, the data are accessed as *.txt, *.xls or *.csv files.In the case of the PQ monitoring, the data should be preferable accessible in standard formats as PQDIF, which enables the data exchange between software applications.
For this purpose, the PQView integrates data from PQ monitors into an open relational database, but presently only for the HV & MV grid area.At the LV level, the PQ data are collected either on-site or remotely from a MEG40-based monitoring system, being uploaded and stored on a dedicated resource server.Originally, the MEG family equipments use own proprietary communication protocols, as well as data storage and interpretation modes for PQ monitoring.
The present stage of interfacing the PQ monitoring system of the local DGO with the PQView's database is given in Fig. 2.
Since the PQView system can ensure the full availability of data if only these are retrieved in pqdif formats, a translation of other proprietary data format should be accomplished if a PQView integration is desired.A solution is supplied by converting these data into an easy-to-handle format, e.g.csv.This format enables further conversion in PQDIF standard, making possible the unification of measurements from different DGO family equipments: MAVOSYS, MEG in the present, and ION, Chauvin Arnoux, Fluke in next stage.
The principle of this approach is using the existing software to extract data under a commonly accepted "csv" format (where applicable), followed by converting "csv" files to "pqdif " files compatible with the existing PQView software.SYMMPQI_PQDM is a customized tool that allows using and extending the capabilities of the PQView platform.SYMMPQI_PQDM is organized on modules with functions for managing both the monitoring of data and the configuration of the data transfer tools.[7].

Report toward National Energy Authority
In the current version, the PQDM Web tool ensures a deterministic way to get and collect measurements from the MEG-system, either manually or automatically.PQDM Web performs further the conversion of raw data collected in PoI of MEG40 type ("csv" format) to PQDIF.The most convenient way of data acquisition is https://doi.org/10.1051/matecconf/201821002045CSCC 2018 the access to the analyzer database by means of SQL query.The data is further prepared for the next step: uploading into the PQ analysis and report system (PQView Data Analyzer).The main functions of PQDM Web module are: -Reading the metering data in the native format (mdat for MEG) and conversion into intermediary format (csv); -Analysis of the data consistency; -Data conditioning by recovering error/missing data; -Building intermediary database; -Data conversion into user proprietary format pqdif).

Internet
PQDM Web application is designed to support any type of acquisition equipment by integrating an appropriate plugin able to process its information.In this way, our system is easy to be expanded and maintained.The PQDM-WebApp application is developed using Java technologies, in NetBeans development environment (IDE).It is running in the Web environment.It has the advantages of exploiting Oracle J2EE which brings robustness, independence from the platform, and efficiency, as well as possibility of using various application servers (case of Apache Tomcat).
PQDM Web Application stores its outputs in a PQDC (Power Quality Data Collection) site.PQDC can be collocated with the web application or can be distributed.Smaller organizations can opt to the collocated solution, while bigger ones can choose the distributed variant.DGO is going to decide about the version, in accordance with its available IT resources.Either of solutions, it provides a unique place from where all PQ information can be retrieved by software packages used for various analyses and reports.
The resulted "pqdif" data files are operated by the PQView Data Analyzer module (PQDA).PQDA contains functions performed by the Excel spreadsheet and the MS Access database.The PQ data can be also stored in the database of PQView Data Manager.

Data extraction and collection
PQ monitors are delivered in a fixed built-in version.
Data upload can be achieved by direct communication (USB/SERIAL RS232) or Ethernet communication protocol from the measuring devices using the software either provided by manufacturers of measuring instruments or as module of existing measurement systems.
The data taken from the measuring devices is stored in the primary format -"csv" -by means of either: -a manufacturer's dedicated software: e.g.Meg4X, MegaMerci or DataViewer for MEG40 (located in LV power grid), DranView or PQDiffractor for Mavosys10 (located in HV & MV power grid); -a customized software module: e.g.SYMMPQI_PQDM applicable presently only for MEG40 devices.
Further, the data are converted to "pqdif" format for advanced storage, processing and analysis within the PQView application.

Collection of data from MEG40-based monitoring system
The retrieving and processing data from MEG40 devices can be achieved by using the proprietary program package MEgA_Merci_II Originally, the measured values of the defined quantities can be stored as MDAT format files for MEG*'s.The file created in this way can be further used for the WebDator system or for DVMEg.exe(an MDAT file viewer) [9].
The output data are stored on a dedicate server, allowing to DGO to perform a basic and single-site analysis of the voltage evolution and power/energy consumption in the monitored grid area.
In the same way, the customized software module, SYMMPQI_PQDM, can be used to retrieve and process data from MEG40 devices.Basically, PQDM-Web application receives the *.csv file for the MEG40 together with the user input (Version Info, File Name, Creation, Subject, Author, Application, Copyright, Trademark, Notes, Language, Owner) and returns the corresponding *.pqdif file.The measurement data flow for format conversion by SYMMPQI_PQDM and PQView integration is given in Fig. 4.
In order to evaluate the *.pqdif files generated by each MEG monitor, it is necessary to subject the monitors to an assortment of known voltage and current variations.In this way, the resulting PQDIF file can be compared to the known disturbance and the data recorded by the monitor.
In order to test the functionality of SYMMPQI_PQDM translator, several preparatory operations were required: 1. Establishing the used communication protocol (MODBUS), the necessary communication parameters and the commands to retrieve the data from the memory card of the measuring device.2. Establishing the data storage mode -the storage is in ASCII code, with some features in the way of marking the measurements and calculating the parameters.
An example of identifying measurements and calculating a parameter such as voltage is given in Fig. 5, in hexadecimal form, with the correspondent resulting *.csv file given as in Fig. 6. https://doi.org/10.1051/matecconf/201821002045CSCC 2018 Further, in order to browse, test and diagnose the converted PQDIF files, three electrical quantities evolutions recorded by MEG40 as PoI located in a LV bus with PV power plant interconnected were followed.The correspondence between graphical evolution plotted in .csvoriginal report of the MEG monitor and plot of variation in PQView-PQDA graphical report is verified.

Identification of measurements data in raw format
Calculation of the medium voltage on A-phase.The steps for testing the functionality of SYMMPQI_PQDM translator are given in Fig. 7-9.
The "csv" file of output data (graphical correspondence given in Fig. 7-9 (a)) are proccesed and concerverted as "pqdif" format, and further uploaded by PQView -PQDA module (graphical report correspondnce given in Fig. 7-9 (c)).The consistency of the new generated "pqdif"files is preliminary tested/validated by using the viewer PQDiffractor (a free PQDIF file viewer utility written by Electrotek Concepts) [10] -see Fig.

Fig. 1 .
Fig. 1.PQView integrability stage of the PQ monitors in the local power distribution grid

Fig. 2 .
Fig. 2. Schematic of the solution of PQ monitor interfacing with PQView system of the local DGO

Fig. 7 .
Fig. 7. RMS voltage variation on the surveilled site: a. graphical output of primary csv file of MEG; b. pqdif file consistency check using PQDiffractor; c. PQView IEEE PQDIF File viewer processing

Table 1 .
Functions of the modular structure of SYMMPQI