Proceedings of 6th International Conference of Young Scientisis on Solutions of Applied Problems in Control and Communications

ПРОГРАММНО-АППАРАТНЫЙ КОМПЛЕКС ДЛЯ СИСТЕМЫ ЭНЕРГОМОНИТОРИНГА С ПРИМЕНЕНИЕМ ГИС-ТЕХНОЛОГИЙ

Сергей АРТЕМОВ1, Алексей КЫЧКИН2

Пермский национальный исследовательский политехнический университет, Пермь, Россия

(1e-mail: Art-sa@bk.ru, 2e-mail: aleksey.kychkin@gmail.com)

Аннотация. В условиях рыночной конкуренции снижение стоимости продукции на любом предприятии является одной из основных задач. Во многих странах принимаются разного типа законодательные акты, регламентирующие сохранения ресурсов предприятиями. В мире уже разрабатываются системы энергосбережения для разного типа объектов, такие систем называются системами энергомониторинга (СЭМ). Для стационарных объектов такие системы широко используются на практике. Для подвижных объектов функции СЭМ ограничены сбором информации и подготовкой отчетной документации, что не может полностью решить проблему сохранения ресурсов. В статье предлагается программно-аппаратный комплекс СЭМ с использованием ГИС-технологий для применения на подвижных объектах. Данное решение также может быть применено для различных объектов, где необходимо производить сбор информации, основываясь на ГИС-технологиях, для систем распределения энергии и ресурсов, различного типа транспорта и другое.

Ключевые слова: энергомониторинг; система энергомониторинга; целевые энергетические показатели; мобильная платформа; транспорт; ГИС.

11

AUTOMATED ENERGY MONITORING SYSTEM OF THE POWER DRIVE LABORATORY OF PERM NATIONAL RESEARCH POLYTECHNIC UNIVERSITY

Georgii MIKRIUKOV1, Aleksey KYCHKIN2

Perm National Research Polytechnic University, Perm, Russia

(1е-mail: mikriukov.georgii@yandex.ru, 2e-mail: aleksey.kychkin@gmail.com)

Abstract. This article discusses the basic principles of automated energy monitoring system for monitoring of energy performance. Such system has been developed based on the power drive laboratory of Perm Polytechnic University. The basic requirements for the composition of energy monitoring systems have been described. The article presents a brief overview of hardware and software used for the proposed system. System’s architecture has been developed and described. Hardware components of energy monitoring system include power analyzers Janitza electronics UMG 104 and UMG 604; software components used are GridVis and OpenJEVis. The system has broad capabilities for collection, displaying and analysing of the obtained energy parameters. In the practical part of the paper, the focus was on technical solutions for the implementation of the system. Two main areas of system’s usage have been reviewed including electricity consumption prediction and calculation of correlation functions of monitoring objects.

Keywords: energy monitoring, data collection system, Janitza, OpenJEVis.

Introduction

Nowadays, design and development of energy-saving technologies are the priorities of energetics. One of the methods of energy saving is utilization of energy management systems (EMaS). Energy monitoring system (EMS) is the key element of EMaS [1, 2]. EMS are being implemented due to the need of improvement of efficiency of energy management in industry, construction, transport and other sectors. Efficiency is achieved by ensuring of transparency and adequacy of the processes of automation, collection and transmission of information about energy state of the objects [3]. It is also combined with a targeted improvement of personnel’s safety.

EMS development in Perm National Research Polytechnic University (PNRPU) includes the analysis of the processes of data’s collection, processing and long-term storage of power consumption statistics of main energy consumers of Electrical Engineering faculty, based on the laboratory of power drive. Subsequently, the collected data can be used to analyze the energy consumption of similar objects, such as lighting systems, electric, heating systems, domestic consumers, computer equipment, etc.

12

EMS usage in PNRPU’s power drive laboratory has two important aspects: positive economic effect of energy saving and possibility of its usage in the research activities of the university.

1. Objectives

To implement a monitoring system, it is necessary to investigate the current state of the electric laboratory and its equipment, to determine the main parameters of monitoring and monitoring points, to develop the structure of EMS. In addition, selection of necessary hardware and software for automation is needed [4].

The following tasks, which PNRPU's EMS shall perform, were formulated:

1.Automated data collection about power consumption by laboratory equipment;

2.Data processing and conversion for long-term storage in DB;

3.Visualization of stored data in the form of graphs and trends;

4.Emergency events generations;

5.Data analysis: energy consumption prediction and calculation of correlation functions.

2. Technology Description

EMS should have a structure of distributed system of data collection. Input devices should be placed throughout the laboratory; received data will flow to a single network storage device. Such structure allows creating unlimited number of input channels [5].

EMS of power drive laboratory should detect the following parameters:

−supply line current;

−supply line voltage;

−active power;

−reactive power;

−apparent power;

EMS of power drive laboratory should include the following elements:

−sensitive elements (sensors, transducers, etc.);

−data collection devices;

−data processing server;

−data storage server;

−workstation with client software.

13

Moreover, it is necessary to create a remote access to monitoring system via the Internet. This requires the duplication of data storage server and workstation on global level.

Requirements to the elements of automation of power drive laboratory of PNRPU presented in Table 1.

Table 1

Software of EMS of power drive laboratory should have the following features:

−open architecture with support of plugin system – c ustom programs for graphical visualization;

−authorization and authentication;

−data visualization with support of scaling and selection of data;

−possibility of comparing and ranking of data for different periods;

−ability to set upper and lower thresholds to identify surges and emergencies;

−ability to export graphs and data.

3. Development

The object of automation is the laboratory of power drive. It is possible to allocate 13 main elements, the state of which EMS should monitor. These objects are laboratory stands with different types of electric motors and lab’s lighting.

14

Power analysers UMG 104 and UMG 604 developed by Janitza electronics GmbH have been selected for data collection. These devices have digital signal processor (DSP), support up to 8 input channels and read data at a frequency of 20 kHz [6]. The main characteristics of these devices presented in Table 2.

The analysers require current transformers in order to detect signals from energy consumers. There are 2 options: current transformers with secondary winding current of 1 A and accuracy class 1 or current transformers with secondary winding current of 5 A and accuracy class 0,5.

Table 3 shows formulas used for monitoring parameters computation by the power analysers. Here, Xpk sands for a mean value of measured value for conductor p (detected every 200 ms), N – num ber of detected Xpk’s during measurement time of effective value (900 s = 15 min).

Table 3

Formulas for computation of monitoring parameters

15

For information support of EMS the following software has been chosen: GridVis by Janitza electronics GmbH and OpenJEVis by ENVIDATEC GmbH.

GridVis is the software bundled with the power analyser UMG 604, it allows to perform a full configuration of parameters of power analysers remotely. Also, it allows to read and to visualize measured values from power analysers in online mode. Moreover, GridVis provides an option to export data to CSV format [6].

In the proposed system, GridVis will be used to read information from measuring devices, to convert data into csv-format; and to configure power analyzers remotely. The reason for this is that OpenJEVis has broader analytic functions and a user-friendly interface.

Open source software OpenJEVis, is an information system, which realizes the possibility of receiving and processing of all types of physical data [7]. OpenJEVis system provides Java-based web-interface. OpenJEVis consists of different program modules, with JEDB, JEConfig, JEGraph, JEChart, JEBench, JEAlarm, JEWebservice being among the mostly used ones.

JEDB is a database based on database management system MySQL. Its advantages include flexible structure, absence of license fees, and possibility of simultaneous use in multiple projects.

JEConfig is used for configuring and editing of measurement nodes. Also, this module allows to import, export and process data, which is extracted from csv-files.

JEChart is a graphical user interface (GUI), which represents the graphics of data comparison. JEChart allows drawing line graphs and bar graphs; it supports a variety of additional graphic options such as scaling.

JEBench is a tool for data comparison and ranking.

JEGraph is a visualization structure, which plugs in different graphic modules such as JEChart and JEBench.

JEAlarm monitors the value of parameters and triggers an alarm in the event of parameters entering an out of bounds range.

JEWebservice connects other modules over the network to the database. Based on this data, a block diagram of EMS of power drive laboratory of PNRPU has been developed, as shown in Fig. 1.

16

Fig. 1. Block diagram of EMS

The system is divided into 2 levels: data collection, data processing and storage. As it can be determined from the block diagram, data is being stored on two servers simultaneously. Remote user’s access is provided by OpenJEVis and its web-interface. Data collection devices have fairly large amount of built-in memory, so they can operate in a standalone mode (without data transmission to the database) for a rather long time. Developed structure satisfies all pre-determined requirements.

Results

In order to test the performance of energy monitoring system, a simplified EMS has been developed, which retains all the basic functionality of the original system. Block diagram of that simplified EMS is shown in Fig. 2.

This system uses only two power analyzers, with signals being read at UMG 104. UMG 604 serves as a Modbus gateway. Data collection server and local workstation are located on the same computer. For data storage, a global server is being used. In this system, file transfer from the collection server to the storage server via FTP is not implemented. Data is converted to CSV format by data collection server and manually exported by JEConfig module from any of the workstations into the database. The structure of the exported file and the export process are shown in Fig. 3.

17

Exported CSV file has the followi ng structure: column 1 – number of measurement; column 2 – date and ti me of measurement; columns 3-5 – average, minimum and maximum values of measured parameter for input L1; columns 6-8 – average, minimum and maximum values of measured parameter for input L2; columns 9-11 – average, minimum and maximum values of measured parameter for input L3. In this case, input L4 was not used.

Fig. 2. Block diagram of simplified EMS

Fig. 3.The export pro cess of CSV-file

18

In JEConfig we specify the date format, a number of rows with headings, and number of columns with date and value parameters. It is also necessary to specify the type of separation.

After data has been exported, it is possible to visualize it using JEGraph. Fig. 4 shows current values for input L1. The upper graph displays data in the GridVis, the lower on e displays data in JEGraph.

Fig. 4. Current values from input L1, GridVis – upper graph,

JEGraph – l ower graph

Based on these results, it can be assumed that the system is operational. EMS with the proposed structure m ay now be implemented in full.

Further development of this syst em will be focused in the following directions:

−energy consumption prediction;

−calculation of correlation functions for the objects of monitoring. Prediction algorithm must take in to account several types of factors:

cyclical, which are the most predicta ble ones; and random [8]. The most significant factors influencing the ene rgy consumption of power drive laboratory of PNRPU include seasonal change of day duration and time of

19

the classes held in the laboratory. Based on this data a prediction algorithm will be developed.

Calculation of correlation functions of objects is needed in order to reduce the number of data collection devices. If we imagine that the power consumption of each element of the object can be described by a function Oi, and power supply of objects is regulated by switch Si (logic «0» & «1»), then the schematic model of the total energy consumption can be presented, as shown in Fig. 5.

Fig. 5. Model of total energy consumption of the object

Total energy consumption will be described by the following formula:

7

C = 3D:9 =D ∙ ED.

In this case, the main task is to implement the analysis block, which will determine the modes of operation of each of the elements (which defines the state of Si). This improvement of EMS would reduce the number of data collection devices to one per object. At the same time, the information about operation of each element will not be lost.

5. Business Benefits

Development of EMS for laboratory of power drive of PNRPU is aimed at quality and pithiness improvement of the educational process. The system will allow to accumulate statistical data about energy consumption by the laboratory and to identify weaknesses in the laboratory’s power supply system.

This is project is a standard one which makes it possible to implement it further in other laboratories of the University.

As mentioned earlier, the development of an EMS is the first step of implementing EMaS in PNRPU.

20