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Seminar Report ’11
Intelligent Management Of Electrical Systems in Industries
Abstract
The automation of public electricity distribution has developed very rapidly in the past few years. The same basis can be used to develop new intelligent applications for electricity distribution networks in industrial plants. Many new applications have to be introduced because of the different environment and needs in industrial sector. The paper includes a system description of industrial electric system management. The paper discusses on the requirements of new applications and methods that can be used to solve problems in the areas of distribution management and condition monitoring of industrial networks.
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CONTENTS 1 Introduction …………………..…………………….………………... 04 2 Applications for supporting the public distribution network management ................................................ 05 3 Description of the system environment …………………………….….08 4 Application functions for distribution management in industrial plants ………………………………............ 11 5 Advanced Distribution Automation ………………...............................………………....……..14 5.1 Distribution System of Future with ADA ………………………………………….….17 6 Distribution Management Functions …...............……………………………….…....18 7Application Functions of Data Management Systems ………………………………........................…......…...…..21 7.1) Load modeling ………..……….….........................................21 7.2) Reliability management………………………….........……..23 7.3) Voltage dip analyses.........……………........……...............…25 7.4) Power quality analyses……………………………................26 7.5) Condition monitoring…………………………………..........26 8 Conclusion….......................................……………..…………….…...29 9 Bibliography.……………….................……………………................30
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Introduction
Industrial plants have put continuous pressure on the advanced process automation. However, there has not been so much focus on the automation of the electricity distribution networks. Although, the uninterrupted electricity distribution is one basic requirement for the process. A disturbance in electricity supply causing the“downrun” of the process may cost huge amount of money. Thus the intelligent management of electricity distribution including, for example, preventive condition monitoring and on-line reliability analysis has a great importance. Nowadays the above needs have aroused the increased interest in the electricity distribution automation of industrial plants. The automation of public electricity distribution has developed very rapidly in the past few years. Very promising results has been gained, for example, in decreasing outage times of customers. However, the same concept as such cannot be applied in the field of industrial electricity distribution, although the bases of automation systems are common. The infrastructures of different industry plants vary more from each other as compared to the public electricity distribution, which is more homogeneous domain. The automation devices, computer systems, and databases are not in the same level and the integration of them is more complicated.
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Applications for supporting the public distribution network management
It was seen already in the end of 80's that the conventional automation system (i.e. SCADA) cannot solve all the problems regarding to network operation. On the other hand, the different computer systems (e.g. AM/FM/GIS) include vast amount of data which is useful in network operation. The operators had considerable heuristic knowledge to be utilized, too. Thus new tools for practical problems were called for, to which AIbased methods (e.g. object-oriented approach, rule-based technique, uncertainty modeling and fuzzy sets, hypertext technique, neural networks and genetic algorithms) offers new problem solving methods. So far a computer system entity, called as a distribution management system (DMS), has been developed. The DMS is a part of an integrated environment composed of the SCADA, distribution automation (e.g. microprocessorbased protection relays), the network database (i.e. AM/FM/GIS), the geographical database, the customer database, and the automatic telephone answering machine system. The DMS includes many intelligent applications needed in network operation. Such applications are, for example, normal state-monitoring and optimization, real-time network calculations, short term load forecasting, switching planning, and fault management.
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The core of the whole DMS is the dynamic object-oriented network model. The distribution network is modeled as dynamic objects which are generated based on the network data read from the network database. The network model includes the real-time state of the network (e.g. topology and loads). Different network operation tasks call for different kinds of problem solving methods. Various modules can operate interactively with each other through the network model, which works as a blackboard (e.g. the results of load flow calculations are stored in the network model, where they are available in all other modules for different purposes).The present DMS is a Windows NT -program implemented by Visual C++. The prototyping meant the iteration loop of knowledge acquisition, modeling, implementation, and testing. Prototype versions were tested in a real environment from the very beginning. Thus the feedback on new inference models, external connections, and the user-interface was obtained at a very early stage. The aim of a real application in the technical sense was thus been achieved. The DMS entity was tested in the pilot company, Koillis-Satakunnan Sähkö Oy, having about 1000 distribution substations and 1400 km of 20 kV feeders. In the pilot company different versions of the fault location module have been used in the past years in over 300 real faults. Most of the faults have been located with an accuracy of some hundred meters, while the distance of a fault from the feeding point has been from a few to tens of kilometers. The fault location system has
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been one reason for the reduced outage times of customers (i.e. about 50 % in the 8 past years) together with other automation.
The experiences as a whole were so encouraging that the DMS was modified as a commercial product. The vendor was first a small Finnish software company. Since 1997 the DMS has been a worldwide software product of ABB Transmit Oybeing integrated to the MicroSCADA platform. At present the DMS is in everyday use in several distribution companies all over the world. Part of the research group behind the development of the DMS works at present as the employees of ABB, which has confirmed the successful commercially phase.
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Description of the system environment A big industrial plant differs from public distribution company by organizatory structure and by system environment. A production is divided into many departments or many companies. These units have the responsibility of production and maintenance. Very often the maintenance is maintained by a service company. An energy department or company is in charge of local energy production and of the distribution network. Above organizations may have some control systems that serve for their needs only, but usually information systems are closely connected together. A process automation system is the most important system in an industrial plant, sometimes including other systems, as illustrated in Fig. 1. For example, all energy production and distribution network control tasks can be done in a process automation system. Normally, because of the reliability reasons, vital parts of distribution network control is independent on the process automation. The independency of process automation system vendor has been one reason for separate systems, too.
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Figure1: Automation and information systems of an industrial plant.
The systems in Fig. 1 utilize many databases, which contain data that can be used in new applications. Process automation systems collect data for process monitoring and optimization tools. The databases contain information of material flow, energy flow and control data of production machines. Maintenance databases include technical specifications and condition data of production machine components. Similar information of electricity network components is supported by network database. Production programs are stored in the databases of administrative systems. Intelligent applications are needed to: - Handle large amount of information available. This includes filtering of data and producing new information by collecting data. - Illustrate complex dependencies of electricity distribution and production processes in abnormal situations. - Give instructions for operators in fault situations. A risk of misoperation in unusual fault situation is obvious and prevents or delay operators’ decision making. - Automize analysis tasks. Continuous information analysis is not possible manually.
In order to introduce new intelligent applications for the management of electric systems in industrial plants, a basis for implementation is needed. The following requirements should be satisfied: - Documentation of electricity distribution network is available for the systems. Network databases can supply this information.
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- Network, process and motor measurements are available for the system. This means, that data acquisition from multiple sources with capability to use various data transfer methods is needed, as illustrated in Fig. 2.
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Application functions for distribution management in industrialplants
As mentioned above the concept of public distribution automation cannot be applied as such in the management of industrial electricity networks. For example, fast and accurate fault location has a great importance for reducing the outage time of customers in the public electricity distribution, while there is no special need of such a function in industrial networks. Predictive condition monitoring, reliability calculations, and protection relay coordination to prevent disturbances in advance are more important. Caused by the features of industrial networks there are needs for methods to model dynamic phenomena and harmonics, and to calculate load-flow and fault currents in ring connected networks. An essential need is the load modeling which differs considerable from the public distribution. The basis of the distribution management system (i.e. the use of network model as the blackboard) is common in the both domains. The network model includes the real-time topology and network calculation results in the prevailing switching and load conditions. The main functions of system entity for the industrial networks are listed in the following: * Real-time network monitoring, state estimation and optimization: - Topology management - load flow and fault currents also as dynamic phenomena - Monitoring and compensation of reactive power - monitoring of harmonics and resonances
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- Minimization of power losses * Planning and simulation of operation actions - switching planning - Automatic load shedding and forming a local island - switching the network as a part of the national grid - fault situations * Management of disturbances - Event analysis - Fault location and network restoration - Preventive condition monitoring - Protection relay coordination - Reliability calculations - reporting
Distribution Automation which includes feeder automation and distribution management systems (DMS) is an important technique in distribution network. The distribution management systems are composed of distribution management functions. The DMF is an entity which incorporates different applications on a single platform over which supervision is made. This mainly supports documentation of network data planning operation and
reliability management of distribution networks. Various
application functions for distribution management in industrial plants are mainly load modeling ,reliability management , power quality analysis, voltage dip analysis and
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condition monitoring .All this are incorporated in a domain of distribution management functions. Advanced distribution automation (ADA) modern day approach towards efficient management of distribution networks.
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ADVANCED DISTRIBUTION AUTOMATION
Traditional distribution systems were designed to perform one function— distributing power to end users. The distribution system of the future will be more versatile and will be multifunctional. Strategic drivers for ADA are to • Improve system performance • Reduce outage times • Allow the efficient use of distributed energy resources • Provide the customer more choices and • To integrate the customer systems For ADA to work, the various intelligent devices must be interoperable both in the electric system architecture and in the communication and control architecture.
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Intelligent Management Of Electrical Systems in Industries . Figure3: ADA architecture
ADA will enable the distribution system to be configured in new ways for such things as looped secondaries or intentional islanding to facilitate easy recovery from outages and to deal with other emergencies.
Fig: 4 The three major components of ADA – Flexible electrical system architecture – Real-time state estimation tools – Communication and control system based on open architecture standards The intelligent universal transformer is a prime example of a new electronic device that will be a cornerstone of ADA. It will provide a variety of functions including – Voltage stepping – Voltage regulation – Power quality enhancement
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– New customer service options such as DC power output – Power electronic replacement for conventional copper and iron transformers
The Flexible Electric Architecture and the Open Communications Architecture synergistically empower each other to create the distribution system of the future.
Each of these is made more valuable by its interaction with the other.
ADA will provide improvements in many areas including – Reliability – System performance – Condition monitoring – Outage detection and restoration – Maintenance practices and prioritization – Automated switching and fault management – Reactive power and voltage management – Loss reduction and load management – Customer service options
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Distribution System of Future with ADA
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DISTRIBUTION MANAGEMENT FUNCTIONS Distribution management functions form an entity of applications supporting documentation of network data, and planning, operation and reliability management of distribution network in industrial plants. The functions can be included into different computer systems, like AM/FM/GIS, Distribution Management System (DMS), and SCADA or case specific customized applications. The main functions of distribution management entity for the industrial networks are listed in the following: • Documentation of network data • Graphical user interfaces • Real-time network monitoring, state estimation and optimization - Topology management, load flow and fault current calculation, monitoring and compensation of reactive power, monitoring of harmonics and resonance, and minimization of power losses • Planning and simulation of operation actions - switching planning, fault situations, automatic load shedding and forming a local island • Management of disturbances and reliability - Preventive condition monitoring, reliability and availability management, protection relay coordination, event analysis, fault location and network restoration, reporting.
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Caused by the features of industrial networks the importance of the distribution management functions are different as in public electricity networks. There are also needs for new methods. An essential need is the load modeling which differs considerable from the public distribution. Predictive condition monitoring, reliability management, and
protection relay coordination to prevent disturbances in advance have a great importance. Some functions of the DMS for the management of public distribution networks can be applied almost as such also in the management of industrial electricity networks, e.g. topology management.
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APPLICATION FUNCTIONS OF DATA MANAGEMENT SYSTEMS
1) Load modeling The essential basis for advanced application functions is the modeling of loads connected to the network. Usually there are only few measurement points in the network. However, loading of every load node of the network must be known in the network calculations. For that purpose the loads are estimated by load models. The essential need for the load models is that they form a basis for the load-flow calculations. Results of load-flow calculations are utilized different kind of tasks as realtime network monitoring and optimization, and switching planning. Information on loads can also be utilized in preventive condition monitoring and reliability analyses. Although, the loads (i.e. the current) of some nodes can be measured on-line, models are needful because of the DMS can be used also in simulated state, when the information of system does not correspond the current real-time state of the distribution network.
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In the domain of public electricity distribution hourly load curves have been determined for each customer group to be used in load-flow calculation and load forecasting. In industrial plants the load modeling should be based mainly on the process
itself and its behavior. Load models can be determined by making enough measurements in different known process conditions. However, the industrialplants vary from each other quite much, which means that load models determined in one plant may not be able to used as such in other one. One aim of the research work is to develop tools and methods by which the determination of the plant specific load models can be achieved during the installation of the automation system when enough measurements have been done and certain process specific parameters are known. Neural networks can be used to learn the correlations between the measurements and the process in order to produce the load model Significant features of the load models are swiftness, simplicity, a capability to utilize measured information, a capability to utilize inaccurate information and a capability to adapt alternating and different conditions. The state monitoring of the DMS acts in real times which appoint demands to the swiftness of the load models. Further the industrial processes will be developed and so the load models must be able to adapt in varied situation. Demands, mentioned above, could be achieved using advanced methods and technologies. This means using neural networks technology, fuzzy logic and self-
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adaptively technologies in further development of load models of the industrial distribution networks.
Fig 5: Network Load Model Determining
Load forecasting in the industrial environment cannot be based on any regularity of behavior. Reliable forecasting assumes use of methods which can utilize production plans in some time distance which also can have a large difference with each other and include inaccurate information. The load forecasting of the network feeding some process bases on the known behavior of the process, earlier measured values and the planned production. Calculation methods for meshed networks
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The DMS for public distribution management included load flow and fault current calculation procedures, which worked only in radial networks. The need for calculating meshed networks in industrial distribution networks is anyway obvious (e.g. there are several fault current sources). Load flow calculation for meshed network leads to a group of non-linear
equations. Classic Newton-Raphson iteration is considered be the most competent method for solving load flow equations, and was selected as the solver. Fault current calculation is performed only in the symmetrical three-phase case. In fact, the calculation can be done simply by inverting a matrix. To calculate inverse of matrix with conventional methods is now too laborious and therefore discarded. Instead an algorithm called Z-bus algorithm is used for calculating inverse effectively. The load flow and fault current algorithms are implemented as a part of the DMS so that they can utilize the common network model and topology analysis. The primary information for the load-flow calculation is the loads of the secondary substations and motors connected to the medium voltage network. The loading information is read from the Access –database including the load models for different situations. The results of load flow and fault current calculations can be studied through the user-interface of the DMS by selecting the desired node.
2) Reliability management The functions related to reliability have considerable economic significance in industry. The losses of production caused by the disturbances and the inputs into the
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investments of the systems including maintenance and operational arrangements join here. The reliability can be studied with both qualitative and quantitative methods. With a qualitative analysis the possible states of the system and reasons which lead to these are determined with non-numerical methods. The failure modes, effects and
criticality analyses are adapted generally on the qualitative methods. Using failure modes, effects and criticality analysis it is aimed to identify those faults of the devices or of the subsystems which affect the capabilities of the system significantly. The system is systematically analyzed and the effects of the component faults of the system are evaluated. In a quantitative analysis indicators describing the capabilities of the system are calculated. For example, availability, fault frequencies, durations of disturbances and indicators which describe the economic appreciation of interruptions can be evaluated. The functions supporting power distribution reliability management can be included in several different systems which are, among others, AM/FM/GIS, the Distribution Management System (DMS), SCADA system, maintenance systems, and documentation systems depending on the total concept.
The load flow calculations and short circuit calculations are applications which have central meaning in reliability analyses. The calculations make it possible to simulate faults, to plan
relaying arrangements and network operations. Switching plans
operational instructions can furthermore be stored in databases. An essential function
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supporting reliability management and analyses is also the management of various instructions and documents. There are many kind of documents which can be used to support the reliability management. The graphical user-interface makes available the developing of the different sophisticated user friendly functions, for example, determination of the feeding routes of the components or loads to be examined The estimation of the reliability technical state and capabilities of the distribution system together with real-time condition supervision and maintenance programmes are in a central position in the anticipating and prevention of disturbances and in the minimization of their effects. The analysis of reliability technical state and capability of power distribution network is closely related to the protection coordination, too. Using fault current and load-flow calculations personnel can evaluate how the distribution and the primary processes will behave in fault situations of the distribution network.
3) Voltage dip analyses A voltage dip is a sudden reduction of the supply voltage to a value between 90 %and 1 % of the declared voltage, followed by a voltage recovery after a short period of time. Possible causes of these dips are typically faults in installations or in feeding public networks and switching of large loads (e.g. motors). In rural areas voltage dips are generally caused by short circuit faults in the public MV overhead network. The interest in voltage dips is mainly due to the problems they cause on several types of equipment e.g. tripping of adjustable-speed drives (both ac and dc drives), process-control
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equipment, computers and contactors in front of some devices. The employment of IUT with the support of ADA is a step towards reduction in these voltage dips.
4) Power quality analyses The term Power Quality (PQ) is used with slightly different meanings. More extensive meaning can be associated with any problems in voltage, current or frequency deviations which result in failure, malfunction, disturbances or combination of voltage quality and current quality. However, the voltage quality is addressed in most cases. Voltage quality is concerned with deviations of the voltage from the ideal and main characteristics
can be described as with regard to frequency, magnitude, waveform, symmetry of the three phase voltages and interruptions. In industrial plants on the other hand increasing amount of disturbing devices (e.g. adjustable drives and power electronics) and on the other hand increasing amount of sensitive devices (computers, process automation ,electronic devices and adjustable drives) have caused growing concern about power quality. Thus there is also a growing need to manage and monitor power quality. Volts
5 ) Condition monitoring There exist many systems for condition monitoring of industrial processes, especially for rotating machines. Monitoring usually covers electric motors that are connected to the monitored processes. There are on-line systems designed mainly for condition monitoring of electric motors, too. These systems usually include measuring device connected with processing device, which can be connected permanently to data bus supplying information for analyzing computer or data can be collected from device
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occasionally. A selection between continuous data transfer and manually performed data collection is made mainly by the costs of instrumentation and labour. Electric motors are often considered to be very reliable, which means that investment not economically justified. On-line condition monitoring of components of electricity distribution network is not commonly used. Protection relays include some functions for condition monitoring such as self diagnostics of relay and counter of operations. The applications described which are required to collect data from various sources, for example from process automation, electricity grid and energy management system. These systems contain data or are able to collect data to be used for condition monitoring purposes. Process automation and energy management can provide energy, power, current and temperature measurements of motors as well as measurement of output quantity of drive, such as mass flow of pump. Electricity grid protection and measuring devices supply quantitative and sometimes also qualitative information of voltage and current. Some useful information of condition of components can be created just by collecting and analyzing information available. Database information is used in condition monitoring and condition planning of network components as follows:
* Component data from the network database: - Date of installation, model, and nominal life time - Plan for service and replacement investments * Operation counters and operation time of switches and disconnectors:
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- Mechanical condition can be estimated - Test instruction for unused disconnectors to prevent sticking * Integrated lifetime (estimate of aging) * Reliability analysis: - Topology information and estimated reliability of components in a given load situation * Analysis (reconstruction) of actual faults: - Simulated network state using topology, load and voltage information of previous situation.
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Conclusion
Requirements of intelligent software applications for supporting the operation of industrial distribution networks are different compared to the public distribution. The domain is more segmented and heterogeneous, and the infrastructure of automation and computer systems for electricity networks are not so sophisticated and advanced as other process automation. On the other hand the chance to apply intelligent software methods is promising from the point of view of end-user attitudes, because the same kind of methods have been successfully applied in process automation, e.g. in fuzzy control and system modeling using neural networks. This paper discusses the requirements of intelligent methods in the new domain, introduces the system environment and presents initial results gained in the research work. Intelligent management will provide improvements inmany areas including Reliability, System performance, loss reduction and load management. The emergence of intelligent management is a promising step towards efficient maintenance and complete automation.
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BIBILIOGRAPHY
1) Jero.A,” Load modeling for distribution management function of industrial medium Voltage distribution networks “, IEEE Transactions on Industry applications, Vol.32 No 4, January 2001. 2) Frank R. Goodman, Jr., Ph.D.” Advanced Distribution Automation”, www.epri.com. 3) Markku Kauppinen, Tampere University of Technology, Finland “Management of electrical systems in industrial plants”, www.energyline.com . 4) Lijun Qin,”A new principle fro system protection in distribution networks”, IEEE transactions on power delivery, Vol 10, No 4, June 2001. 5) Monclar F.R,” Intelligent support system for distribution network management “, International conference on Intelligent system application to power systems “, Sweden, June 2000.
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Abstract
The automation of public electricity distribution has developed very rapidly in the past few years. The same basis can be used to develop new intelligent applications for electricity distribution networks in industrial plants. Many new applications have to be introduced because of the different environment and needs in industrial sector. The paper includes a system description of industrial electric system management. The paper discusses on the requirements of new applications and methods that can be used to solve problems in the areas of distribution management and condition monitoring of industrial networks.
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CONTENTS 1 Introduction …………………..…………………….………………... 04 2 Applications for supporting the public distribution network management ................................................ 05 3 Description of the system environment …………………………….….08 4 Application functions for distribution management in industrial plants ………………………………............ 11 5 Advanced Distribution Automation ………………...............................………………....……..14 5.1 Distribution System of Future with ADA ………………………………………….….17 6 Distribution Management Functions …...............……………………………….…....18 7Application Functions of Data Management Systems ………………………………........................…......…...…..21 7.1) Load modeling ………..……….….........................................21 7.2) Reliability management………………………….........……..23 7.3) Voltage dip analyses.........……………........……...............…25 7.4) Power quality analyses……………………………................26 7.5) Condition monitoring…………………………………..........26 8 Conclusion….......................................……………..…………….…...29 9 Bibliography.……………….................……………………................30
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Introduction
Industrial plants have put continuous pressure on the advanced process automation. However, there has not been so much focus on the automation of the electricity distribution networks. Although, the uninterrupted electricity distribution is one basic requirement for the process. A disturbance in electricity supply causing the“downrun” of the process may cost huge amount of money. Thus the intelligent management of electricity distribution including, for example, preventive condition monitoring and on-line reliability analysis has a great importance. Nowadays the above needs have aroused the increased interest in the electricity distribution automation of industrial plants. The automation of public electricity distribution has developed very rapidly in the past few years. Very promising results has been gained, for example, in decreasing outage times of customers. However, the same concept as such cannot be applied in the field of industrial electricity distribution, although the bases of automation systems are common. The infrastructures of different industry plants vary more from each other as compared to the public electricity distribution, which is more homogeneous domain. The automation devices, computer systems, and databases are not in the same level and the integration of them is more complicated.
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Applications for supporting the public distribution network management
It was seen already in the end of 80's that the conventional automation system (i.e. SCADA) cannot solve all the problems regarding to network operation. On the other hand, the different computer systems (e.g. AM/FM/GIS) include vast amount of data which is useful in network operation. The operators had considerable heuristic knowledge to be utilized, too. Thus new tools for practical problems were called for, to which AIbased methods (e.g. object-oriented approach, rule-based technique, uncertainty modeling and fuzzy sets, hypertext technique, neural networks and genetic algorithms) offers new problem solving methods. So far a computer system entity, called as a distribution management system (DMS), has been developed. The DMS is a part of an integrated environment composed of the SCADA, distribution automation (e.g. microprocessorbased protection relays), the network database (i.e. AM/FM/GIS), the geographical database, the customer database, and the automatic telephone answering machine system. The DMS includes many intelligent applications needed in network operation. Such applications are, for example, normal state-monitoring and optimization, real-time network calculations, short term load forecasting, switching planning, and fault management.
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The core of the whole DMS is the dynamic object-oriented network model. The distribution network is modeled as dynamic objects which are generated based on the network data read from the network database. The network model includes the real-time state of the network (e.g. topology and loads). Different network operation tasks call for different kinds of problem solving methods. Various modules can operate interactively with each other through the network model, which works as a blackboard (e.g. the results of load flow calculations are stored in the network model, where they are available in all other modules for different purposes).The present DMS is a Windows NT -program implemented by Visual C++. The prototyping meant the iteration loop of knowledge acquisition, modeling, implementation, and testing. Prototype versions were tested in a real environment from the very beginning. Thus the feedback on new inference models, external connections, and the user-interface was obtained at a very early stage. The aim of a real application in the technical sense was thus been achieved. The DMS entity was tested in the pilot company, Koillis-Satakunnan Sähkö Oy, having about 1000 distribution substations and 1400 km of 20 kV feeders. In the pilot company different versions of the fault location module have been used in the past years in over 300 real faults. Most of the faults have been located with an accuracy of some hundred meters, while the distance of a fault from the feeding point has been from a few to tens of kilometers. The fault location system has
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been one reason for the reduced outage times of customers (i.e. about 50 % in the 8 past years) together with other automation.
The experiences as a whole were so encouraging that the DMS was modified as a commercial product. The vendor was first a small Finnish software company. Since 1997 the DMS has been a worldwide software product of ABB Transmit Oybeing integrated to the MicroSCADA platform. At present the DMS is in everyday use in several distribution companies all over the world. Part of the research group behind the development of the DMS works at present as the employees of ABB, which has confirmed the successful commercially phase.
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Description of the system environment A big industrial plant differs from public distribution company by organizatory structure and by system environment. A production is divided into many departments or many companies. These units have the responsibility of production and maintenance. Very often the maintenance is maintained by a service company. An energy department or company is in charge of local energy production and of the distribution network. Above organizations may have some control systems that serve for their needs only, but usually information systems are closely connected together. A process automation system is the most important system in an industrial plant, sometimes including other systems, as illustrated in Fig. 1. For example, all energy production and distribution network control tasks can be done in a process automation system. Normally, because of the reliability reasons, vital parts of distribution network control is independent on the process automation. The independency of process automation system vendor has been one reason for separate systems, too.
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Figure1: Automation and information systems of an industrial plant.
The systems in Fig. 1 utilize many databases, which contain data that can be used in new applications. Process automation systems collect data for process monitoring and optimization tools. The databases contain information of material flow, energy flow and control data of production machines. Maintenance databases include technical specifications and condition data of production machine components. Similar information of electricity network components is supported by network database. Production programs are stored in the databases of administrative systems. Intelligent applications are needed to: - Handle large amount of information available. This includes filtering of data and producing new information by collecting data. - Illustrate complex dependencies of electricity distribution and production processes in abnormal situations. - Give instructions for operators in fault situations. A risk of misoperation in unusual fault situation is obvious and prevents or delay operators’ decision making. - Automize analysis tasks. Continuous information analysis is not possible manually.
In order to introduce new intelligent applications for the management of electric systems in industrial plants, a basis for implementation is needed. The following requirements should be satisfied: - Documentation of electricity distribution network is available for the systems. Network databases can supply this information.
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- Network, process and motor measurements are available for the system. This means, that data acquisition from multiple sources with capability to use various data transfer methods is needed, as illustrated in Fig. 2.
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Application functions for distribution management in industrialplants
As mentioned above the concept of public distribution automation cannot be applied as such in the management of industrial electricity networks. For example, fast and accurate fault location has a great importance for reducing the outage time of customers in the public electricity distribution, while there is no special need of such a function in industrial networks. Predictive condition monitoring, reliability calculations, and protection relay coordination to prevent disturbances in advance are more important. Caused by the features of industrial networks there are needs for methods to model dynamic phenomena and harmonics, and to calculate load-flow and fault currents in ring connected networks. An essential need is the load modeling which differs considerable from the public distribution. The basis of the distribution management system (i.e. the use of network model as the blackboard) is common in the both domains. The network model includes the real-time topology and network calculation results in the prevailing switching and load conditions. The main functions of system entity for the industrial networks are listed in the following: * Real-time network monitoring, state estimation and optimization: - Topology management - load flow and fault currents also as dynamic phenomena - Monitoring and compensation of reactive power - monitoring of harmonics and resonances
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- Minimization of power losses * Planning and simulation of operation actions - switching planning - Automatic load shedding and forming a local island - switching the network as a part of the national grid - fault situations * Management of disturbances - Event analysis - Fault location and network restoration - Preventive condition monitoring - Protection relay coordination - Reliability calculations - reporting
Distribution Automation which includes feeder automation and distribution management systems (DMS) is an important technique in distribution network. The distribution management systems are composed of distribution management functions. The DMF is an entity which incorporates different applications on a single platform over which supervision is made. This mainly supports documentation of network data planning operation and
reliability management of distribution networks. Various
application functions for distribution management in industrial plants are mainly load modeling ,reliability management , power quality analysis, voltage dip analysis and
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condition monitoring .All this are incorporated in a domain of distribution management functions. Advanced distribution automation (ADA) modern day approach towards efficient management of distribution networks.
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ADVANCED DISTRIBUTION AUTOMATION
Traditional distribution systems were designed to perform one function— distributing power to end users. The distribution system of the future will be more versatile and will be multifunctional. Strategic drivers for ADA are to • Improve system performance • Reduce outage times • Allow the efficient use of distributed energy resources • Provide the customer more choices and • To integrate the customer systems For ADA to work, the various intelligent devices must be interoperable both in the electric system architecture and in the communication and control architecture.
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Intelligent Management Of Electrical Systems in Industries . Figure3: ADA architecture
ADA will enable the distribution system to be configured in new ways for such things as looped secondaries or intentional islanding to facilitate easy recovery from outages and to deal with other emergencies.
Fig: 4 The three major components of ADA – Flexible electrical system architecture – Real-time state estimation tools – Communication and control system based on open architecture standards The intelligent universal transformer is a prime example of a new electronic device that will be a cornerstone of ADA. It will provide a variety of functions including – Voltage stepping – Voltage regulation – Power quality enhancement
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– New customer service options such as DC power output – Power electronic replacement for conventional copper and iron transformers
The Flexible Electric Architecture and the Open Communications Architecture synergistically empower each other to create the distribution system of the future.
Each of these is made more valuable by its interaction with the other.
ADA will provide improvements in many areas including – Reliability – System performance – Condition monitoring – Outage detection and restoration – Maintenance practices and prioritization – Automated switching and fault management – Reactive power and voltage management – Loss reduction and load management – Customer service options
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Distribution System of Future with ADA
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DISTRIBUTION MANAGEMENT FUNCTIONS Distribution management functions form an entity of applications supporting documentation of network data, and planning, operation and reliability management of distribution network in industrial plants. The functions can be included into different computer systems, like AM/FM/GIS, Distribution Management System (DMS), and SCADA or case specific customized applications. The main functions of distribution management entity for the industrial networks are listed in the following: • Documentation of network data • Graphical user interfaces • Real-time network monitoring, state estimation and optimization - Topology management, load flow and fault current calculation, monitoring and compensation of reactive power, monitoring of harmonics and resonance, and minimization of power losses • Planning and simulation of operation actions - switching planning, fault situations, automatic load shedding and forming a local island • Management of disturbances and reliability - Preventive condition monitoring, reliability and availability management, protection relay coordination, event analysis, fault location and network restoration, reporting.
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Caused by the features of industrial networks the importance of the distribution management functions are different as in public electricity networks. There are also needs for new methods. An essential need is the load modeling which differs considerable from the public distribution. Predictive condition monitoring, reliability management, and
protection relay coordination to prevent disturbances in advance have a great importance. Some functions of the DMS for the management of public distribution networks can be applied almost as such also in the management of industrial electricity networks, e.g. topology management.
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APPLICATION FUNCTIONS OF DATA MANAGEMENT SYSTEMS
1) Load modeling The essential basis for advanced application functions is the modeling of loads connected to the network. Usually there are only few measurement points in the network. However, loading of every load node of the network must be known in the network calculations. For that purpose the loads are estimated by load models. The essential need for the load models is that they form a basis for the load-flow calculations. Results of load-flow calculations are utilized different kind of tasks as realtime network monitoring and optimization, and switching planning. Information on loads can also be utilized in preventive condition monitoring and reliability analyses. Although, the loads (i.e. the current) of some nodes can be measured on-line, models are needful because of the DMS can be used also in simulated state, when the information of system does not correspond the current real-time state of the distribution network.
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In the domain of public electricity distribution hourly load curves have been determined for each customer group to be used in load-flow calculation and load forecasting. In industrial plants the load modeling should be based mainly on the process
itself and its behavior. Load models can be determined by making enough measurements in different known process conditions. However, the industrialplants vary from each other quite much, which means that load models determined in one plant may not be able to used as such in other one. One aim of the research work is to develop tools and methods by which the determination of the plant specific load models can be achieved during the installation of the automation system when enough measurements have been done and certain process specific parameters are known. Neural networks can be used to learn the correlations between the measurements and the process in order to produce the load model Significant features of the load models are swiftness, simplicity, a capability to utilize measured information, a capability to utilize inaccurate information and a capability to adapt alternating and different conditions. The state monitoring of the DMS acts in real times which appoint demands to the swiftness of the load models. Further the industrial processes will be developed and so the load models must be able to adapt in varied situation. Demands, mentioned above, could be achieved using advanced methods and technologies. This means using neural networks technology, fuzzy logic and self-
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adaptively technologies in further development of load models of the industrial distribution networks.
Fig 5: Network Load Model Determining
Load forecasting in the industrial environment cannot be based on any regularity of behavior. Reliable forecasting assumes use of methods which can utilize production plans in some time distance which also can have a large difference with each other and include inaccurate information. The load forecasting of the network feeding some process bases on the known behavior of the process, earlier measured values and the planned production. Calculation methods for meshed networks
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The DMS for public distribution management included load flow and fault current calculation procedures, which worked only in radial networks. The need for calculating meshed networks in industrial distribution networks is anyway obvious (e.g. there are several fault current sources). Load flow calculation for meshed network leads to a group of non-linear
equations. Classic Newton-Raphson iteration is considered be the most competent method for solving load flow equations, and was selected as the solver. Fault current calculation is performed only in the symmetrical three-phase case. In fact, the calculation can be done simply by inverting a matrix. To calculate inverse of matrix with conventional methods is now too laborious and therefore discarded. Instead an algorithm called Z-bus algorithm is used for calculating inverse effectively. The load flow and fault current algorithms are implemented as a part of the DMS so that they can utilize the common network model and topology analysis. The primary information for the load-flow calculation is the loads of the secondary substations and motors connected to the medium voltage network. The loading information is read from the Access –database including the load models for different situations. The results of load flow and fault current calculations can be studied through the user-interface of the DMS by selecting the desired node.
2) Reliability management The functions related to reliability have considerable economic significance in industry. The losses of production caused by the disturbances and the inputs into the
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investments of the systems including maintenance and operational arrangements join here. The reliability can be studied with both qualitative and quantitative methods. With a qualitative analysis the possible states of the system and reasons which lead to these are determined with non-numerical methods. The failure modes, effects and
criticality analyses are adapted generally on the qualitative methods. Using failure modes, effects and criticality analysis it is aimed to identify those faults of the devices or of the subsystems which affect the capabilities of the system significantly. The system is systematically analyzed and the effects of the component faults of the system are evaluated. In a quantitative analysis indicators describing the capabilities of the system are calculated. For example, availability, fault frequencies, durations of disturbances and indicators which describe the economic appreciation of interruptions can be evaluated. The functions supporting power distribution reliability management can be included in several different systems which are, among others, AM/FM/GIS, the Distribution Management System (DMS), SCADA system, maintenance systems, and documentation systems depending on the total concept.
The load flow calculations and short circuit calculations are applications which have central meaning in reliability analyses. The calculations make it possible to simulate faults, to plan
relaying arrangements and network operations. Switching plans
operational instructions can furthermore be stored in databases. An essential function
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supporting reliability management and analyses is also the management of various instructions and documents. There are many kind of documents which can be used to support the reliability management. The graphical user-interface makes available the developing of the different sophisticated user friendly functions, for example, determination of the feeding routes of the components or loads to be examined The estimation of the reliability technical state and capabilities of the distribution system together with real-time condition supervision and maintenance programmes are in a central position in the anticipating and prevention of disturbances and in the minimization of their effects. The analysis of reliability technical state and capability of power distribution network is closely related to the protection coordination, too. Using fault current and load-flow calculations personnel can evaluate how the distribution and the primary processes will behave in fault situations of the distribution network.
3) Voltage dip analyses A voltage dip is a sudden reduction of the supply voltage to a value between 90 %and 1 % of the declared voltage, followed by a voltage recovery after a short period of time. Possible causes of these dips are typically faults in installations or in feeding public networks and switching of large loads (e.g. motors). In rural areas voltage dips are generally caused by short circuit faults in the public MV overhead network. The interest in voltage dips is mainly due to the problems they cause on several types of equipment e.g. tripping of adjustable-speed drives (both ac and dc drives), process-control
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equipment, computers and contactors in front of some devices. The employment of IUT with the support of ADA is a step towards reduction in these voltage dips.
4) Power quality analyses The term Power Quality (PQ) is used with slightly different meanings. More extensive meaning can be associated with any problems in voltage, current or frequency deviations which result in failure, malfunction, disturbances or combination of voltage quality and current quality. However, the voltage quality is addressed in most cases. Voltage quality is concerned with deviations of the voltage from the ideal and main characteristics
can be described as with regard to frequency, magnitude, waveform, symmetry of the three phase voltages and interruptions. In industrial plants on the other hand increasing amount of disturbing devices (e.g. adjustable drives and power electronics) and on the other hand increasing amount of sensitive devices (computers, process automation ,electronic devices and adjustable drives) have caused growing concern about power quality. Thus there is also a growing need to manage and monitor power quality. Volts
5 ) Condition monitoring There exist many systems for condition monitoring of industrial processes, especially for rotating machines. Monitoring usually covers electric motors that are connected to the monitored processes. There are on-line systems designed mainly for condition monitoring of electric motors, too. These systems usually include measuring device connected with processing device, which can be connected permanently to data bus supplying information for analyzing computer or data can be collected from device
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occasionally. A selection between continuous data transfer and manually performed data collection is made mainly by the costs of instrumentation and labour. Electric motors are often considered to be very reliable, which means that investment not economically justified. On-line condition monitoring of components of electricity distribution network is not commonly used. Protection relays include some functions for condition monitoring such as self diagnostics of relay and counter of operations. The applications described which are required to collect data from various sources, for example from process automation, electricity grid and energy management system. These systems contain data or are able to collect data to be used for condition monitoring purposes. Process automation and energy management can provide energy, power, current and temperature measurements of motors as well as measurement of output quantity of drive, such as mass flow of pump. Electricity grid protection and measuring devices supply quantitative and sometimes also qualitative information of voltage and current. Some useful information of condition of components can be created just by collecting and analyzing information available. Database information is used in condition monitoring and condition planning of network components as follows:
* Component data from the network database: - Date of installation, model, and nominal life time - Plan for service and replacement investments * Operation counters and operation time of switches and disconnectors:
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- Mechanical condition can be estimated - Test instruction for unused disconnectors to prevent sticking * Integrated lifetime (estimate of aging) * Reliability analysis: - Topology information and estimated reliability of components in a given load situation * Analysis (reconstruction) of actual faults: - Simulated network state using topology, load and voltage information of previous situation.
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Conclusion
Requirements of intelligent software applications for supporting the operation of industrial distribution networks are different compared to the public distribution. The domain is more segmented and heterogeneous, and the infrastructure of automation and computer systems for electricity networks are not so sophisticated and advanced as other process automation. On the other hand the chance to apply intelligent software methods is promising from the point of view of end-user attitudes, because the same kind of methods have been successfully applied in process automation, e.g. in fuzzy control and system modeling using neural networks. This paper discusses the requirements of intelligent methods in the new domain, introduces the system environment and presents initial results gained in the research work. Intelligent management will provide improvements inmany areas including Reliability, System performance, loss reduction and load management. The emergence of intelligent management is a promising step towards efficient maintenance and complete automation.
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BIBILIOGRAPHY
1) Jero.A,” Load modeling for distribution management function of industrial medium Voltage distribution networks “, IEEE Transactions on Industry applications, Vol.32 No 4, January 2001. 2) Frank R. Goodman, Jr., Ph.D.” Advanced Distribution Automation”, www.epri.com. 3) Markku Kauppinen, Tampere University of Technology, Finland “Management of electrical systems in industrial plants”, www.energyline.com . 4) Lijun Qin,”A new principle fro system protection in distribution networks”, IEEE transactions on power delivery, Vol 10, No 4, June 2001. 5) Monclar F.R,” Intelligent support system for distribution network management “, International conference on Intelligent system application to power systems “, Sweden, June 2000.
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