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Exploiting Intelligent Systems Techniques within an Autonomous Regional Active Network Management System Euan M. Davidson, Member, IEEE, Stephen D. J. McArthur, Senior Member, IEEE, Michael. J. Dolan, James R. McDonald, Senior Member, IEEE
Abstract—This paper discusses AuRA-NMS, an autonomous regional active network management system currently being developed in the UK through a partnership between several UK universities, two distribution network operators (DNO) and ABB. The scope of control to be undertaken by AuRANMS includes: automatic restoration, voltage control, power flow management and implementation of network performance optimisation strategies. Part of the scientific aims of the AuRA-NMS programme is the investigation and comparison of the use of different techniques for making the control decisions above. The techniques under consideration range from the use of optimisation techniques, such as OPF, to the use of intelligent systems techniques, such as constraint programming, casebased reasoning and AI planning. In this paper the authors consider the role that intelligent systems techniques could play within active network management and reports on preliminary results gathered from the testing of prototype software running on commercially available IEC 61850 compliant substation computing hardware, connected to a real time power systems simulator. The importance of an appropriate comparative testing methodology, as well as the need for assessing the robustness of different techniques in the presence of communication failures and measurement errors, is also discussed. A key element in the development of AuRA-NMS is the use of multi-agent systems (MAS) technology to provide a flexible and extensible software architecture in which the techniques above can be deployed. As a result, the use (MAS) in conjunction with IEC 61850 and the common information model within AuRA-NMS is described. Index Terms— Cooperative systems, distributed control, intelligent systems.
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I. INTRODUCTION
he development of “smart” or “intelligent” energy networks has been proposed by both the European SmartGrids Technology Platform [1] and EPRI’s IntelliGrid initiative [2] as a key step in meeting our future energy needs. One of the challenges in delivering the energy networks of the future is the judicious selection and development of an appropriate set of technologies which will form “a toolbox of proven technical solutions that can This work was supported in part by the UK’s Engineering and Physical Science Research Council (EPSRC), SP Energy Networks, EDF Energy and ABB. E. M. Davidson , S. D. J. McArthur and J. R. McDonald are with the Institute for Energy and Environment, University of Strathclyde, Glasgow, Scotland, G1 1XW, UK (e-mail:
[email protected],
[email protected],
[email protected] ).
be deployed rapidly and cost effectively” [1], i.e. the building blocks for SmartGrids. In this paper we explore the role that intelligent systems techniques may fill in the population of part of that “toolbox”. In particular the following areas are examined: • The use of intelligent systems techniques for providing decision-making functionality for active network management systems; • The use of intelligent systems techniques for building flexible and extensible software architectures for active network management systems; and • The use of the intelligent system techniques along side existing standards, power systems measurement, protection and control hardware, and communication technologies. Importantly the creation of a toolbox of proven technical solutions requires that potential approaches, such as the use of intelligent systems, are adequately tested and then demonstrated through field trials. In order to explore the issues above, this paper reports on preliminary results from the development of intelligent systems for potential use within AuRA-NMS (Autonomous Regional Active Network Management System), a collaborative research project involving several UK universities, two distribution network operators (DNO) and ABB, which aims to develop a demonstrable flexible and extensible active network management system. II. THE ROLE OF INTELLIGENT SYSTEMS IN AURA-NMS AuRA-NMS represents a move from bespoke single issue active network management solutions to a more generic solution that can be rapidly deployed a variety of MV networks and deal with multiple issues in a coordinated manner, e.g. power flow management, steady state voltage control, restoration, and minimisation of losses. Details of the industrial drivers behind the development of AuRA-NMS can be found in [3] and [4] but for the sake of brevity can be summarised as follows: • Increasing network access for distributed generation. The renewable obligation certificate (ROC) scheme has provided the incentive for generators to connect to distribution networks in the UK. However, the burden of any requisite network reinforcement can be prohibitive. As a result, utilities are looking to active network management (ANM) solutions [5] which allow generators to connect to existing networks under a range of connection agreements, while avoiding, or at least reducing or
09GM1139 deferring, the costs associated with network reinforcement. • Reducing complexity of constraint management schemes for the operator’s perspective. DNOs currently employ bespoke standalone constraint management schemes which deal with particular contingencies, e.g. loss of a transformer or a line. As more generation is connected to the network and more contingencies need to be considered, dealing with contingencies on a piecemeal basis can lead a large number of schemes on the same area of network. These schemes may interact in a manner that may make it difficult to discern root cause. In some cases the complexity of control and the possible negative impact on network performance can become the main barriers to additional generation being connected to the network. Hence, DNOs wish to replace these disparate schemes with a coordinated solution [4]. • The requirement for flexible and extensible active network management solutions. DNOs recognize that, once in place, active network management systems must be flexible and extensible. Flexibility is the ability to easily reconfigure the active network management system in the face of: changes to network topology and plant ratings; the connection or removal of new generation or energy storage; changes to the installed protection and control equipment; the installation or removal of measurement and monitoring equipment; and changes to the regulatory framework in which DNOs operate and the markets in which generators connected to the network participate. Extensibility is the ability to easily: add additional network management and control functionality in the future; and replace existing functionality when improved network management and control techniques are developed. • The potential increasing network performance. There may be ancillary benefits of levels of automation if, as well as increasing DG access, it can improve network performance, i.e. reduce customer minutes lost (CML), customer interruptions (CI) and minimize losses, through automated restoration or automated network control which takes action to minimize losses. AuRA-NMS is being developed in response to these industrial drivers and can be seen as part of the drive to develop “smart” or “intelligent” networks. A. AuRA-NMS Like the IntelliGrid and SmartGrid vision, AURA-NMS is predicated on the deployment of distributed computing resources with the use of cost effective communication links between those resources. From a technical perspective, AuRA-NMS can be thought of as distributable active network management software running on a distributed hardware platform. The hardware platform comprises: substation computing equipment, on which control software with the required network management and control functionality is installed; IEDS; potentially PLCs and PACs used for measurement gathering and simple control
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functionality; and the communications infrastructure linking this hardware in different locations The substation computing hardware currently being used for the development and testing of AuRA-NMS is ABB’s COM600 series substation automation product. The COM600 is a Windows XP Embedded industrial computer that, designed for robustness, has no moving parts. Full details of functionality and specification of the COM600 series can be found on ABB's website [6]. The COM600 has been designed to act, in part, as a substation gateway which translates between various master and slave protocols using IEC 61850 as a standard data model and collection of OPC clients and servers with interfaces to legacy intra and inter substation communication protocols. These protocols include: DNP3; MODBUS; PROFIBUS; and IEC 61850 using OPC and MMS [6]. As a result, the COM600 provides AuRA-NMS software with ready-made software interface to existing monitoring, protection and control equipment, as well as existing communications networks that use the protocols the COM600 supports. Moreover, it provides a powerful substation computing platform on which power system management and control software can run. A key part of the AuRA-NMS concept is the notion of an active network management system that integrates a number of disparate approaches to a number of different power system management and control tasks. This, in part, is recognition that, in addition to the requirement for flexibility and extensibility, different DNOs will have different power system management and control requirements.
Figure 1: 33kV interconnected test network with distributed generation.
Figure 2: 11kV radial test network with connected DG
09GM1139 Two different areas of network were chosen in order to explore how a single solution, such as AuRA-NMS, could be deployed on different networks. These networks are: • An area of interconnected 33kV distribution network, with a relatively large amount of distributed generation connected (figure 1); and • An area of traditional radial 11kV distribution network with small scale DG (Landfill Gas) connected (figure 2). It is envisaged that by placing one or more COM600 units at the most appropriate locations on the network, given the existing or possibly augmented communications infrastructure, power systems data gathering, management and control functionality can be distributed in order to meet the utilities active network management requirements. B. The role of intelligent systems Techniques and technologies from the intelligent systems canon are being investigated for two distinct roles within AuRA-NMS: • As a means of providing control decision-making functionality; and • As a means of providing a flexible and extensible software architecture for AuRA-NMS which supports the integration of disparate approaches to the different power systems management and control tasks. We begin by examining the use of intelligent systems as a means of providing control decision-making functionality and present results from initial laboratory tests of prototypes of intelligent systems for power flow management and voltage control. III. INTELLIGENT SYSTEMS TECHNIQUES FOR PROVIDING CONTROL DECISION-MAKING FUNCTIONALITY The development of AuRA-NMS began with the elicitation of requirements from two DNOs with differing active network management needs. A formal specification of requirements identified three core active network management tasks that the utilities believe an active network management solution should be capable of. These tasks are: • Automatic restoration; • Power flow management; and • Voltage control. Any solution to these three tasks should also meet the utilities’ requirements for flexibility and extensibility described in the previous section. Solutions must be safe, secure, tolerant to failure, and possess the ability to exhibit graceful degradation in performance during adverse or unanticipated network conditions. It is in the area of graceful degradation in active network management that intelligent systems techniques may come into their own. Many intelligent systems techniques have been developed with graceful degradation in mind. We shall examine this idea in more detail when considering specific techniques in sections IV and V. Below we consider each of the power systems management and control tasks in turn and the role intelligent systems techniques may play in providing the appropriate decision-making functionality.
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A. Automatic restoration Of the three core power systems and control management requirements of partner DNOs, automatic restoration has arguable the longest history. Expert system based, modelbased, artificial neural network (ANN) based, agent-based, and genetic algorithm (GA) based approaches have been reported in the literature [7][8][9][10]. Although not widely reported in the power systems literature, the AI planning community has been using the restoration of distribution networks as a benchmark problem for testing the effectiveness of domain independent planning tools [11]. [12] and [13] describe the results of the application of existing planning tools with respect to the benchmark, including graceful degradation of performance when dealing with uncertain or erroneous data. The current prototype supply restoration software developed by AuRA-NMS consortium partners at the University of Cardiff does not employ an intelligent system technique and, as a result, is not presented in this paper. However, intelligent systems approaches, such as the use of an off-the-shelf AI planner for restoration may be investigated for use in AuRA-NMS in the future. B. Power flow management In the context of AuRA-NMS, power flow management entails management distributed generators in a manner that thermal limits of plant are not exceeded. Thermal limits place a limitation on the firm DG connections that a network can support without having to reinforce the network. It has been shown that ANM techniques, such as curtailment of generator output, can accommodate DG connections above the level allowed by conventional power system planning techniques [14]. To the authors’ knowledge, no work has been published to date for an active network management scheme which uses intelligent systems techniques for power flow management. The authors are currently investigating a number of approaches to providing decision making functionality for power flow management within AuRA-NMS: • A constraint programming approach; • A Current tracing technique described in [15] ; and • An optimal power flow (OPF) based approach. We describe the constraint programming approach, which is an intelligent systems technique, in section IV. C. Steady state voltage control One of the key problems encountered when connecting generation to distribution networks is variation in voltage outside statutory limits, in particular, voltage rise, and a large number of papers have been devoted to this issue, e.g. [16][17][18] provide examples of the novel AVC schemes and the use of DG to provide voltage support [19]. In terms of reported investigation of the use of intelligent systems techniques to voltage control outside operator decision support, results have been published on the use of genetic algorithms [20] and the use of multi-agent system technology [21][22] to provide coordinated voltage control. Within the AuRA-NMS programme, case-based reasoning is being investigated as a potential flexible and extensible approach to coordinated voltage control [23] using on load tap changers and control of the real and
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reactive power output of DG as control measures. This and the rationale for its use are described in more detail in section V. Research yet to be reported in the power systems literature has investigated the use of AI planning for setting voltage targets on the transmission network [24]. This research may be applicable to voltage control on distribution networks and be able to incorporate load and generation forecasts into planning on making voltage control decisions. IV. A CONSTRAINT PROGRAMMING APPROACH TO POWER FLOW MANAGEMENT
At the time of writing, the most mature prototyped approach to power flow management produced by the AuRA-NMS consortium was an approach using constraint programming developed by the authors. A. Constraint programming Constraint programming (CP) [25] is a form of declarative programming used, in the main, to solve constraint satisfaction problems (CSP). It has its roots in the logic programming sub-field of artificial intelligence and has been applied in a number of different fields: its use in operations research as a means of solving scheduling problems and configuration problems in electronics serve as examples. In power engineering, the interpretation of fault recorder data has been modeled as a CSP and then attacked using CP techniques [26]. There are several classes of CSP and research into CP has led to the development of growing set of algorithms for solving these different problems. The particular class of CSP discussed in this paper is a finite discrete domain CSP. Modeled as a CSP a problem has 2 facets: variables and constraints. Each variable in a problem has a finite domain of discrete values. Constraints place limitations on the values that those variables can hold. Finding a solution to a CSP involves finding assignments to variables from their domains that meet all the constraints. CP provides tools for doing this called CSP solvers. B. CP applied to power flow management The use of CP is based on the assumption that power flow management can be modeled as a constraint satisfaction problem. To do so the problem must be expressed as a set of variables with finite discrete domains and a set of constraints. In the case of power flow management, we assume that the problem we wish to solve is that of deciding what control action to take in order to remove a thermal overload from the network. The variables of the CSP are the controllable plant items, i.e. the generators. Associated with each generator is a domain of discrete values. These discrete values are the control signals that can be sent to the generator. In the current prototype, these actions are: • Run unconstrained. The generator can generate as much or a little real power as it wishes. • Run constrained. The generator must reduce its output to below a given threshold or it will be disconnected from the network. Each generator is given a number of curtailment bandings, e.g. 80% rated output, 70% rated output, 60% rated output etc.
In addition to variables and their domains, the constraints on the solution must be modeled. In the current prototype the following constraints are used. • Power flow constraints. Any potential solution in the form of control actions sent to generators must fulfill a power flow constraint, i.e. a given set of control actions will not result in the thermal overload of any plant given the current network conditions. It is assumed that this constraint can be checked using a suitable network model and load flow engine. • Contractual constraints. Generator access rights place further constraints on possible solutions. Generators are connected to the network with first in last off network access rights. These access rights can be expressed as a constraint or set of constraints. • Preference constraints. For a given situation, there may be many sets of control actions which meet the constraints above. For example, due to the network design rules, thermal overloads are unlikely to occur when all generation is tripped. As a result the CSP solver requires a preference constraint or set of preference constraints. The constraints are used by the CSP solver to decide when one solution in better than another. When a preference constraint is applied, the CSP solver uses a best-first strategy to search the space of possible solutions. In the current prototype a preference constraint is used to make the CSP solver search for solutions that maximize DG network access. The prototype software (figure 3) for the constraint programming approach comprises: an off-the-shelf load flow engine; an off-the-shelf constraint satisfaction problem solver; and an interface to the COM600’s OPC servers (not shown in figure 3) allowing it to read measurements from the network and send control signals to generators.
Figure 3: CSP solver for power flow management
As the network model and list of generators and their control actions are all configurable, this approach meets the requirements for flexibility and extensibility. It is also able to return a rank list of N solutions for a given situation. If the top ranked solution does not result in the removal of the thermal overload, due to measurement/model errors, the next best solution is then implemented. Hence, it is envisaged that this technique will degrade gracefully. C. Initial results At the time of writing the AuRA-NMS consortium was in the process of unit testing of the software in figure 3, running on a COM600 connected to a real time simulator. A key element in the feasibility of this approach is the time required by the power flow management software to find a given number of solutions to thermal overload. The case study presented here is based on the management of power flows on the network in figure 1. The
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load at Sub A increases to force a thermal overload on the line 1 (figure 4). The software detects the overload (figure 5). Based on the contractual, load flow, and preference constraints, the CSP solver works out that having an output lower than 50% at Gen 5 will result in the removal of the thermal overload from the network and curtails the generation appropriately (Figures 4 and 5). Network access rights determine that Gen 5 should be curtailed before the other generators. It is possible to run the CSP solver without applying the contractual constraint and that case it will attempt maximize DG access regardless of generator rights. 25 20 Load MW
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Generation
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Curtailed Generation
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12 23 34 45 56 67 78 89 100 111 122 time step
Figure 4: The graph above shows the output of the curtailed generation, the load at Sub A, and the amount and duration of the curtailment the generator experienced. These results were achieved via real time simulation.
voltage control prototype software is based in that technique. A. Case-based reasoning Case-based reasoning emerged from the intelligent systems community in the early 1980s [27]. As a reasoning technique, CBR solves a given problem by using solutions to similar past problems. CBR systems are generally composed of a case-base, a database containing previous cases and their solutions, and a case-based reasoning engine which, when presented with a new case, searches the casebase for the most similar cases and their solutions. The CBR research community has produced a set of techniques for evaluating the similarity between cases. B. CBR applied to voltage control The rationale behind the use of CBR is based on the assumption that the burden of computing a solution to the voltage control problem may be too great for some hardware. By carrying out the required simulation and analysis off-line in order to populate the case-base, CBR offers an alternative to computing a solution using a technique such as CP or OPF with searching through a database of pre-enumerated solutions, a task which is completed in a predictable timescale, without risk of nonconvergence. CBR may offer other advantages. Errors in less important measurands may be less likely to cause errors in the most suitable retrieved case, resulting in a robust voltage control system. CBR could also meet the requirement for graceful degradation: CBR can return a number of solutions to a problem. Should the preferred solution fail to remove a voltage excursion from the network, then the next best solution can be attempted as appropriate.
Figure 6: CBR software for voltage control Figure 5: GUI of power flow management software running on COM600 showing the PFM software detecting a thermal overload, curtailing generation, and then removing that curtailment when network conditions allow.
Running on commercially available hardware, tests such as these indicate that it is possible to compute solutions using this technique in feasible timescales for use on the real power system. Figure 5 shows that the approach takes around 2 seconds to find the solution to this case study. Similar tests have been run on the network on figure 2 to show network agnostic nature of the approach. Timing results can be found in [15]. Time to compute a solution is only one aspect of testing these techniques and the goals of further unit testing will be discussed in section VII. V.
APPLYING CASE-BASED REASONING TO STEADY STATE VOLTAGE CONTROL
One of the intelligent systems techniques being investigated for voltage control is case-based reasoning (CBR) and the AuRA-NMS consortium’s most mature
The current prototype CBR software uses the approach to case matching in [28] and does the following: • Detects of a voltage excursion and formulates a problem case; • Retrieves the N most similar case from the case base based on the problem case; and • Evaluates whether or not the proposed solution will remove the fault from the network using online simulation. A more detailed description of the CBR approach can be found in [23]. C. Initial results The same real time simulation environment used for unit testing of the constraint programming approach to power flow management was used to test the prototype CBR engine. The case study presented here involves a voltage excursion at bus 11 and “gen bus B”, cause by an increase in load at buses 4 and 12, on the network in figure 2. The resulting voltage excursion can be seen in figure 7.
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Figures 8 and 9 show the response of the CBR engine to the excursion. The CBR engine detects the excursion and then forms a problem case (figure 8.) The 5 most similar cases are retrieved from the case base and then the CBR engine runs a set of simulations to evaluate the effectiveness of the proposed solutions (figure 9). Running on the COM600 unit, it does this in under two seconds.
Figure 7: Voltage excursion on network in figure 2.
Excursion detected
Problem Case
Figure 8: GUI of CBR software running on COM600 unit showing the detection of voltage excursions, the formulation of a problem case, and the retrieval of cases from the case base for an excursion on the network in figure 2.
Figure 9: GUI of CBR software running on the COM600 showing the retrieval of cases and their subsequent evaluation via online testing. Solutions in red fail, solutions in green pass.
It is important to note that the technique described above need not be limited to voltage control. The CBR engine developed by the authors is both network and control task agnostic: it simply matches a current set of network conditions against cases in its case base. Two outstanding research issues need be addressed before concrete claims about the feasibility of a CBR approach to voltage control: • Case-based population methodology. Testing to date has been using a manually populated case-base. Given that the performance of CBR is related to the cases in its case base, a robust methodology for ensuring the case base covers all expected network states and conditions is required. • Evaluation of the size of case-base required for particular networks. The number of cases in a case base affects the time taken to search for similar cases. If the case base is very large, searching the database may take as much time as computing a solution using techniques such as OPF and CP. Further investigation is required in order to evaluate factors on the size of the case-based required and comparison to other techniques which carry out online computation of a solution from first principles. The authors are currently exploring these issues with partners at Durham University and Imperial College London. VI. TESTING THROUGH REAL TIME SIMULATION One of the aims of the AuRA-NMS programme is the testing and evaluation of techniques for voltage control, power flow management, restoration and network optimization through real-time simulation. In collaboration with partners at Imperial College London, a real time simulator has been developed that can feed COM600 units with network data and respond to control signal issues by the COM600 unit. The real-time simulator also has the ability to introduce noise and variance to measurands, and is being extending to simulate communication link delays and
09GM1139 failures. Using the capabilities of the simulator with models of the test networks, and historical load and generation output profiles, the approaches described in sections IV and V are undergoing unit testing. A more detail description of the simulator can be found in [23]. VII. COMPARING CONTROL TECHNIQUES As section III describes, the authors are currently in the process of assessing the relative strengths and weaknesses of a number of different techniques to each of the core power system management and control tasks. Using the test networks in figures 2 and 3 as benchmarks, the authors aim to assess the performance of each technique through comparative testing. By examining the performance of the approaches under measurement errors and communications problems, we hope to empirically assess the relative robustness of the techniques in order to make recommendations on which techniques are most feasible and can be proceed to site trials. For the case of the three techniques to power flow management investigated thus far, initial comparative testing based on timing and the solution found by the technique indicates that not only do the three techniques find the same solution to the test cases used thus far, they do so in similar timescales. As a result, it is possible the performance in the presence of erroneous measurements that may be the discriminating factor when choosing between these techniques.
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B. A MAS based software architecture While the use of MAS technology for distribution automation has been mooted by several authors, AuRANMS is different in that it does not map an agent architecture onto the existing topology of the power system, i.e. in AuRA-NMS agents do not represent specific generators, circuit breakers, feeders or busbars. AuRANMS exploits MAS technology as a means of providing a flexible, extensible and distributable software integration framework. Details of the use of MAS for AuRA-NMS, the rationale for its use in AuRA-NMS can be found in [3] and [4]. The proposed software architecture from [32] can be seen in figure 10. A foundation for intelligent physical agents (FIPA) compliant MAS is installed on each COM600. Software modules with the control decision-making ability described in the preceding sections are wrapped as agents and deployed within the architecture. Distribution state estimation (DSE) is integrated into the architecture by exploiting the ability of the COM600 to manage virtual IEDs [6] via a virtual IED OPC server. System voltage, currents and loads estimated by the DSE can be written to these virtual IEDs and then used by the AuRA-NMS agents which can connect to OPC servers that support the IEC 61850 data model
VIII. PROVIDING A FLEXIBLE AND EXTENSIBLE SOFTWARE ARCHITECTURE FOR ACTIVE NETWORK MANAGEMENT SYSTEMS
AuRA-NMS is being developed to be flexible and extensible. As well as potentially providing some of the control and systems management software for specific tasks, such as voltage control and power flow management, intelligent systems techniques, in the form of multi-agent systems (MAS), are being investigated as a means of developing a flexible and extensible software architecture. A. Exploiting MAS Technology [29] describes the potential uses of MAS technology within power systems: as a modeling approach or as a systems integration technology. In AuRA-NMS MAS is being investigated as a means of integrating disparate power system management and control approaches within a single architecture. Arguably, the use of MAS technology to that end, i.e. system integration, is one that has already been demonstrated in the power industry [30] through deployment at a utility. A number of other MAS have been developed at the Institute for Energy and Environment which use MAS technology in a similar way. The condition monitoring multi-agent system (COMMAS) [31] and a monitoring system developed for British Energy are currently being rolled out, strengthening the case that MAS can be used to develop distributed systems in an incremental, flexible and extensible manner. Flexibility and extensibility are achieved partly through the adoption of standards for building open multi-agent systems and partly through the properties we imbue our agents with.
Figure 10: The proposed AuRA-NMS MAS architecture
Agents within the architecture communicate with each other using the FIPA messaging standards and an agent ontology based on IEC61850 and the common information model. IX. CONCLUSIONS In this paper the authors have discussed the role intelligent systems techniques may play in active network management through discussion of the research into their use within AuRA-NMS. Initial results for the AuRA-NMS programme indicate that, running on currently commercially available hardware, intelligent systems techniques for power flow management and voltage control can reach control decisions in potentially feasible timescales for use on real distribution networks. However, further testing is required
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to assess the impact of measurement errors and communication system performance on the efficacy of those techniques. If intelligent systems, such as those discussed in this paper, are to become part of the toolbox of proven technical solutions suggested in [1], then demonstration of the industrial application of these systems is required. Moreover, the lessons learnt from testing and industrial trials need to be reported to the power engineering community. X. ACKNOWLEDGEMENTS The authors wish to acknowledge the contribution of their industrial and academic partners on the AuRA-NMS programme. XI. REFERENCES [1] [2] [3]
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