Search

In order to overcome the limitations of the existing classical and solid-state Marx pulse generators, this paper proposes a new modular multilevel voltage-boosting Marx pulse generator (BMPG). The proposed BMPG has hardware features that allow modularity, redundancy, and scalability as well as operational features that alleviate the need of series-connected switches and allows generation of a wide range of pulse waveforms. In the BMPG, a controllable, low-voltage input boost converter supplies, via directing/blocking (D/B) diodes, two arms of a series modular multilevel converter half-bridge sub-modules (HB-SMs). At start up, all the arm's SM capacitors are resonantly charged in parallel from 0 V, simultaneously via directing diodes, to a voltage in excess of the source voltage. After the first pulse delivery, the energy of the SM capacitors decreases due to the generated pulse. Then, for continuous operation without fully discharging the SM capacitors or having a large voltage droop as in the available Marx generators, the SM capacitors are continuously recharged in parallel, to the desired boosted voltage level. Because all SMs are parallelly connected, the boost converter duty ratio is controlled by a single voltage measurement at the output terminals of the boost converter. Due to the proposed SMs structure and the utilization of D/B diodes, each SM capacitor is effectively controlled individually without requiring a voltage sensor across each SM capacitor. Generation of the commonly used pulse waveforms in electroporation applications is possible, while assuring balanced capacitors, hence SM voltages. The proposed BMPG has several topological variations such as utilizing a buck-boost converter at the input stage and replacing the HB-SM with full-bridge SMs. The proposed BMPG topology is assessed by simulation and scaled-down proof-of-concept experimentation to explore its viability for electroporation applications. - 1986-2012 IEEE. Manuscript received June 12, 2018; revised October 23, 2018 and December 29, 2018; accepted February 5, 2019. Date of publication February 18, 2019; date of current version August 29, 2019. This work was supported by the Qatar National Research Fund (a member of the Qatar Foundation) under NPRP Grant 7-203-2-097. Recommended for publication by Associate Editor J. Clare. (Corresponding author: Mohamed A. Elgenedy.) M. A. Elgenedy is with the Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1RD, U.K., and also with the Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt (e-mail:,mohamed.elgenedy@strath.ac.uk). Scopus

Pulse forming networks and Marx generators are the classical rectangular waveform pulse generators (PGs). They are inflexible and their capacitors must be fully charged to the required voltage from 0 V before delivering each high-voltage (HV) pulse. They are only able to generate unipolar pulses; if bipolar pulses are sought another generator fed from a negative supply voltage is added. Recently, several power electronics based PGs have been proposed. This study presents an HV power electronics based PG, which is based on half-bridge modular multilevel converter (MMC) sub-modules (SMs) charged sequentially in a voltage-boost mode. Each SM capacitor and main switch form a boost converter with the charging input supply and inductor. As a result, all SM capacitors are charged to a voltage greater than the input. During the discharging process, the SM capacitors are connected in series, producing a rectangular HV pulse across the load. The proposed charging method allows a reduction in the converter footprint in comparison with recently proposed MMC sequentially charged PG topologies. Although only rectangular pulse waveforms are sought in this study, an SM-capacitor voltage balance method allows multilevel pulse generation. The viability of the proposed converter is confirmed by MATLAB/Simulink simulation and scaled-down experimentation.

The power industry in many parts of the industrialized world faces major challenges over the next two decades to renew ageing networks, accommodate new generation, especially renewables, and make grid operation “smarter”. This requires increasing numbers of professionals, but in many places, the industry faces great losses in personnel and experience as senior engineers retire. As a consequence, the industry needs to recruit a new generation of well-qualified electrical engineering graduates. This paper describes one particular initiative to attract school leavers into engineering and electric power engineering in particular: the U.K.'s “Power Academy”. The main features of the scheme and its success to date are described along with the new challenges and various issues that remain to be addressed. These include the need for “engineering leadership” and career progression for technical specialists. It is argued that companies' continued investment in schemes like the Power Academy is essential to meet the challenges faced by the power industry and to ensure a healthy future supply of professional engineers.

In this paper an analytical method by which the transient stability of an induction machine is maintained regardless of the fault clearance times is introduced. The method can be applied in order to improve the transient stability of a large penetration of low-voltage (LV) connected microgeneration that can be directly interfaced by single-phase induction generators within domestic premises. The analysis investigates the effectiveness of using resistive-type superconducting fault current limiters (RSFCLs) as remedial measures to prevent the microgenerators from reaching their speed limits during remote faults, and hence improving their transient stability. This will prevent unnecessary disconnection of a large penetration of LV-connected microgeneration and thus avoiding the sudden appearance of hidden loads, and unbalanced voltage conditions. The minimum required value of a resistive element of RSFCL for mitigating the transient instability phenomena of LV-connected microgeneration based on the system and connected machine parameters is determined. The analytical method has been validated by conducting informative transient studies by using detailed models of a small microwind turbine with constant mechanical output interfaced directly within residential dwellings by a single-phase induction generator, a transient model of resistive superconducting fault current limiter (RSFCL), and a typical suburban distribution network with residential loads. All the models are developed in the time-domain PSCAD/EMTDC dynamic simulation.

The effect of a short circuit fault and a voltage sag fault on a distributed grid-connected solid oxide fuel cell (SOFC) is investigated in this paper. The fuel cell is modeled in Simulink and the performance is verified against experimental load testing data. Grid faults are simulated, and conclusions are drawn on the fuel cell response and the effects of the fault condition on internal fuel cell parameters.

The paper investigates the effects of emissions constraints and Emissions Trading Scheme (ETS) on the generation scheduling outcome. ETS is a cap-and-trade market mechanism that has been introduced in European Union in order to facilitate CO2 emissions management. This scheme gives generators certain amount of CO2 allowances which they can use to cover emissions produced during energy generation. In a current setting, most of the allowances are given for free. However, under ETS generators also have an opportunity to buy and sell CO2 allowances on the market. Since generation power outputs are bounded by the amount of CO2 emissions that they are allowed to produce over time, it is becoming increasingly important for generating units to manage their allocations in the most profitable way and decide when and how much of permissions to spent to produce electricity. The method proposed here allows for modeling of this new limitation by including costs of buying and selling of CO2 allowance in the generation scheduling procedure. It also introduces additional emissions constraints in the problem formulation. Although CO2 permissions and energy are traded in separate markets, the proposed formulation permits analysis on how emission caps and emission market prices can influence market outcome. The method is illustrated on a 5-unit system. Given examples compare (i) a base-case when all generators have made a decision to use portions of their total free allocations that do not cause any shortfall during the investigated time period; (ii) two cases when the least expensive generators' decisions on the amount of free allowances they are willing to use during the considered period are insufficient. In all cases generators also submit prices at which they expect to be able to 'top-up' or sell allowances on the market, however, only in the second and third case the 'buying' option becomes active and affects generation scheduling and total costs. In addition, the paper investigates how aggregation of emissions allowances of generators belonging to the same company can affect market clearing.

Increasing penetration of distributed generation within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise cost, algorithms for the measurement of AC voltage and current waveforms can be implemented on a single microcontroller, which also carries out other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although analogue to digital converters (ADCs) can be oversampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality, which may arise within grid-connected or islanded (e.g. emergency, battlefield or marine) power system scenarios. This study presents a 'Clarke-FLL hybrid' architecture, which combines a three-phase Clarke transformation measurement with a frequency-locked loop (FLL). This hybrid contains suitable algorithms for the measurement of frequency, amplitude and phase within dynamic three-phase AC power systems. The Clarke-FLL hybrid is shown to be robust and accurate, with harmonic content up to and above 28% total harmonic distortion (THD), and with the major algorithms executing at only 500 samples per second. This is achieved by careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal through low-sample-rate Fourier analysis to sub-harmonic ripple removal. Platform-independent algorithms for three-phase nodal power flow analysis are benchmarked on three processors, including the Infineon TC1796 microcontroller, on which only 10% of the 2000 mus frame time is required, leaving the remainder free for other algorithms.

A procedure to estimate the capacity of minihydro plants based on water flow time series forecasting is presented. First, the classic method used for this purpose is introduced and then a set of methodologies to assess the feasibility of minihydro generation and to determine system capacity based on time series forecast is described. The water flow time series is processed to determine the theoretical power generation and to assess if a minihydro plant can be installed. Finally the characteristics of each of the electro-mechanical components for the proposed minihydro plant are selected.

Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data.

This paper provides an analysis of the “Hydrogen from the Sun” project at the “Ecological House” in northern Italy. The modeling and analysis work is being performed in conjunction with the International Energy Agency Hydrogen Implementing Agreement Annex 18: Integrated Systems Evaluation. A customized library of Matlab/Simulink component models is used to simulate the system and evaluate the hydrogen economics and energy production efficiencies. Two control algorithms are developed for the house using a fuzzy logic and an adaptive control strategy. The economic and steady state effects of these two strategies are compared as are the energy sources used to supply the energy demand of the house. The hydrogen production system consists of an electrolyzer, a photo-voltaic collector, and a battery, linked to both a metal hydride and high pressure gas storage system. The hydrogen supplies a fuel cell, which powers a residential estate. The analysis shows the contribution of the different system components to the overall efficiency and cost of hydrogen. However, the control systems presented also have a significant effect on the hydrogen and electricity cost. Reduction of these costs and an increase in system efficiency require optimal use of the hydrogen stored, as well as the optimized distribution of power supply from the generating components. The analysis shows the initial cost of hydrogen to be 9.36 $/kg, with electricity produced at 0.65 $/kWh using a fuzzy logic control system at an electrical efficiency of 50% (of the full hydrogen house system), based on the lower heating value of hydrogen. The result of using an active control strategy is presented.

Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems.

In this paper, we report on nonlinearity compensation for a solid-state fiber bragg grating (FBG) sensor interrogation system based on an arrayed waveguide grating (AWG) device. A lookup table with calibration data is used to improve system linearity. A reduction in the absolute value of the measurement error from 120 mustrain or 15degC to 4.8 mustrain or 0.6degC, respectively, is experimentally demonstrated.

In this paper we present, for the first time, the hybrid fiber-optic voltage and current sensors interrogated using an arrayed waveguide grating (AWG) device. Due to the excellent dynamic capabilities of an AWG-based interrogator and its improved robustness, the proposed system would be suitable for voltage and current monitoring within an aircraft electrical system. The voltage sensor comprises a multilayer piezoelectric stack, acting as a voltage-to-strain transducer, and a fiber Bragg grating (FBG) used to convert voltage induced strain changes within the stack into wavelength shifts. These wavelength shifts are then analyzed by an AWG. To measure current, the same sensor type is used to monitor a specially designed ferrite-core current transformer. Alternatively, a magnetostrictive transducer is employed instead of piezoelectric stack. It is shown that the system is capable of measuring variable frequency of voltage and current waveforms, typical of those anticipated in the next generation aero-electric power systems. It is also demonstrated that the system can be used for voltage and current harmonic analysis and power quality measurement in such networks.

In this paper, we consider the possibility of improving the accuracy of a solid-state fiber Bragg grating (FBG) interrogation system by using two, instead of one, FBGs inscribed on the same location of the fiber, with a small difference in the peak wavelength that each reflects. Signal processing can be applied to the outputs of the photodetectors that are connected to an arrayed waveguide grating to provide a linear response from the sensor. We demonstrate here that this method reduces errors when compared to the setup employing a single FBG with the same bandwidth as the dual sensor.

Two tradeoffs in the effectiveness of automatic voltage regulators (AVRs) and power system stabilizers (PSSs) are investigated together for the first time. The first is the effect of a high-gain fast response AVR on decreasing power system oscillation stability as well as increasing transient stability, and vice versa. The second is that a PSS can reduce transient stability by overriding the voltage signal to the exciter as well as increasing oscillation stability, and vice versa. In essence, the actions of the AVR and PSS devices are dynamically interlinked. A novel Bode frequency response framework for dynamic analysis of AVR and PSS performance and tradeoffs is presented. Bode frequency response also assists with the determination of suitable generator locations for PSSs and the assessment of robustness under changing power system operating conditions.

Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.

The Institute for Energy and Environment (IEE) at the University of Strathclyde has developed various fuel cell (FC) systems for stationary and vehicular applications. In particular the author is involved in the development of alkaline fuel cell (AFC) systems. To understand the dynamic behaviour of the system's key element, the alkaline fuel cell stack, a dynamic model was developed allowing the characterisation of the electrochemical parameters. The model is used to forecast the behaviour of the fuel cell stack under various dynamic operating conditions. The so-called Nernst potential, which describes the open circuit voltage of the stack, is calculated using thermodynamic theory. Electrochemistry theory has been used to model the sources of the electric losses within the FC, such as activation, ohmic and concentration losses. The achievable value of this paper is the first publication of a detailed dynamic AFC based on mass balance, thermodynamics and electrochemical theory. The effects of the load changes on various fuel cell parameters, such as electrolyte concentration and concentrations of dissolved hydrogen and oxygen were covered in this investigation using the author's model. The model allows a detailed understanding of the dynamic effects within the AFC during load change events, which lead to the experienced electric response of the overall FC stack.

A novel methodology is proposed for the customer security assessment with high penetration of wind power in modern distribution networks. The customer security is quantified through customer damage costs by operating wind farms without standing reserve, which causes an inadequate supply of power to meet loads at some operating conditions. Necessary reserve to mitigate intermittency of wind is quantified through shed load. The ability to estimate a network firm import capacity that can be replaced with a wind plant, which is operated with the necessary reserve, is an added advantage of the approach. Monte Carlo simulation is the prime tool of the assessment, which incorporates linear programming-based re-dispatching of generation and load shedding to eliminate constraint violations. Steady-state analysis is used for the assessment incorporating random contingencies, subsequent tripping, intermittency of wind power, and demand variations. A case study is presented considering a medium voltage island network that has a grid connection, and this is used to demonstrate the customer security assessment integrating uncertainty models and a formulation to extract the impact of intermittency.

This paper proposes an innovative way of making a decision to island (or synchronize) distributed generators when operating conditions of a distribution system are varied. Islanding decisions are made when a network undergoes disturbances. In this approach, there is a decision-making mechanism, which samples states of the network operating condition to either synchronize or island in an identified period of time. Decision making is achieved through extended sequential sampling, which manages generator states within specific time periods. The approach can minimize the duration of islanded operation and manage generators' re-connection to the grid. Use of the proposed mechanism and sampling approach offer benefits over conventional relays; incorrect operations due to errors in measurements are reduced because the operational actions are not based on a single decision sample, but instead rely on extended sequential sampling over a number of samples. A case study is investigated using the IEEE 30-bus test system and the results are presented for this new approach

This paper presents the design details of a biogas gas plant and fuel cell installation that will provide a practical solution on an island (and be applicable in other remote and rural areas) where connection to the grid can be expensive, and where biofuels can be produced on site at no significant extra cost. In situ use of biomass and organic waste streams have the potential to provide the key to energy recovery and sustainability from biodegradable waste. The scheme described in this paper meets the new EU regulations covering (i) the landfill directive, (ii) the animal by-products regulations, and (iii) the biowaste directive. Traditionally biogas fuels have been used in combustion engines for electric power generation. However, there are now clear indications that given the current developments in reformer technologies, hydrogen gas can be produced from methane-derived anaerobic digestion of organic waste material-much of which is currently land filled. Hence, when utilized in fuel cells this offers the prospect of achieving higher generating efficiencies, and additionally, important environmental benefits will be achieved through mitigating greenhouse gas emissions. The solid waste from the process provides a valuable organic fertiliser which can be returned to the land thereby displacing chemical fertilisers currently in wide spread use.

We have highlighted how optical sensors can be exploited to solve many measurement problems within selected power and energy systems applications. Clearly, their development is defined at present by the industry demand, often unsatisfied with the functionality and performance of electrical instrumentation. In these cases, the overall gain from being able to monitor a plant will outweigh the higher cost associated with optical measurement systems. However, as the technology advances and costs decrease, it is hoped that photonic sensors will make their way into more areas of industry and become as widely accepted as a simple strain gauge or thermocouple. We hope that this article will provide a useful perspective and will inspire a renewed interest in photonic sensors among a broad range of researchers, engineers, and industrial end-users involved in the field of instrumentation and measurement

To trade effectively and profitably in new electricity market structures, participants need to identify how best to use information available to them. In many cases only incomplete information will be available for short-term planning, trading and decision-making. This paper simulates a group of generators who adapt bidding behaviours in different segments of liberalised electricity markets based on historic market information, observed strategies, and their view of other market participants. Results show that even in the incomplete information case efficient bidding strategy for market participants can be identified. Specifically, this paper presents some key findings from an active electricity market and utilises them within an electricity market simulation. The benefit of market simulation for participants is identified and reported.

In this paper, a sensor that is capable of measuring both ac current and temperature using a single fiber Bragg grating (FBG) is described. The device consists of a magnetically biased magnetostrictive alloy bonded to an FBG. The magnetic field generated by the current in a specially designed coil produces internal strains in the magnetostrictive alloy which can be detected by the FBG. The temperature at the sensing point is determined through averaging of the sensor output signal. The measurement range of the sensor can be varied by altering the design of the sensor coil. A prototype sensor that is capable of measuring ac currents up to 1 A has been constructed while simultaneously measuring temperatures of up to 100 degC

This paper presents a review of existing dynamic electrical battery models and subsequently describes a new mathematical model of a lead acid battery, using a non-linear function for the maximum available energy related to the battery discharge rate. The battery state of charge (SOC) is expressed in a look-up table relative to the battery open circuit voltage (VOC). This look-up table has been developed through low discharge experiments of the battery modelled. Further, both the internal resistance and self-discharge resistance of the battery are subsequently expressed as functions of the open circuit voltage. By using an electrical model with these characteristics and a temperature compensation element to model different rates of charge and discharge, a relatively simple and accurate battery model has been developed. The new model takes into account battery storage capacity, internal resistance, self-discharge resistance, the electric losses and the temperature dependence of a lead acid battery. It is shown in this paper how the necessary parameters for the model were found. The battery modelled was a Hawker Genesis 42 Ah rated gelled lead acid battery. The simulation results of the new model are compared with test data recorded from battery discharge tests, which validate the accuracy of the new model.

It is now becoming increasingly clear that the in situ use of biomass and organic waste streams are likely to provide the key to energy self sustainability for islands and remote communities. Traditionally biofuels have been used in combustion engines for electric power generation, however, when replaced by fuel cells there is the prospect of achieving higher generating efficiencies, coupled with, in some instances, the opportunity to produce biofuel at a cheaper rate than conventional fuels. Additionally, important environmental benefits can be achieved by way of mitigating greenhouse gas emissions, whilst providing a carbon sink. This paper presents the design details of such an installation that will provide a practical solution on an island (and be applicable in other remote and rural areas) where connection to the grid can be expensive, and where biofuels can be produced on site at no significant extra cost.

Online diagnostics and online condition monitoring are important functions within the operation and maintenance of a power plant. When there is knowledge of the relationships between the raw data and the underlying phenomena within the plant item, typical intelligent system-based interpretation algorithms can be implemented. Increasingly, health data is captured without any underlying knowledge concerning the link between the data and their relationship to physical and electrical phenomena within the plant item. This leads to the requirement for dynamic and learning condition monitoring systems that are able to determine the expected and normal plant behavior over time. This paper describes how multi-agent system technology can be used as the underpinning platform for such condition monitoring systems. This is demonstrated through a prototype multi-agent anomaly detection system applied to a 2.5-MW diesel engine driven alternator system.

In this paper, we propose an interrogation system capable of multiplexing four identical Fabry-Perot (FP) interferometric sensors using two wavelength-division multiplexing devices. One is a 40-channel DWDM channel monitor, the other a four-channel CWDM device. The sensors are connected to the output channels of the CWDM device in order to assign a portion of the spectrum to each sensor. The reflected spectra are then analyzed using the DWDM channel monitor. By monitoring the power incident on each of the DWDM channels, the four sensor reflection spectra can be reconstructed in software and information relating to the measurand obtained. Based on software simulations and previous laboratory experiments with single sensors, it is predicted that this system would be capable of interrogating four EFPI strain sensors simultaneously at frequencies greater than 5 kHz with a resolution of approximately 2 /spl mu//spl epsiv/ and a range of 3000 /spl mu//spl epsiv/.

This paper summarises the energy policies that the UK Government has enacted in order to achieve its renewable targets by 2010. Current policies are designed primarily to support large-scale renewable projects through Renewable Obligation Certificates, Levy Exemption Certificates and capital grant schemes. Non-profit domestic and non-profit community renewable projects are also eligible for grant support. First-hand experience of privately owned renewable projects indicate that existing renewable policy is insufficient in its support of both small-scale and community-based profit oriented renewable energy (RE) schemes. Primary and secondary survey information suggests that people living in regions where RE will be situated may generally be inclined to support broader uses of renewables in these regions. Small-scale renewables can make a significant cumulative contribution to the RE mix. The results reported in this paper support the contention that the Government could go further towards approaching its targets through rural-focused changes to its energy incentive programmes.

In safety critical components, the capability of an inspection may need to be demonstrated by an independent process of inspection qualification (IQ). Due to the skill level of the manpower involved, IQ is both costly and time consuming. Therefore, an approach that can aid the production of the required documentation would be beneficial. This paper presents a practical example in the form of a case study, which demonstrates how an intelligent approach to IQ, utilising smart software operating within the context of a non-destructive testing Workbench, can be used to evaluate the capability of an ultrasonic inspection. This is accomplished using four interactive software tools which include: a beam-coverage analyser, a worst-case defect search engine, a flaw modelling expert system and a case-based reasoning system to retrieve previously measured data. This paper discusses the motivation for such an approach and the benefits it provides.

Market liberalisation has resulted in significant changes not only in the way electricity is traded, but also for the market participants themselves. The bidding behaviour of market participants who are active in a liberalised UK‐like market has been modelled. Both operational and technical parameters associated with the market and its participants are accounted for. Explicit characterization of risk (value at risk) is made with respect to market participants and their attitude to trading. Profit maximization strategies for market participants are then developed based on the minimization of price‐risk under uncertainty. Results are presented for a selected case study and the effect of alternative strategies is compared. The case study concerns several generators who need to determine what proportion of their production they should sell to the market. The results show that based on cost and price forecasts there is scope for generators to profitably take advantage of both contractual and within‐day market trades.

In this paper, we demonstrate an interrogation system, based on an arrayed waveguide grating, capable of monitoring dynamic strain in a cantilever beam at frequencies up to 5 kHz (limited by the actuator) with a similar precision to resistive strain gauges.

This paper details the optimization of bulk material port handling systems through the use of an evolutionary based approach. The effective management of port systems requires effective solutions for the design, operation and maintenance of these facilities. This has to be achieved through a reduction in financial costs, and an increase in the utilization of equipment and other resources. The operation of a port system is complex and difficult to model mathematically. Consequently, through the explicit characterization of port components, a port modeling tool was developed that permits the generic construction of port simulation models. A genetic algorithm based approach was developed to provide an optimization capability to the port simulation tool. Two case studies based on real world port systems are presented and the results are discussed. A significant improvement is demonstrated in both the operational and economic performance as a result of the GA/model interaction.

This paper describes a software-based toolset which aids the application and development of model-based reasoning systems for validation and diagnosis in power systems applications. The validation of the operation of power systems protection equipment using disturbance recorder data is used as an example throughout the paper.

Evaluation of the data produced during the refuelling process in a nuclear power plant is required to ensure proper ‘set-down’ of the fuel assembly, thereby allowing the continued and safe operation of the station. The process of evaluating the data can be time consuming owing to the large amounts of data requiring considerable domain experience and interpretation. This paper presents an intelligent system (IS) to automate the process of data analysis, thereby shortening the evaluation time and providing an explanation of the reasoning behind its conclusions. The intelligent system utilizes a knowledge-based system (KBS), neural network based classification, K-means clustering techniques and rule induction methods to evaluate the data and inform the operator of any errors encountered.

Condition monitoring of power transformers is a significant issue for electrical utilities. Research has demonstrated the efficacy of employing ultra high frequency measurement of partial discharge in the monitoring of transformers. A condition monitoring architecture is described that supports the capture and interpretation of diagnostic data, and provides engineers with meaningful diagnostic advice using intelligent system technologies.

Condition monitoring, within the power industry, is essential to ensure continuous operation and health of electrical plans. This is a complex activity that requires the combination of different artificial intelligence techniques. This article describes a multiagent framework to support such a condition-monitoring approach. The resulting condition monitoring multiagent system (COMMAS) was applied to two different case studies, to monitor the start-up sequences of industrial gas turbines and identification of partial discharge signal defects in gas insulated substations.

Engineers within the electrical power industry are becoming increasingly challenged by the volume of data that must be analysed. In reality, they are interested in the significant information that can be interpreted from the data. Intelligent systems are seen as a solution to this problem, as they are able to interpret data to provide information, thereby offering decision support. This article discusses the use of intelligent systems for decision support applied to power system control and management.

Within the field of power engineering, forecasting and prediction techniques underpin a number of applications such as fault diagnosis, condition monitoring and planning. These applications can now be enhanced due to the improved forecasting and prediction capabilities offered through the use of artificial neural networks. This paper demonstrates the maturity of neural network based forecasting and prediction through four diverse case studies. In each case study the authors have developed diagnostic, monitoring or planning applications (within the power engineering field) using neural networks and industrial data. The engineering applications discussed in the paper are: condition monitoring and fault diagnosis applied to a power transformer; condition monitoring and fault diagnosis applied to an industrial gas turbine; electrical load forecasting; monitoring of the refuelling process within a nuclear power station. For each case study the data sources, data preparation, neural network methods and implementation of the resulting application is discussed. The paper will show that the forecasting and prediction techniques discussed offer significant engineering benefits in terms of enhanced decision support capabilities.

The application of intelligent systems for data interpretation and condition monitoring is an advancing field of research. In recent years autonomous intelligent agents and multi-agent systems have gained much attention within different real time applications. The novel idea of COMMAS (COndition Monitoring Multi-Agent System) introduces a hierarchical decentralised multi-agent architecture developed for data interpretation and condition monitoring applications. By definition condition monitoring is concerned with detecting and distinguishing faults occurring in plant that is being monitored [1]; therefore the early diagnosis and identification of faults has a number of benefits (improvement in the plant economy, reduction in operational costs, improving the level of safety etc). A variety of intelligent techniques have been applied in plant monitoring, which resulted in the development of centralised approaches for condition monitoring, e.g., Knowledge Based Systems (KBS) [2], Model Based Reasoning (MBR) Systems [3], Case Based Reasoning (CBR) Systems [4], Artificial Neural Networks (ANN) [5] etc. These approaches tend to be fixed, so they lack flexibility and extensibility. Moving to an agent-based architecture allows simultaneous complex tasks to be performed in real-time; better handling of inaccurate data is achieved and each agent can be independently updated.

This paper presents a dynamic model of a protection system based on a differential feeder protection along with the primary power system. A commercially available relay is modelled and its functions explained. A general primary power system model structure that includes a fully integrated protection system model is discussed. The protection system model contains dynamic models of both the relays and the transducers. A technique will be described for developing an accurate dynamic model of a numerical protection relay using actual microprocessor codes from a commercially available relay. Finally, the practical application of the analysis under abnormal system conditions is demonstrated. Different faults are applied to the internal and the external regions of the feeder, and the response of the relays examined.

This paper considers the problem of generator maintenance scheduling (GMS) in power systems. The applications of computer-based modern heuristic solutions, namely simulated annealing (SA) and genetic algorithm (GA), are proposed in order to overcome some of the limitations of conventional solution methods. A test problem is formulated as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution techniques are discussed. The results obtained demonstrate that these techniques are effective methods for solving GMS problems.

The design process of a SOFC plant dynamic model for a power systems simulation (PSS) commercial software package has revealed the trade-off between the satisfaction of the network dynamic requirements and a safe and durable cell operation that the plant controller should implement. This paper describes the initial fuel cell stack and power conditioner modelling methodologies that have addressed such issues.

In this paper we consider the problem of generator maintenance scheduling (GMS) in power systems. A genetic algorithm with a fuzzy evaluation function is proposed in order to overcome some of the limitations of conventional modelling and solution methods. A test GMS problem is formulated with a reliability objective and flexible and crisp constraints. A rule base and fuzzy sets are formulated for the objective and the flexible constraint using experience of solutions to the problem, and used to define a fuzzy evaluation function. This is used in a genetic algorithm (GA) with an integer representation of the schedule to solve the test problem. The results obtained from the GA with the fuzzy evaluation function are compared with those obtained using crisp evaluation functions. The comparison shows that the GA with the fuzzy evaluation function is an effective and practical approach for finding good solutions for GMS with flexible constraints.

Condition monitoring of electrical plant represents an area of great interest to both manufacturing and utility companies within the electricity supply industry. De-regulation and privatisation entail that utilities must operate their systems in an optimal fashion and one of the technologies which can facilitate this is condition monitoring. Condition monitoring has a number of important benefits: unexpected failures can be avoided through the possession of quality information relating to the on-line condition of the plant and the consequent ability to identify faults or problems while still in the incipient phases of development; maintenance programmes can be condition based rather than periodically based; the plant may be utilised more optimally through the use of information relating to the plant's real-time condition and/or performance – for example, the plant may be driven temporarily beyond its stated capacity if it is known that this will not cause any short-term problems. This paper will cover the generic capabilities of artificial neural networks, in both estimation and classification mode, for condition monitoring applications, using examples based around work that the authors have carried out with respect to the monitoring of a power transformer.

This paper presents the background to the ongoing research and development work into optical current measurement devices at Strathclyde University. Details of a new reflective sensor geometry and potted epoxy housing are given, and problems associated with the epoxy curing are noted. A temperature compensation system for the current sensor is described and results showing an accuracy improvement from ≈31% to 1.4% are given.

In this letter, we discuss the use of cyclic arrayed waveguide gratings in the construction of a multiplexed fiber-optic sensor system. The basic components are described and their role in interrogating sensors is discussed. The letter concludes with a proposed design for an expansive, low cost, highly maintainable system