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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

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.

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.

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 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.

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

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

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

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/.

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 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.

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

This paper discusses model-based reasoning applied to power network protection system performance analysis. Appropriate modelling and reasoning methodologies are indicated, and their advantages considered. The necessity of incorporating protection system performance analysis within a decision support system (DSS), designed for electric utility protection engineers, is presented. Alarm processing and fault diagnosis are demonstrated as further requirements of the DSS for protection engineers, within which knowledge-based reasoning and model-based reasoning are combined to provide the overall functionality required.

Significant improvements have been taking place in the power and extent of the monitoring and control systems employed by electrical power utilities, with the result that strategic decisions are having to be made concerning the use of the increased data now available. The evolutionary changes being made to power systems are introducing increased expectations of the performance of system management and control. However, more effective system control can only be exercised if the increased volume of data is matched by an increased capacity to interpret system conditions from the control room. The decision support system described in this paper makes use of expert system techniques in order to provide intelligent reasoning based on the incoming alarm data. Both the readily available benefits, and the advance towards the next generation of power systems which this type of system will afford shall be presented along with a description of the decision support system.

This paper investigates the effectiveness of the artificial neural network (ANN) approach to short term load forecasting in electrical power systems. Using examples, the learning process and capabilities of a neural network in the prediction of peak load of the day are demonstrated. Different data normalizing approaches and input patterns are employed to exploit the correlation between historical load and temperatures and expected load patterns. A number of ANN's are included with emphasis given to their practical implementation for electrical power system control and planning purposes. The networks have been trained on actual power utility load data using a backpropagation algorithm. The prospects for applying a combined solution using artificial neural networks and expert systems, called the expert network are also discussed. Consideration is given to expert networks as a more complete solution to the forecasting problem which neither system alone can provide. >

The findings of research in distribution system planning methodologies indicate: discontinuity between the theory and practice of distribution system planning; set of new requirements for distribution system planning in the light of electricity industry restructuring and other developments in the industry and technology; lack of topical research in general distribution system planning methodologies focused on live utility requirements; and several immediate challenges for researchers and developers in distribution planning methodology. Some of the challenges in distribution system planning for the future have been noted in this letter and several more are added to the list. The future challenges for distribution system planning include: further development of techniques based on heuristics, cases, and tacit knowledge; incorporation of the multiple drivers in present-day electricity distribution planning (e.g., reliability, distributed generation, load, and condition); integration of planning techniques with other energy or utility service planning; incorporation of best practice in planning methodology; and consideration of customer needs in an environment of changing customer base under restructuring.

This letter describes a method of "tuning" an ATP/EMTP-based distribution network model for the purposes of harmonic analysis. When the impact of the nonlinear devices (loads or generators) is studied in terms of network harmonic distortion, it is important to build a reliable model of the network as well as the model of the specific nonlinear device. The letter employs substation-monitoring data to adjust the network model to characterize an existing level of distortion. The letter suggests a method to adjust the harmonic current and voltage sources in order to obtain a specified current and voltage harmonic spectrum at particular locations.

The authors present two expert system developments which are each concerned with utilizing, to the best effect, the increasing volume of SCADA (supervisory control and data acquisition) system data available to power system control staff. The systems presented, APEX and RESPONDD, are aimed at the two related fields of alarm processing and fault diagnosis, respectively. The areas of commonality between these systems are discussed, as well as details specific to each separate system, including a case of study illustrating the practical operation of each. >

Recent IEEE publications have been of great interest to those who have been investigating the requirements for power system planning in restructured, unbundled and competitive electricity markets. In particular, the increased uncertainty regarding generator siting and timing are problems that will tend to increase as competition in generation increases. The authors have been investigating a generic planning framework to meet the requirements of distribution companies in the United Kingdom with particular regard to embedded generation. The generic framework has been developed from an extensive survey of planning methodologies, with reference to standard industry planning practices and in recognition of the set of issues regarding embedded generation that require to be addressed through evaluation.

This letter reports on the evaluation of the performance of an optical current transducer (OCT) prior to commencement of a year-long engineering assessment, which is scheduled to take place within the British Short Circuit Testing Station. The authors report on the operation of an automatic signal conditioning facility that has been implemented to compensate for deficiencies in the sensor performance associated with temperature variations and mechanical vibrations. Their trials indicate that protection class operation is feasible with the present system.

The uncertainties relating to the forced outage rates (FOR) of power system generating units are important in studies such as long-term generation expansion planning. These uncertainties might be better understood by experts in the field. Expert evaluations can be modeled with fuzzy set theory, and the fuzzy-FOR so calculated reflects the conditions of operation and maintenance of the units more realistically.

This letter presents the outcomes of a trial of an active network management (ANM) scheme on part of the North-Scotland electricity network. The ANM scheme has as its goal the facilitation of increased connections of distributed generation (DG) from renewable resources on the Orkney Isles. The ANM scheme is the first multigenerator application of ANM at distribution level in the U.K. The ANM scheme is briefly discussed prior to the trial of the ANM scheme being introduced. The implications of the trial for full ANM roll-out are discussed. The results of the trial of the ANM scheme are of direct relevance to those working in the areas of ANM, smart grids, and advanced distribution automation. The results confirm that wind farms can be controlled within the thermal constraints of a distribution network subject to certain operating margin considerations.

This paper discusses a novel approach to model integration applied within electrical power engineering. A model integration methodology, exploiting the novel approach developed and underpinned by agent technology, is proposed.

This letter demonstrates the use of modal logic for knowledge representation for condition monitoring of gas turbine start-up sequences. The potentially large amounts of data and the complex real-time processes behind on-line fault detection indicate the need for software entities that can reason and react to changing environmental conditions. These are known as intelligent software agents. As a consequence, data interpretation is achieved by converting the data into appropriate information and combining individual agents' knowledge, resulting in an automatic fault diagnosis.

This letter presents a simulation-based case study that indicates the need for accurate dynamic modeling of protection systems. In many cases, a static analysis based on current and voltage phasors may be sufficient to assess the performance of protection systems. There are several circumstances under which such a simplified study does not provide the depth of analysis necessary to obtain accurate results, however. This letter presents a study of the influence of transformer magnetizing inrush current on the performance of different types of distance protection relays. Three types of distance relays have been investigated: Micromho, THR, and REL 100. The studies have been performed in order to demonstrate the difference in responses to the same event when detailed dynamic models of protection relays are utilized.

This letter has presented an overview of activities to date with respect to the development and demonstration of anomaly detection functions, which analyze key parameters from an industrial gas turbine during its starting sequence. The software modules have been shown to be able to successfully detect anomalies in the start sequences. The software has been developed using a combination of simulated and actual plant performance data and is currently in the process of being implemented in the field. In the longer term, as the level of operational experience and knowledge grows, the anomaly detection functions can be augmented to include prognostic and diagnostic functionality. This will permit not only the detection of anomalous behavior but also the provision of information to plant operators as to the suspected cause of the anomaly

This letter demonstrates the use of modal logic for knowledge representation for condition monitoring of gas turbine start-up sequences. The potentially large amounts of data and the complex real-time processes behind on-line fault detection indicate the need for software entities that can reason and react to changing environmental conditions. These are known as intelligent software agents. As a consequence, data interpretation is achieved by converting the data into appropriate information and combining individual agents' knowledge, resulting in an automatic fault diagnosis.