Showing papers in "Electric Power Systems Research in 2004"
TL;DR: In this article, the authors introduce a survey of this revolutionary approach of DGs, which will change the way electric power systems operate along with their types and operating technologies, and survey the operational and economical benefits of implementing DGs in the distribution network.
Abstract: As a result of the application of deregulation in the electric power sector, a new identity appeared in the electric power system map known as “distributed generation” (DG). According to new technology, the electric power generation trend uses disbursed generator sized from kW to MW at load sits instead of using traditional centralized generation units sized from 100 MW to GW and located far from the loads where the natural recourses are available. This paper introduces a survey of this revolutionary approach of DGs, which will change the way electric power systems operate along with their types and operating technologies. Some important definitions of DGs and their operational constraints are discussed to help in understanding the concepts and regulations related to DGs. Furthermore, we will survey the operational and economical benefits of implementing DGs in the distribution network. Most DG literatures are based on studying the definitions, constructions or benefits of DGs separately. However, in our paper we aim to give a comprehensive survey by adding new classifications to relate the DG types, technologies and applications to each other.
966 citations
TL;DR: The proposed method out-performs and provides quality solutions compared to other existing techniques for EDP considering valve-point effects are shown in general.
Abstract: This paper presents a novel and efficient method for solving the economic dispatch problem (EDP), by integrating the particle swarm optimization (PSO) technique with the sequential quadratic programming (SQP) technique. PSO is the main optimizer and the SQP is used to fine tune for every improvement in the solution of the PSO run. PSO is a derivative free optimization technique which produces results quickly and proves itself fit for solving large-scale complex EDP without considering the nature of the incremental fuel cost function it minimizes. SQP is a nonlinear programming method which starts from a single searching point and finds a solution using the gradient information. The effectiveness of the proposed method is validated by carrying out extensive tests on three different EDP with incremental fuel cost function takes into account the valve-point loadings effects. The proposed method out-performs and provides quality solutions compared to other existing techniques for EDP considering valve-point effects are shown in general.
538 citations
TL;DR: Various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants.
Abstract: In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are classical particle swarm optimization, hybrid particle swarm optimizations and hybrid genetic algorithm simulated annealing. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations. The gains obtained by particle swarm optimization are more optimal than those obtained by GA/hybrid GA-simulated annealing. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses.
293 citations
TL;DR: A novel time-varying weather and load model for solving the short-term electric load-forecasting problem using moving window of current values of weather data as well as recent past history of load and weather data is presented.
Abstract: This paper presents a novel time-varying weather and load model for solving the short-term electric load-forecasting problem. The model utilizes moving window of current values of weather data as well as recent past history of load and weather data. The load forecasting is based on state space and Kalman filter approach. Time-varying state space model is used to model the load demand on hourly basis. Kalman filter is used recursively to estimate the optimal load forecast parameters for each hour of the day. The results indicate that the new forecasting model produces robust and accurate load forecasts compared to other approaches. Better results are obtained compared to other techniques published earlier in the literature.
256 citations
TL;DR: In this paper, an interactive fuzzy satisfying method based on evolutionary programming technique for short-term multiobjective hydrothermal scheduling is presented, which is formulated considering two objectives: (i) cost and (ii) emission.
Abstract: This paper presents an interactive fuzzy satisfying method based on evolutionary programming technique for short-term multiobjective hydrothermal scheduling. The multiobjective problem is formulated considering two objectives: (i) cost and (ii) emission. Assuming that the decision maker (DM) has fuzzy goals for each of the objective functions, evolutionary programming technique based fuzzy satisfying method is applied for generating a corresponding optimal noninferior solution for the DM’s goals. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. Thermal plants with nonsmooth fuel cost and emission level function are also taken into consideration. Results of the application of the proposed method are presented.
235 citations
TL;DR: The authors in this article reviewed the progress made in self-excited induction generator (SEIG) research and development since its inception and highlighted the current and future issues involved in the development of induction generator technology for its large-scale future applications.
Abstract: The increasing importance of fuel saving has been responsible for the revival of interest in so-called alternative source of energy. Thus, the drive towards the decentralization of power generation and increasing use of non-conventional energy sources such as wind energy, bio-gas, solar and hydro potential, etc. has become essential to adopt a low cost generating system, which is capable of operating in the remote areas, and in conjunction with the variety of prime movers. With the renewed interest in wind turbines and micro-hydro-generators as an alternative energy source, the induction generators are being considered as an alternative choice to the well-developed synchronous generators because of their lower unit cost, inherent ruggedness, operational and maintenance simplicity. The induction generator’s ability to generate power at varying speed facilitates its application in various modes such as self-excited stand-alone (isolated) mode; in parallel with synchronous generator to supplement the local load, and in grid-connected mode. The research has been underway for the last three decades to investigate the various issues related to the use of induction generator as potential alternative to the synchronous generator to utilize the small hydro and wind energy to accomplish the future energy requirement, and to feed the power to remote locations and far flung areas, where extension of grid is economically not feasible. This paper, therefore, reviews the progress made in induction generator particularly, the self-excited induction generator (SEIG) research and development since its inception. Attempts are made to highlight the current and future issues involved in the development of induction generator technology for its large-scale future applications.
226 citations
TL;DR: The PSO and its variants are applied to a synthetic test system of five types of candidate units with 6- and 14-year planning horizon and the results obtained are compared with dynamic programming in terms of speed and efficiency.
Abstract: This paper presents the application of particle swarm optimization (PSO) technique and its variants to least-cost generation expansion planning (GEP) problem. The GEP problem is a highly constrained, combinatorial optimization problem that can be solved by complete enumeration. PSO is one of the swarm intelligence (SI) techniques, which use the group intelligence behavior along with individual intelligence to solve the combinatorial optimization problem. A novel ‘virtual mapping procedure’ (VMP) is introduced to enhance the effectiveness of the PSO approaches. Penalty function approach (PFA) is used to reduce the number of infeasible solutions in the subsequent iterations. In addition to simple PSO, many variants such as constriction factor approach (CFA), Lbest model, hybrid PSO (HPSO), stretched PSO (SPSO) and composite PSO (C-PSO) are also applied to test systems. The differential evolution (DE) technique is used for parameter setting of C-PSO. The PSO and its variants are applied to a synthetic test system of five types of candidate units with 6- and 14-year planning horizon. The results obtained are compared with dynamic programming (DP) in terms of speed and efficiency.
215 citations
TL;DR: In this article, a review on methods of generation scheduling in both regulated and deregulated power markets since 1951 is presented, covering a wide span of deterministic, meta-heuristic, and hybrid approaches.
Abstract: This paper presents a review on methods of generation scheduling in both regulated and deregulated power markets since 1951. It covers a wide span of deterministic, meta-heuristic, and hybrid approaches.
209 citations
TL;DR: Results indicate that the proposed greedy improvement heuristic methodology represents an effective solution strategy for this problem of determining the locations of wind generators in a wind farm consisting of many generators.
Abstract: This paper considers the problem of determining the locations of wind generators in a wind farm consisting of many generators. The objective is to find a generator placement that maximizes profit, which is the product of the cost efficiency of the generators and the total power output from the wind farm. Generator placement is significant because if generator A is located close to generator B and is located downwind of generator B then the power output of generator A is reduced by an amount that varies with the distance between the generators. The problem can be formulated using mathematical programming but to solve the problem one cannot employ traditional optimization methods. Therefore, a greedy improvement heuristic methodology is developed and described in detail. The effectiveness of the proposed heuristic is demonstrated on a suite of test problems. These results indicate that the proposed method represents an effective solution strategy for this problem.
171 citations
TL;DR: It is proved that optimal PID gains are superior to suboptimal, arbitrary PID gains and optimal integral gains as well with respect to transient responses by plotting transient responses analytically by MATLAB based software program and by “SIMULINK of MATLAB software.
Abstract: Optimal integral gains (for integral gain control) and proportional-integral-derivative gains (for PID control) are computed by genetic algorithm (GA) and then hybrid genetic algorithm-simulated annealing (GA-SA) techniques for nominal values of area input parameters and optimal transient responses of area frequency deviations in terms of settling times, undershoots, overshoots and d f /d t as output with incremental increase of area load for interconnected three equal generating areas. Though it is well known that the normal PID control is usually superior to integral control because of the advantages of each of the three individual control actions (proportional, integral and derivative), the author’s contribution in the paper is optimizing these individual PID gains through GA or GA-SA methods to obtain an optimal PID controller, which would be further better than an optimal integral controller. These optimal PID gains are tested by plotting transient responses analytically by MATLAB based software program and then by “SIMULINK of MATLAB software.” Both methods yield same results and prove that optimal PID gains are superior to suboptimal, arbitrary PID gains and optimal integral gains as well with respect to transient responses. The author’s next contribution is to show optimal PID gains as determined by hybrid GA-SA technique to be more globally optimal than those determined by GA method. For off-nominal input parameters, transient responses as determined by fast acting Sugeno fuzzy logic technique reflect the same superiority of GA-SA based optimized gains, specially for PID control, the same has also been verified by “MATLAB–SIMULINK” software.
151 citations
TL;DR: In this article, a robust coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with an eigenvalue-based objective function.
Abstract: Power system stability enhancement via robust coordinated design of a power system stabilizer (PSS) and a thyristor-controlled series capacitor (TCSC)-based stabilizer is thoroughly investigated in this paper. The coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. This study also presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The damping characteristics of the proposed control schemes are also evaluated in terms of the damping torque coefficient over a wide range of loading conditions. The proposed stabilizers were tested on a weakly connected power system. The damping torque coefficient analysis, nonlinear simulation results, and eigenvalue analysis show the effectiveness and robustness of the proposed approach over a wide range of loading conditions.
TL;DR: In this article, a particle swarm optimization (PSO) based approach to achieve optimal capacitors placement in radial distribution systems is presented, where Harmonic distortion effects, discrete nature of capacitors, and different load levels are all taken into consideration in the problem formulation.
Abstract: This paper presents a particle swarm optimization (PSO) based approach to achieve optimal capacitor placement in radial distribution systems. Harmonic distortion effects, discrete nature of capacitors, and different load levels are all taken into consideration in the problem formulation. Mathematically, the capacitor placement problem is a non-linear and non-differentiable mixed integer optimization problem with a set of equality and inequality operating constraints. Most conventional optimization techniques are incapable to solve this hard combinatorial problem, whereas PSO algorithm is very suitable. The proposed solution method employs PSO to search for optimal locations, types, and sizes of capacitors to be placed and optimal numbers of switched capacitor banks at different load levels. Computation procedures of applying the method to the capacitor placement problem are given in detail. The proposed approach has been implemented and tested on a distorted IEEE 9-bus test system with promising results.
TL;DR: Transmission congestion distribution factors based on sensitivity of ac power flow in the lines due to the unit change in the power injection at the buses have been proposed by which the congestion zones are identifies to reschedule the generators and loads in that zone for the congestion management.
Abstract: In a deregulated electricity market, one of the major concerns of system operator (SO) is to ensure the free and fair electricity trading while maintaining system security and stability in meeting the pool and contract demands. Achieving a commercially transparent and technically feasible solution during transmission congestion, therefore, poses a great challenge to SO. Transmission congestion distribution factors (TCDF) based on sensitivity of ac power flow in the lines due to the unit change in the power injection at the buses have been proposed by which the congestion zones are identifies to reschedule the generators and loads in that zone for the congestion management. A conceptually reasonable and computationally feasible approach for the solution of this problem has been developed and is illustrated on two test systems having both pool and contracts loads.
TL;DR: In this article, an improved genetic algorithm with multiplier updating (IGA_MU) was proposed to solve the combined heat and power economic dispatch (CHPED) problem, and the proposed approach integrates the IGA and the MU such that it has the merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the CHPED problem.
Abstract: This paper presents an improved genetic algorithm with multiplier updating (IGA_MU) to solve the combined heat and power economic dispatch (CHPED) problem. The improved genetic algorithm (IGA) equipped with an improved evolutionary direction operator (IEDO) and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed approach integrates the IGA and the MU such that it has the merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the CHPED problem. Extensive simulations using the proposed method are carried out on various-size systems, and the results are compared with that of the previous methods. Numerical results indicate that the proposed approach has more advantages than other methods in application. Moreover, the proposed algorithm provides an efficacious approach for large-scale systems of the CHPED problem.
TL;DR: In this paper, an approach using Lagrangian method to solve the optimal chiller loading (OCL) problem and to improve the deficiencies of conventional methods is presented, where the coefficient of performance (COP) of a chiller is chosen as the objective function for the reason of being a concave function.
Abstract: This paper presents an approach using Lagrangian method to solve the optimal chiller loading (OCL) problem and to improve the deficiencies of conventional methods. The coefficient of performance (COP) of a chiller is chosen as the objective function for the reason of being a concave function. The potential performance of the proposed method is demonstrated by mean of two example systems. Compared with the conventional methods, the proposed method has much less power consumption and good accuracy, and is very suitable for application in air-condition system operation.
TL;DR: In this article, a novel hysteresis current control for active power filter (APF) is suggested which is based on optimal voltage vector and in the meantime with constant switching frequency.
Abstract: In this paper a novel hysteresis current control for active power filter (APF) is suggested which is based on optimal voltage vector and in the meantime with constant switching frequency. In the method the location region of the reference voltage vector is detected quickly by a set of hysteresis comparators through one try-and-error process. Two appropriate switches are then selected to control the corresponding two line-to-line currents independently with constant switching frequency. The new method has the advantages of fast allocation of reference voltage space vector, good current tracking accuracy, and constant switching frequency. Therefore, it is efficient and safe in operation. Computer simulation results show that the new current control method can improve APF performance noticeably.
TL;DR: In this paper, a multi-objective optimization method is used to develop tradeoff curves for different types of emissions and the incremental cost of a unit emission reduction (emission reduction rate) is defined for all non-inferior points on a tradeoff curve.
Abstract: With the increase in the environmental awareness and the passage of environmental regulations, the environmental constraints are having a significant impact on the operation of power systems Traditional economic dispatch to minimize the fuel cost is inadequate when environmental emissions are also to be included in the operation of power plants This paper presents a method to include the fuel cost and the environmental impacts of emission in the optimization process Since minimizing the fuel cost and emissions are conflicting in nature, a multiobjective optimization method is being used to develop tradeoff curves for different types of emissions The incremental cost of a unit emission reduction (emission reduction rate) is defined Emission reduction rates are calculated for all non-inferior points on a tradeoff curve These rates provide important information to the system dispatcher to run the system according to his/her preferences Furthermore, emission reduction rates for a particular pollutant can be used for emissions allowance trading
TL;DR: In this paper, a review on the insulation and partial discharge monitoring for gas-insulated switchgear/transmission lines (GIS/GITL) is presented with focusing on the measurement techniques.
Abstract: Widespread applications of pressurized SF6 gas and its mixtures as insulating media in many electric power apparatus are the result of recent advances in modern technologies. Partial discharge (PD) is a natural phenomenon occurring in such gas-insulated electric power systems, which invariably contains tiny defects and non-uniformities. The quality of an electrical insulation system can be characterized by PD measurements, which serve to identify the type and status of a defect. A review on the insulation and PD monitoring for gas-insulated switchgear/transmission lines (GIS/GITL) is presented with focusing on the measurement techniques. Since PD monitoring systems can generate a large amount of data, therefore computer-aided interpretation and classification of defects are also discussed.
TL;DR: In this article, an artificial neural networks-based fault locator for extra high voltage (EHV) transmission lines is presented. But the authors focus on one end of the line only and use the radial basis function (RBF) networks to classify and locate faults.
Abstract: This paper describes the design and implementation of an artificial neural networks-based fault locator for extra high voltage (EHV) transmission lines. This locator utilizes faulted voltage and current waveforms at one end of the line only. The radial basis function (RBF) networks are trained with data under a variety of fault conditions and used for fault type classification and fault location on the transmission line. The results obtained from testing of RBF networks with simulated fault data and recorded data from a 400 kV system clearly show that this technique is highly robust and very accurate. The technique takes into account all the practical limitations associated with a real system. Thereby making it possible to effectively implement an artificial intelligence (AI) based fault locator on a real system.
TL;DR: A new method to locate the source of voltage sag in a power distribution system using the polarity of the real current component to determine the sag location relative to the monitoring point is proposed.
Abstract: Voltage sag can cause hours of downtime, substantial loss of product and also can attribute to malfunctions, instabilities and shorter lifetime of the load. Accurate voltage sag source location can help to minimize the loss and problems caused by voltage sag in a power distribution system. This paper proposes a new method to locate the source of voltage sag in a power distribution system. The proposed method uses the polarity of the real current component to determine the sag location relative to the monitoring point. The product of the RMS current and the power factor angle at the monitoring point is employed for the sag source location. A graph of this product against time is plotted. The voltage sag source location is determined by examining the polarity of the RMS current at the beginning of the sag. The proposed method has been verified by simulations and the results are proven to be in agreement when compared with the slope of system trajectory method.
TL;DR: In this paper, the authors proposed an algorithm to minimize simultaneously and iteratively both kinds of security indices such as J P relating to line flow and J V relating to bus voltage.
Abstract: This paper presents principles about installation and operation of flexible ac transmission systems (FACTS) devices to enhance the steady-state security of power system. Three generic types of FACTS devices are introduced and the proper location of each kind of FACTS devices is determined in accordance with the individual purpose of use. It is desirable to install shunt controllers where there is a need for voltage support according to V – Q sensitivity analysis and to install series controllers where line flow control is required to increase system loadability. It is also important to install combined series–shunt controllers such as UPFC in order to get most of their merits, that is, to control both the active and reactive power flow. An algorithm to minimize simultaneously and iteratively both kinds of security indices such as J P relating to line flow and J V relating to bus voltage is proposed in this paper. For minimization of security indices, each sensitivity vector of both indices is derived analytically. Results from the minimization show that optimal operating points of FACTS devices under various conditions of power system have been achieved. The proposed methodology to install and operate FACTS devices properly is verified on the IEEE 57-bus system where FACTS devices were operated under normal condition and in a line-faulted contingency.
TL;DR: The investigations reveal that the proposed algorithm for solving security constrained optimal power flow problem through the application of evolutionary programming is relatively simple, reliable and efficient and suitable for on-line applications.
Abstract: This paper presents an algorithm for solving security constrained optimal power flow problem through the application of evolutionary programming (EP). The controllable system quantities in the base-case state are optimised to minimize some defined objective function subject to the base-case operating constraints as well as the contingency-case security constraints. An IEEE 30-bus system is taken for investigation. The security constrained optimal power flow results obtained using EP are compared with those obtained using conventional security constrained optimal power flow. The investigations reveal that the proposed algorithm is relatively simple, reliable and efficient and suitable for on-line applications.
TL;DR: An integrated evolving fuzzy neural network and simulated annealing (AIFNN) for load forecasting method to improve the shortcoming of the traditional ANN training where the weights and biases are always trapped into a local optimum.
Abstract: An integrated evolving fuzzy neural network and simulated annealing (AIFNN) for load forecasting method is presented in this paper. First we used fuzzy hyper-rectangular composite neural networks (FHRCNNs) for the initial load forecasting. Then we used evolutionary programming (EP) and simulated annealing (SA) to find the optimal solution of the parameters of FHRCNNs (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). We knew that the EP has a good capability for searching for globe optimal value, but a poor capability for searching for the local optimal value. And, the SA only had a good capability for searching for a local optimal value. Therefore, we combined both methods to obtain both advantages, and so improve the shortcoming of the traditional ANN training where the weights and biases are always trapped into a local optimum. Finally, we use the AIFNN to see if we could improve the solution quality, and if we actually could reduce the error of load forecasting. The proposed AIFNN load forecasting scheme was tested using data obtained from a sample study including 1 year, 1 month and 24 h time periods. The result demonstrated the accuracy of the proposed load forecasting scheme.
TL;DR: In this article, the authors presented an approach for service restoration and optimal reconfiguration of distribution network using GA and Tabu search (TS) method, which is a combination of GA and TS.
Abstract: This paper represents an approach for service restoration and optimal reconfiguration of distribution network using Genetic algorithm (GA) and Tabu search (TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve within feasible times, because the distribution network is so complicated with the combination of many tie-line switches and sectionalizing switches and also has to satisfy radial operation conditions and reliability indices. Therefore, this paper applied Genetic-Tabu algorithm (GTA) to find optimum value with reasonable computation time. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to find a global solution. The case studies with 7-feeder model showed that not only the loss reduction but also the reliability should be considered at the same time to achieve the optimal service restoration and reconfiguration in the distribution network.
TL;DR: A hybrid model between Lagrangian relaxation and genetic algorithm to solve the unit commitment problem is presented and the optimal bidding curves as a function of generation schedule are derived.
Abstract: This paper presents a hybrid model between Lagrangian relaxation (LR) and genetic algorithm (GA) to solve the unit commitment problem. GA is used to update the Lagrangian multipliers. The optimal bidding curves as a function of generation schedule are also derived. An IEEE 118-bus system is used to demonstrate the effectiveness of the proposed hybrid model. Simulation results are compared with those obtained from traditional unit commitment.
TL;DR: In this approach load demand, reserve requirements, and production cost are expressed by fuzzy set notations, while unit generation limits, ramp rate limits, and minimum up/down limits are handled as crisp constraints.
Abstract: This paper presents a new fuzzy optimization based approach to solve the thermal unit commitment (UC) problem. In this approach load demand, reserve requirements, and production cost are expressed by fuzzy set notations, while unit generation limits, ramp rate limits, and minimum up/down limits are handled as crisp constraints. A fuzzy optimization based algorithm is then, developed to find the optimal solution by using fuzzy operations and “if-then” rules. Some heuristics such as dividing hourly load and generating units into levels are used to speed the solution. The approach has been applied to a 38 units thermal power system. The results are compared with that obtained by the dynamic programming (DP), the Lagrangiane–relaxation (LR), constraint logic programming (CLP), and simulated annealing (SA) methods. The achieved results prove the effectiveness, and validity of the proposed approach to solve the large-scale UC problem. The effects of unit ramp rate limits and minimum up/down times are also, investigated.
TL;DR: In this paper, a novel cerebellar model articulation controller (CMAC) neural network (NN) method is presented for the fault diagnosis of power transformers, by introducing the IEC standard 599 to generate the training data, and using the characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN fault diagnosis scheme enables a powerful, straightforward and efficient fault diagnosis.
Abstract: Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. In this paper, a novel cerebellar model articulation controller (CMAC) neural network (NN) method is presented for the fault diagnosis of power transformers. By introducing the IEC standard 599 to generate the training data, and using the characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. With application of this scheme to published transformers data, the diagnoses demonstrate the new scheme with high accuracy and high noise rejection ability. Moreover, the results also proved the ability of multiple incipient faults detection. © 2004 Elsevier B.V. All rights reserved.
TL;DR: Jirutitijaroen et al. as mentioned in this paper developed detailed models relating maintenance parameters to reliability and cost and then investigated the effect of varying model parameters, such as mean time to the first failure, maintenance cost, inspection cost, and failure cost.
Abstract: Transformer is an equipment common to most power systems. Preventive maintenance is performed to extend the equipment lifetime. Models relating probability of failure to maintenance activity are proposed in [Panida Jirutitijaroen, Chanan Singh, Oil-immersed transformer inspection and maintenance: probabilistic models, in: Proceedings of the 2003 North American Power Symposium Conference, pp. 204–208]. The model parameters which are mean time in each stage, inspection rate of each stage, and probabilities of transition from one stage to others, have an effect on reliability and cost of maintenance. In order to establish a cost-effective maintenance process, analysis of model parameters should be conducted thoroughly. This paper develops detailed models relating maintenance parameters to reliability and cost and then investigates the effect of varying model parameters. Simulation results from the proposed model are shown and corroborated by mathematical analysis of a simpler equivalent model. The analysis covers mean time to the first failure, maintenance cost, inspection cost, and failure cost.
TL;DR: Signals obtained from monitoring system have been processed using wavelet transform with suitably modified algorithms to extract detailed information for induction machine fault diagnosis and it is depicted that the application of WT for processing and analysis of the vibration signal to different frequency regions in time domain improves the extraction of the information.
Abstract: Condition monitoring is used for increasing machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring scheme is capable of providing warning and predicting the faults at early stages. The monitoring system obtains information about the machine in the form of primary data and through the use of modern signal processing techniques; it is possible to give vital information to equipment operator before it catastrophically fails. The suitability of a signal processing technique to be used depends upon the nature of the signal and the required accuracy of the obtained information. Therefore, in this paper, signals obtained from monitoring system have been processed using wavelet transform (WT) with suitably modified algorithms to extract detailed information for induction machine fault diagnosis. The results of this investigation depict that the application of WT for processing and analysis of the vibration signal to different frequency regions in time domain improves the extraction of the information that can enhance the ability of the system for diagnosis.
TL;DR: In this paper, the authors proposed a fuzzy model for reliability evaluation of a power plant maintenance scheduling optimizing safety and reliability incorporating uncertain forced outage rate and load representation, which is a combination of probabilistic fuzzy state (PROFUST) model and fuzzy load model.
Abstract: This paper proposes a fuzzy model for reliability evaluation of captive power plant maintenance scheduling optimizing safety and reliability incorporating uncertain forced outage rate and load representation. Such a model is a combination of probabilistic fuzzy state (PROFUST) model and fuzzy load model. In PROFUST model, fuzzy numbers represent the failure and repair rates of generating units because it is inadequate from pragmatic prospective to represent them by crisp numbers. As the maintenance tends to be based on experience-based skills, therefore the fuzzy forced outage rate (fuzzy FOR) based on expert evaluation reflects the condition of operation and maintenance of thermal generating units more realistically compared to a constant failure and repair rate model yielding constant FOR value. The uncertainties due to load forecasting lead to fuzzy load model. Particularly the utilities catered by captive power plants are very sensitive to power failure and the reliability evaluation corroborates the effect of uncertainties through fuzzy loss of load probability (FLOLP) index. Case studies for the maintenance scheduling of a captive power plant catering to an aluminum smelter have been formulated based on both classical probabilistic as well as fuzzy model and comparisons of FLOLP demonstrate the efficacy of the proposed model.