Showing papers in "IEEE Transactions on Power Systems in 2005"
TL;DR: In this article, the authors present the major conclusions drawn from the presentations and ensuing discussions during the all day session, focusing on the root causes of grid blackouts, together with recommendations based on lessons learned.
Abstract: On August 14, 2003, a cascading outage of transmission and generation facilities in the North American Eastern Interconnection resulted in a blackout of most of New York state as well as parts of Pennsylvania, Ohio, Michigan, and Ontario, Canada. On September 23, 2003, nearly four million customers lost power in eastern Denmark and southern Sweden following a cascading outage that struck Scandinavia. Days later, a cascading outage between Italy and the rest of central Europe left most of Italy in darkness on September 28. These major blackouts are among the worst power system failures in the last few decades. The Power System Stability and Power System Stability Controls Subcommittees of the IEEE PES Power System Dynamic Performance Committee sponsored an all day panel session with experts from around the world. The experts described their recent work on the investigation of grid blackouts. The session offered a unique forum for discussion of possible root causes and necessary steps to reduce the risk of blackouts. This white paper presents the major conclusions drawn from the presentations and ensuing discussions during the all day session, focusing on the root causes of grid blackouts. This paper presents general conclusions drawn by this Committee together with recommendations based on lessons learned.
1,220 citations
TL;DR: In this paper, a modified particle swarm optimization (MPSO) was proposed to deal with the equality and inequality constraints in the economic dispatch (ED) problems with nonsmooth cost functions.
Abstract: This work presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve-point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hopfield neural network approaches.
1,172 citations
TL;DR: In this paper, the authors present the operation of a multiagent system (MAS) for the control of a microgrid and a classical distributed algorithm based on the symmetrical assignment problem for the optimal energy exchange between the production units of the Microgrid and the local loads, as well the main grid.
Abstract: This paper presents the operation of a multiagent system (MAS) for the control of a Microgrid. The approach presented utilizes the advantages of using the MAS technology for controlling a Microgrid and a classical distributed algorithm based on the symmetrical assignment problem for the optimal energy exchange between the production units of the Microgrid and the local loads, as well the main grid.
1,035 citations
TL;DR: Basic features, algorithms, and a variety of case studies are presented in this paper to illustrate the capabilities of the presented tool and its suitability for educational and research purposes.
Abstract: This paper describes the Power System Analysis Toolbox (PSAT), an open source Matlab and GNU/Octave-based software package for analysis and design of small to medium size electric power systems. PSAT includes power flow, continuation power flow, optimal power flow, small-signal stability analysis, and time-domain simulation, as well as several static and dynamic models, including nonconventional loads, synchronous and asynchronous machines, regulators, and FACTS. PSAT is also provided with a complete set of user-friendly graphical interfaces and a Simulink-based editor of one-line network diagrams. Basic features, algorithms, and a variety of case studies are presented in this paper to illustrate the capabilities of the presented tool and its suitability for educational and research purposes.
890 citations
TL;DR: In this paper, the authors present a critical literature review and an up-to-date and exhaustive bibliography on the AGC of power systems, highlighting various control aspects concerning the AGG problem.
Abstract: An attempt is made in This work to present critical literature review and an up-to-date and exhaustive bibliography on the AGC of power systems. Various control aspects concerning the AGC problem have been highlighted. AGC schemes based on parameters, such as linear and nonlinear power system models, classical and optimal control, and centralized, decentralized, and multilevel control, are discussed. AGC strategies based on digital, self-tuning control, adaptive, VSS systems, and intelligent/soft computing control have been included. Finally, the investigations on AGC systems incorporating BES/SMES, wind turbines, FACTS devices, and PV systems have also been discussed.
836 citations
TL;DR: A novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models is proposed, where the historical and usually ill-behaved price series is decomposed using the wavelets to reconstruct the future behavior of the price series and therefore to forecast prices.
Abstract: This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.
815 citations
TL;DR: In this article, a new methodology is presented which quantifies the reserve needed on a system taking into account the uncertain nature of the wind power, and the reliability of the system is used as an objective measure to determine the effect of increasing wind power penetration.
Abstract: With wind power capacities increasing in many electricity systems across the world, operators are faced with new problems related to the uncertain nature of wind power. Foremost of these is the quantification and provision of system reserve. In this paper a new methodology is presented which quantifies the reserve needed on a system taking into account the uncertain nature of the wind power. Generator outage rates and load and wind power forecasts are taken into consideration when quantifying the amount of reserve needed. The reliability of the system is used as an objective measure to determine the effect of increasing wind power penetration. The methodology is applied to a model of the all Ireland electricity system, and results show that as wind power capacity increases, the system must increase the amount of reserve carried or face a measurable decrease in reliability.
795 citations
TL;DR: In this article, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed, which permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, and cost of energy not supplied.
Abstract: In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure.
767 citations
TL;DR: In this paper, the impact of wind turbine inertial response characteristics on frequency control of small isolated power systems is discussed. But, due to differing electromechanical characteristics, this inherent link is not present in wind turbine generators.
Abstract: Increasing levels of wind generation has resulted in an urgent need for the assessment of their impact on frequency control of power systems. Whereas increased system inertia is intrinsically linked to the addition of synchronous generation to power systems, due to differing electromechanical characteristics, this inherent link is not present in wind turbine generators. Regardless of wind turbine technology, the displacement of conventional generation with wind will result in increased rates of change of system frequency. The magnitude of the frequency excursion following a loss of generation may also increase. Amendment of reserve policies or modification of wind turbine inertial response characteristics may be necessary to facilitate increased levels of wind generation. This is particularly true in small isolated power systems.
708 citations
TL;DR: In this article, an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general.
Abstract: Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed.
700 citations
TL;DR: In this article, an improved genetic algorithm with multiplier updating (IGA/spl I.bar/MU) was proposed to solve power economic dispatch (PED) problems of units with valve-point effects and multiple fuels.
Abstract: This paper presents an improved genetic algorithm with multiplier updating (IGA/spl I.bar/MU) to solve power economic dispatch (PED) problems of units with valve-point effects and multiple fuels. The proposed IGA/spl I.bar/MU integrates the improved genetic algorithm (IGA) and the multiplier updating (MU). The IGA equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions, and the MU is employed to handle the equality and inequality constraints of the PED problem. Few PED problem-related studies have seldom addressed both valve-point loadings and change fuels. To show the advantages of the proposed algorithm, which was applied to test PED problems with one example considering valve-point effects, one example considering multiple fuels, and one example addressing both valve-point effects and multiple fuels. Additionally, the proposed algorithm was compared with previous methods and the conventional genetic algorithm (CGA) with the MU (CGA/spl I.bar/MU), revealing that the proposed IGA/spl I.bar/MU is more effective than previous approaches, and applies the realistic PED problem more efficiently than does the CGA/spl I.bar/MU. Especially, the proposed algorithm is highly promising for the large-scale system of the actual PED operation.
TL;DR: A solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented and it is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches.
Abstract: Reactive power dispatch in power systems is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm optimization (PSO) algorithm. An agent in MAPSO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors, and it can also learn by using its knowledge. Making use of these agent-agent interactions and evolution mechanism of PSO, MAPSO realizes the purpose of optimizing the value of objective function. MAPSO applied to optimal reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system. Simulation results show that the proposed approach converges to better solutions much faster than the earlier reported approaches. The optimization strategy is general and can be used to solve other power system optimization problems as well.
TL;DR: In this article, the authors proposed a new integrated model for solving the distribution system planning (DSP) problem by implementing distributed generation (DG) as an attractive option in distribution utilities territories.
Abstract: This paper proposes a new integrated model for solving the distribution system planning (DSP) problem by implementing distributed generation (DG) as an attractive option in distribution utilities territories. The proposed model integrates a comprehensive optimization model and planner's experience to achieve optimal sizing and siting of distributed generation. This model aims to minimize DG's investment and operating costs, total payments toward compensating for system losses along the planning period, as well as different costs according to the available alternative scenarios. These scenarios vary from expanding of an existing substation and adding new feeders to purchasing power from an existing intertie to meet the load demand growth. Binary decision variables are employed in the proposed optimization model to provide accurate planning decisions. The present worth analysis of different scenarios is carried out to estimate the feasibility of introducing DG as a key element in solving the DSP problem.
TL;DR: A new probabilistic load-flow solution algorithm based on an efficient point estimate method that can be used directly with any existing deterministic load- flow program and compared with those obtained from Monte Carlo simulation technique and combined simulation and analytical method.
Abstract: A new probabilistic load-flow solution algorithm based on an efficient point estimate method is proposed in this paper. It is assumed that the uncertainties of bus injections and line parameters can be estimated or measured. This paper shows how to estimate the corresponding uncertainty in the load-flow solution. The proposed method can be used directly with any existing deterministic load-flow program. For a system with m uncertain parameters, it uses 2m calculations of load flow to calculate the statistical moments of load-flow solution distributions by weighting the value of the solution evaluated at 2m locations. The moments are then used in the probability distribution fitting. Performance of the proposed method is verified and compared with those obtained from Monte Carlo simulation technique and combined simulation and analytical method using several IEEE test systems.
TL;DR: In this paper, a new methodology is developed using linear programming to determine the optimal allocation of embedded generation (EG) with respect to the technical constraints faced by EG projects, and a methodology is implemented and tested on a section of the Irish distribution network, demonstrating that the proper placement and sizing of EG is crucial to the accommodation of increasing levels of EG on distribution networks.
Abstract: As a result of the restructuring of electricity markets and the targets laid down for renewable energy, increasing amounts of embedded generation (EG) are being connected to distribution networks. To accommodate this new type of generation, the existing distribution network should be utilized and developed in an optimal manner. This paper explains the background to the technical constraints faced by EG projects, and a new methodology is developed using linear programming to determine the optimal allocation of EG with respect to these constraints. The methodology is implemented and tested on a section of the Irish distribution network. Results are presented, demonstrating that the proper placement and sizing of EG is crucial to the accommodation of increasing levels of EG on distribution networks.
TL;DR: A probabilistic methodology for estimating the energy costs in the market for wind generators associated with wind prediction errors is proposed in this paper, where the prediction error is modeled through a probability density function that represents the accuracy of the prediction model.
Abstract: In this paper, a probabilistic methodology for estimating the energy costs in the market for wind generators associated with wind prediction errors is proposed. Generators must buy or sell energy production deviations due to prediction errors when they bid in day-ahead or hour-ahead energy markets. The prediction error is modeled through a probability density function that represents the accuracy of the prediction model. Production hourly energy deviations and their associated trading costs are then calculated. Three study cases based on real wind power installations in Spain are analyzed. The three study cases show that the error prediction costs can reach as much as 10% of the total generator energy incomes.
TL;DR: In this article, a particle swarm optimization (PSO) technique was used for loss reduction in the IEEE 118-bus system by using a developed optimal power flow based on loss minimization function by expanding the original PSO.
Abstract: This paper presents a particle swarm optimization (PSO) as a tool for loss reduction study. This issue can be formulated as a nonlinear optimization problem. The proposed application consists of using a developed optimal power flow based on loss minimization function by expanding the original PSO. The study is carried out in two steps. First, by using the tangent vector technique, the critical area of the power system is identified under the point of view of voltage instability. Second, once this area is identified, the PSO technique calculates the amount of shunt reactive power compensation that takes place in each bus. The proposed approach has been examined and tested with promising numerical results using the IEEE 118-bus system.
TL;DR: In this article, a stochastic security-constrained multi-period electricity market clearing problem with unit commitment is formulated, where reserve services are determined by economically penalizing the operation of the market by the expected load not served.
Abstract: The first of this two-paper series formulates a stochastic security-constrained multi-period electricity market-clearing problem with unit commitment. The stochastic security criterion accounts for a pre-selected set of random generator and line outages with known historical failure rates and involuntary load shedding as optimization variables. Unlike the classical deterministic reserve-constrained unit commitment, here the reserve services are determined by economically penalizing the operation of the market by the expected load not served. The proposed formulation is a stochastic programming problem that optimizes, concurrently with the pre-contingency social welfare, the expected operating costs associated with the deployment of the reserves following the contingencies. This stochastic programming formulation is solved in the second companion paper using mixed-integer linear programming methods. Two cases are presented: a small transmission-constrained three-bus network scheduled over a horizon of four hours and the IEEE Reliability Test System scheduled over 24 h. The impact on the resulting generation and reserve schedules of transmission constraints and generation ramp limits, of demand-side reserve, of the value of load not served, and of the constitution of the pre-selected set of contingencies are assessed.
TL;DR: This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining techniques, applied on the different stages of the process.
Abstract: This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company.
TL;DR: An efficient SCUC approach with ac constraints that obtains the minimum system operating cost while maintaining the security of power systems is introduced.
Abstract: In a restructured power market, the independent system operator (ISO) executes the security-constrained unit commitment (SCUC) program to plan a secure and economical hourly generation schedule for the day-ahead market. This paper introduces an efficient SCUC approach with ac constraints that obtains the minimum system operating cost while maintaining the security of power systems. The proposed approach applies the Benders decomposition for separating the unit commitment (UC) in the master problem from the network security check in subproblems. The master problem applies the augmented Lagrangian relaxation (LR) method and dynamic programming (DP) to solve UC. The subproblem checks ac network security constraints for the UC solution to determine whether a converged and secure ac power flow can be obtained. If any network violations arise, corresponding Benders cuts will be formed and added to the master problem for solving the next iteration of UC. The iterative process will continue until ac violations are eliminated and a converged optimal solution is found. In this paper, a six-bus system and the IEEE 118-bus system with 54 units are analyzed to exhibit the effectiveness of the proposed approach.
TL;DR: In this paper, a price-based unit commitment (PBUC) problem based on the mixed integer programming (MIP) method was formulated for a GENCO with thermal, combined-cycle, cascaded-hydro, and pumped storage units.
Abstract: This paper formulates the price-based unit commitment (PBUC) problem based on the mixed integer programming (MIP) method. The proposed PBUC solution is for a generating company (GENCO) with thermal, combined-cycle, cascaded-hydro, and pumped-storage units. The PBUC solution by utilizing MIP is compared with that of Lagrangian relaxation (LR) method. Test results on the modified IEEE 118-bus system show the efficiency of our MIP formulation and advantages of the MIP method for solving PBUC. It is also shown that MIP could be applied to solve hydro-subproblems including cascaded-hydro and pumped-storage units in the LR-based framework of hydro-thermal coordination. Numerical experiments on large systems show that the MIP-based computation time and memory requirement would represent the major obstacles for applying MIP to large UC problems. It is noted that the solution of large UC problems could be accomplished by improving the MIP formulation, the utilization of specific structure of UC problems, and the use of parallel processing.
TL;DR: In this article, the design and implementation of a novel control scheme for a doubly fed induction generator (DFIG), of the type employed with wind turbines, to provide support to power system operation is addressed.
Abstract: This paper addresses the design and implementation of a novel control scheme for a doubly fed induction generator (DFIG), of the type employed with wind turbines, to provide support to power system operation. It is shown that this controller provides a DFIG-based wind farm with operational and control compatibility with conventional power stations, the ability to contribute to voltage support and recovery following network faults, the ability to provide a power system stabilizer capability that improves overall system damping, and the capability of contributing short-term frequency support following loss of network generation. A simple but realistic test network that combines synchronous and wind farm generation has been modeled and used to assess dynamic performance. Simulation results are presented and discussed that demonstrate the capabilities and contributions of the new DFIG controller to network support.
TL;DR: The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem.
Abstract: Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
TL;DR: In this paper, the authors proposed a real-time GPS-synchronized wide-area frequency monitoring network (FNET), which consists of frequency disturbance recorders and an information management system.
Abstract: Frequency dynamics is one of the most important measures of an electrical power system status. To better understand power system dynamics, an accurately measured wide-area frequency is needed. The concept of building an Internet-based real-time GPS-synchronized wide-area frequency monitoring network (FNET) was proposed in 2000 by Qiu et al., and this concept has been realized. The FNET system consists of frequency disturbance recorders and an information management system. The FNET has made the synchronized observations of the entire U.S. power network possible with very little cost for the first time. This paper summarizes the implementation of the FNET system and shows some preliminary observations and analyses of the data that have been collected from the FNET.
TL;DR: In this paper, the problem of state estimation in very large power systems, which may contain several control areas, is investigated, and an estimation approach which coordinates locally obtained decentralized estimates while improving bad data processing capability at the area boundaries is presented.
Abstract: This paper investigates the problem of state estimation in very large power systems, which may contain several control areas. An estimation approach which coordinates locally obtained decentralized estimates while improving bad data processing capability at the area boundaries is presented. Each area is held responsible for maintaining a sufficiently redundant measurement set to allow bad data processing among its internal measurements. It is assumed that synchronized phasor measurements from different area buses are available in addition to the conventional measurements provided by the substation remote terminal units. The estimator is implemented and tested using different measurement configurations for the IEEE 118-bus test system and the 4520-bus ERCOT system.
TL;DR: In this article, a functional relationship between failure rate and maintenance measures has been developed for a cable component and the results show the value of using a systematic quantitative approach for investigating the effect of different maintenance strategies.
Abstract: This paper proposes a method for comparing the effect of different maintenance strategies on system reliability and cost. This method relates reliability theory with the experience gained from statistics and practical knowledge of component failures and maintenance measures. The approach has been applied to rural and urban distribution systems. In particular, a functional relationship between failure rate and maintenance measures has been developed for a cable component. The results show the value of using a systematic quantitative approach for investigating the effect of different maintenance strategies.
TL;DR: In this paper, an input-output hidden Markov model (IOHMM) is proposed for analyzing and forecasting electricity spot prices in the Spanish electricity market, which provides both good predictions in terms of accuracy as well as dynamic information about the market.
Abstract: In competitive electricity markets, in addition to the uncertainty of exogenous variables such as energy demand, water inflows, and availability of generation units and fuel costs, participants are faced with the uncertainty of their competitors' behavior. The analysis of electricity price time series reflects a switching nature, related to discrete changes in competitors' strategies, which can be represented by a set of dynamic models sequenced together by a Markov chain. An input-output hidden Markov model (IOHMM) is proposed for analyzing and forecasting electricity spot prices. The model provides both good predictions in terms of accuracy as well as dynamic information about the market. In this way, different market states are identified and characterized by their more relevant explanatory variables. Moreover, a conditional probability transition matrix governs the probabilities of remaining in the same state, or changing to another, whenever a new market session is opened. The model has been successfully applied to real clearing prices in the Spanish electricity market.
TL;DR: In this article, a method for analyzing the competition among transmission-constrained generating companies (GENCOs) with incomplete information is presented. But the authors do not consider the impact of transfer capability on GENCOs' bidding strategies.
Abstract: This work describes a method for analyzing the competition among transmission-constrained generating companies (GENCOs) with incomplete information. Each GENCO models its opponents' unknown information with specific types for transforming the incomplete game into a complete game with imperfect information. The proposed methodology employs the supply function equilibrium for modeling a GENCO's bidding strategy. The competition is modeled as a bilevel problem with the upper subproblem representing individual GENCOs and the lower subproblem representing the independent system operator (ISO). The upper subproblem maximizes the individual GENCOs' payoffs and the lower subproblem solves the ISO's market clearing problem for minimizing consumers' payments. The bilevel problem is solved by developing sensitivity functions for a GENCO's payoff with respect to its bidding strategies. An eight-bus system is employed to illustrate the proposed method, and the numerical results show the impact of transfer capability on GENCOs' bidding strategies.
TL;DR: In this paper, a model of a field-oriented controlled doubly-fed induction generator based on a fifth-order induction generator model is described and implemented in a reference frame that allows the factors affecting the inertial response of a doubly fed induction generator to be easily examined.
Abstract: The inertial response of a generator is influenced by the sensitivity of the generator's electromagnetic torque to changes in the power system frequency. This paper deals with the inertial response of wind turbines employing induction-machine-based generators. A model of a field-oriented controlled doubly fed induction generator based on a fifth-order induction-generator model is described. The proposed model is implemented in a reference frame that allows the factors affecting the inertial response of a doubly fed induction generator to be easily examined. A comparison between the inertial response of a squirrel-cage and doubly fed induction-machine-based wind-turbine generator is performed using the developed models. It is found that the inertial response of a doubly fed induction generator employing field-oriented control is strongly influenced by the rotor current-controller bandwidth.
TL;DR: Two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting are described, which aim is to develop more robust load forecasters.
Abstract: The importance of short-term load forecasting has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and nonstationarity of loads are becoming worse, due to the dynamics of energy prices. Besides, the number of nodal loads to be predicted does not allow frequent interactions with load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. This paper describes two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting. Its main goal is to develop more robust load forecasters. Hourly load and temperature data for North American and Slovakian electric utilities have been used to test the proposed methodology.