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Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on

Date 11-15 April 2011

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  • [Front cover]

    Publication Year: 2011 , Page(s): c1
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  • [Copyright notice]

    Publication Year: 2011 , Page(s): 1
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  • Table of contents

    Publication Year: 2011 , Page(s): iii - vi
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  • IEEE CIASG 2011 Committee

    Publication Year: 2011 , Page(s): vii
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  • Particle swarm optimization applied to integrated demand response resources scheduling

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB) |  | HTML iconHTML  

    The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches. View full abstract»

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  • Encoding distributed search spaces for virtual power plants

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (399 KB) |  | HTML iconHTML  

    The optimization task in many virtual power plant (VPP) scenarios comprises the search for appropriate schedules in search spaces from distributed energy resources. In scenarios with a decoupling of plant modeling and plant control, these search spaces are distributed as well. If merely the controller unit of a plant knows about the subset of operable schedules that are allowed to be considered by the central scheduling unit, then these sets have to be effectively communicated. We discuss an approach of learning the envelope that separates operable from non-operable schedules inside the space of all schedules by means of support vector data description. Then, only the comparatively small set of support vectors has to be transmitted as a classifier for distinguishing schedules during optimization. We applied this approach to simulated VPP. View full abstract»

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  • Adaptive critic design based dynamic optimal power flow controller for a smart grid

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB) |  | HTML iconHTML  

    An adaptive critic design (ACD) based dynamic optimal power flow control (DOPFC) is proposed in this paper as a solution to the smart grid operation in a high short-term uncertainty and variability environment. With the increasing penetration of intermittent renewable generation, power system stability and security need to be ensured dynamically as the system operating condition continuously changes. The proposed DOPFC dynamically tracks the power system optimal operating point by continuously adjusting the steady-state set points from the traditional OPF algorithms. The ACD technique, specifically the dual heuristic dynamic programming (DHP), is used to provide nonlinear optimal control, where the control objective is formulated explicitly to incorporate system operation economy, stability and security considerations. A 12 bus test power system is used to demonstrate the development and effectiveness of the proposed ACD-based DOPFC using recurrent neural networks. View full abstract»

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  • An optimal scheduling problem in distribution networks considering V2G

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2546 KB) |  | HTML iconHTML  

    This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles. View full abstract»

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  • Viterbi algorithm with sparse transitions (VAST) for nonintrusive load monitoring

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (281 KB) |  | HTML iconHTML  

    Implementation of smart grid provides an opportunity for concurrent implementation of nonintrusive appliance load monitoring (NIALM), which disaggregates the total household electricity data into data on individual appliances. This paper introduces a new disaggregation algorithm for NIALM based on a modified Viterbi algorithm. This modification takes advantage of the sparsity of transitions between appliances' states to decompose the main algorithm, thus making the algorithm complexity linearly proportional to the number of appliances. By consideration of a series of data and integrating a priori information, such as the frequency of use and time on/time off statistics, the algorithm dramatically improves NIALM accuracy as compared to the accuracy of established NIALM algorithms. View full abstract»

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  • Methods for generating TLPs (typical load profiles) for smart grid-based energy programs

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1058 KB) |  | HTML iconHTML  

    Most electric power companies implement an automatic meter reading (AMR) system operating at quarter-hour intervals. The companies install the system (an electric meter) at the homes of their high-voltage (HV) customers who consume a significant amount of power each month. When first introduced to the industry, the AMR system simply measured customers' peak power and billed them. Recent studies are examining the same system's applicability in the cutting-edge smart grid technology. A growing number of studies are focusing on AMR-based distribution network load analysis and demand prediction to promote the dissemination of smart grid-based services. Researchers are basically using AMR customers' usage data to analyze loads and generate the virtual load profile (VLP) of non-automatic meter reading (nAMR) customers. Generating VLP requires clustering and classification that are among the various data mining techniques adopted by researchers. This study reviewed previous research findings that reported AMR-based typical load profile (TLP) generation, and utilized the AMR data of some KEPCO HV customers for TLP generation. Analyses were performed via three clustering techniques, and the strengths of the techniques were compared. View full abstract»

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  • Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (281 KB) |  | HTML iconHTML  

    In this paper, a Higher Order Wavelet Neural Network (HOWNN) trained with an Extended Kalman Filter (EKF) is implemented to solve the wind forecasting problem. The Neural Network based scheme is composed of high order terms in the input layer, two hidden layers, one incorporating radial wavelets as activation functions and the other using classical logistic sigmoid, and an output layer with a linear activation function. A Kalman filter based algorithm is employed to update the synaptic weights of the wavelet network. The size of the regression vector is determined by means of the Lipschitz quotients method. The proposed structure captures more efficiently the complex nature of the wind speed time series. The proposed model is trained and tested using real wind speed data values. View full abstract»

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  • Neural networks ensembles for short-term load forecasting

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (646 KB) |  | HTML iconHTML  

    This paper proposes a new approach for short-term load forecasting based on neural networks ensembling methods. A comparison between traditional statistical linear seasonal model and ANN-based models has been performed on the real-world building load data, considering the utilisation of external data such as the day of the week and building occupancy data. The selected models have been compared to the prediction of hourly demand for the electric power up to 24 hours for a testing week. Both neural networks ensembles achieved lower average and maximum errors than other models. Experiments showed how the introduction of external data had helped the forecasting. View full abstract»

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  • Monitoring of multivariate wind resources with self-organizing maps and slow feature analysis

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (371 KB) |  | HTML iconHTML  

    Wind power is an important part of a sustainable and smart energy grid. Wind energy production datasets from hundreds of wind farms and thousands of windmills are collected, and have to be analyzed and understood. As wind is a volatile energy source, state observation has an important part to play for grid management, fault analysis and planning strategies of grid operators. We demonstrate how two approaches from unsupervised neural computation help to understand high-dimensional wind resource time series. The first approach for visualization of multivariate sequences is based on self-organizing feature maps. The output sequence allows the monitoring of the overall system state with a low-dimensional linear visualization that reflects the topological characteristics of the original wind data. We demonstrate the visualization on real-world wind resource measurements. The second approach shows how to identify the slowest feature in a multivariate wind time series, also known as driving force, with the help of slow feature analysis. Experiments, parameter analyses, and first interpretations demonstrate the capabilities of the approaches. View full abstract»

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  • Optimal phasor measurement unit placement for power system observability — A heuristic approach

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (173 KB) |  | HTML iconHTML  

    In this paper a heuristics based method is proposed for optimal phasor measurement unit (PMU) placement in a power system. The objective is to determine the strategic locations for PMUs so that the power system is made completely observable with minimum number of PMUs. Zero injection buses are considered as virtual measurements in the proposed method. The optimal phasor measurement unit placement problem (OPPP) is solved using simple heuristics and network connectivity information. Simulation results for IEEE 14-bus, 24-bus, 30-bus, 57-bus, 118-bus and New England 39bus test system are presented and compared with the existing techniques. The results show that the proposed method is simple to implement and compares well with the other existing methods. View full abstract»

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  • Discrete-time reduced order neural observer for Linear Induction Motors

    Publication Year: 2011 , Page(s): 1 - 7
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (374 KB) |  | HTML iconHTML  

    This paper focusses on a discrete-time reduced order neural observer applied to a Linear Induction Motor (LIM) model, whose model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. Simulation results are included in order to illustrate the applicability of the proposed scheme. View full abstract»

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  • Power system state estimation with dynamic optimal measurement selection

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (218 KB) |  | HTML iconHTML  

    Power system measurement devices continue to evolve towards higher accuracy and update rate. On the other hand, the computation required for processing the enormous amounts of measurement data associated with large complex power systems makes real-time estimation a major challenge. In this paper we present the Lower Dimensional Measurement-space (LoDiM) state estimation method for large-scale and wide-area interconnected power systems. We present the method in the context of the Kalman filter and Extended Kalman filter, however our measurement selection procedure is not filter-specific, i.e. it can also be applied on other state estimation methods such as particle filters and unscented filters. Our method can also take advantage of large-scale parallel computation techniques for further improvement. Moreover, the concept of LoDiM should be applicable to other large-scale, real-time and computationally-intensive state tracking systems beyond the power systems, such as weather forecasting systems, gas-pipeline systems, and other critical infrastructure. View full abstract»

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  • Wide area monitoring in power systems using cellular neural networks

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (708 KB) |  | HTML iconHTML  

    The demand of power and the size and complexity of the power system is increasing. Wide area monitoring and control is an integral part in transitioning from the traditional power system to a Smart Grid. However, wide area monitoring becomes challenging as the size of the electric power grid, and consequently the number of components to be monitored, grows. Wide area monitor (WAM) designed using feed-forward and feedback neural network architectures do not scale up to handle the growing complexity of the Smart Grid. In this paper, cellular neural network (CNN) is presented as a way to provide scalability in the development of a WAM for Smart Grid. The CNN based WAM is compared with multilayer perceptrons (MLP) based WAM on two different power systems. The results show that the CNN has better or comparable performance with, and scales up much better than, MLP. View full abstract»

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  • Prediction of critical clearing time using artificial neural network

    Publication Year: 2011 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (148 KB) |  | HTML iconHTML  

    This paper is concerned with the application of feed forward artificial neural networks for the prediction of the critical clearing time of a fault in power systems. The training of ANNs is done using selected features as inputs and the critical clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulations. Multi layer feed forward neural network trained with Levenberg-Marquardt (LM) back propagation algorithm is used to provide the estimated CCT. The simulation results show that ANNs is capable to provide fast and accurate mapping. This makes it attractive for real-time stability assessment. View full abstract»

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  • Comparison of TDNN and RNN performances for neuro-identification on small to medium-sized power systems

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1151 KB) |  | HTML iconHTML  

    For Artificial Neural Networks (ANN) to become more widely used in power systems and the future smart grids, ANN based algorithms must be capable of scaling up as they try to identify and control larger and larger parts of a power system. This paper goes through the process of scaling up an ANN based identifier as it is driven to identify increasingly larger portions of a power system. Distributed and centralized approaches for scaling up are taken and the pros and cons of each are presented. The New England/New York 68-bus power network is used as the test bed for the studies. It is shown that while a fully-connected (centralized) ANNs is capable of identification of the system with appropriate accuracy, the increase in the training times required to obtain an acceptable set of weights becomes prohibitive as the system size is increased. View full abstract»

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  • Fuzzy based operation of renewable energy based autonomous micro-grids with hydrogen storage

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (365 KB) |  | HTML iconHTML  

    Electrical supply of remotely located objects - such as telecommunication relay stations, alpine huts, or farms and small settlements in developing countries - requires autonomously operated micro-grids, favorably based on renewable energy sources. Proper design as well as reasonable control of such small scale power systems are posing a challenge which was taken up by the development of a comprehensive counseling software tool which provides for reasonable system design by selection of expedient renewable source(s) as well as short and/or long term energy storage paths; the present paper deals with the setup of optimal operation strategies for the complete plants, based on fuzzy logic. View full abstract»

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  • Optimal location and sizing of energy storage modules for a smart electric ship power system

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (347 KB) |  | HTML iconHTML  

    The Navy's next generation electric ship's power system will support high energy loads and critical equipment. Energy storage modules will be needed to meet the demands of these loads as well as increase the overall high quality of service. This paper describes an approach to evaluate the impact of energy storage module location and sizing for ship survivability and quality of service. Specifically, a multi-objective optimization algorithm, the multi-objective particle swarm optimization - is used to obtain Pareto optimal solutions considering survivability, quality of service, and cost. Results based multi-objective particle swarm optimization study show that optimal ESM location and sizing improves ship power system's survivability and quality of service with a possible minimum cost. View full abstract»

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  • Energy management system for a renewable based microgrid with a demand side management mechanism

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (431 KB) |  | HTML iconHTML  

    A novel energy management system for a renewable based microgrid is proposed. It provides on-line set points for each generation unit, operation modes for a water supply system, and signals for consumers based on a demand side management mechanism. The smart microgrid is composed of photovoltaic panels, a wind turbine, a diesel generator, a battery bank, and a water supply system. The energy management system (EMS) minimizes the operational costs while supplying the water and electric load demands. It considers a two days ahead prediction of the weather conditions. Also, a neural network for a two days ahead electric consumption forecasting is designed. The system is implemented and tested using a real data set from a reference location. Results show the economic sense of the set points and management, for a practical implementation of the system in a specific location in Chile. View full abstract»

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  • Genetic algorithm methodology applied to intelligent house control

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1914 KB) |  | HTML iconHTML  

    In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers' preferences. The proposed approach is compared with a mixed integer non-linear approach. View full abstract»

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  • Design of a smart grid management system with renewable energy generation

    Publication Year: 2011 , Page(s): 1 - 4
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (258 KB) |  | HTML iconHTML  

    This paper proposes a hybrid smart grid integrating wind, photovoltaic and batteries into an AC bus. The wind speed and solar irradiance variables are characterized with experimental data from a meteorological station and the generators are sized according to the power demand of the Mechatronics building in the UADY Faculty of Engineering. A management system based on multiagents is designed in order to measure and control the loads inside the building. The knowledge base of the multi agent system is aided with contrains functions and power generation forecast using neural networks. View full abstract»

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  • Sizing a self-sustaining wind-Diesel power supply by Particle Swarm Optimization

    Publication Year: 2011 , Page(s): 1 - 7
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (969 KB) |  | HTML iconHTML  

    Hybrid wind-Diesel systems can be an attractive solution for the supply of remotely located consumers or the provision of enhanced energy independence in the Near-East region, especially in Gaza strip. Such stand-alone system contains a wind turbine, batteries as short term storage as well as hydrogen storage tanks as long term storage devices. The proposed system leads to a remarkable reduction in the fuel consumption, in comparison with Diesel only systems. For proper sizing of the particular system components under minimization of the Life Cycle Cost (LCC), the metaheuristic computational method of Particle Swarm Optimization (PSO) was successfully applied; this is exemplarily shown by the electricity supply of a hospital in Gaza strip where the energy requirements are fully met under regard of specific constraints and restrictions. View full abstract»

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