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A neural network-based approximate dynamic programming (ADP) method, the direct neural dynamic programming (direct NDP), is introduced in this paper. The paper covers the basic principle of this learning scheme and an illustrative example of how direct NDP can be implemented. The paper focuses on how direct NDP can be applied to power system stability control. In this case direct NDP is based on realtime...
Nowadays, power transmission networks are operated through supervisory control and data acquisition (SCADA) systems. In incident situations, SCADA systems are prone to accumulate huge amounts of information potentially with temporal, non-monotonic and incompleteness problems. To support operator's decisions, on-line fault diagnosis is needed due to the critical nature of their work. In this paper,...
Automatic data processing and information retrieval is a desirable approach in dealing with the curse of the large volume of data recorded for the condition assessment of equipment in power systems. This approach involves developing a methodology that utilizes the inherent relationships of the data to automate the characterization of system behavior. This paper discusses a practical approach to process...
Power distribution systems play an important role in modern society. When outages occur, fast and proper restorations are crucial to improve system reliability. Proper outage root cause identification is often essential for effective restorations. This paper reports on the investigation of two classification methods: logistic regression and neural network, applied in power distribution fault cause...
The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried...
Classifying consumers, namely LV consumers, in order to assign them typical load diagrams, was always a concern of the electric utilities, which used this kind of information to better manage their distribution networks. Now, with the transition to a completely open market, the need for settlement between distribution operators and traders requires hourly consumption records that are not generally...
In this paper, we investigate the effect of sample size to the estimation of harmonic sources in electric power systems. A blind source separation technique called independent component analysis (ICA) is used to estimate the load profiles of harmonic sources. The ICA algorithm is based on the statistical independence of loads. Results are obtained for several sample sizes. Simulation results show...
Multi-agent systems (MAS) have proven to be an effective platform for diagnostic and condition monitoring applications in the power industry. For example, a multi-agent system architecture, entitled condition monitoring multi-agent system (COMMAS) (McArthur et al., 2004), has been applied to the ultra high frequency (UHF) monitoring of partial discharge activity inside transformers. Additionally,...
Summary form only given. The Northeast US blackout, and the after effects of Hurricane Katrina, remind us of our critical dependence on reliable electric supply. This concern arises both from the minute to minute operations of the system and from the long term environmental and geopolitical concerns with dependence on fossil fuels. It has become increasingly clear that an electric power system based...
This paper proposes a new probabilistic method for short-term load forecasting with the Gaussian processes (GP). In recent years, the degree of uncertainty increases as the power system becomes more deregulated and competitive. The power system players are concerned with maximizing the profit while minimizing the risk in the power market. As a result, it is important to consider the uncertainty of...
The main focus of this paper is to explore the dynamic behavior of an auction system for an electricity market. In order to cope with this complex problem, agent-based simulation has been previously used, where autonomous agents learn through the results of repeated auctions. In this paper, the replicator equation is introduced as a learning algorithm that can be applicable to agent-based simulation...
An efficient method to address the multistage planning of open loop structured mv distribution networks under uncertainty, taking into account distributed generation connected to distribution system, has been proposed. The fuzzy model can cope with important features implicit in planning studies such as time-phased representation, consideration of conflicting objectives and uncertainty in loads, distributed...
This paper motivates the importance of equipment failure rates in three decision problems central to the power engineering industry. Traditional methods of computing equipment failure rates are described, together with three new methods. Illustrations for each are provided
This paper presents a semi-group based neural network architecture for extrapolating steam enthalpies along the temperature axis in the water-steam cycle in a power plant. The assumption is that steam pressures and temperatures are known (available) in a limited high-pressure, high-temperature region, which allows enthalpy calculations to proceed in this limited region but are not available in an...
Speed control during startup is one of the most critical tasks of gas turbine power plant operation. This paper introduces the PI fuzzy gain-scheduling (PI-FGS) controller to solve the foremost speed control problems, including tracking of the acceleration pattern and rejection of disturbances caused by operation events throughout startup. Fundamentally, the PI-FGS synthesizes a gain-scheduling controller...
Speed governors are key elements in the dynamic performance of electric power systems. Therefore, accurate governor models are of great importance in simulating and investigating the power system transient phenomena. Model parameters of such devices are, however, usually unavailable or inaccurate, especially when old generators are involved. Most methods for speed governor parameter estimation are...
The knowledge of loads' future behavior is very important for decision making in power system operation. During the last years, many load models have been proposed, and the neural ones have presented the best results. One of the disadvantages of the neural models for load forecasting is the possibility of excessive adjustment of the training data, named overfitting, which degrades the generalization...
This paper provides a different approach for electricity price forecast from risk management point of view. Making use of neural networks, the methodology presented here has as main concern finding the maximum and the minimum system marginal price (SMP) for a specific programming period, with a certain confidence level. To train the neural network, probabilistic information from past years is used...
This paper presents a new approach to unit commitment problem using absolute stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in simulated annealing method, a higher cost feasible solution is accepted with temperature dependent probability, but other solutions are accepted deterministically. That may lead to the near optimization...
The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the...
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