A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

Nature-Inspired Topology Optimization of Recurrent Neural Networks

Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, this work presents three nature-inspired (NI) algorithms for neural architecture search (NAS), introducing the subfield of nature-inspired neural architecture search (NI-NAS). These algorithms, based on ant colony optimization (ACO), progress from memory cell structure optimization, to bounded discrete-space architecture optimization, and finally to unbounded continuous-space architecture optimization. These methods were applied to real-world data sets representing challenging engineering problems, such as data from a coal-fired power plant, wind-turbine power generators, and aircraft flight data recorder (FDR) data. Initial work utilized ACO to select optimal connections inside recurrent long short-term memory (LSTM) cell structures. Viewing each LSTM cell as a graph, ants would choose potential input and output connections based on the pheromones previously laid down over those connections as done in a standard ACO search. However, this approach did not optimize the overall network of the RNN, particularly its synaptic parameters. I addressed this issue by introducing the Ant-based Neural Topology Search (ANTS) algorithm to directly optimize the entire RNN topology. ANTS utilizes a discrete-space superstructure representing a completely connected RNN where each node is connected to every other node, forming an extremely dense mesh of edges and recurrent edges. ANTS can select from a library of modern RNN memory cells. ACO agents (ants), in this thesis, build RNNs from the superstructure determined by pheromones laid out on the superstructure\u27s connections. Backpropagation is then used to train the generated RNNs in an asynchronous parallel computing design to accelerate the optimization process. The pheromone update depends on the evaluation of the tested RNN against a population of best performing RNNs. Several variations of the core algorithm was investigated to test several designed heuristics for ANTS and evaluate their efficacy in the formation of sparser synaptic connectivity patterns. This was done primarily by formulating different functions that drive the underlying pheromone simulation process as well as by introducing ant agents with 3 specialized roles (inspired by real-world ants) to construct the RNN structure. This characterization of the agents enables ants to focus on specific structure building roles. ``Communal intelligence\u27\u27 was also incorporated, where the best set of weights was across locally-trained RNN candidates for weight initialization, reducing the number of backpropagation epochs required to train each candidate RNN and speeding up the overall search process. However, the growth of the superstructure increased by an order of magnitude, as more input and deeper structures are utilized, proving to be one limitation of the proposed procedure. The limitation of ANTS motivated the development of the continuous ANTS algorithm (CANTS), which works with a continuous search space for any fixed network topology. In this process, ants moving within a (temporally-arranged) set of continuous/real-valued planes based on proximity and density of pheromone placements. The motion of the ants over these continuous planes, in a sense, more closely mimicks how actual ants move in the real world. Ants traverse a 3-dimensional space from the inputs to the outputs and across time lags. This continuous search space frees the ant agents from the limitations imposed by ANTS\u27 discrete massively connected superstructure, making the structural options unbounded when mapping the movements of ants through the 3D continuous space to a neural architecture graph. In addition, CANTS has fewer hyperparameters to tune than ANTS, which had five potential heuristic components that each had their own unique set of hyperparameters, as well as requiring the user to define the maximum recurrent depth, number of layers and nodes within each layer. CANTS only requires specifying the number ants and their pheromone sensing radius. The three applied strategies yielded three important successes. Applying ACO on optimizing LSTMs yielded a 1.34\% performance enhancement and more than 55% sparser structures (which is useful for speeding up inference). ANTS outperformed the NAS benchmark, NEAT, and the NAS state-of-the-art algorithm, EXAMM. CANTS showed competitive results to EXAMM and competed with ANTS while offering sparser structures, offering a promising path forward for optimizing (temporal) neural models with nature-inspired metaheuristics based the metaphor of ants

Simulating The Impact of Emissions Control on Economic Productivity Using Particle Systems and Puff Dispersion Model

A simulation platform is developed for quantifying the change in productivity of an economy under passive and active emission control mechanisms. The program uses object-oriented programming to code a collection of objects resembling typical stakeholders in an economy. These objects include firms, markets, transportation hubs, and boids which are distributed over a 2D surface. Firms are connected using a modified Prim’s Minimum spanning tree algorithm, followed by implementation of an all-pair shortest path Floyd Warshall algorithm for navigation purposes. Firms use a non-linear production function for transformation of land, labor, and capital inputs to finished product. A GA-Vehicle Routing Problem with multiple pickups and drop-offs is implemented for efficient delivery of commodities across multiple nodes in the economy. Boids are autonomous agents which perform several functions in the economy including labor, consumption, renting, saving, and investing. Each boid is programmed with several microeconomic functions including intertemporal choice models, Hicksian and Marshallian demand function, and labor-leisure model. The simulation uses a Puff Dispersion model to simulate the advection and diffusion of emissions from point and mobile sources in the economy. A dose-response function is implemented to quantify depreciation of a Boid’s health upon contact with these emissions. The impact of emissions control on productivity and air quality is examined through a series of passive and active emission control scenarios. Passive control examines the impact of various shutdown times on economic productivity and rate of emissions exposure experienced by boids. The active control strategy examines the effects of acceptable levels of emissions exposure on economic productivity. The key findings on 7 different scenarios of passive and active emissions controls indicate that rate of productivity and consumption in an economy declines with increased scrutiny of emissions from point sources. In terms of exposure rates, the point sources may not be the primary source of average exposure rates, however they significantly impact the maximum exposure rate experienced by a boid. Tightening of emissions control also negatively impacts the transportation sector by reducing the asset utilization rate as well as reducing the total volume of goods transported across the economy

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

© 2016 Elsevier B.V. Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator

New Modeling Approaches for the Prediction of Combustion Pollutants

Combustion processes are ubiquitous to human technological development and provide many benefits such as large-scale power generation for electricity and transportation along with residential and commercial heating for manufacturing, cooking, and warmth. However, these various processes can also have harmful effects on human health and the environment via emission of CO2 and other pollutants such as NOx and particulate matter (PM; often in the form of soot). For these reasons, there is a continued need for controlling, improving, and optimizing combustion processes. Modeling of these processes provides powerful insights into system-level dynamics and their control. Due to the size and complexity of industrial-scale combustion systems, there is a pressing need for the development of computationally-inexpensive models that can accurately predict gaseous and PM emissions. The research described in this dissertation addresses this need by: 1) careful evaluation of existing soot formation models for application in oxygen-enriched flames, 2) development of new, robust soot modeling capabilities with improved accuracy for flames outside of the normal fuel/air condition, and 3) production of a large data set for the development of machine learning-based algorithms for predicting pollutant emissions under a wide range of combustion operating conditions. Over the past decades, several semi-empirical soot models have been developed for specific applications with unique characteristic timescales and/or validated only under fuel-air combustion conditions. Hence, their universal use, especially under oxygen-enriched combustion conditions, could lead to highly inaccurate predictions. Twelve semi-empirical models (1-step or 2-step) are evaluated based on their ability to respond to changes in stoichiometric mixture fraction (Zst) and strain in a series of ethylene counterflow flames spanning across the sooting-to-non-sooting (yellow to blue) transition. Results show that no existing model is able to predict a blue flame when Zst is increased beyond the experimentally-measured sooting limit. Motivated by this finding, a novel modeling approach is presented to account for the unique flame characteristics at elevated-Zst environments and their effect on soot formation. This modeling approach is designed to capture both the formation and the reversible processes that occur on the fuel-side of a diffusion flame in a robust yet simple manner and can be utilized in many industrial combustion applications. A new semi-empirical formulation is presented that achieves this goal. In addition, extensions are presented for two widely-used semi-empirical models (Leung-Lindstedt and Moss-Brookes) which would otherwise be inaccurate at these conditions. Upon application of this approach to counterflow flame systems, the predicted soot volume fraction profiles agree well with experimental findings reported by previous studies under low Zst. This improved approach also resulted in the prediction of blue (soot-free) limit conditions in a non-premixed counterflow flame for the first time. Thus, the performance of semi-empirical soot formation models can be dramatically improved when the reversible nature of soot formation at high temperature is considered. The next goal was to develop a machine-learning based modelling approach for combustion systems. As part of this collaborative effort, a series of experiments were performed using a lab-scale (25 kW) combustor that was operated under varying the fuel and air ratios. Measurements were made of temperature profiles along the reactor wall and gas composition and pollutants (CO, NOx, PM) in the exhaust. A series of tests were performed totaling 60 hours of runtime and 140,000 data points corresponding to each parameter. Findings from these experiments highlight a series of trends in the reactor: low and high primary air flows lead to elevated PM and NOx emission levels, respectively; NOx levels correlate with varying swirl ratios under fixed fuel / air ratio. Based on the generated data set, a model may be developed to accurately predict pollutant levels and subsequently recommend optimized operating conditions for the combustion system

Architecture for intelligent power systems management, optimization, and storage.

The management of power and the optimization of systems generating and using power are critical technologies. A new architecture is developed to advance the current state of the art by providing an intelligent and autonomous solution for power systems management. The architecture is two-layered and implements a decentralized approach by defining software objects, similar to software agents, which provide for local optimization of power devices such as power generating, storage, and load devices. These software device objects also provide an interface to a higher level of optimization. This higher level of optimization implements the second layer in a centralized approach by coordinating the individual software device objects with an intelligent expert system thus resulting in architecture for total system power management. In this way, the architecture acquires the benefits of both the decentralized and centralized approaches. The architecture is designed to be portable, scalable, simple, and autonomous, with respect to devices and missions. Metrics for evaluating these characteristics are also defined. Decentralization achieves scalability and simplicity through modularization using software device objects that can be added and deleted as modules based on the devices of the power system are being optimized. Centralization coordinates these software device objects to bring autonomy and intelligence of the whole power system and mission to the architecture. The centralization approach is generic since it always coordinates software device objects; therefore it becomes another modular component of the architecture. Three example implementations illustrate the evolution of this power management system architecture. The first implementation is a coal-fired power generating station that utilized a neural network optimization for the reduction of nitrogen oxide emissions. This illustrates the limitations of this type of black-box optimization and serves as a motivation for developing a more functional architecture. The second implementation is of a hydro-generating power station where a white-box, software agent approach illustrates some of the benefits and provides initial justification of moving towards the proposed architecture. The third implementation applies the architecture to a vehicle to grid application where the previous hydro-generating application is ported and a new hybrid vehicle application is defined. This demonstrates portability and scalability in the architecture, and linking these two applications demonstrates autonomy. The simplicity of building this application is also evaluated