Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Analysis, simulation and testing of ITS applications based on wireless communication technologies

Intelligent Transportation Systems (ITS) aim to improve road transport safety and efficiency, to manage road networks in the interest of the society and to provide real time responses to events. In order to reach these goals, real time feedback to the drivers is expected through the integration of telecommunications, sensing and information technologies with transport engineering. Wireless communication technologies, that have been used in industrial applications for more than 30 years, play a crucial role in ITS, as based on the concept of multiple devices (on both vehicle and infrastructure side) interconnected in different ways. Connectivity, in tandem with sensing technologies, is fuelling the innovations that will inevitably lead to the next big opportunity for road transport: autonomous vehicles. Therefore, this study has investigated - through analysis, simulation and field testing – on applications based on wireless communication technologies meant to support both Data acquisition and Data diffusion as fundamental aspects/ phases in ITS, where data is widely individuated as being the key element

Coordinated Voltage and Reactive Power Control for Renewable Dominant Smart Distribution Systems

Driven by their economic and environmental advantages, smart grids promote the deployment of active components, including renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs), for sustainability and environmental benefits. As a result of smart grid technologies and the amount of data collected by smart meters, better operation and control schemes can be developed to allow for cleaner energy with high efficiency, and without breaching network operating constraints. Power distribution networks may face some operational and control challenges as the integration of intermittent energy sources (wind and PV power systems) increases. Some of these challenges include voltage rise and fluctuation, reverse power flow, and the malfunction of conventional Volt/Var control devices. Depending on their location, RESs may introduce two issues related to the Volt/Var control problem, the first of which is that the severity of loading variations will be greater than the case without RESs. The second occurs when the RES is connected between the load center and any regulating devices. The power in-feed from the intermittent RESs may not only mislead the regulator’s control circuit, resulting in unfavorable voltage, but may also enforce the regulator taps to operate randomly following bus voltage variations. This thesis investigates and presents a methodology for the Volt/Var control problem in Smart Distribution Grids (SDGs) under the high penetration and fluctuation of RESs. The research involves the application of predictive control actions to optimally set Volt/Var control devices before the predicted voltage violation takes place. The main objective of this controller is to manage and control the operation of Volt/Var devices in an optimal way that improves the voltage profile along the feeders, reduces real power losses and minimizes the number of Volt/Var device taps and/or switching movements under all loading conditions and for high penetration RESs. This thesis first presents a very Short-Term Stacking Ensemble (STSE) forecasting model for solar PV and wind power outputs that is developed to predict the generated power for intervals of 15 minutes. The proposed model combines heterogeneous machine learning algorithms composed of three well-established models: Support Vector Regression (SVR); Radial Basis Function Neural Network (RBFNN); and Random Forest (RF) heuristically via SVR. The STSE model aims to minimize the prediction error associated with renewable resources when used in the real-time operation of power distribution networks. Secondly, a day-ahead Predictive Volt/Var Control (PVVC) model is developed to find the optimal coordination between Volt/Var control devices under the high penetration and power variations of RESs. The objective of the PVVC model is defined as simultaneous minimization of voltage deviation at each bus, power losses, operating cycle of regulation equipment, and RES curtailment. The benefit of using smart inverter interface RESs with the capability of injective/absorbing reactive power is examined and applied as ancillary services for voltage support. Thirdly, a Sequential Predictive Control (SPC) Strategy for smart grids is developed. The model uses the past and currently available data to forecast demand and RES outputs for intervals of 15 minutes, with real-time updating mechanisms. It then schedules the settings and operations of Volt/Var control devices by solving the Volt/Var control problem in a rolling horizon optimization framework. Because the optimization must be solved in a short interval with a global solution, a solution methodology for linearizing the nonlinear optimization problem is adapted. The original control problem, which is a Mixed-Integer Nonlinear Programming (MINLP) optimization problem, is transformed into a Mixed Integer Second Order Conic Programming (MISOCP) problem that guarantees a global solution through convexity and remarkably reduces the computational burden. Case studies carried out to compare the proposed model against state-of-the-art models provides evidence for the proposed model’s effectiveness. Results indicate that the SPC is capable of accurately solving the control problem within small time slots. The proposed models aim to efficiently operate SDGs at a high penetration level of RES for a day-ahead, as well as in real-time, depending on the preference of network operators. The primary purpose is to minimize operating costs while increasing the efficiency and lifespan of Volt/Var control devices

Critical analysis of determining induction motor operating power factor using measurement and estimation techniques

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonInduction motors are the most used in commercial and industrial areas that consume the majority of generated electrical energy. The induction motors always create a low power factor. The low power factor not only create a penalty charge for industrial customers, but also produces energy losses in electrical systems. To prevent such issues, the users responsible to maintain the power factor to unity. Many researchers expressed that reactive power compensation by capacitors bank can be a substantial solution to maintain the power factor in the desired level at any loads, but providing the optimal reactive power still is a controversial topic. In the last decade, the power factor correction formula leads to obtain the optimal reactive power using measurement of input power and the operating power factor. However, measurement of these values synchronously create difficulties at any loading points. This research will examine a solution to determine the operating power factor of induction motors against input power from no-load to full/over-load conditions using measurement and estimation techniques. In this thesis, estimation techniques including Kriging, regression, neural network and support vector regression are implemented in three different induction motors with the size of 250 W, 10 HP and 100 HP in order to identify the best estimation technique. In these cases, the support vector regression technique with some inputs data determined the power factor and input power at every desired loading points with high accuracy. These estimated values contributed to obtain the optimal reactive power and so prevent under or over correction at any loading points

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Power System Dynamics Enhancement Through Phase Unbalanced and Adaptive Control Schemes in Series FACTS devices

This thesis presents novel series compensation schemes and adaptive control techniques to enhance power system dynamics through damping Subsynchronous Resonance (SSR) and low-frequency power oscillations: local and inter-area oscillations. Series capacitive compensation of transmission lines is used to improve power transfer capability of the transmission line and is economical compared to the addition of new lines. However, one of the impeding factors for the increased utilization of series capacitive compensation is the potential risk of SSR, where electrical energy is exchanged with turbine-generator shaft systems in a growing manner which can result in shaft damage. Furthermore, the fixed capacitor does not provide controllable reactance and does not aid in the low-frequency oscillations damping. The Flexible AC Transmission System (FACTS) controllers have the flexibility of controlling both real and reactive power which could provide an excellent capability for improving power system dynamics. Several studies have investigated the potential of using this capability in mitigating the low-frequency (electromechanical) as well as the subsynchronous resonance (SSR) oscillations. However, the practical implementations of FACTS devices are very limited due to their high cost. To address this issue, this thesis proposes a new series capacitive compensation concept capable of enhancing power system dynamics. The idea behind the concept is a series capacitive compensation which provides balanced compensation at the power frequency while it provides phase unbalance at other frequencies of oscillations. The compensation scheme is a combination of a single-phase Thyristor Controlled Series Capacitor (TCSC) or Static Synchronous Series Compensator (SSSC) and a fixed series capacitors in series in one phase of the compensated transmission line and fixed capacitors on the other two phases. The proposed scheme is economical compared to a full three-phase FACTS counterpart and improves reliability of the device by reducing number of switching components. The phase unbalance during transients reduces the coupling strength between the mechanical and the electrical system at asynchronous oscillations, thus suppressing the build-up of torsional stresses on the generator shaft systems. The SSR oscillations damping capability of the schemes is validated through detailed time-domain electromagnetic transient simulation studies on the IEEE first and second benchmark models. Furthermore, as the proposed schemes provide controllable reactance through TCSC or SSSC, the supplementary controllers can be implemented to damp low-frequency power oscillations as well. The low-frequency damping capability of the schemes is validated through detail time-domain electromagnetic transient simulation studies on two machines systems connected to a very large system and a three-area, six-machine power system. The simulation studies are carried out using commercially available electromagnetic transient simulation tools (EMTP-RV and PSCAD/EMTDC). An adaptive controller consisting of a robust on-line identifier, namely a robust Recursive Least Square (RLS), and a Pole-Shift (PS) controller is also proposed to provide optimal damping over a wide range of power system operations. The proposed identifier penalizes large estimated errors and smooth-out the change in parameters during large power system disturbances. The PS control is ideal for its robustness and stability conditions. The combination results in a computationally efficient estimator and a controller suitable for optimal control over wider range of operations of a non-linear system such as power system. The most important aspect of the controller is that it can be designed with an approximate linearized model of the complete power system, and does not need to be re-tuned after it is commissioned. The damping capability of such controller is demonstrated through detail studies on a three-area test system and on an IEEE 12-bus test system. Finally, the adaptive control algorithm is developed on a Digital Signal Processing Board, and the performance is experimentally tested using hardware-in-the-loop studies. For this purpose, a Real Time Digital Simulator (RTDS) is used, which is capable of simulating power system in real-time at 50 µs simulation time step. The RTDS facilitates the performance evaluation of a controller just like testing on a real power system. The experimental results match closely with the simulation results; which demonstrated the practical applicability of the adaptive controller in power systems. The proposed controller is computationally efficient and simple to implement in DSP hardware