Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

Power system security boundary visualization using intelligent techniques

In the open access environment, one of the challenges for utilities is that typical operating conditions tend to be much closer to security boundaries. Consequently, security levels for the transmission network must be accurately assessed and easily identified on-line by system operators;Security assessment through boundary visualization provides the operator with knowledge of system security levels in terms of easily monitorable pre-contingency operating parameters. The traditional boundary visualization approach results in a two-dimensional graph called a nomogram. However, an intensive labor involvement, inaccurate boundary representation, and little flexibility in integrating with the energy management system greatly restrict use of nomograms under competitive utility environment. Motivated by the new operating environment and based on the traditional nomogram development procedure, an automatic security boundary visualization methodology has been developed using neural networks with feature selection. This methodology provides a new security assessment tool for power system operations;The main steps for this methodology include data generation, feature selection, neural network training, and boundary visualization. In data generation, a systematic approach to data generation has been developed to generate high quality data. Several data analysis techniques have been used to analyze the data before neural network training. In feature selection, genetic algorithm based methods have been used to select the most predicative precontingency operating parameters. Following neural network training, a confidence interval calculation method to measure the neural network output reliability has been derived. Sensitivity analysis of the neural network output with respect to input parameters has also been derived. In boundary visualization, a composite security boundary visualization algorithm has been proposed to present accurate boundaries in two dimensional diagrams to operators for any type of security problem;This methodology has been applied to thermal overload, voltage instability problems for a sample system

Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%

A Wide Area Hierarchical Voltage Control for Systems with High Wind Penetration and an HVDC Overlay

The modern power grid is undergoing a dramatic revolution. On the generation side, renewable resources are replacing fossil fuel in powering the system. On the transmission side, an AC-DC hybrid network has become increasingly popular to help reduce the transportation cost of electricity. Wind power, as one of the environmental friendly renewable resources, has taken a larger and larger share of the generation market. Due to the remote locations of wind plants, an HVDC overlay turns out to be attractive for transporting wind energy due to its superiority in long distance transmission of electricity. While reducing environmental concern, the increasing utilization of wind energy forces the power system to operate under a tighter operating margin. The limited reactive capability of wind turbines is insufficient to provide adequate voltage support under stressed system conditions. Moreover, the volatility of wind further aggravates the problem as it brings uncertainty to the available reactive resources and can cause undesirable voltage behavior in the system. The power electronics of the HVDC overlay may also destabilize the gird under abnormal voltage conditions. Such limitations of wind generation have undermined system security and made the power grid more vulnerable to disturbances. This dissertation proposes a Hierarchical Voltage Control (HVC) methodology to optimize the reactive reserve of a power system with high levels of wind penetration. The proposed control architecture consists of three layers. A tertiary Optimal Power Flow computes references for pilot bus voltages. Secondary voltage scheduling adjusts primary control variables to achieve the desired set points. The three levels of the proposed HVC scheme coordinate to optimize the voltage profile of the system and enhance system security. The proposed HVC is tested on an equivalent Western Electricity Coordinated Council (WECC) system modified by a multi-terminal HVDC overlay. The effectiveness of the proposed HVC is validated under a wide range of operating conditions. The capability to manage a future AC/DC hybrid network is studied to allow even higher levels of wind

Data generation and model usage for machine learning-based dynamic security assessment and control

The global effort to decarbonise, decentralise and digitise electricity grids in response to climate change and evolving electricity markets with active consumers (prosumers) is gaining traction in countries around the world. This effort introduces new challenges to electricity grid operation. For instance, the introduction of variable renewable energy generation like wind and solar energy to replace conventional power generation like oil, gas, and coal increases the uncertainty in power systems operation. Additionally, the dynamics introduced by these renewable energy sources that are interfaced through converters are much faster than those in conventional system with thermal power plants. This thesis investigates new operating tools for the system operator that are data-driven to help manage the increased operational uncertainty in this transition. The presented work aims to an- swer some open questions regarding the implementation of these machine learning approaches in real-time operation, primarily related to the quality of training data to train accurate machine- learned models for predicting dynamic behaviour, and the use of these machine-learned models in the control room for real-time operation. To answer the first question, this thesis presents a novel sampling approach for generating ’rare’ operating conditions that are physically feasible but have not been experienced by power systems before. In so doing, the aim is to move away from historical observations that are often limited in describing the full range of operating conditions. Then, the thesis presents a novel approach based on Wasserstein distance and entropy to efficiently combine both historical and ’rare’ operating conditions to create an enriched database capable of training a high- performance classifier. To answer the second question, this thesis presents a scalable and rigorous workflow to trade-off multiple objective criteria when choosing decision tree models for real-time operation by system operators. Then, showcases a practical implementation for using a machine-learned model to optimise power system operation cost using topological control actions. Future research directions are underscored by the crucial role of machine learning in securing low inertia systems, and this thesis identifies research gaps covering physics-informed learning, machine learning-based network planning for secure operation, and robust training datasets are outlined.Open Acces

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system