A data analytic approach to automatic fault diagnosis and prognosis for distribution automation

Distribution Automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as ‘pick-up activity’. This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This paper details the design of a novel decision support system to achieve fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with rule-based, data mining and clustering techniques to deliver the diagnostic and prognostic functions. These are applied to 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) as provided by a leading UK network operator. This novel automated analysis system diagnoses the nature of a circuit’s previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault. The novel contributions include the tackling of ‘semi-permanent faults’ and the re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios

Power Quality Concerns in Implementing Smart Distribution-Grid Applications

This paper maps the expected and possible adverse consequences for power quality of introducing several smart distribution-grid technologies and applications. The material presented in this paper is the result of discussions in an international CIGRE-CIRED joint working group. The following technologies and applications are discussed: 1) microgrids; 2) advanced voltage control; 3) feeder reconfiguration; and 4) demand-side management. Recommendations are given based on the mapping

Tuningless Load Frequency Control Through Active Engagement of Distributed Resources

The increasing share of volatile and inverter-based energy sources render electric power grids increasingly susceptible to disturbances. Established Load Frequency Controls (LFCs) schemes are rigid and require careful tuning, making them unsuitable for dynamically changing environments. In this paper, we present a fast and tuningless frequency control approach that tackles these shortcomings by means of modern grid monitoring and communications infrastructures in a two-fold concurrent process. First, direct observation of supply and demand enables fast power balancing decoupled from the total system dynamics. Second, primary resources are actively involved in frequency restoration by systematic adjustment of their frequency reference setpoints. In contrast to the commonly used Automatic Generation Control (AGC), the proposed Direct Load Frequency Control (DLFC) does not require an integrator for frequency control in the closed loop even under partial grid observability. The approach is Lyapunov-stable for a wide range of system parameters, including ramping limits of controlled resources. A performance study against AGC has been conducted on a three area power system in simulations as well as in a real laboratory grid with an installed generation capacity of 110kW

Investigate Centralized and Decentralized Information Infrastructure for Future Electricity Market

The power grid is undergoing a transformation from a monopolized control system to a more decentralized one. Distributed renewable energy generation, responsive loads, and distribution automation are posing a new challenge to the traditional centralized control method. To address these challenges, we propose two innovative centralized and decentralized solutions for the information infrastructure of the future electricity market. For the centralized approach, we investigate the applications of an open-source control system platform VOLTTRON in the areas of building control and electric vehicle charging. For the case study, we implement the VOLTTRON platform to solve the economic dispatch (ED) problem. The VOLTTRON platform is used as a central message bus and 16 single-board computers are used to simulate distributed generators and dispatchable loads. For the decentralized approach, we propose an innovative Bitcoin-style distributed transactional model “Bit-Energy” using radically different Internet-of-Things technologies (Blockchain and Ethereum’s smart contract). “Bit-Energy” enables transparent, auditable, and peer-to-peer energy transactions between active market participants. We implement a highly efficient buyer/seller matching algorithm. Case studies demonstrate the accuracy, robustness, effectiveness, and scalability of the proposed Bit-Energy platform under various operating conditions.Master of Science in EngineeringElectrical Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/136613/1/MS Thesis JingweiLuo - 11F.pdfDescription of MS Thesis JingweiLuo - 11F.pdf : Thesi

Probabilistic hosting capacity and risk analysis for distribution networks

Hereby I present a PhD thesis by publications. Altogether, the thesis includes: a) two journal papers, b) three IEEE conference papers. The journals include IEEE Transactions on Industrial Informatics while the second has been submitted. The conference list includes World Renewable Energy Congress (WREC), Asian conference on energy, power and transportation electrification (ACEPT) and IEEE Conference on Probabilistic Methods Applied in Power Systems (PMAPS). The PMAPS conference is the only event that exclusively discusses probability and statistic methods applied to power system analysis. The thesis presents several novel methods. The first novelty is the development of a new probabilistic model for estimating the solar radiation incident to residential roofs which is compatible with the Australian meteorological conditions. The second is the development of new probabilistic approach called “probabilistic hosting capacity” to estimate the hosting capacity of distribution networks. The third one is the utilization of sparse grid numerical approximation techniques in handling the uncertainty computations. The last contribution is the new assessment method for quantifying the risk of connecting a large number of correlated distributed generators (DGs) into the distribution networks. In glance, these contributions are highlighted in the following paragraphs. The development of the probabilistic method to estimate the solar irradiation is aimed to represent the uncertainty of produced power from residential solar panels. By utilizing the relation between clearness index and diffuse fraction, a probability density function (PDF) of produced power is derived from the total radiance quantity incident of a tilted area to the horizontal plane. Given the characteristics of the day time and the place, the uncertainty associated with power production by solar panels can be probabilistically estimated from the total solar irradiation of a tilted area. Two mathematical models are proposed: the first utilizes the HDKR (Hay, Davies, Klucher, Reindl) mathematical representation for total irradiance, while the second one involves the use of Hay-Davies mathematical representation. Without losing the scope of the work, only the first model is compared with real data obtain from a site in Adelaide. The second model is used for conducting the power flow calculations due to the low computational time is required to deliver results. The interest in the development of probabilistic hosting capacity comes as DGs in the distribution networks rely mainly on the renewable energy. Probabilistic hosting capacity is aimed to deliver a probabilistic estimate of the maximum amount of DGs that can be connected into the existing distribution network without jeopardizing the utility’s system operation and/or customers’ connected appliances. The approach is built up after defining the main uncertainties, resulted from the stochastic behaviours of the small-scale of wind turbines and solar panels as well as domestic loads. The impacts of these uncertainties on the operation of a distribution network are assessed by establishing a set of operational performance indices and the use of the probability of occurrence notion. Three types of hazardous impacts are defined (tolerable, critical and serious). The approach is time-dependent and includes network bi-directionality feature which complies with the fundamentals of automation approaches for active distribution networks. The third contribution is the use of sparse grid numerical techniques (SGTs) as an efficient tool to handle the uncertainty computation which is multi-dimensional problem. It replaces the use of classical numerical techniques based on tensor product grids which suffers from the curse of dimensionality. Additionally, the SGT in comparison with Monte Carlo Technique (MCT) is able to achieve improved efficiency in computation with acceptable accuracy. The last contribution is the development of a new risk analysis approach to quantify the effect of increasing levels of DG penetration on distribution networks. The proposed novel analysis utilises the following techniques and concepts: the Nataf transformation to represent spatial correlation of the DGs connected in the same distribution network; the consideration of likelihood (relative frequency of event occurrence) as well as severity (accumulative depth of event occurrence) of the performance indices in assessing the operation of distribution networks with the increase of DG connections. The Nataf transformation was used to ensure the rank correlation modelling among the non-Gaussian uncertainty representations in which the inter-dependences are modelled. The risk components, likelihood and severity, are visualized along with the increase of correlated DG connections. The purpose of this analysis is to provide an estimate of degree of risk in assessing the operational performance of a distribution network as whole, instead of the traditional methods that assess the network by parts, such as assessing individually a line or bus. The effectiveness of developed methods in this thesis is demonstrated by performing tests on two actual distribution networks: small and large. The small network consists of 11 buses with one substation transformer; while the existing large distribution network, situated in South Australia, consists of 59 (11/0.4 kV) feeder-transformers serving commercial, residential and industrial loads. The large network is segmented into different zones according to their likelihood of having DGs. The results are visualized, analysed and discussed for each proposed methods or approaches. All system modelling and algorithms are performed using MATLAB software and implemented on the distribution networks modelled in the industry accepted software OpenDSS, introduced by Electrical Power Research Institute (EPRI).Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Load Modeling and Evaluation of LEDs for Hardware Test Bed Application

The lighting industry was revolutionized with the emergence of LED lighting. Over the last 15 years, LED lighting device sales and utilization have grown immensely. The growth and popularity of LEDs is due to improved operation of the device when compared to previous lighting technologies. Efficient performance of the device is critical due to the growth of global energy consumption. As nonrenewable generation fuel is finite, utilities have begun the transition to renewable energy generation. Generation and distribution systems become inherently complex to comprehend and maintain with incorporation of emerging supply and load technologies. With the unprecedented growth of LED bulbs, there are concerns regarding the impact of their integration on power systems. In determination of the effects, which LED bulb adoption posed within the power grid, investigation of this device as a grid-load was pursued. This thesis reviews existing studies pertaining to LEDs and power grid load modeling methodologies. Load modeling aids in establishing a balance between energy generation and consumption, comprehensively characterizing relationships between electrical generation, transmission, distribution, and loads. Due to the complexities of large networked systems, device load models are constructed and aggregated in emulation of the interactive relationships throughout the power grid. This thesis includes a study of preestablished LED bulb ZIP load models and formulation of a component-based load model for improved characterization of a conventional LED lighting device. Load modeling was conducted with reference to the UTK HTB, for future integration and improved grid emulation. Factors, such as shape, size, illumination, and the power rating of popular LED bulbs is examined. Through investigation of typical LED bulb topologies, a model is formulated, in representation of device behavior as a load. The established load model’s characteristics are tested with comparison to physical device operation in a laboratory environment. The LED bulb component-based model is simulated under dynamic conditions in portrayal of device behavior under fault scenarios. An interactive interface is formulated for simulation of load behavior throughout grid level events. Detailed analysis of data and methods of implementation is provided, in characterization of the LED bulb’s load profile

City-Friendly Smart Network Technologies and Infrastructures: The Spanish Experience

Efficient, resilient, and sustainable electricity delivery is a key cornerstone in increasingly large and complex urban environments, where citizens expect to keep or rise their living standards. In this context, cost-effective and ubiquitous digital technologies are driving the transformation of existing electrical infrastructures into truly smart systems capable of better providing the services a low-carbon society is demanding. The goal of this paper is twofold: 1) to review the dramatically evolving landscape of power systems, from the old framework based on centralized generation and control, aimed at serving inelastic customers through alternating current (ac) transmission networks and one-way distribution feeders, to a new paradigm centered mainly around two main axes: renewable generation, both centralized and distributed, and active customers (prosumers), interacting with each other through hybrid ac/dc smart grids; 2) to illustrate, through featured success stories, how several smart grid concepts and technologies have been put into practice in Spain over the last few years to optimize the performance of urban electrical assets