An effective optimisation method for multifactor and reliability-related structural design problems

This thesis first presents a systematic design procedure which satisfies the required strength and stiffness, and structural mass for conceptual engineering structural designs. The procedure employs a multi-objective and multi-disciplinary (MO–MD) optimisation method (multifactor optimisation of structure techniques, MOST) which is coupled with finite element analysis (FEA) as an analysis tool for seeking the optimum design. The effectiveness of the MOST technique is demonstrated in two case studies.Next, a reliability-related multi-factor optimisation method is proposed and developed, representing a combination of MOST (as a method of multi-factor optimisation) and the reliability-loading case index (RLI) (as a method of calculating the reliability index). The RLI is developed based on a well-known reliability method: the first-order reliability method (FORM). The effectiveness and robustness of the proposed methodology are demonstrated in two case studies in which the method is used to simultaneously consider multi-objective, multi-disciplinary, and multi-loading-case problems. The optimised designs meet the targeted performance criteria under various loading conditions.The results show that the attributes of the proposed optimisation methods can be used to address engineering design problems which require simultaneous consideration of multi-disciplinary problems. An important contribution of this study is the development of a conceptual MO–MD design optimisation method, in which multi-factor structural and reliability design problems can be simultaneously considered

A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

Reliability assessment for electrical power generation system based on advanced Markov process combined with blocks diagram

This paper presents the power generation system reliability assessment using an advanced Markov process combined with blocks diagram technique. The effectiveness of the suggested methodology is based on HL-I of IEEE_EPS_24_bus. The proposed method achieved the generation reliability and availability of an electrical power system using the Markov chain which based on the operational transition from state to state which represented in matrix. The proposed methodology has been presented for reliability performance evaluation of IEEE_EPS_24_bus. MATLAB code is developed using Markov chain construction. The transition between probability states is represented using changing the failure and repair rates. The reduced number of generation system are used with Markov process to assess the availability, unavailability, and reliability for the generation system. Additionally, the proposed technique calculates the frequency, time duration of states, the probability of generation capacity state which get out of service or remained in service for each state of failure, and reliability indices. A considerable improvement in reliability indices is found with using blocks diagram technique which is used to reduce the infinity number of transition states and assess the system reliability. The proposed technique succeeded at achieving accurate and faster reliability for the power system

Bridging Machine Learning for Smart Grid Applications

This dissertation proposes to develop, leverage, and apply machine learning algorithms on various smart grid applications including state estimation, false data injection attack detection, and reliability evaluation. The dissertation is divided into four parts as follows.. Part I: Power system state estimation (PSSE). The PSSE is commonly formulated as a weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, zero-emission technologies (e.g., electric vehicles), and demand response programs. Efficient approaches for PSSE are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. The first part of this dissertation develops a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and a multivariate-linear regressor as a meta-learner. Historical measurements and states are utilized to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data acquisition. This work adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. Part II: Cyber-attacks on Voltage Regulation. Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. The second part of this dissertation develops a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in real-time. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system.Part III: Cyber-attacks on Distributed Generators. Part III of the dissertation proposes a deep learning-based multi-label classification approach to detect coordinated and simultaneously launched data falsification attacks on a large number of distributed generators (DGs). The proposed approach is developed to detect power output manipulation and falsification attacks on DGs including additive attacks, deductive attacks, and combination of additive and deductive attacks (attackers use the combination of additive and deductive attacks to camouflage their attacks). The proposed approach is demonstrated on several systems including the 240-node and IEEE 123-node distribution test system. Part IV: Composite System Reliability Evaluation. Traditional composite system reliability evaluation is computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been utilized only to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In the fourth part of this dissertation, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF. Unavoidable load curtailments due to failures are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test System and Saskatchewan Power Corporation in Canada

Power system reliability enhancement with reactive power compensation and operational risk assessment with smart maintenance for power generators

Modern power systems incorporate advanced contingency measures with the aim of enhancing system performance. Among them, the strategical installation of reactive power compensators into a power system is commonly practised to minimize power losses and improve system reliability. Such a practice requires a robust optimization technique that could reduce the computational burden and provide optimal planning and operation of the compensators. This thesis proposes an advanced optimization technique, named as Accelerated Quantum Particle Swarm Optimization (AQPSO) to determine the optimal placement, sizing and dispatch strategy of the reactive power compensators with the aim of improving the system level reliability. The uniqueness of the technique is the incorporation of the concept ‘best observation’, which accelerates the search towards the optimal solution. The implementation of advanced maintenance strategies is another common contingency measure used to enhance system performance. In this context, this thesis proposes a novel Smart Maintenance (SM) strategy for power generators that maximize the generation adequacy and provide increased economic benefits in a framework of system reliability. The uniqueness of the SM approach is the incorporation of the ‘obsolescence’ state through the stages of the bathtub curve and half-arch shape to model the aging process and then quantify the operational risk of the generators using fuzzy logic theory. Further, SM combines the proposed AQPSO and Sequential Median Latin Hypercube to obtain a comprehensive maintenance schedule. The investigation presented in this thesis contributes with novel AQPSO-based algorithms to enhance power system reliability with the operation of reactive power compensation; a more realistic and accurate aging reliability model of power generators; a detailed SM mathematical framework and an algorithm for the scheduling of proactive maintenance of generators of small and large-power systems. The proposed models are significant in the journey to the smart operation of a power system with diverse levels of applications

New Approach for Stochastic Modelling of Microgrid Containing CHP

The focus in this article is on long term load forecasting and expansion generation planning of the microgrids containing multiple load type consumption. The novel approach of planning cogeneration systems by introducing the function of simultaneous heat and electricity duration is presented. The function is extended form of load duration curve in such a way as to capture the simultaneity of different type of energy load. The paper contains seven chapters. In the second chapter the load forecasting methods are summarized. In continuation as an introduction to the main goal of this article, definition, application and restriction of load duration curve is presented. In the fourth chapter, some outlines about cogeneration system planning are given. The new approach for planning of microgrid contacting CHP based on probabilistic approach is presented in the fifth chapter. In the sixth chapter are demonstrated some application functions of simultaneous heat and electricity duration as a tool for planning multi energy systems

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes