Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

Navigation of mobile robot in cluttered environment

Now a day’s mobile robots are widely used in many applications. Navigation of mobile robot is primary issue in robotic research field. The mobile robots to be successful, they must quickly and robustly perform useful tasks in a complex, dynamic, known and unknown surrounding. Navigation plays an important role in all mobile robots activities and tasks. Mobile robots are machines, which navigate around their environment extracting sensory information from the surrounding, and performing actions depend on the information given by the sensors. The main aim of navigation of mobile robot is to give shortest and safest path while avoiding obstacles with the help of suitable navigation technique such as Fuzzy logic. In this, we build up mobile robot then simulation and experiments are carried out in the lab. Comparison between the simulation and experimental results are done and are found to be in good

Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots

Navigational path planning problems of the mobile robots have received considerable attention over the past few decades. The navigation problem of mobile robots are consisting of following three aspects i.e. locomotion, path planning and map building. Based on these three aspects path planning algorithm for a mobile robot is formulated, which is capable of finding an optimal collision free path from the start point to the target point in a given environment. The main objective of the dissertation is to investigate the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach. Firstly, three different standalone computational intelligence approaches based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) algorithm and Invasive Weed Optimization (IWO) are presented to address the problem of path planning in unknown environments. Next two different hybrid approaches are developed using CS-ANFIS and IWO-ANFIS to solve the mobile robot navigation problems. The performance of each intelligent navigational controller is demonstrated through simulation results using MATLAB. Experimental results are conducted in the laboratory, using real mobile robots to validate the versatility and effectiveness of the proposed navigation techniques. Comparison studies show, that there are good agreement between them. During the analysis of results, it is noticed that CS-ANFIS and IWO-ANFIS hybrid navigational controllers perform better compared to other discussed navigational controllers. The results obtained from the proposed navigation techniques are validated by comparison with the results from other intelligent techniques such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and other hybrid algorithms. By investigating the results, finally it is concluded that the proposed navigational methodologies are efficient and robust in the sense, that they can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Decision Support System Approach for the Management of Complex Systems in Transportation and Logistics

L'analisi e la gestione dei sistemi complessi e delle loro ripercussioni in diversi aspetti della vita quotidiana sono tematiche che continuano ad attrarre molta attenzione nella letteratura scientifica. Si considerino, ad esempio, il trasporto marittimo e su strada, i moderni sistemi di assistenza sanitaria, le catene di distribuzione integrate, i processi industriali o, ancora, il nuovo paradigma di citt\ue0 intelligente: \ue8 evidente come in tutti questi contesti vi sia sempre pi\uf9 la necessit\ue0 di analizzare e gestire elementi eterogenei, collegati tra loro al fine di raggiungere un obiettivo comune altrimenti non realizzabile. Tuttavia, il processo decisionale in tali ambiti richiede competenze trasversali che abbracciano svariate discipline, rendendo la gestione di questi sistemi molto complessa e, spesso, inefficace. I Sistemi di Supporto alle Decisioni (DSS) ben si adattano alla previsione ed al controllo dei sistemi complessi grazie a: la loro capacit\ue0 di integrare varie fonti di dati ed informazioni; l'applicazione di modelli formali tipici di diverse discipline; la possibilit\ue0 di interagire costantemente con il sistema considerato. L'obiettivo di questo lavoro di tesi \ue8 quello di definire un approccio generale basato sul concetto di DSS per la gestione di sistemi complessi nel settore dei trasporti e della logistica, e di applicare tale approccio a tre problemi di grande interesse oggigiorno: 1) il problema della ricollocazione dei veicoli nei servizi di car sharing, 2) la gestione intelligente delle operazioni di carica dei veicoli elettrici presso le infrastrutture pubbliche e 3) l'ottimizzazione delle operazioni di drayage nel trasporto container. In particolare, il focus della ricerca \ue8 rivolto al cuore del DSS, ovvero alla parte che direttamente supporta il processo decisionale: i moduli di ottimizzazione e simulazione e le loro interazioni. Vengono considerati diversi approcci di modellazione, simulazione ed ottimizzazione, evidenziando il carattere totalmente generale dell' approccio considerato. I risultati ottenuti nelle diverse applicazioni sottolineano l'efficacia dei DSS nel migliorare il processo decisionale, portando ad un miglioramento generale delle prestazioni dei sistemi in esame. In particolare: 1) l'applicazione del DSS permette di ottimizzare i set-point per l'introduzione di un sistema di incentivi economici atto a risolvere il problema di ricollocazione dei veicoli nei servizi di car sharing, garantendo un miglioramento delle prestazioni del sistema, anche in condizioni di quasi saturazione; 2) il DSS permette la formalizzazione di un approccio leader-follower per il coordinamento delle operazioni di ricarica di veicoli elettrici che tenga conto contemporaneamente sia dei requisiti dell'utente che quelli della rete elettrica; infine, 3) il DSS consente di migliorare l'efficienza delle operazioni di drayage nel trasporto containter, riducendo i costi di trasporto.In recent years, the analysis and management of complex systems and their impacts in many aspects of the every-day life are topics that attract a lot of attention in the scientific literature. Consider for instance road and maritime transportation, modern healthcare systems, integrated supply chains, industrial processes or the new paradigm of smart cities: it is apparent that in all these contexts there is an increasing need of analysing and managing heterogeneous elements, networked together in order to reach a common goal otherwise not achievable. However, making decisions concerning such systems requires specific competences from many disciplines, leading to a very complex and often ineffective management process. Decision Support Systems (DSSs) can strengthen the capacity of predicting and controlling complex systems by integrating various sources of data and information, applying formal models typical of diverse and isolated disciplines and constantly interacting with the considered system. The goal of this work is to define a general approach based on the DSS concept for the management of complex systems in transportation and logistics and to apply it to three problems of great interest nowadays: 1) the user-based vehicle relocation problem} in car sharing services, 2) the smart management of electric vehicles charging operations and 3) the container drayage problem. In particular, the focus of the research is on the core of the DSS, i.e., on the part that directly supports the decision making process: optimization modules, simulation modules and their interactions. Different modelling, simulation and optimization approaches are applied, highlighting the generality of the considered approach regardless the specific context analysed. Results show the ability of DSSs to enhance the effectiveness of the decision process, thus leading to an improvement of the considered systems performance. In particular: 1) the application of the DSS allows to optimize the set-points of an incentive policy designed to solve the vehicle relocation problem in car sharing services, guaranteeing an effective relocation and improving the system performance even in the case of nearly saturated offer; 2) the DSS allows the formalization of a leader-follower approach for the coordination of electric vehicles charging operations which takes into account simultaneously electric grid and drivers requirements; finally, 3) the DSS allows to improve the efficiency of drayage operations in container transportation, reducing total transportation costs

Advanced Communication and Control Methods for Future Smartgrids

Proliferation of distributed generation and the increased ability to monitor different parts of the electrical grid offer unprecedented opportunities for consumers and grid operators. Energy can be generated near the consumption points, which decreases transmission burdens and novel control schemes can be utilized to operate the grid closer to its limits. In other words, the same infrastructure can be used at higher capacities thanks to increased efficiency. Also, new players are integrated into this grid such as smart meters with local control capabilities, electric vehicles that can act as mobile storage devices, and smart inverters that can provide auxiliary support. To achieve stable and safe operation, it is necessary to observe and coordinate all of these components in the smartgrid

Investigation of domestic level EV chargers in the Distribution Network: An Assessment and mitigation solution

This research focuses on the electrification of the transport sector. Such electrification could potentially pose challenges to the distribution system operator (DSO) in terms of reliability, power quality and cost-effective implementation. This thesis contributes to both, an Electrical Vehicle (EV) load demand profiling and advanced use of reactive power compensation (D-STATCOM) to facilitate flexible and secure network operation. The main aim of this research is to investigate the planning and operation of low voltage distribution networks (LVDN) with increasing electrical vehicles (EVs) proliferation and the effects of higher demand charging systems. This work is based on two different independent strands of research. Firstly, the thesis illustrates how the flexibility and composition of aggregated EVs demand can be obtained with very limited information available. Once the composition of demand is available, future energy scenarios are analysed in respect to the impact of higher EVs charging rates on single phase connections at LV distribution network level. A novel planning model based on energy scenario simulations suitable for the utilization of existing assets is developed. The proposed framework can provide probabilistic risk assessment of power quality (PQ) variations that may arise due to the proliferation of significant numbers of EVs chargers. Monte Carlo (MC) based simulation is applied in this regard. This probabilistic approach is used to estimate the likely impact of EVs chargers against the extreme-case scenarios. Secondly, in relation to increased EVs penetration, dynamic reactive power reserve management through network voltage control is considered. In this regard, a generic distribution static synchronous compensator (D-STATCOM) model is adapted to achieve network voltage stability. The main emphasis is on a generic D-STATCOM modelling technique, where each individual EV charging is considered through a probability density function that is inclusive of dynamic D-STATCOM support. It demonstrates how optimal techniques can consider the demand flexibility at each bus to meet the requirement of network operator while maintaining the relevant steady state and/or dynamic performance indicators (voltage level) of the network. The results show that reactive power compensation through D-STATCOM, in the context of EVs integration, can provide continuous voltage support and thereby facilitate 90% penetration of network customers with EV connections at a normal EV charging rate (3.68 kW). The results are improved by using optimal power flow. The results suggest, if fast charging (up to 11 kW) is employed, up to 50% of network EV customers can be accommodated by utilising the optimal planning approach. During the case study, it is observed that the transformer loading is increased significantly in the presence of D-STATCOM. The transformer loading reaches approximately up to 300%, in one of the contingencies at 11 kW EV charging, so transformer upgrading is still required. Three-phase connected DSTATCOM is normally used by the DSO to control power quality issues in the network. Although, to maintain voltage level at each individual phase with three-phase connected device is not possible. So, single-phase connected D-STATCOM is used to control the voltage at each individual phase. Single-phase connected D-STATCOM is able maintain the voltage level at each individual phase at 1 p.u. This research will be of interest to the DSO, as it will provide an insight to the issues associated with higher penetration of EV chargers, present in the realization of a sustainable transport electrification agenda

On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Ph.DDOCTOR OF PHILOSOPH

Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU