Optimal distributed generation planning based on NSGA-II and MATPOWER

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe UK and the world are moving away from central energy resource to distributed generation (DG) in order to lower carbon emissions. Renewable energy resources comprise a big percentage of DGs and their optimal integration to the grid is the main attempt of planning/developing projects with in electricity network. Feasibility and thorough conceptual design studies are required in the planning/development process as most of the electricity networks are designed in a few decades ago, not considering the challenges imposed by DGs. As an example, the issue of voltage rise during steady state condition becomes problematic when large amount of dispersed generation is connected to a distribution network. The efficient transfer of power out or toward the network is not currently an efficient solution due to phase angle difference of each network supplied by DGs. Therefore optimisation algorithms have been developed over the last decade in order to do the planning purpose optimally to alleviate the unwanted effects of DGs. Robustness of proposed algorithms in the literature has been only partially addressed due to challenges of power system problems such multi-objective nature of them. In this work, the contribution provides a novel platform for optimum integration of distributed generations in power grid in terms of their site and size. The work provides a modified non-sorting genetic algorithm (NSGA) based on MATPOWER (for power flow calculation) in order to find a fast and reliable solution to optimum planning. The proposed multi-objective planning tool, presents a fast convergence method for the case studies, incorporating the economic and technical aspects of DG planning from the planner‟s perspective. The proposed method is novel in terms of power flow constraints handling and can be applied to other energy planning problems

Modular Decomposition of Hierarchical Finite State Machines

In this paper we develop an analogue of the graph-theoretic `modular decomposition' in automata theory. This decomposition allows us to identify hierarchical finite state machines (HFSMs) equivalent to a given finite state machine (FSM). We provide a definition of a module in an FSM, which is a collection of nodes which can be treated as a nested FSM. We identify a well-behaved subset of FSM modules called thin modules, and represent these using a linear-space directed graph we call a decomposition tree. We prove that every FSM has a unique decomposition tree which uniquely stores each thin module. We provide an $O(n^2k)$ algorithm for finding the decomposition tree of an $n$-state $k$-alphabet FSM. The decomposition tree allows us to extend FSMs to equivalent HFSMs. For thin HFSMs, which are those where each nested FSM is a thin module, we can construct an equivalent maximally-hierarchical HFSM in polynomial time.Comment: 38 pages, 11 figures. Submitted to Theoretical Computer Scienc

Decentralized robust interval type-2 fuzzy model predictive control for Takagi–Sugeno large-scale systems

Here, decentralized robust interval type-2 (IT2) fuzzy model predictive control (MPC) for Takagi–Sugeno (T-S) large-scale systems is studied. The large-scale system consists of many IT2 fuzzy T–S subsystems. Important necessities that limit the practical application of MPC are the online computational cost and burden of the frameworks. For MPC of T–S fuzzy large-scale systems, the online computational burden is even worse, and in some cases, they cannot be solved timely. Especially for severe, large-scale systems with disturbances, the MPC of T–S fuzzy large-scale systems usually give a conservative solution. So, researchers have many challenges and in finding a reasonable solution in a short time. Although more comfortable results can be achieved by the proposed fuzzy MPC approach, which adopts T–S large-scale systems with nonlinear subsystems, many restrictions are not considered. In this paper, challenges are solved, and the MPC is designed for a nonlinear IT2 fuzzy large-scale system with uncertainties and disturbances. Besides, the online optimization problem is solved, and results are proposed. Consequently, the online computational cost of the optimization problem is reduced considerably. Finally, the effectiveness of the proposed algorithm is illustrated with two practical examples

Hierarchical Optimization-Based Model Predictive Control for a Class of Discrete Fuzzy Large-Scale Systems Considering Time-Varying Delays and Disturbances

Altres ajuts: Acord transformatiu CRUE-CSICIn this manuscript, model predictive control for class of discrete fuzzy large-scale systems subjected to bounded time-varying delay and disturbances is studied. The considered method is Razumikhin for time-varying delay large-scale systems, in which it includes a Lyapunov function associated with the original non-augmented state space of system dynamics in comparison with the Krasovskii method. As a rule, the Razumikhin method has a perfect potential to avoid the inherent complexity of the Krasovskii method especially in the presence of large delays and disturbances. The considered large-scale system in this manuscript is decomposed into several subsystems, each of which is represented by a fuzzy Takagi-Sugeno (TS) model and the interconnection between any two subsystems is considered. Because the main section of the model predictive control is optimization, the hierarchical scheme is performed for the optimization problem. Furthermore, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are studied. The linear matrix inequalities (LMIs) method is performed for our computations. So the closed-loop large-scale system is asymptotically stable. Ultimately, by two examples, the effectiveness of the proposed method is illustrated, and a comparison with other papers is made by remarks

Safe Learning of Linear Time-Invariant Systems

We consider safety in simultaneous learning and control of discrete-time linear time-invariant systems. We provide rigorous confidence bounds on the learned model of the system based on the number of utilized state measurements. These bounds are used to modify control inputs to the system via an optimization problem with potentially time-varying safety constraints. We prove that the state can only exit the safe set with small probability, provided a feasible solution to the safety-constrained optimization exists. This optimization problem is then reformulated in a more computationally-friendly format by tightening the safety constraints to account for model uncertainty during learning. The tightening decreases as the confidence in the learned model improves. We finally prove that, under persistence of excitation, the tightening becomes negligible as more measurements are gathered.Comment: Accepted in NeurIPS 2021 Workshop on Safe and Robust Control of Uncertain System

Nonlinear Pseudo State-Feedback Controller Design for Affine Fuzzy Large-Scale Systems with H∞ Performance

Acord transformatiu CRUE-CSICThis paper treats robust controller design for Affine Fuzzy Large-Scale Systems (AFLSS) composed of Takagi-Sugeno-Kang type fuzzy subsystems with offset terms, disturbances, uncertainties, and interconnections. Instead of fuzzy parallel distributed compensation, a decentralized nonlinear pseudo state-feedback is developed for each subsystem to stabilize the overall AFLSS. Using Lyapunov stability, sufficient conditions with low codemputational effort and free gains are derived in terms of matrix inequalities. The proposed controller guarantees asymptotic stability, robust stabilization, and H∞ control performance of the AFLSS. A numerical example is given to illustrate the feasibility and effectiveness of the proposed approach

Development of a stability-indicating high performance liquid chromatography method for assay of erythromycin ethylsuccinate in powder for oral suspension dosage form

In this study an effective method was developed to assay erythromycin ethylsuccinate for an oral suspension dosage form. The chromatographic separation was achieved on an X-Terra[superscript ™] C[subscript 18] analytical column. A mixture of acetonitrile–ammonium dihydrogen phosphate buffer (0.025 mol L[superscript -1]) (60:40, V/V) (pH 7.0) was used as the mobile phase, effluent flow rate monitored at 1.0 mL min[superscript −1], and UV detection at 205 nm. In forced degradation studies, the effects of acid, base, oxidation, UV light and temperature were investigated showing no interference in the peak of drug. The proposed method was validated in terms of specificity, linearity, robustness, precision and accuracy. The method was linear at concentrations ranging from 400 to 600 μg mL[superscript −1], precise (intra- and inter-day relative standard deviations <0.65), accurate (mean recovery; 99.5%). The impurities and degradation products of erythromycin ethylsuccinate were selectively determined with good resolution in both the raw material and the final suspension forms. The method could be useful for both routine analytical and quality control assays of erythromycin ethylsuccinate in commercial powder for an oral suspension dosage form and it could be a very powerful tool to investigate the chemical stability of erythromycin ethylsuccinate.Chemi Darou Industrial Compan