Online monitoring of voltage stability margin using PMU measurements

With the growing smart grid concept it becomes important to monitor health of the power system at regular intervals for its secure and reliable operation. Phasor Measurement Units (PMUs) may play a vital role in this regard. This paper presents voltage stability monitoring in real time framework using synchrophasor measurements obtained by PMUs. Proposed approach estimates real power loading margin as well as reactive power loading margin of most critical bus using PMU data. As system operating conditions keep on changing, loading margin as well as critical bus information is updated at regular intervals using fresh PMU measurements. Simulations have been carried out using Power System Analysis Toolbox (PSAT) software. Accuracy of proposed Wide Area Monitoring System (WAMS) based estimation of voltage stability margin has been tested by comparing results with loading margin obtained by continuation power flow method (an offline approach for accurate estimation of voltage stability margin) under same set of operating conditions. Case studies performed on IEEE 14-bus system, New England 39-bus system and a practical 246-bus Indian power system validate effectiveness of proposed approach of online monitoring of loading margin

‘Optimulation’ in Chemical Reaction Engineering: The Oxidative Coupling of Methane as a Case Study

The optimization of reacting systems, including chemical, biological, and macromolecular reactions, is of great importance from both theoretical and practical standpoints. Even though several classical deterministic and stochastic modeling and simulation approaches have been routinely examined to understand and control reacting systems from lab- to industrial-scales, almost all tackling the same problem, i.e., how to predict reaction outputs from any given set of reaction input variables. Development and application of an effective and versatile mathematical tool capable of appropriately connecting preset desired reaction outputs to corresponding inputs have always been the ideal goal for experts in the related fields. Hence, there definitely exists the need to predict a priori optimum reaction conditions in a computationally-demanding multi-variable space for both keeping the chemical and biological reactions in optimal conditions and at the same time satisfying preset desired targets. As a novel and powerful solution, we hereby introduce a robust and functional computational tool capable of simultaneously simulating and optimizing, i.e. ‘optim-ulating’ intricate chemical, biological, and macromolecular reactions via the amalgamation of the Kinetic Monte Carlo (KMC) simulation approach and the multi-objective version of Genetic Algorithms (NSGA-II). The synergistic interplay of KMC and NSGA-II for the optimulation of Oxidative Coupling of Methane (OCM) as an example of a challenging chemical reaction engineering system has clearly demonstrated the outstanding capabilities of the proposed method. Undoubtedly, the proposed novel hybridized technique is very powerful and can address a variety of unsolved optimization questions in chemical, biological, and macromolecular reaction engineering

Power demand and supply allocation using inherent structural theory of network systems and voltage stability index based on multi-bus reactive power loading.

Masters Degree. University of KwaZulu- Natal, Durban.Power availability is a crucial factor in determining new load centers. There is a need for adequate power reserve and maximum load capacity allocation to ensure continued power demand and supply electrical systems. In modern interconnected power systems, a high peak load power demand is met by the contribution of the available generator units. There is an urgency to solve the challenges arising from interconnected network configurations such as the loss of generation, inadequate supply capacity to meet load demand during peak time, transmission losses and significant voltage drop at the heavily loaded buses. This dissertation investigates the influence of inter-connected load buses on the system’s voltage profile and the electrical proximity from generation sites to load centers as captured by the Y-admittance matrix. The inherent structural theory of networks was used in determining the required power reserve and load capacity allocation using the ideal generator contribution index. PowerWorld simulator, Dig SILENT Power Factory and MATLAB were used as simulation and presentation tools for the modified IEEE 14 bus system and the Southern Indian 10 bus system. From the analysis of the results, much of the load capacity needed for electrical load growth is feasible for the bus that is most electrically proximal to a high-rated power source. The use of the ideal generator contribution index exploits the structural properties of the network. That being the case, its advantages include minimum expansion of existing structures and minimal transmission active power losses. Also, in this dissertation, a V-Q curve characteristic approach was used to identify the weak load buses in an interconnected power system. This was done by simulating uniformly distributed multi-bus loading conditions and the conventional analysis of the sole bus loading method in a power network system up to the minimum acceptable per unit voltage point. This lead to the formulation of a novel V-Q curve-based index. The voltage critical multi-bus index is a variable state-based index. This index was compared with the self-sensitivity index of the reduced Jacobian matrix and the ‘load structural electrical attraction region’ index of the inherent structural theory of power networks, giving a deeper insight into the system characteristics under light and heavy loading states

Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

Voltage instability or voltage collapse, observed in many blackout events, poses a significant threat to power system reliability. To prevent voltage collapse, the countermeasures suggested by the post analyses of the blackouts usually include the adoption of better online voltage stability monitoring and control tools. Recently, the variability and uncertainty imposed by the increasing penetration of renewable energy further magnifies this need. This work investigates the methodologies for online voltage stability margin (VSM) monitoring and control in the new era of smart grid and big data. It unleashes the value of online measurements and leverages the fruitful results in machine learning and demand response. An online VSM monitoring approach based on local regression and adaptive database is proposed. Considering the increasing variability and uncertainty of power system operation, this approach utilizes the locality of underlying pattern between VSM and reactive power reserve (RPR), and can adapt to the changing condition of system. LASSO (Least Absolute Shrinkage and Selection Operator) is tailored to solve the local regression problem so as to mitigate the curse of dimensionality for large-scale system. Along with the VSM prediction, its prediction interval is also estimated simultaneously in a simple but effective way, and utilized as an evidence to trigger the database updating. IEEE 30-bus system and a 60,000-bus large system are used to test and demonstrate the proposed approach. The results show that the proposed approach can be successfully employed in online voltage stability monitoring for real size systems, and the adaptivity of model and data endows the proposed approach with the advantage in the circumstances where large and unforeseen changes of system condition are inevitable. In case degenerative system conditions are identified, a control strategy is needed to steer the system back to security. A model predictive control (MPC) based framework is proposed to maintain VSM in near-real-time while minimizing the control cost. VSM is locally modeled as a linear function of RPRs based on the VSM monitoring tool, which convexifies the intricate VSM-constrained optimization problem. Thermostatically controlled loads (TCLs) are utilized through a demand response (DR) aggregator as the efficient measure to enhance voltage stability. For such an advanced application of the energy management system (EMS), plug-and-play is a necessary feature that makes the new controller really applicable in a cooperative operating environment. In this work, the cooperation is realized by a predictive interface strategy, which predicts the behaviors of relevant controllers using the simple models declared and updated by those controllers. In particular, the customer dissatisfaction, defined as the cumulative discomfort caused by DR, is explicitly constrained in respect of customers\u27 interests. This constraint maintains the applicability of the control. IEEE 30-bus system is used to demonstrate the proposed control strategy. Adaptivity and proactivity lie at the heart of the proposed approach. By making full use of real-time information, the proposed approach is competent at the task of VSM monitoring and control in a non-stationary and uncertain operating environment