Communications and control for electric power systems: Power system stability applications of artificial neural networks

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed

Forecasting hospital bed availability using computer simulation and neural networks

The success of hospitals in treating patients and staying in business relies on their efficient use of resources. In particular, the utilization of hospital beds is a critical concern, since over-crowding will result in delays or transfers of patients, and under-utilization will result in lost opportunity to treat patients and generate profit. To this end, hospital decision makers must have reliable forecasts of patient demand and bed availability. The objective of this thesis was to create a general method to forecast the availability of hospital beds in the short term, up to 2 days into the future. Specifically, this thesis employed a computer simulation model of the hospital and a time-dependent neural network to learn from the simulated model and forecast the availability of beds. The computer simulation model was found to be well suited to the task of describing a general hospital system and creating training data for a neural network. The neural network was found to provide accurate performance in predicting bed availability in the short term. The network incorporated the effect of time explicitly to capture the non-stationary behavior of hospital systems. These findings have a number of implications that will be discussed

Transient stability of power systems with non-linear load models using individual machine energy functions

Using the classical model of power system, Fouad and Vittal developed individual machine energy functions in order to identify the transient energy tending to separate a particular machine from the rest of the system(\u271);To improve the confidence in first swing transient stability assessment a more realistic model of the loads is used. In the proposed non-linear model, the loads are represented by any desired combination of constant impedance, constant current or constant-MVA models. The effect of the non-linear loads is reflected at the internal nodes of the generators as injected currents. The non-linear injected currents account for the sudden voltage changes immediately following the disturbance;Expressions for the individual machine energy functions, which represent the effects of the non-linear load models, are obtained. Using the criterion of stability suggested by Fouad and Vittal, first swing transient stability assessments are made for two multimachine test power networks. A number of cases, under a variety of load compositions, were tested. Analysis of the results obtained have demonstrated the effects of non-linear load representation on the energy absorbing capacity of the network, critical fault clearing time and the mode of instability. Comparisons have also been made between the critical fault clearing times obtained above and those;obtained using time solution such as Philadelphia Electric Co.\u27s transient stability program; (\u271)Vittal, V. Power System Transient Stability using the Critical Energy of Individual Machines. Ph.D. dissertation. Iowa State University, Ames, IA, 1982

"Enhancing Power System Stability with Fuzzy-PI Cascade Controllers and TCSC: A Comprehensive Study"

This study endeavours to develop cascade controllers employing Fuzzy-PI methodology to enhance power system stability, with a specific focus on generation control. The primary goal is to mitigate system instabilities and improve the reliability and performance of contemporary power grids. The efficacy of the Fuzzy-PI controller is assessed through extensive simulations involving significant fluctuations in wind energy generation, with comparative analysis against traditional PI controllers. Furthermore, the research emphasizes fortifying the stability of modern power systems by deploying optimized controllers, specifically targeting challenges related to frequency and voltage stability through the application of tailored controllers for Thyristor Controlled Series Capacitor (TCSC). The overarching objectives include enhancing both frequency and voltage stability through the utilization of TCSC units. Moreover, the research integrates hybrid optimization techniques, such as Improved meta-heuristic MPR-RSA, to fine-tune controller parameters, ensuring optimal performance across diverse operating conditions. The selection of controller gains relies on the integral absolute time error function, serving as the cost function for problem optimization

Optimizing the Load Frequency of a Two-Area Interlinked Power System using Artificial Intelligence Techniques

Power costs are increasing on a daily basis, generating changes in system frequency and causing serious concerns with system stability. It has become a major problem to offer customers with uninterrupted and high-quality power. To mitigate these issues, a linked power system's load distribution and network frequency should be constantly reviewed. Load frequency control adjusts the generator's energy output and tie line power between prescribed limits. As well as regulating generator output power, load frequency control also adjusts tie line power. The disturbance in the frequency due to different load changes is regulated using the proposed scheme. In this article, a two-area load frequency control system is constructed and evaluated using various control approaches, including a proportional integral derivative (PID) controller, a proportional integral (PI) controller, a fuzzy logic-based controller, and an Artificial Neural Network (ANN). The goal is to assess the power system's resilience under different loading conditions with these control schemes. The performance of the controllers is compared based on peak-undershoot, peak-overshoot, and settling time, focusing on tie line power and frequency response. To achieve this, the design is implemented using MATLAB/SIMULINK software

Innovative solutions for grids with high penetration of low-inertia variable renewable generation

Analisi di tecnologie alternative atte a migliorare la stabilità di frequenza in reti ad elevata penetrazione di rinnovabile: controllori di tipo SEBIR e VSM come possibili soluzioni al problema della riduzione dell'inerzia nella rete

Application of HVDC transmission within Nigerian transmission system: technical and economic evaluation

High Voltage Direct Current (HVDC) technology has recently emerged as a significant option in current power networks to address power transmission challenges. Therefore, this research project examines the potential impact of HVDC technology on the Nigerian transmission system. Based on the information provided by the Nigerian Electricity System Operator (NESO), a model of the 330 kV transmission network in Nigeria was created using DIgSILENT PowerFactory. Summer and winter load flow scenarios were examined to enhance the model's precision and consistency. The system performance was evaluated through stability and reliability studies, which helped identify suitable locations for HVDC links. The load point indices (LPIs) and system indices were used for the reliability evaluation, while the PV and QV analyses were used for the stability evaluation. An HVDC model was created and connected to the transmission network at two locations identified by the studies, Gombe and Yola buses. A comparison was conducted using two separate HVDC connections (Case 1: Azura-Gombe and Case 2: Azura-Yola) to identify the critical impact of HVDC on the system reliability and stability. The reliability and stability simulation showed that HVDC technology significantly boosts the reliability and stability of the transmission network and reduces energy losses. However, adding an HVDC link between Azura and Yola, Case 2, significantly improved overall performance, as evidenced by improving the system and load point indices and a better voltage profile and reactive power margin. The system indices, SAIDI improved by 0.07 h/yr, SAIFI by 0.19 interruption/yr, and ASAI enhanced by 0.11%, while the LPIs improved effectively by zeroing the Load Point Interruption Frequency (LPIF) and Load Point Interruption Time (LPIT). During the stability analysis, the loadability increased by 119% while the critical point improved by 0.061 pu/ -394 MVAR. Economically, the cost incurred in Case 1 is 79.17% lower than the base case even though new equipment (HVDC) is installed; this also applies to Case 2, which is 85% lower than the base case. Integrating HVDC technology could improve financial gain, considering improved energy supply, increased production, and general economic growth. This research has contributed significantly to revealed improvements in system and load indices, increased loadability, increased stability, and boosted financial gain with the HVDC connected

Topological changes in data-driven dynamic security assessment for power system control

The integration of renewable energy sources into the power system requires new operating paradigms. The higher uncertainty in generation and demand makes the operations much more dynamic than in the past. Novel operating approaches that consider these new dynamics are needed to operate the system close to its physical limits and fully utilise the existing grid assets. Otherwise, expensive investments in redundant grid infrastructure become necessary. This thesis reviews the key role of digitalisation in the shift toward a decarbonised and decentralised power system. Algorithms based on advanced data analytic techniques and machine learning are investigated to operate the system assets at the full capacity while continuously assessing and controlling security. The impact of topological changes on the performance of these data-driven approaches is studied and algorithms to mitigate this impact are proposed. The relevance of this study resides in the increasingly higher frequency of topological changes in modern power systems and in the need to improve the reliability of digitalised approaches against such changes to reduce the risks of relying on them. A novel physics-informed approach to select the most relevant variables (or features) to the dynamic security of the system is first proposed and then used in two different three-stages workflows. In the first workflow, the proposed feature selection approach allows to train classification models from machine learning (or classifiers) close to real-time operation improving their accuracy and robustness against uncertainty. In the second workflow, the selected features are used to define a new metric to detect high-impact topological changes and train new classifiers in response to such changes. Subsequently, the potential of corrective control for a dynamically secure operation is investigated. By using a neural network to learn the safety certificates for the post-fault system, the corrective control is combined with preventive control strategies to maintain the system security and at the same time reduce operational costs and carbon emissions. Finally, exemplary changes in assumptions for data-driven dynamic security assessment when moving from high inertia to low inertia systems are questioned, confirming that using machine learning based models will make significantly more sense in future systems. Future research directions in terms of data generation and model reliability of advanced digitalised approaches for dynamic security assessment and control are finally indicated.Open Acces