Power system security enhancement by HVDC links using a closed-loop emergency control

In recent years, guaranteeing that large-scale interconnected systems operate safely, stably and economically has become a major and emergency issue. A number of high profile blackouts caused by cascading outages have focused attention on this issue. Embedded HVDC (High Voltage Direct Current) links within a larger AC power system are known to act as a “firewall” against cascading disturbances and therefore, can effectively contribute in preventing blackouts. A good example is the 2003 blackout in USA and Canada, where the Québec grid was not affected due to its HVDC interconnection. In the literature, many works have studied the impact of HVDC on the power system stability, but very few examples exist in the area of its impact on the system security. This paper presents a control strategy for HVDC systems to increase their contribution to system security. A real-time closed-loop control scheme is used to modulate the DC power of HVDC links to alleviate AC system overloads and improve system security. Simulations carried out on a simplified model of the Hydro-Québec network show that the proposed method works well and can greatly improve system security during emergency situations.Peer reviewedFinal Accepted Versio

Distribution network development planning with quality of supply (QOS) costing

Includes bibliographical references.The report outlines details of research in distribution network development with consideration of costs due to quality. Network planning methods are diverse with the common objective of establishing minimum cost options without violating network constraints. The selected network alternative is directed to meet customer requirements. Network planning models have evolved from consideration of simplistic models to multi variable and more realistic approaches. It is not always possible to achieve the desired outcome because planning is a difficult and complex task. There are usually uncertainties due to vague or no information available about the long-term (15-20 years) planning. The uncertainties generally result in risks, which have to be sufficiently analysed before reaching planning decisions. The recently proposed Minimum Risk Criterion is not a preferred risk resolution approach because it suggests that utilities should not establish expensive networks due to cost risk. Uncertainty modeling approaches based on fuzzy logic are proposed as the solution for analysis of uncertain conditions where very limited information is available. Costs in distribution lines are usually due to capital investment and operating costs. Distribution capital costs are primarily due to cost of conductor, s ucture and insulator. The cost of conductor and structure varies with size and type. Insulator costs do not vary significantly with variations in insulator type and properties. Quality related costs are a relatively new concept in distribution costing and are developed in the research. They are primarily due to mitigation, condition monitoring and interruptions. Quality mitigation costs are defined in the mitigation cost models in Figure 4- 8 and Figure 4- 9. The impact cost values in the models were established on the basis of assumptions, which require further research. According to CTLab [12], quality-monitoring equipment costs could vary from R50, 000 to R250, 000. Interruption costs are incurred through penalty cost and revenue losses. The penalty cost is similar to the revenue loss cost in many respects but is incurred when the standard limits are violated. Revenue loss costs are applicable whenever the frequency or voltage deviates from the nominal. It may be preferred to accept revenue losses where mitigation is expensive

Enhancing Grid Reliability With Phasor Measurement Units

Over the last decades, great efforts and investments have been made to increase the integration level of renewable energy resources in power grids. The New York State has set the goal to achieve 70% renewable generations by 2030, and realize carbon neutrality by 2040 eventually. However, the increased level of uncertainty brought about by renewables makes it more challenging to maintain stable and robust power grid operation. In addition to renewable energy resources, the ever-increasing number of electric vehicles and active loads have further increased the uncertainties in power systems. All these factors challenge the way the power grids are operated, and thus ask for new solutions to maintain stable and reliable grids. To meet the emerging requirements, advanced metering infrastructures are being integrated into power grids that transform traditional grids into \u27\u27 smart grids . One example is the widely deployed phasor measurement units (PMUs), which enable generating time-synchronized measurements with high sampling frequency, and pave a new path to realize real-time monitoring and control in power grids. However,the massive data generated by PMUs raises the questions of how to efficiently utilize the obtained measurements to understand and control the present system. Additionally, to meet the communication requirements between the advanced meters, the connectivity of the cyber layer has become more sophisticated, and thus is exposed to more cyber-attacks than before. Therefore, to enhance the grid reliability with PMUs, robust and efficient grid monitoring and control methods are required. This dissertation focuses on three important aspects of improving grid reliability with PMUs: (1) power system event detection; (2) impact assessment regarding both steady-state and transient stability; and (3) impact mitigation. In this dissertation, a comprehensive introduction of PMUs in the wide-area monitoring system, and comparisons with the existing supervisory control and data acquisition (SCADA) systems are presented first. Next, a data-driven event detection method is developed for efficient event detection with PMU measurements. A text mining approach is utilized to extract event oscillation patterns and determine event types. To ensure the integrity of the received data, the developed detection method is further designed to identify the fake events, and thus is robust against cyber-threat. Once a real event is detected, it is critical to promptly understand the consequences of the event in both steady and dynamic states. Sometimes, a single system event, e.g., a transmission line fault, may cause subsequent failures that lead to a cascading failure in the grid. In the worst case, these failures can result in large-scale blackouts. To assess the risk of an event in steady state, a probabilistic cascading failure model is developed. With the real-time phasor measurements, the failure probability of each system component at a specific operating condition can be predicted. In terms of the dynamic state, a failure of a system component may cause generators to lose synchronism, which will damage the power plant and lead to a blackout. To predict the transient stability after an event, a predictive online transient stability assessment (TSA) tool is developed in this dissertation. With only one sample of the PMU voltage measurements, the status of the transient stability can be predicted within cycles. In addition to the impact detection and assessment, it is also critical to identify proper mitigations to alleviate the failures. In this dissertation, a data-driven model predictive control strategy is developed. As a parameter-based system model is vulnerable to topology errors, a data-driven model is developed to mimic the grid behavior. Rather than utilizing the system parameters to construct the grid model, the data-driven model only leverages the received phasor measurements to determine proper corrective actions. Furthermore, to be robust against cyber-attacks, a check-point protocol, where past stored trustworthy data can be used to amend the attacked data, is utilized. The overall objective of this dissertation is to efficiently utilize advanced PMUs to detect, assess, and mitigate system failure, and help improve grid reliability

Optimization of Islanded Microgrid Operation

Presently a lot of effort is being deployed in the area of microgrid development. In this aspect, the work presented here is in the direction of developing and coordinating various operational modules in an isolated microgrid system. The work presented in this report looks at the prospects of incorporating a consumer side load-scheduling algorithm that works in conjunction with the unit commitment and economic load dispatch. The unit commitment and economic load dispatch are run a day in advance to determine generator outputs for the following day. From the microgrid operator point of view, the load side scheduling helps reduce the stress on the system especially during peak hours thereby ensuring system stability and security. From the consumers’ point of view, the dynamic electricity prices within a day, which are a reflection of this time varying stress on the system, encourage them to endorse such a scheme and reduce their bills incurred. Owing to unpredictable weather conditions, running unit commitment and economic load dispatch in advance does not guarantee planned real-time generation in the microgrid scenario. Such variability in forecasted generation must be handled in any microgrid, while accounting for load demand uncertainties. To address this issue a load side energy management system and power balance scheme is proposed in this paper. The objective is to ascertain uninterrupted power to critical loads while managing other non-critical loads based on their priorities

Assessment and Enhancement of Power System Security using Soft Computing and Data Mining Approaches

The power system is a complex network with numerous equipment’s interconnected for it’s reliable operation. These power system networks are forced to operate under highly stressed conditions closer to their limits. One of the key objective of the power system operators is to provide safe, economic and reliable power to it’s consumers. However, such network experiences perturbations due to many factors. These perturbations may lead to system collapse or even black out, which impacts the reliability of the system. Thus, one of the major aspect for the secure operation of the system can be achieved through security assessment. In this context, the power system static security assessment is necessary to evaluate the security status under contingency scenario. One of the approach for the security assessment is by contingency ranking, where the severity of a specific contingency is computed and ranked with highest severity to the lowest one. Initially, this approach is implemented using several load flow methods in order to identify the limit violations. However, these approaches are complex, time consuming and not feasible for real time implementation. These approaches are applied to a specific system operating condition. Thus in this context, this thesis focusses to implement soft computing and data mining approaches for security assessment by contingency ranking and classification approach. Along with the security assessment, this thesis also focusses on a control mechanism approach for the security enhancement under contingency scenario using evolutionary computing techniques. In this thesis, the various aspects of the power system security such as it’s assessment, and it’s enhancement are studied. The conventional contingency ranking approach by NRLF method is presented for the security assessment. In order to predict the system severity, a ranking module is designed with two neural network models namely, MFNN and RBFN for security assessment under different load conditions. Both neural network models are quite accurate in predicting the performance indices in less time. Another aspect of power system static security assessment is by classification approach, where the security states are classified into secure, critically secure, insecure and highly insecure. This approach helps in proper security monitoring. Thus, this thesis also presents the design and implementation of two security pattern classifier models namely the decision tree and the random forest classifiers. The classifiers are trained and tested with several security patterns generated in an offline mode. The proposed models are compared with MLP, RBFN and SVM classifier models in order to prove their efficiency in classifying the security levels. Further, this thesis work also focusses on a control mechanism for security enhancement under N-1 line outage contingency scenario. Initially contingency analysis is carried out under N-1 line outage case and critical contingencies are identified. The objective is to reschedule the generators with minimum fuel cost in such a way that the overloaded lines are relieved from stress. In order to enhance the power system security, an evolutionary computing algorithm, namely an enhanced cuckoo search algorithm is proposed for the contingency constrained economic load dispatch. To study the robustness and effectiveness of the proposed algorithm, the results are compared with CS, BA and PSO algorithms

Aeronautical engineering: A continuing bibliography with indexes (supplement 293)

This bibliography lists 476 reports, articles, and other documents introduced into the NASA scientific and technical information system in July, 1992. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Agent-based technology applied to power systems reliability

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200