Intelligent tutoring systems for power system control centers

The authors introduce the methodology and techniques used in constructing an expert system designed to serve as a decision-making aid for the operators of power system control centers in the area of systems security. Fundamentally, the system is designed for complete contingency analysis on an extensive power transmission network, as well as for proposing possible corrective actions aimed at taking the network to safe states. One of the main characteristics of this expert system is its close combination with the algorithmic techniques traditionally used in such control centers and heuristic methods, in order to provide solutions to problems. >

This paper presents a parallel particle swarm optimization (PSO) technique for reactive power and voltage control (Volt/Var Control: VVC) investigating dependability. High penetration of renewable energies and deregulation of power systems force power flow to change suddenly and operators in control centers have to control voltage in wider power systems. Therefore, VVC is required to shorten the control interval and handle larger-scale power systems. One of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, sustainable voltage control is crucial and dependability is strongly required for VVC. This paper investigates not only fast computation by parallel computation but also dependability of parallel PSO for VVC with IEEE 14, 30, 57, and 118 bus systems.

A memory output sliding mode load frequency control (MOSMLFC) strategy is proposed for multi-area interconnected power systems under historical-frequency-triggered denial-of-service (DoS) attacks. Due to the use of the open network, the multi-area power system is prone to cyber-attacks. Different types of attack models have been built to describe the actual attack behavior, so that effective strategies can be quickly formulated in the event of an attack. Therefore, a historical-frequency-triggered DoS attacks model is presented from the perspective of attackers, with the aim of destroying the stable state of the multi-area power system. It is assumed that attackers determine the timing of DoS attacks by monitoring the operational status of multi-area power systems and designing the triggering condition with historical frequency. A MOSMLFC strategy is investigated to ensure the security performance of multi-area power systems under historical-frequency-triggered DoS attacks, which applies the memory output information of the power system to realize the controller design. The security condition of multi-area power systems under historical-frequency-triggered DoS attacks is obtained by Lyapunov’s theorem and linear matrix inequality (LMI). Numerical examples are tested over the IEEE 10-generator 39-bus system and the results prove the usefulness and superiority of the proposed method. Note to Practitioners—Load frequency control is widely applied in multi-area power systems to achieve a balance between the load demand and generation. Frequent cyber-attacks are a threat to the normal operation of the power system. It is therefore necessary to develop appropriate strategies to defend against cyber-attacks. So far, there have been many different forms of cyber-attacks. This has prompted defenders to build different types of attack models to describe the actual attack behavior in order to preemptively formulate appropriate defensive strategies. Smart attacker may notice that certain characteristics of the target system are important, such as the power system frequency. This motivates us to propose a historical frequency-triggered DoS attack model that contributes to a deep understanding of the impact of cyber-attacks on the power system. We propose a unique sliding mode control approach to ensure the stable performance of power system state and output simultaneously.

ANALYSIS OF BAD DATA IN POWER SYSTEM STATE ESTIMATION UNDER NON-GAUSSIAN MEASUREMENT NOISE

A power system operation at minimum cost is no longer the only measure for electrical power dispatch. Combined Economic Emission Dispatch (CEED) problem is attained by considering both the economy and emission objectives with necessary constraints. The stability of the power system is also considered as an important factor in the performance of the power system. Several optimization techniques are slow for such complex optimization tasks and are not suitable for on-line use. This paper presents an effective optimization algorithm, for solving security constrained combined economic emission dispatch problem. Also, the power system stability plays an important role in power system. Power system stability criteria are also considered in this paper for better performance of the entire power system. In this paper, an efficient optimization technique called Modified Artificial Bee Colony Optimization is used to solve the CEED problem. The performance of the proposed approach is compared with the other optimization techniques. The experimental result shows that the proposed system results in better solution for CEED problem with the maintenance of system stability.

During the last decade, many of the Intelligent Tutoring Systems (ITS) have been developed in different domains. ITS deliver important learning gains outside classroom environments. Expert systems have been extensively used to implement ITS in education. Frames are widely used as a knowledge representation for the expert systems. This paper proposes and discusses the construction of student's knowledge model in ITS using a frame-based expert system that can adapt to the students’ cognitive characteristics.

The 115/22 kV transformer is a significant equipment to transmit the energy to the customers. The power outage occurred, if there was a transformer tripping. The control center operator has to identify the severity of alarms on SCADA while the event occurred, by indicating the transformer tripping and how many events of the power outages, in order to quickly transfer the electrical load to other transformer. The process explained above, the operator would require less time to reduce the impact of power outage. Therefore, this paper proposes the Artificial Intelligence (AI) Fault Tree Analysis on Python Platform. It was developed to be as a substitute for operators work in analyzing the message alarm. This paper also presents the determination of the previous loads by Weighted Moving Average technique. This technique was used in planning to transfer the load from the transformer outage to another and can prevent the power overload event on the power system. Moreover, this article presents an automatically tripping event reported via Line application. All information was sent to distribution company control center and maintenance staff. As a result, AI can replace control center operator work. AI can more accurately identify the severity of alarms that occurred simultaneously than operator. AI also spends only 12 seconds which is less than the operator spends at388 seconds.

Automated driving has started to be used commercially for individual mobility and public transport in recent years. As soon as automated driving is commercially exploited, automated vehicle fleets require assistance which can be provided by an operational control center as known from air traffic, public transport or process technology. The need for assistance is shown by two aspects. The aspects are human interaction and efficiency of journeys. Based on that, a control center is proposed to address those aspects. Further, a concept for a control center is derived. For that, the methods of interviewing experts, observation and literature research are used. Specific tasks for the control center are defined. These tasks were examined and categorized into three service categories. The three service categories are emergency service, fleet service and teleoperation service. Due to the categories, future-built control centers will be scalable and adaptable to its demand. Whereas two of three categories are well covered by industry or research, the teleoperation service as the essential problem solving technique needs further development. Moreover, further research will be required to quantify the control center demand.

098 Developing an Intelligent tutoring system for prostate cryosurgery

This paper proposes the concept and framework of smart operating system based on the artificial intelligence U+0028 AI U+0029 techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

Power system performance is affected by dynamic characteristics of hydraulic governor-turbines during and following any disturbance, such as occurrence of a fault, loss of a transmission line or a rapid change of load. Accurate modelling of hydraulic governor-turbines is essential to characterise and diagnose the system response during an emergency. In this article, both detailed and simplified hydraulic systems governed by proportional-integral-derivative and proportional-integral controllers are modelled. This article examines their transient responses to disturbances through simulation in Matlab/Simulink. The article also investigates the dynamic performance of an isolated hydraulic system through evaluating stability margins, eigenvalues, root loci and frequency deviation time responses of the system. The results obtained provide an insight into the stability of the power system governed by different governor settings. References P. Kundur. Power System Stability and Control. McGraw-Hill, 1994. D. H. Thorne and E. F. Hill. Field testing and simulation of hydraulic turbine governor performance. IEEE Transactions on Power Apparatus and Systems, PAS-93(4):1183--1191, July 1974. doi:10.1109/TPAS.1974.294066. J. M. Undrill and J. L. Woodward. Nonlinear hydro governing model and improved calculation for determining temporary droop. IEEE Transactions on Power Apparatus and Systems, PAS-86(4):443--453, April 1967. doi:10.1109/TPAS.1967.291853. Richard C. Dorf and Robert H. Bishop. Modern Control Systems. Pearson Educational, Inc, 2005. L. N. Hannett, J. W. Feltes, and B. Fardanesh. Field tests to validate hydro turbine-governor model structure and parameters. IEEE Transactions on Power Systems, 9(4):1744--1751, November 1994. doi:10.1109/59.331426. IEEE. IEEE guide for the application of turbine governing systems for hydroelectric generating units. IEEE Std 1207-2004, pages 1--121, 2004. IEEE. Hydraulic turbine and turbine control models for system dynamic studies. IEEE Transactions on Power Systems, 7(1):167--179, Feb 1992. doi:10.1109/59.141700.

Measuring instruments in a power system are numbered in thousands and are dispersed geographically. The readings of those instruments are telemetered to the control centre and then used in a power system state estimation program for further processing. Field technicians calibrate these instruments routinely at great cost. The purpose of remote meter calibration is to determine, using analytical methods in the control centre, which instrument needs calibration and to estimate the calibration error. Results of many runs of the power system state estimation program are used to demonstrate the feasibility of remote meter calibration in a power system. The IEEE 14-bus test system is used for testing. It is shown that the simulated fixed (calibration) errors in all types of power system measurements can be advantageously estimated.

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration.

Guest Editorial: Situational awareness of integrated energy systems

Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student's learning behavior and presents to the student the learning material (content, questions‐answers, assignments) accordingly. In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents.Student modeling is a key component in any Intelligent Tutoring System (ITS). In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar group. In the present paper we present a methodology where using Case Based Reasoning (CBR), the ITS provides student modeling for online learning in a distributed environment with the help of agents.The Chapter describes the approach, the architecture, and the agent characteristics for the student modeling in the ITS. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students' learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and students' model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system.