Intelligent Dispatcher Training Simulator: Comparison
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Please note this is a comparison between Version 3 by Jessie Wu and Version 4 by Jessie Wu.

The concept of dispatcher training simulator (DTS) was first proposed by Latimer in 1977, and the first DTS device was developed in the same year. After a large discussion on DTS organized by Electric Power Research Institute (EPRI) in 1978, DTS was rapidly promoted in the power industry. In the 1990s, dynamic simulation and object-oriented techniques were introduced into DTS. Traditional DTS has been unable to meet the needs of the continuously developing smart grid. As artificial intelligence (AI) advances, more intelligent techniques are introduced into DTS, and the intelligent dispatcher training simulator (IDTS) is taking the historical stage. Nico Brose designed the first visualized IDTS in Germany, using software PRINS, which is coupled via a tele-control server with the Root-Mean-Square (RMS) real-time simulation core (with the software PowerFactory) for dynamic grid simulations.

  • dispatcher training simulator
  • simulation
  • power systems

1. Intelligent Power System Simulation

1.1. Functionality

The power system model is the core of the traditional dispatcher training simulator (DTS); similarly, the intelligent power system simulation is a vital subsystem of the  intelligent dispatcher training simulator (IDTS). At present, there are still many problems to be studied in power system simulation, such as the limitations of the model, the enormity of modeling, and the puzzling nature of the results, which can be solved by introducing artificial intelligence (AI) technology. Two major functions of this subsystem are intelligent model generation and intelligent model solving.
(1)
 Intelligent model generation.
Compared with the traditional DTS, the primary system to be simulated in the IDTS is the smart grid, and the secondary system to be simulated is the corresponding dispatching system integrated with the substation monitoring. The system should also realize the simulation of online monitoring, analysis, and early warning of interconnected grids, as well as the simulation of renewable energy integration, control, and dispatch [1]. Currently, research on the application of intelligent simulation techniques in IDTS is widely carried out. Different intelligent simulation techniques enable a complete description of the smart grid, making the simulated grid in IDTS more realistic [2][3][4].
(2) 
Intelligent model solving.
Dr. T.E. Dy Liacco, who is known as the father of dispatch automation, has described a beautiful blueprint for intelligent dispatching [5]. He argued that by continuously generating training sets through pattern recognition and decision tree and by automatically learning the features of the power system, an experienced dispatching robot would eventually be formed. This robot would be able to react faster and more accurately than experienced dispatchers when faced with problems [6]. With such dispatching robots being trainees, the IDTS will judge trainers’ decisions with better accuracy and efficiency compared with the traditional DTS.

1.2. Techniques

Several AI algorithms and techniques can be adopted to realize the above functions [7]:
(1) 
Expert systems.
Expert systems are one of the most used AI techniques in the field of power systems and dispatching problems [8][9]. Expert systems allow computers to simulate the decision-making process of human experts, solving practical problems that cannot be mathematically modeled and must rely on expert experience [10]. As shown in Figure 1, an expert system solves a specific problem with a knowledge base and an inference engine. The knowledge base is an organized collection of all the facts about power dispatching, and the inference engine interprets the input question and provides an answer according to inference rules. The expert system can solve various power dispatching problems in different regions and under different conditions, effectively alleviating the burden of human trainers in IDTS.
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Figure 1. Expert systems.
(2)
 Artificial neural networks.
Artificial neural networks are inspired by the neural system of humans and can effectively solve nonlinear problems [11]. In power systems, the classification and parallel processing abilities of artificial neural networks can be used for fault detection [12], load forecasting [13][14], security assessment [15][16], automatic control [17], system restoration [18], and many other tasks.
(3) 
Fuzzy set theory.
Fuzzy set theory can deal with ambiguous, subjective, or imprecise judgments and thus reduce the complexity of the problem [19]. This theory has been applied extensively in the fields of reliability evaluation [20], hybrid algorithm [21], and parameter optimization [22] of power systems.
(4) 
Heuristic search.
Heuristic search obtains a new solution in a randomized manner, compares it with the previous results, keeps the better one, and iterates until an optimal or approximate solution is obtained [23]. In power system simulation, this method can solve problems with arbitrary objective functions and obtain numerical results with arbitrary precision [24][25].
(5)
 Cloud computing.
Cloud computing technology, through distributed computing and storage, can reduce hardware investment costs and operation and maintenance costs, as well as enhance the flexibility of the application architecture [26]. IDTS can be deployed on the cloud platform and use the large grid model on the cloud to conduct joint training among multiple dispatching centers [27][28], thus solving the shortcomings of traditional DTS. Practice proves that cloud-IDTS can realize multi-service and multi-scene co-simulation among dispatching centers. Not only does it improve the off-site interactive coordination capability of joint simulation, but it also realizes the flexible deployment of the simulation environment [29]. The simulation resources are better used and can be better adapted to the rapid development of the power grid.

2. Intelligent Training Control

As shown in Figure 2, the intelligent training control subsystem includes four modules: training demand identification module, training plan generation module, training control module, and training evaluation module [30]. These four modules cycle to form a complete training process.
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Figure 2. Intelligent training control subsystem.
The training demand identification module is the beginning of the training process. The information to be identified includes the trainee’s training records, learning preferences, and the purpose of training.
The training plan generation module develops personalized training strategies and training plans according to the identified training demand. Besides, it will also analyze and modify the training contents to better meet the needs of different trainers.
The training control module controls the training process in real-time according to the training plans. It is the commander of the entire training process. The training control module can use the fuzzy set theory to indicate the understanding level of the trainee and compare the trainee’s operations with the results given by the expert system so as to guide the subsequent training. In regular training, this module monitors each step of the trainee’s operations, pointing out the deficiencies and providing advice after simulation by the expert system; in the fault handling training, it simulates the inference process of the expert and determines the cause, type, and location of the fault, aiding the trainee’s judgment.
The training evaluation module comprehensively assesses the performance of trainees, with the aim of examining the effectiveness of each training phase. At the end of the training, the trainee’s operations on accident management are automatically scored, showing the skills they learned and the problems they exposed. This process greatly reduces the human trainer’s workload and improves the reasonableness of the grading. After that, the assessment results are fed back to the training demand identification module to update the trainee’s record, which then further improves the training plan generation module. The trainee will receive feedback at the end of the training process.

3. Intelligent Database Management

Smart grids contain more components compared with traditional grids for which they are horizontally integrated and vertically coherent [31]. Therefore, the IDTS contains more data to be processed. This imposes requirements on the intelligent database management subsystem for simple maintenance, convenient expansion, fully functional, real-time control, and easy operation.
The required functions of the intelligent database management subsystem include:
  • Manage all static and dynamic data in the power grid model and support users to query, update, backup, and restore data.
  • Provide data sources for all modules and support data exchange between modules.
  • Support graphic database for editing, saving, and reproducing graphic data.
  • Provide an interface to EMS, which is the key to integrating DTS with EMS.
To fulfill the above requirements, this module should be built as follows.
The database should contain component model parameters, network model parameters, system parameters, simulated SCADA data, training data, graphical data, etc.
Two databases, an offline database, and an online database, need to be established and coordinated with each other [32]. The training program is set up and initialized with the offline database. Relevant model parameters will be passed into the intelligent power system simulation subsystem to start the simulation, and the simulation results will be read from or written to the online database. The graphic module obtains simulation results from the online database and displays them to the trainer and trainee. The trainee performs operations that modify the online database, and the intelligent power system simulation subsystem simultaneously reads in data and re-simulates.
The offline database is managed by ORACLE and can be exported in various forms as required. It supports creating, retrieving, updating, and deleting (CRUD) operations with high reliability, fault tolerance, and self-recovery capability [33], which can guarantee the integrity and security of the database.
Data sharing in online databases can be realized through the shared memory mechanism in UNIX systems, which can provide continuous storage that can be accessed by different processes [34]. The use of an online database can significantly accelerate data access, improving the real-time performance of IDTS.

4. Intelligent Graphic Support

The intelligent graphic support subsystem is oriented to trainees and trainers, mainly consisting of diagrams of electrical wiring, power flow, power generation, device configuration, network topology, etc. [35]. It displays the operating conditions of the simulated grid and allows trainees to operate directly on the graphic interface.
Traditional graphic support systems often suffer from the following problems:
  • The graphic editing module is designed in a process-oriented way, with individual programming for each component, which is not scalable and has a high maintenance workload [36].
  • The network topology module does not take full advantage of the graphical data for analysis and requires separate preparation of topology data.
  • The graphical display cannot highlight the specific connection and thus cannot effectively check whether the power system calculation results are correct [37].
To deal with these problems, the intelligent graphic support subsystem mainly includes the graphics editing module, data management module, display, and dynamic update module, and printing module.
  • The graphics editing module contains the commonly used basic symbols (points, lines, circles, etc.), electrical components (generators, transformers, etc.), and vector words. An object-oriented approach is used to encapsulate the individual graphic elements in the form of classes, and then different types of data are placed in different layers.
  • The data management module mainly uses large commercial databases, such as ORACLE, to meet security and efficiency requirements.
  • The display and dynamic update module support dynamic graph update, screen switching, and graph zooming. It also allows trainees to perform operations on the graph [38].
  • The graphics printing module converts vector graphics to PostScript files for direct output or converts graphics windows to X Windows files for output with the XPR command.

References

  1. Wang, Z.; Fan, J. Dispatcher Training Simulation System Oriented to Intelligent Scheduling. East China Electr. Power 2012, 40, 772–775.
  2. Zhong-zheng, T.; Yang-zi, S. Study on Unbalanced Power Flow in Distribution Network with Distributed Generators of Power Grid. In Proceedings of the 2016 China International Conference on Electricity Distribution (CICED), Xi’an, China, 10–13 August 2016; pp. 1–4.
  3. Zhao, X.; Gao, F.; Wang, G.; Shi, J.; Yang, J.; Sun, A. Design and Implementation of the “Internet plus” Intelligent Power Simulation and Learning System. In Proceedings of the 7th International Conference on Electrical Engineering, Control and Robotics, EECR 2021, Xiamen, China, 21–23 January 2021; IOP Publishing Ltd.: Fuzhou, China, 2021; Volume 1887.
  4. Li, Q. Discussion on Application of Computer Simulation in Power System. China Comput. Commun. 2018, 8, 18–19.
  5. Dy Liacco, T.E. The Adaptive Reliability Control System. IEEE Trans. Power Appar. Syst. 1967, PAS-86, 517–531.
  6. Dy Liacco, T.E. Real-Time Computer Control of Power Systems. Proc. IEEE 1974, 62, 884–891.
  7. Zhang, J.J.; Wang, F.-Y.; Wang, Q.; Hao, D.; Yang, X.; Gao, D.W.; Zhao, X.; Zhang, Y. Parallel Dispatch: A New Paradigm of Electrical Power System Dispatch. IEEECAA J. Autom. Sin. 2018, 5, 311–319.
  8. Mayadevi, N.; Vinodchandra, S.S.; Ushakumari, S. A Review on Expert System Applications in Power Plants. Int. J. Electr. Comput. Eng. IJECE 2014, 4, 116–126.
  9. Fossati, J.P.; Galarza, A.; Martín-Villate, A.; Echeverría, J.M.; Fontán, L. Optimal Scheduling of a Microgrid with a Fuzzy Logic Controlled Storage System. Int. J. Electr. Power Energy Syst. 2015, 68, 61–70.
  10. Shu-Hsien Liao Expert System Methodologies and Applications—A Decade Review from 1995 to 2004. Expert Syst. Appl. 2005, 28, 93–103.
  11. Yang, G.R.; Wang, X.-J. Artificial Neural Networks for Neuroscientists: A Primer. Neuron 2020, 107, 1048–1070.
  12. Jana, S.; De, A. A Novel Zone Division Approach for Power System Fault Detection Using ANN-Based Pattern Recognition Technique. Can. J. Electr. Comput. Eng. 2017, 40, 275–283.
  13. Tudose, A.M.; Sidea, D.O.; Picioroaga, I.I.; Boicea, V.A.; Bulac, C. A CNN Based Model for Short-Term Load Forecasting: A Real Case Study on the Romanian Power System. In Proceedings of the 2020 55th International Universities Power Engineering Conference (UPEC), Torino, Italy, 1–4 September 2020; pp. 1–6.
  14. Wu, L.; Kong, C.; Hao, X.; Chen, W. A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model. Math. Probl. Eng. 2020, 2020, e1428104.
  15. Swarup, K.S.; Corthis, P.B. ANN Approach Assesses System Security. IEEE Comput. Appl. Power 2002, 15, 32–38.
  16. Gao, Z.; Hu, S.; Sun, H.; Liu, J.; Zhi, Y.; Li, J. Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN. IEEE Access 2021, 9, 142411–142424.
  17. Saxena, N.K.; Kumar, A. Reactive Power Control in Decentralized Hybrid Power System with STATCOM Using GA, ANN and ANFIS Methods. Int. J. Electr. Power Energy Syst. 2016, 83, 175–187.
  18. Taher, S.A.; Sadeghkhani, I. Estimation of Magnitude and Time Duration of Temporary Overvoltages Using ANN in Transmission Lines during Power System Restoration. Simul. Model. Pract. Theory 2010, 18, 787–805.
  19. Kabir, S.; Papadopoulos, Y. A Review of Applications of Fuzzy Sets to Safety and Reliability Engineering. Int. J. Approx. Reason. 2018, 100, 29–55.
  20. Murthy, C.; Varma, K.A.; Roy, D.S.; Mohanta, D.K. Reliability Evaluation of Phasor Measurement Unit Using Type-2 Fuzzy Set Theory. IEEE Syst. J. 2014, 8, 1302–1309.
  21. Ghadimi, N. A New Hybrid Algorithm Based on Optimal Fuzzy Controller in Multimachine Power System. Complexity 2015, 21, 78–93.
  22. Berrazouane, S.; Mohammedi, K. Parameter Optimization via Cuckoo Optimization Algorithm of Fuzzy Controller for Energy Management of a Hybrid Power System. Energy Convers. Manag. 2014, 78, 652–660.
  23. Salhi, S. Heuristic Search: The Emerging Science of Problem Solving; Springer: Berlin/Heidelberg, Germany, 2017; ISBN 978-3-319-49355-8.
  24. Banhidarah, A.K.; Al-Sumaiti, A.S. Heuristic Search Algorithms for Optimal Locations and Sizing of Distributed Generators in the Grid: A Brief Recent Review. In Proceedings of the 2018 Advances in Science and Engineering Technology International Conferences (ASET), Abu Dhabi, United Arab Emirates, 6 February–5 April 2018; pp. 1–5.
  25. Singh, M.; Dhillon, J.S. Multiobjective Thermal Power Dispatch Using Opposition-Based Greedy Heuristic Search. Int. J. Electr. Power Energy Syst. 2016, 82, 339–353.
  26. Haber, M.J.; Chappell, B.; Hills, C. Cloud Computing. In Cloud Attack Vectors: Building Effective Cyber-Defense Strategies to Protect Cloud Resources; Haber, M.J., Chappell, B., Hills, C., Eds.; Apress: Berkeley, CA, USA, 2022; pp. 9–25. ISBN 978-1-4842-8236-6.
  27. Guo, C.; Feng, F.; Bian, H.; Chen, J.; Zhou, B. Design and Research of Intelligent Simulation and Analysis System for Large Power Grid Based on Internet Cloud Platform. Smart Power 2017, 45, 28–31+61.
  28. Lin, J. Cloud Computing Based System Design for Power Grid Dispatching and Control Training Simulation. Autom. Electr. Power Syst. 2017, 41, 164–170.
  29. Ma, F.; Luo, X.; Litvinov, E. Cloud Computing for Power System Simulations at ISO New England—Experiences and Challenges. IEEE Trans. Smart Grid 2016, 7, 2596–2603.
  30. Dong, Z.; Wang, C.; He, N. The Research on Power System Dispatcher Training Intelligent Simulation. Proc. CSU-EPSA 2003, 5, 91–95.
  31. Peng, H.; Tao, H.; Yan, Y.; Wang, J.; Ji, X.; Xie, X.; Liu, T. Database Management Technology for Smart Grid Dispatching and Control Systems. Autom. Electr. Power Syst. 2015, 39, 19–25.
  32. Liu, J.; Sun, Y.; Li, H. The Research of Real-Time Database Technique Used in Power System Dispatcher Training Simulator. In Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control, Dalian, China, 18–20 June 2008; p. 9.
  33. Lahiri, T.; Chavan, S.; Colgan, M.; Das, D.; Ganesh, A.; Gleeson, M.; Hase, S.; Holloway, A.; Kamp, J.; Lee, T.-H.; et al. Oracle Database In-Memory: A Dual Format in-Memory Database. In Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, Seoul, Republic of Korea, 13–17 April 2015; pp. 1253–1258.
  34. Gong, W.; Ge, Z.; Zhao, W. Study on the Data Synchronization Mechanism of Dispatcher Training Simulator. In Proceedings of the 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Xi’an, China, 25–28 October 2016; pp. 1380–1383.
  35. Kober, F.; Manzoni, A.; Lemos, F.A.B. An Objected-Oriented Approach to Development and Integration of Graphical User Interface and Power System Framework. In Proceedings of the 2003 IEEE Bologna Power Tech Conference Proceedings, Bologna, Italy, 23–26 June 2003; Volume 3, p. 7.
  36. Sun, Y. The Study of Graphic Database Integration Support Environmental in Dispatcher Training System. Master’s Thesis, North China Electric Power University, Beijing, China, 2007.
  37. Zheng, C. Multi-Computer Display and Manager of the Graphics Base on Database in DTS. Master’s Thesis, North China Electric Power University, Beijing, China, 2007.
  38. Lyu, Z.; Lian, C.; Qin, S.; Li, Z.; Wei, H. Research and Implementation of Dispatcher Power Flow Based on Cloud Computing Technology. In Proceedings of the 2020 4th International Conference on Power and Energy Engineering (ICPEE), Xiamen, China, 19–21 November 2020; pp. 108–113.
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