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Co-optimal Placement in Wide Area Measurement Systems

Co-optimal Placement in Wide Area Measurement Systems

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Co-optimal Placement in Wide Area Measurement Systems

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  1. Co-optimal Placement in Wide Area Measurement Systems Xiaoxia Zhang x79zhang@bbcr.uwaterloo.ca [1] M. Shahraeini, M.S. Ghazizadeh, and M.H. Javidi. Co-optimal placement of measurement devices and their related communication infrastructure in wide area measurement systems. Smart Grid, IEEE Transactions on, (99):1-8, 2012. [2] M. Shahraeini, M.H. Javidi, and M.S. Ghazizadeh. Comparison between communication infrastructures of centralized and decentralized wide area measurement systems. Smart Grid, IEEE Transactions on, 2(1):206-211, 2011.

  2. Outline • Introduction • Problem Formulation • Simulation Results • Conclusions

  3. Introduction

  4. Wide Area Measurement System • Wide area measurement system (WAMS) is a measurement system include digital metering devices and communication system designed to monitor, operate and control in wide geographical area. • WAMS process features three functions: • Data acquisition • Data transmitting • Data processing

  5. Data Acquisition • Performed by measuring devices • Devices are responsible for providing raw data for different applications • Examples of devices: • Phasor measurement unit (PMU) • Supervisory control and data acquisition (SCADA) • Remote terminal unit (RTU) • Digital protective relay (DPR) • Digital fault recorder (DFR)

  6. Data transmitting • A communication infrastructure (CI) should be established

  7. Data processing • Performed by software packages in energy management systems (EMS) • EMS applications has functions of operation, control and optimization in power systems based on the acquired dara. • EMS functions include: • Online state estimation (SE) • Load flow (LF) • Optimal power flow (OPF) • Load forecast (LF) • Online low-frequency oscillation (LFO) analyses

  8. State Estimation • Data acquired by measuring devices are raw data • Cannot be used by EMS applications directly • Online state estimation (SE) extracts creditable data from raw data • Creditable data can be used by applications • SE is the basis of EMS applications

  9. Phasor Measurement Unit (PMU) • A data resource commonly used • Measures voltage and current phasors • Lead to a simplified linear state estimator • Sample rate is very high (30-60 samples per second) • High data rate transmission is required • Focus of this work

  10. Research issues • Measurement placement study: • Observability: A system is observable if #of measured var. >= # of var. that should be estimated • EMS application design: • Fast and efficient algorithms for EMS applications

  11. Research issues • Communication infrastructure (CI) design and planning • Dependent media (parts of power network elements): power line communication (PLC), all-dielectric self supporting (ADSS), and optical power ground wire (OPGW). • Independent media: wireless and satellite communication. • Dependent media can be co-optimally designed in conjunction with power system planning problems.

  12. Problem Formulation

  13. Problem Formulation • Objective: co-optimize the meter placement and its CI for state estimation problem. • Measurement device: PMU • Transmission media: OPGW • Optimization tool: genetic algorithm (GA) due to its accurate solution where high complexity is not a major concern

  14. Subproblem 1:PMU placement opt. • A PMU on a bus can observe this bus and all its incident buses. • PMU placement: find minimum set of PMUs such that the entire system can be observed. Indicates whether there is a PMU on bus i Cost for one PMU n-dimentional arrays

  15. Subproblem 1 Cont’d • Define adjacency matrix • Observability: • Add up the columns i of adjacency matrix if PMUi=1. If the array of summation vector are equal or bigger than 1.

  16. Subproblem 1 Cont’d • Define adjacency matrix • Observability: • Add up the columns i of adjacency matrix if PMUi=1. If the array of summation vector are equal or bigger than 1.

  17. Subproblem 1 Cont’d • Gene (objective+constraint): • Optimal case: fitness<1 Total # of zero arrays in OBS vector Total # of PMUs

  18. Subproblem 2: Communication Links optimization • OPGW cables perform both grounding and communication. • Objective: find a minimal OPGW plan which covers all PMU enabled buses. Total # of OPGW links Cost per km cable Length of ithlink

  19. Subproblem 2 Cont’d • penalty: • PMU enabled bus with maximum conjunction is found as starting node • The path from starting node to all other PMU enabled bus is examined • If a path does not exist, increase the penalty function by 1 • Optimal case: fitness<1 Total length of transmission lines

  20. Co-optimal Placement of PMUs and Links • Objective: find the optimal set of PMUs and its required communication links simultaneously. • Can be solved by multiobjective genetic algorithm (MOGA)

  21. Simulation Results • Comparison of two methods of placement: • Independent • Find the optimum placement of PMUs, then determine the communication links • Simultaneous • Determine the placement of PMUs and links together

  22. Simulation Results • IEEE 30, 57, 118-bus test networks

  23. Simulation Results

  24. CI Nodes: the number of nodes in their corresponding CIs OPGW Coverage: the length percentage of transmission lines which should be equipped by OPGW cable. Total Cost: the total cost of PMU sites and their corresponding CI.

  25. Conclusions • Optimal placement of PMUs and their required communication infrastructure of power systems are co-optimally designed for state estimation problem. • Although the number of measurement devices, for full observability of the system, may be increased by proposed approach, the considerable reduction in communication media decreases the total cost of WAMS implementation.

  26. Questions and Discussion? Thank you!