Railway Load Control Using Forecasted Load Components
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Solution Overview
Problem
Current methods for managing load peaks in railway systems are inefficient, leading to unnecessary load shutdowns, discomfort for passengers, and instability in the operating network, as they rely on complex evaluations of timetables and train running data, and fail to accurately predict and manage sporadic load peaks.
Innovation Solution
A method that uses a load computer to determine and forecast load components, allowing for precise prediction and mitigation of load peaks by shifting or reducing power consumption of operating units, and providing kinetic energy back into the network, without relying on timetables or production plans, thereby minimizing disruptions and maintaining a smooth load curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If loads are switched off over longer periods to avoid load peaks, then load peaks are reduced, but passenger comfort is degraded and functional impairments occur
Solution Approach 1:
The system performs preliminary analysis of timetable data and train running data to predict future load peaks before they occur. By identifying anticipated load peaks in advance, the system can prepare appropriate countermeasures such as switching off non-critical loads or adjusting train operations proactively, rather than reacting after peaks have already degraded comfort.
Solution Approach 2:
The system continuously monitors actual load values and compares them against predicted values and threshold values. This feedback mechanism allows the system to adjust its control strategies in real-time, switching off loads only when necessary and only for the duration needed to maintain stability, thereby minimizing impact on passenger comfort while still preventing load peaks.
2Reliability
If complex evaluations of timetables and train running data are performed to manage load peaks, then load management capability is improved, but system complexity and computational effort increase
Solution Approach 1:
The system segments the load management task into distinct components: timetable data analysis, train running data analysis, load component determination, prediction generation, and control execution. This segmentation allows each component to be processed independently and efficiently, reducing overall system complexity while maintaining comprehensive load management capability.
Solution Approach 2:
The system introduces an intermediary prediction mechanism that translates complex timetable and running data into simplified anticipated load peak predictions. This intermediary layer filters and processes the complex input data, presenting only the essential information needed for control decisions, thereby reducing the complexity of subsequent control operations.
3Reliability
If loads are switched off during predicted peak periods, then load peaks are prevented, but unnecessary shutdowns occur when load volume is lower than expected
Solution Approach 1:
The system continuously compares actual load values against predicted load values during the control period. If actual loads are lower than predicted and do not exceed threshold values, the system automatically cancels or adjusts the load shutdown plan, preventing unnecessary shutdowns. This real-time feedback ensures loads are switched off only when truly necessary.
Solution Approach 2:
The system dynamically adjusts the load control strategy based on real-time conditions. Rather than executing a fixed shutdown schedule, the system continuously monitors actual load development and adapts its control actions accordingly, switching off loads only for the specific duration and intensity needed to maintain stability, thereby minimizing energy loss from unnecessary shutdowns.
4Reliability
If isolated load peaks outside scheduled periods occur, then network stability is compromised, but these peaks are not detected by traditional load management
Solution Approach 1:
The system implements continuous real-time monitoring of actual load values against dynamically updated threshold values throughout the entire operating period, not just during pre-scheduled peak periods. This continuous feedback mechanism enables detection of isolated load peaks that occur outside predicted time windows, allowing the system to respond promptly to maintain network stability.
Solution Approach 2:
The system generates predictions for multiple possible load scenarios including isolated peaks outside scheduled periods. By preparing for various potential peak patterns in advance and maintaining continuous monitoring, the system can quickly identify and respond to unexpected isolated load peaks, improving detection accuracy and response time.
Data Source
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Figure 3a~3b
AI summary
The method serves to control or regulate an operating device, in particular a railway track, which is supplied by an operating network (BN) connected to at least one power supply system (EVN1, EVN2), in order to maintain a desired load profile within the operating network (BN), in particular to avoid or compensate for load peaks, with a load computer (RL), to which the data of the profile of the total load (PG) occurring in the operating network (BN) are supplied, according to which a) several profiles of load components (PS0, PS1, PS2) are determined from the total load (PG), b) the future profile (PS0E, PS1E, PS2E) is determined for each of the load components (PS0, PS1, PS2); c) the future profiles (PS0E, PS1E, PS2E) determined for the load components (PS0, PS1, PS2) are superimposed on each other in order to determine a future profile (PGE) of the total load (PG) and future load peaks;and d) the operating device and/or the at least one energy supply system (EVN1, EVN2) is controlled or regulated taking into account the determined future profile (PGE) of the total load (PG) and specified load limits in order to avoid future load peaks or to provide the energy required for them.;