Description:SYSTEM AND METHOD FOR ADVANCED SIGNAL FILTERING
TECHNICAL FIELD
Generally, the present invention relates to the field of signal processing systems. Particularly, the present disclosure relates to a system and method for transforming, analyzing, and refining multi-phase signals.
BACKGROUND
In modern electrical and electronic systems, especially those involving motor drives, power converters, and renewable energy sources, the need for accurate and reliable signal processing of multi-phase electrical inputs has become critical. The multi-phase signals generated from complex energy sources, such as, but not limited to, a three-phase AC system or advanced inverter outputs, are subject to inherent noise, harmonics, and phase imbalances. The distortions introduce inaccuracies in measurement and control operations, affecting the overall system performance and efficiency.
Conventional signal processing approaches typically rely on basic filtering techniques or time-domain analysis, such as, but not limited to, median filter, moving average filter, gaussian filter, wavelet transform, and bilateral filter. Specifically, the median filter is one of the most widely used techniques for noise reduction. The median filter works by sorting the dataset and selecting the median value within a moving window. Further, the moving average filter is another commonly used technique for smoothing noisy signals. The moving average filter computes the average of values within a moving window and is simple to implement. Furthermore, the Gaussian filter applies a weighted average based on a Gaussian function, providing smoother transitions and better preservation of edge information compared to the moving average filter. Furthermore, the wavelet transform method is often used for noise reduction in multi-scale analysis. The wavelet transform technique works by decomposing the signal into multiple frequency bands and selectively filtering out the high-frequency noise. Furthermore, the bilateral filter is an edge-preserving filter that reduces noise while maintaining sharp edges.
However, there are certain problems associated with the existing or above-mentioned mechanism of filtering a multi-phase signal supplied by an energy source. For instance, most traditional filters, such as median and Gaussian filters, involve computationally expensive operations such as sorting or complex weight computations, which is unsuitable for real-time processing in low-resource environments. Further, filters such as the moving average filter struggle with noise, which degrades the performance in noisy conditions. Furthermore, many filtering techniques, including the moving average and Gaussian filters, lack in preserving edges or important features in motion-related data, particularly in dynamic environments. Additionally, there is a lack of a robust solution that transforms signals into a rotating reference frame, identifies key boundary values, calculates representative signal points, and reconstructs cleaner signal profiles, offering significant advantages in control accuracy and system responsiveness
Therefore, there exists a need for a mechanism for filtering a multi-phase signal supplied by an energy source that is efficient, accurate, and overcomes one or more problems as mentioned above.
SUMMARY
An object of the present disclosure is to provide a system signal processing system for filtering a multi-phase signal supplied by an energy source.
Another object of the present disclosure is to provide a filtering of a multi-phase signal supplied by an energy source.
Yet another object of the present invention is to provide a signal processing system capable of accurately filtering and conditioning electrical signals derived from an energy source using coordinated phase alignment, transformation, and advanced filtering techniques.
In accordance with an aspect of the present disclosure, there is provided a signal processing system for filtering a multi-phase signal supplied by an energy source, the system comprises:
- a sensing unit communicably coupled to the energy source;
- at least one analog-to-digital converter communicably coupled to the sensing unit;
- a control unit communicably coupled to the at least one analog-to-digital converter;
- at least one gate driver communicably coupled to the control unit; and
- at least one utilization unit electrically coupled to the control unit,
wherein the control unit is configured to control at least one utilization unit based on at least one input received from the at least one gate driver.
The system for filtering a multi-phase signal supplied by an energy source, as described in the present disclosure, is advantageous in terms of enhanced accuracy in signal processing by implementing a filtering technique that selects representative signal values based on statistical characteristics of sampled data. The above-mentioned approach reduces the influence of transient noise and irregularities without relying on traditional averaging or low-pass filtering, thereby preserving signal integrity. The transformation into a rotating reference frame (d-q domain) further enables efficient decoupling of signal components, simplifying subsequent control operations. Further, the use of representative value selection improves the precision of reconstructed signals used to drive critical systems, leading to increased system responsiveness, stability, and robustness under varying electrical conditions. Therefore, the above-mentioned advantages collectively enable reliable performance in applications such as motor drives, power converters, and industrial automation systems.
In accordance with another aspect of the present disclosure, there is provided a method for controlling power flow of a battery pack, the method comprises, the method comprises:
- sensing at least one electrical parameter of the multi-phase signal, via a sensing unit;
- converting the sensed electrical parameter into a digital signal, via at least one analog-to-digital converter;
- transforming the synchronized signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component using a coordinate transformation module;
- selecting a representative signal value from a sampling window using a filtering module; and
- reconstructing a filtered signal based on the selected representative value.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
Figures 1 and 2 illustrate block diagrams of a signal processing system for filtering a multi-phase signal supplied by an energy source, in accordance with different embodiments of the present disclosure.
Figure 3 illustrates a flow chart for filtering a multi-phase signal supplied by an energy source, in accordance with another embodiment of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
As used herein, the terms “signal processing system” and “system” are used interchangeably and refer to a hardware and software-based architecture configured to acquire, digitize, filter, and condition analog signals to enhance signal quality and extract required information. In the power and energy applications, the system operates on single-phase and multi-phase electrical signals derived from external energy sources. The core components of a signal processing system include sensing units, analog-to-digital converters (ADCs), control units, filtering modules, and signal transformation modules. Further, the signal processing involves domain conversion, such as, but not limited to, Clarke and Park transformations, to convert three-phase signals into d-q components for enhanced interpretability and control. Furthermore, the signal processing systems are typically classified as analog, digital, or hybrid based on the nature of signal operation. The Digital signal processing (DSP) systems dominate in precision applications due to programmability, robustness, and ease of integration with control logic and decision-making algorithms. The signal processing procedure begins with a sensing unit acquiring electrical parameters such as voltage or current from an external energy source. The acquired analog signals undergo sampling through one or more ADCs to produce corresponding multi-phase digital signals. Subsequently, the digital signal is routed to a control unit, with a phase alignment module synchronizing the input signals, followed by transformation into a rotating reference frame using a coordinate transformation module. Furthermore, the Euclidean-distance-based filtering module processes the transformed signal to suppress noise by identifying representative signal values from predefined sampling windows. Thereafter, the filtered signals are either retained in the transformed domain or reconstructed into the original signal frame, routed to gate drivers for regulated delivery to utilization units. The above-mentioned procedure ensures consistent and accurate signal quality for real-time operational control.
As used herein, the terms “multi-phase signal ” and “signal” are used interchangeably and refer to a set of two or more alternating electrical signals with phase differences between them, commonly used in power systems for efficient transmission, distribution, and utilization of electrical energy. Specifically, the three-phase signal is the most prevalent type, comprising three sinusoidal waveforms offset by 120 electrical degrees. Further, the additional configurations include two-phase and six-phase systems, depending on the application requirements. The multi-phase signals ensure balanced load distribution, improved power density, and reduced pulsation in rotating machines. The signals may represent voltage or current components and originate from generators, inverters, or energy conversion devices in industrial, vehicular, or grid systems. The generation of the multi-phase signal typically originates from a rotating electrical machine or power electronic converter configured to produce phase-shifted outputs. Further, each phase signal is acquired via a dedicated sensing unit designed to capture voltage or current parameters. The sensed analog signals undergo digitization through analog-to-digital converters for further processing. Subsequently, the filtered and conditioned signal outputs are reconstructed or utilized directly for downstream control, monitoring, or actuation in power conversion, load regulation, or system protection applications.
As used herein, the terms “energy source” and “source” are used interchangeably and refer to a physical or electrical origin that supplies power in the form of voltage and current for performing electrical work or driving a load. Energy sources include electrical generators, batteries, photovoltaic panels, fuel cells, and power grids. Further, depending on the configuration, the energy output is single-phase or multi-phase in nature and continuous or pulsed. Further, sources are classified as AC or DC based on output characteristics, and as renewable or non-renewable based on origin. The energy sources operate independently or in conjunction with other systems to supply power to connected signal processing or power conversion units. Specifically, the energy delivery from the source initiates with the generation or storage of electrical energy, followed by distribution through conductors or power interfaces. Subsequently, the voltage and current output from the source are routed to sensing elements that measure one or more electrical parameters. Output signals are then processed by downstream modules such as analog-to-digital converters, control units, and gate drivers. The characteristics of the source determine signal form, magnitude, and frequency, which directly influence the design and behavior of the signal processing system.
As used herein, the terms “sensing unit” and “unit” are used interchangeably and refer to a hardware module configured to detect and measure one or more electrical parameters from a signal provided by an energy source. The unit includes sensors such as voltage sensors, current sensors, or a combination of both in the form of a unified electrical sensor. The voltage sensor measures potential difference across two points, and the current sensor detects the flow of electrical current through a conductor. The sensing unit may include isolation mechanisms, signal conditioning circuitry, and output interfaces to ensure accurate signal transmission to subsequent processing components. Further, the sensor types include resistive dividers, Hall effect sensors, Rogowski coils, and current transformers, each selected based on signal characteristics and application context. The sensing unit operates by acquiring real-time electrical signals directly from an energy source or an intermediate electrical path. The acquired analog signals undergo preliminary conditioning to improve quality or compatibility, followed by transmission to one or more analog-to-digital converters. The sensing function supports various tasks such as system protection, energy metering, and intelligent control of downstream elements, including gate drivers and utilization units. Proper configuration of the sensing unit directly influences overall system responsiveness and stability.
As used herein, the terms “analog-to-digital converter”, “ADC”, and “converter” are used interchangeably and refer to an electronic component that transforms continuous analog electrical signals into discrete digital representations. The ADC performs sampling of the analog input at specified intervals and assigns quantized values corresponding to signal amplitude at each sampling point. The core functional blocks include a sample-and-hold circuit, a quantizer, and encoding logic. The ADC types applicable to signal processing systems include successive approximation register (SAR), sigma-delta, flash, and pipeline converters. The selection of ADC architecture depends on resolution, sampling speed, power efficiency, and noise tolerance requirements for the target signal. The procedure of operation involves receiving analog signals from a sensing unit and periodically converting them into binary-coded digital values for subsequent digital processing. A sampling frequency is chosen based on the Nyquist criterion and signal bandwidth. The output from the ADC comprises a stream of numerical values corresponding to amplitude levels of the input signal over time. Therefore, the precision of the ADC directly affects the fidelity of the signal in the digital domain and influences downstream control and decision-making processes.
As used herein, the terms “control unit” and “controller unit” are used interchangeably and refer to a digital processing module configured to interpret, process, and manage input signals for driving and regulating downstream components within a signal processing system. The control unit comprises processing logic, memory elements, and firmware or algorithms for decision-making and command generation. The common types include microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs). The control unit operates as a centralized logic system that coordinates data flow and signal transformation across interconnected modules, including filtering, coordinate transformation, and gate driving circuits. The technique of operation involves receiving digitized input from an analog-to-digital converter, executing signal conditioning procedures such as synchronization and transformation into rotating reference frames, and applying filtering algorithms to isolate or enhance specific signal features. The processed signal is used to generate control commands or actuation signals, which are then transmitted to a gate driver or output interface linked to a utilization unit. The output from the control unit influences switching behavior, energy delivery, or system feedback mechanisms, thereby ensuring precise control of power or signal flow based on predefined logic or real-time input variations.
As used herein, the term “gate driver” refers to an electronic interface circuit designed to receive low-power control signals from a processing unit and deliver appropriately amplified signals to the gates of power semiconductor devices, such as MOSFETs or IGBTs. The gate driver serves as a key intermediary between control logic and high-power switching devices. The types of gate drivers include high-side, low-side, half-bridge, and full-bridge configurations, with isolation or non-isolation features depending on system requirements. The procedure of operation involves receiving gate command signals from the control unit and translating into voltage and current levels sufficient to turn power switches on or off with minimal delay and loss. The gate driver adjusts the switching characteristics to control parameters such as timing, duty cycle, and synchronization in coordination with the control unit logic. The output from the gate driver connects to the power stage of the utilization unit, enabling efficient energy transfer or actuation. The gate driver ensures reliable isolation, noise immunity, and fast response to maintain signal integrity and safeguard system performance.
As used herein, the terms “utilization unit”, “sink unit,” and “end unit” are used interchangeably and refer to the end component or load that receives the processed or controlled electrical signal for performing a functional operation. The utilization unit represents the terminal stage of a signal processing system, as electrical energy is converted into mechanical, thermal, or another form of usable energy. The types of utilization units include electric motors, actuators, lighting systems, inverters, resistive loads, and power conversion modules. The selection of the utilization unit depends on application-specific requirements such as power rating, voltage class, and operational dynamics. The procedure of operation involves receiving controlled output signals from a gate driver or control unit, which regulate the magnitude, frequency, and phase of the electrical input supplied to the utilization unit. The utilization unit responds to the signals to perform designated output actions, such as rotation in motors or heating in resistive elements. The signal conditioning and regulation by upstream modules ensure the utilization unit operates with optimal performance, minimal losses, and high reliability under varying load conditions.
As used herein, the term “phase alignment module” refers to a functional unit designed to receive multi-phase digital signals and adjust phase discrepancies between individual signals to achieve temporal or spatial alignment. The module ensures that all input phase components are synchronized to a common temporal reference, enabling consistent downstream processing. The types of phase alignment modules include zero-crossing detectors, phase-locked loops (PLLs), and software-based digital phase alignment algorithms. The hardware or firmware implementations may vary based on precision, signal type, and real-time performance requirements. The approach of operation involves detecting the phase angle or timing deviation of each input signal and adjusting each signal’s reference frame or time base to achieve synchronization. The aligned signals are then passed to subsequent modules, such as coordinate transformation or filtering units. Further, the accurate phase alignment improves signal integrity and enhances the effectiveness of transformations and filtering operations, particularly in rotating reference frame applications involving d-q axis processing or real-time control of electrical systems.
As used herein, the term “coordinate conversion module” refers to a functional component that converts multi-phase signals from one reference frame to another, typically from the stationary three-phase frame to a rotating reference frame. The transformation enables simplified analysis and control by representing complex multi-phase signals as direct (d) and quadrature (q) components. The types of coordinate transformation modules include Clarke transformation, which converts three-phase signals to two-phase stationary coordinates, and Park transformation, which further converts stationary coordinates to rotating d-q coordinates. The procedure of operation involves receiving synchronized multi-phase digital signals and applying mathematical transformations to convert the signals into the desired reference frame. The conversion facilitates decoupling of signal components, easing filtering, control, and modulation processes. The transformed signals are utilized by filtering modules or control units for enhanced performance in tasks such as noise suppression, signal smoothing, and precise control of electrical loads or machines within the system.
As used herein, the term “filtering Module” refers to a system component designed to process input signals by reducing unwanted noise and extracting essential signal features. Specifically, the module applies specific algorithms or techniques to smooth the signal while preserving the important characteristics, thereby enhancing overall signal quality. The types of filtering modules include digital filters such as, but not limited to, low-pass, high-pass, band-pass, notch filters, adaptive filtering, and distance-based filtering techniques. The procedure of operation involves receiving transformed signals, such as those in the d-q domain, and performing noise reduction through a systematic selection of representative values within a defined sampling window. The filtering process includes identifying boundary values, calculating reference points, and selecting signal samples based on proximity criteria to the reference. The filtered output is then reconstructed into a refined signal that supports further control or analysis operations, ensuring improved accuracy and reliability in subsequent system functions.
As used herein, the term “electrical parameter” and “parameter” are used interchangeably and refer to measurable quantities that characterize the behavior and properties of electrical signals or systems. The parameters comprise, but not limited to, voltage, current, frequency, phase angle, power factor, and impedance. Further, each parameter provides critical information required for understanding, monitoring, and controlling electrical systems, enabling effective operation and fault detection. The types of electrical parameters encompass instantaneous and steady-state measurements. Particularly, the voltage and current represent the fundamental parameters, while frequency and phase angle offer insights into the signal’s temporal and spatial characteristics. The power-related parameters, such as active, reactive, and apparent power, quantify energy consumption and transfer. The approach involves sensing these parameters through appropriate sensors, converting analog signals into digital representations, and processing them for further analysis, filtering, or control within the signal processing system.
As used herein, the terms “sampling window” and “sampling frame” are used interchangeably and refer to a specific interval or subset of signal samples selected from a continuous or discrete signal for focused analysis or processing. Specifically, the window limits the number of data points considered at one time, enabling localized operations such as filtering, averaging, or feature extraction. The size and position of the sampling window directly influence the resolution and responsiveness of the signal processing task. The Types of sampling windows include fixed-length windows, sliding windows, and adaptive windows. The Fixed-length windows maintain a constant size throughout the processing, and the sliding windows move across the signal data to enable continuous analysis. Further, the adaptive windows vary in size or position based on signal characteristics or processing requirements. The technique involves capturing signal samples within the defined window, performing computational operations such as boundary detection, mean calculation, or distance measurement, and utilizing the results to improve signal quality or extract meaningful information.
As used herein, the terms “rotating reference frame” and “frame” are used interchangeably and refer to a coordinate system that moves in synchrony with a rotating vector, commonly used to simplify the analysis of multi-phase signals by transforming signals into components aligned with the rotating frame. The frame typically consists of a direct (d) axis and a quadrature (q) axis, which represent projections of the original multi-phase signals onto orthogonal axes rotating at the same angular velocity as the signal vector. Further, employing a rotating reference frame facilitates easier control and filtering of dynamic signals by converting time-varying quantities into steady or slowly varying components. The types of rotating reference frames include synchronous frames that rotate at the fundamental frequency of the signal and arbitrary rotating frames that may rotate at different speeds or angles depending on the application. The procedure involves mathematically transforming multi-phase signals from the stationary frame into the rotating frame using coordinate transformation techniques such as Park or Clarke transforms. The transformations yield the d-axis and q-axis components, which serve as inputs for subsequent processing steps such as, filtering, control, or reconstruction back into the original multi-phase format.
As used herein, the term “first characteristic boundary value” refers to a defined extremum within a sampling window of a signal, marking one of the limit points that characterize the range of signal variations in the selected interval. The boundary value is essential for identifying the scope of signal fluctuations, providing a reference for further processing such as filtering or feature extraction. The first characteristic boundary value is typically either the minimum or maximum data point within the sampling window, serving as a critical parameter to assess signal behavior and guide noise reduction techniques. The types of first characteristic boundary values include minimum values, which denote the lowest amplitude in the sample set, and maximum values, which indicate the highest amplitude. The method to determine the first characteristic boundary value involves scanning the sampled data within a predefined window and selecting the point that meets the extremum condition. The value forms a basis for calculating central tendencies or reference points, such as the mean or median and aids in subsequent signal conditioning steps such as filtering, smoothing, or representative value selection based on proximity measures.
As used herein, the term “second characteristic boundary value” refers to a defined extremum within a sampling window of a signal, representing a limit point that complements the first characteristic boundary value to define the full range of signal variation within the selected interval. The boundary value plays a crucial role in understanding the signal’s amplitude spread and assists in establishing thresholds for processing tasks such as filtering and noise suppression. The second characteristic boundary value is typically the extremal point opposite to the first boundary value, either the maximum in case the first is minimum, or, contrarywise, thereby providing a complete boundary framework for the signal segment. The manner for determining the second characteristic boundary value involves analyzing the sampled data within the predefined window and selecting the point that defines the extremum opposite to the first characteristic boundary value. The value, along with the first boundary value, enables calculation of intermediate metrics such as the mean or median and facilitates subsequent filtering processes by defining the range within which signal smoothing or noise reduction techniques operate effectively.
As used herein, the terms “reference point” and “mid-point” are used interchangeably and refer to a specific value derived from a set of signal samples within a sampling window, serving as a benchmark for comparison and selection during filtering operations. The point typically corresponds to a central tendency measure, such as the mean or median, calculated using characteristic boundary values or other significant signal features. The reference point acts as a representative indicator of the signal’s true or nominal state, facilitating the identification of samples most closely aligned with the underlying signal trend and minimizing the influence of noise and outliers. The types of reference points include arithmetic mean, median, mode, or any statistically representative value calculated from the range of signal samples. The method for determining the reference point involves first identifying characteristic boundary values within the sampling window and subsequently computing the central value that best represents the overall signal profile. The reference point guides the filtering process by serving as a criterion to select the sample or set of samples with minimum deviation, ensuring accurate signal reconstruction and noise reduction in the processed output.
As used herein, the term “representative signal value” refers to a selected sample within a set of signal data that most accurately reflects the true characteristic of the underlying signal amid noise and variations. The value is chosen based on proximity to a predefined reference point, ensuring minimal deviation from the expected signal behavior. The representative signal value serves as a reliable indicator for reconstructing or filtering the signal, enhancing overall signal integrity and stability during processing. The types of representative signal values include the sample with the smallest Euclidean distance to the reference point, the median sample within a data window, or any statistically significant value that finest captures the essential features of the signal. The procedure for determining the representative signal value involves calculating the distance or difference between each sample in a defined window and the reference point, followed by selecting the sample exhibiting the closest alignment. The approach ensures the filtered output accurately represents the true signal while effectively suppressing noise and anomalies.
In accordance with an aspect of the present disclosure, there is provided a signal processing system for filtering a multi-phase signal supplied by an energy source, the system comprises:
- a sensing unit communicably coupled to the energy source;
- at least one analog-to-digital converter communicably coupled to the sensing unit;
- a control unit communicably coupled to the at least one analog-to-digital converter;
- at least one gate driver communicably coupled to the control unit; and
- at least one utilization unit electrically coupled to the control unit ,
wherein the control unit is configured to control at least one utilization unit based on at least one input received from the at least one gate driver.
Referring to figure 1, in accordance with an embodiment, there is described a signal processing system 100 for filtering a multi-phase signal supplied by an energy source 102. The system 100 comprises a sensing unit 104 communicably coupled to the energy source 102, at least one analog-to-digital converter 106 communicably coupled to the sensing unit 104, a control unit 108 communicably coupled to the at least one analog-to-digital converter 106, at least one gate driver 110 communicably coupled to the control unit 108 and at least one utilization unit 112 electrically coupled to the control unit 108. Further, the control unit 108 is configured to control at least one utilization unit 112 based on at least one input received from the at least one gate driver 110.
The signal processing system 100, as mentioned above, operates by first capturing a multi-phase signal from an energy source 102 through the sensing unit 104. The sensing unit 104 accurately detects at least one electrical parameter of the multi-phase signal, which is further converted into a digital format by the analog-to-digital converter 106. The digitized multi-phase signal is forwarded to the control unit 108, with advanced processing, including phase alignment, coordinate transformation, and noise filtering, is performed to enhance signal quality and ensure precise representation of the original input. The control unit 108 processes the filtered multi-phase digital signal and generates control commands, which are sent to at least one gate driver 110. The gate driver 110 acts as an interface to regulate and modulate the operation of the utilization unit 112 based on the commands received from the control unit 108. The utilization unit 112 responds according to the regulated signals, executing the intended energy distribution or load control with improved stability and reduced interference, thereby optimizing the overall system performance. Advantageously, the system 100 provides enhanced noise suppression, improved signal accuracy, and efficient control of the utilization unit 112 through digitally refined inputs. The system 100 ensures robust handling of multi-phase signals by filtering out distortions and maintaining signal integrity throughout the processing stages. Further, the system 100 encompasses increased reliability, reduced signal degradation, and precise control over the energy delivery or load management, resulting in improved operational efficiency and longevity of connected devices or systems.
Referring to figure 2, in accordance with an embodiment, there is described a signal processing system 100 for filtering a multi-phase signal supplied by an energy source 102. The system 100 comprises a sensing unit 104 communicably coupled to the energy source 102, at least one analog-to-digital converter 106 communicably coupled to the sensing unit 104, a control unit 108 communicably coupled to the at least one analog-to-digital converter 106, at least one gate driver 110 communicably coupled to the control unit 108 and at least one utilization unit 112 electrically coupled to the control unit 108. Further, the control unit 108 is configured to control at least one utilization unit 112 based on at least one input received from the at least one gate driver 110. The control unit 108 comprises a phase alignment module 114, a coordinate conversion module 116, and a filtering Module 118. The control unit 108 incorporates a phase alignment module 114, which receives the digitized multi-phase signal and synchronizes the phases to a common reference, ensuring coherent timing and accurate phase relationships across all signal components. Further, following phase alignment, the coordinate transformation module 116 converts the synchronized multi-phase digital signals into a rotating reference frame, represented by direct (d) and quadrature (q) components, simplifying the analysis and manipulation of the signals. Subsequently, the filtering module 118 processes the transformed signals to suppress noise and extract a representative signal value, enhancing signal clarity and reliability. Specifically, the phase alignment module 114 operates by adjusting phase discrepancies among the incoming signals, aligning them to eliminate phase shifts that affect downstream processing. Afterwards, the coordinate transformation module 116 transforms the multi-phase signals from the stationary reference frame to a rotating frame, which facilitates the decoupling of components and simplifies control algorithms. The filtering module 118 applies a distance-based filtering approach that identifies characteristic boundary values and selects representative signal samples within a predefined sampling window, reducing noise and transient disturbances while preserving critical signal information. Furthermore, the combined operation of the above-mentioned modules in the control unit 108 results in a significant improvement in signal fidelity and control accuracy. The synchronization of phases ensures consistent signal timing; coordinate transformation reduces computational complexity and enables more effective filtering. The filtering module’s 118 noise reduction technique enhances the precision of signal representation, leading to more reliable generation of control signals for downstream components such as gate drivers 110 and utilization units 112. Advantages of the above-mentioned modules include improved system stability, enhanced noise immunity, and optimized control performance, ultimately contributing to higher efficiency and durability of the entire signal processing system 100.
In an embodiment, the sensing unit 104 is configured to sense at least one electrical parameter of the multi-phase signal and send the sensed at least one electrical parameter to the at least one analog-to-digital converter 106. The sensing unit 104 detects at least one electrical parameter of the multi-phase signal supplied by the energy source 102. Specifically, the electrical parameters include voltage, current, or any other measurable characteristic essential for accurate signal analysis. Further, the sensing unit 104 captures real-time analog data corresponding to the multi-phase signal and transmits the data to at least one analog-to-digital converter 106 for further processing. Furthermore, the sensing unit 104 employs appropriate sensors designed to ensure high precision and fast response to variations in the electrical parameters. By continuously monitoring the multi-phase signal, the sensing unit 104 provides accurate and timely information required for subsequent signal conditioning steps. The accurate data ensures that the analog-to-digital converter 106 receives reliable and representative analog signals to digitize and forward to the control unit 108. Further, the integration of the sensing unit 104 in the system 100 facilitates real-time monitoring and accurate measurement of electrical parameters, leading to improved signal fidelity and overall system performance. Furthermore, early detection of anomalies or disturbances within the multi-phase signal is enabled, allowing prompt corrective measures through the control unit 108. Additionally, the advantages of the sensing unit 108 include enhanced system reliability, better noise management, and optimized control of downstream components such as gate drivers and utilization units.
In an embodiment, the at least one analog-to-digital converter 106 is configured to receive the sensed at least one electrical parameter and sample the received at least one electrical parameter to generate a multi-phase sampling window of at least two samples representing the multi-phase signal. The at least one analog-to-digital converter 106 receives the sensed electrical parameter from the sensing unit 104 and performs sampling to convert the continuous analog signal into a discrete digital representation. The sampling occurs within defined intervals, capturing at least two samples per multi-phase signal cycle to form a multi-phase sampling window. The samples preserve the essential characteristics of the original signal, enabling accurate digital analysis. The sampling process uses precise timing mechanisms to ensure synchronization with the multi-phase signal's frequency and phase. Further, by generating the multi-phase sampling window, the analog-to-digital converter 106 provides a structured digital dataset that reflects variations in amplitude and phase of the multi-phase signal. Subsequently, the digital representation forms the basis for advanced signal processing techniques executed within the control unit 108. Furthermore, employing the analog-to-digital converter 106 enhances signal accuracy and resolution by minimizing distortion and noise present in analog domains. The above-mentioned approach allows real-time digitization, supporting rapid and reliable downstream processing such as filtering and phase alignment. Additionally, the advantages include improved signal fidelity, better noise immunity, and enhanced system responsiveness, ultimately optimizing control of gate drivers and utilization units.
In an embodiment, the phase alignment module 114 is configured to receive the generated multi-phase digital signal and synchronize the phases of the received multi-phase digital signal. The phase alignment module 114 receives the multi-phase digital signal generated by the analog-to-digital converter 106 and performs phase synchronization across all phases. The synchronization practice involves detecting phase differences among the individual signal components and adjusting phase differences to a common reference, ensuring temporal alignment. Specifically, the phase alignment module 114 employs algorithms that compensate for phase shifts produced by transmission delays or signal distortions, maintaining coherence across the multi-phase digital signal. Further, the phase synchronization enables accurate comparison and processing of the multi-phase signal components, which is critical for subsequent modules such as coordinate transformation and filtering. The advantage of phase alignment is enhanced signal integrity and improved system stability, as misaligned phases lead to incorrect control decisions. The approach ensures precise timing, reduces errors due to phase mismatch, and facilitates effective signal conditioning and utilization in downstream processing.
In an embodiment, the coordinate transformation module 116 is configured to receive the synchronized multi-phase digital signal and transform the received multi-phase digital signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component. The coordinate transformation module 116 receives the synchronized multi-phase digital signal from the phase alignment module 114 and converts the signal from the stationary reference frame to a rotating reference frame. The transformation separates the multi-phase signal into two orthogonal components: a direct (d) component and a quadrature (q) component. The rotating reference frame aligns with the instantaneous phase angle of the signal, and thereby simplifies the analysis and control of the multi-phase system by reducing the time-varying components into steady-state values. Further, the transformation into the d-q domain enables efficient processing by isolating active and reactive components of the signal, which are crucial for power system monitoring and control. The above-mentioned approach facilitates the application of filtering and control techniques in a frame as signal variations are minimized, improving accuracy and response time. The approach supports advanced algorithms for signal conditioning, fault detection, and control without the complexity of handling multi-phase signals in the original form. The coordinate transformation provides enhanced signal clarity and reduced computational complexity in downstream modules such as filtering and control. Further, the advantages of the coordinate transformation include improved dynamic response and stability of the system, enabling precise control of the utilization unit 112 and effective mitigation of noise and distortions in the multi-phase signal. The procedure ensures reliable performance in various operating conditions by simplifying signal representation and facilitating accurate real-time analysis.
In an embodiment, the filtering module 118 is configured to receive the transformed d-q components and identify a first characteristic boundary value and a second characteristic boundary value within a multi-phase sampling window of the multi-phase digital signal. The filtering module 118 receives the transformed d-q components from the coordinate transformation module 116 and analyses a multi-phase sampling window of the multi-phase digital signal. Within the sampling window, the module 118 identifies a first characteristic boundary value, representing the minimum signal amplitude, and a second characteristic boundary value, representing the maximum signal amplitude. The boundary values define the range of signal variation within the window, serving as reference points for subsequent filtering operations. The procedure involves examining signal samples within the predefined window to establish the dynamic range and detect noise components that fall outside the boundary values. Further, by capturing the extreme values within the window, the filtering module 118 ensures that noise spikes or anomalies do not influence the representative signal selection. The step enhances the robustness of the filtering process by focusing on the most relevant signal data points and excluding distortions caused by transient events or interference. The identification of the characteristic boundary values improves signal quality and stability by enabling precise noise suppression without loss of critical signal information. Advantages of the characteristic boundary values include enhanced accuracy in signal reconstruction and improved reliability of control commands generated downstream.
In an embodiment, the filtering module 118 is configured to derive a reference point based on the identified first characteristic boundary value and the second characteristic boundary value, and compute the proximity of each signal sample to the reference point. The filtering module 118 derives a reference point by calculating the mean value between the identified first characteristic boundary value and the second characteristic boundary value within the multi-phase sampling window. The reference point serves as a central benchmark representing the typical amplitude level of the filtered signal segment. Subsequently, the module 118 evaluates the proximity of each signal sample in the sampling window relative to the reference point, using a distance metric that quantifies the deviation of each sample from the mean. The approach involves comparing each sample's value against the reference point to determine which samples lie closest to the expected signal level, thereby identifying the most representative signal data within the noisy environment. Further, by prioritizing samples nearest to the reference point, the module effectively suppresses outlier samples that represent noise or transient disturbances. The proximity computation supports the selection of a signal sample that best characterizes the true underlying signal dynamics in the d-q domain. The derivation of the reference point and computing sample proximity enhances filtering accuracy and noise rejection capability. Advantages of the reference point include improved signal fidelity and stability, enabling more precise control of downstream components such as the gate driver 110 and utilization unit 112. Furthermore, the above-mentioned approach reduces signal distortion and improves overall system reliability by ensuring that control commands are generated from accurately filtered signal inputs, supporting robust and efficient operation of the signal processing system 100.
In an embodiment, the filtering module 118 is configured to determine a representative signal value, and wherein the representative value is the sample value with minimal deviation from the computed reference point. The filtering module 118 determines a representative signal value by analyzing the proximity of each sample within the multi-phase sampling window to the previously computed reference point. The representative signal value corresponds to the sample exhibiting the minimal deviation from the reference point, thereby serving as the most accurate reflection of the true signal amid noise and fluctuations. The selection process ensures that the chosen sample most closely matches the expected signal characteristics defined by the reference point. The approach involves comparing all sampled values within the window and identifying the one with the smallest absolute difference or distance metric relative to the reference point. By prioritizing this minimal deviation, the filtering module 118 effectively suppresses outliers and transient disturbances, which deviate significantly from the typical signal behavior. The representative value thus serves as a reliable and stable indicator of the signal’s actual state in the transformed d-q domain. The selection of the minimal deviation sample as the representative signal enhances overall signal integrity and robustness of the filtering process. Advantages of the minimal deviation include improved noise immunity and increased precision in subsequent control decisions made by the control unit. The representative value provides a stable input to downstream components, such as gate drivers and utilization units, thereby optimizing system performance and reliability in managing the multi-phase signal.
In an embodiment, the filtering module 118 is configured to generate a reconstructed signal in the d-q domain based on the selected representative signal value and selectively convert the reconstructed signal from the d-q domain to a multi-phase signal format. The filtering module 118 generates a reconstructed signal in the d-q domain by utilizing the selected representative signal value as the foundational component. The reconstruction process involves arranging the filtered representative values to form a continuous and coherent signal within the rotating reference frame. The reconstructed signal maintains the essential characteristics of the original multi-phase signal while eliminating noise and distortions present in the raw sampled data. Subsequently, the reconstructed signal undergoes a coordinate transformation from the d-q domain back into the multi-phase signal format. The reverse transformation ensures compatibility with the downstream components and systems designed to operate on multi-phase signals. Further, by converting the filtered data into the original multi-phase domain, the system preserves signal integrity while enabling accurate control and utilization in applications requiring multi-phase inputs. The reconstruction and conversion of the signal enhances overall system accuracy and stability by providing a noise-reduced and coherent multi-phase signal output. Advantages of the reconstruction include improved responsiveness and precision in controlling utilization units and gate drivers, leading to optimized energy management and reduced operational errors. The above-mentioned approach strengthens the robustness of the entire signal processing system, facilitating reliable performance in dynamic and noisy electrical environments.
In an embodiment, the control unit 108 is configured to transmit the converted multi-phase signal to the at least one gate driver 110 for controlling the utilization unit 112. The control unit 108 transmits the converted multi-phase signal, reconstructed from the d-q domain, to the at least one gate driver 110 as an actionable control signal. The transmission process involves encoding and dispatching signal values that represent the desired electrical behavior or operational state. The multi-phase format ensures that the signal aligns with the hardware architecture and timing requirements of the gate driver 110, enabling precise control actions. The gate driver 110 interprets the received control signal and adjusts the switching behavior of power electronic components interfacing with the utilization unit 112. Further, the control signal modulates parameters such as pulse width, timing, and sequencing, directly influencing the flow of electrical power or signal characteristics to the utilization unit. The control unit 108 maintains signal fidelity and phase accuracy to ensure that the gate driver operates synchronously with the system’s real-time demands. The above-mentioned transmission process enhances the dynamic performance and reliability of the control architecture. Additionally, the advantages of the gate driver 110 include accurate signal reproduction in multi-phase form, reduced latency in actuation, and improved coordination between processing and execution layers. The use of filtered and transformed signals ensures efficient operation of utilization units, reduction of switching losses, and precise modulation of power or signals, supporting high-performance electrical systems.
In an exemplary embodiment, an electric vehicle motor control system uses a signal processing system configured to refine real-time current feedback. The energy source is a 400 V lithium-ion battery supplying a three-phase inverter. The inverter drives a three-phase induction motor, and the sensing unit 104 measures the phase currents from the motor stator terminals. The analog-to-digital converter 106 samples each of the three-phase currents at a rate of 10 kHz, producing a digital signal stream. Further, at a given instant, the sampled d-axis component values in a 5-sample window are: [3.1, 2.8, 3.4, 3.0, 2.9] amperes. The filtering module 118 identifies the maximum and minimum values in the window, 3.4 A and 2.8 A, respectively and computes the difference (0.6 A). The midpoint or average of the max and min values is determined to be 3.1 A. The module 118 further evaluates each sample’s distance from the central value and selects the sample closest to the midpoint. For instance, the 3.1 acts as the representative signal value. Subsequently, the selected value forms the basis for reconstructing a clean signal in the d-q domain. Further, after optional back-transformation into the original three-phase system, the refined signal is transmitted to a gate driver. The gate driver uses the filtered information to issue precise switching commands to the inverter, resulting in smoother torque output and reduced electrical noise in the motor operation, thereby improving driving efficiency and system robustness.
In accordance with a second aspect, there is described a method for filtering a multi-phase signal supplied by an energy source, the method comprises:
- sensing at least one electrical parameter of the multi-phase signal, via a sensing unit;
- converting the sensed electrical parameter into a digital signal, via at least one analog-to-digital converter;
- transforming the synchronized signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component using a coordinate conversion module;
- selecting a representative signal value from a sampling window using a filtering module; and
- reconstructing a filtered signal based on the selected representative value.
Figure 3 describes a method 200 for filtering a multi-phase signal supplied by an energy source 102. The method 200 starts at a step 202. At the step 202, the method 200 comprises sensing at least one electrical parameter of the multi-phase signal, via a sensing unit 108. At a step 204, the method 200 comprises converting the sensed electrical parameter into a digital signal, via at least one analog-to-digital converter. At a step 206, the method 200 comprises transforming the synchronized signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component using a coordinate conversion module 116. At a step 208, the method 200 comprises selecting a representative signal value from a sampling window using a filtering module 118. At a step 210, the method 200 comprises reconstructing a filtered signal based on the selected representative value.
In an embodiment, the method 200 comprises sampling the least one electrical parameter to generate a multi-phase sampling window of at least two sample representing the multi-phase signal, via the at least one analog-to-digital converter 106.
In an embodiment, the method 200 comprises synchronizing the phases of the received multi-phase digital signal via the phase alignment module 114.
In an embodiment, the method 200 comprises identifying a first characteristic boundary value and a second characteristic boundary value within multi-phase sampling window of the multi-phase digital signal, via the filtering module 118.
In an embodiment, the method 200 comprises deriving a reference point based on the identified first characteristic boundary value and the second characteristic boundary value and computing the proximity of each signal sample to the reference point, via the filtering module 118.
In an embodiment, the method 200 comprises transmitting the converted multi-phase signal to the at least one gate driver 110 for controlling the utilization unit 112, via the control unit 108.
In an embodiment, the method 200 comprises sampling the least one electrical parameter to generate a multi-phase sampling window of at least two sample representing the multi-phase signal, via the at least one analog-to-digital converter 106. Further, the method 200 comprises synchronizing the phases of the received multi-phase digital signal via the phase alignment module 114. Furthermore, the method 200 comprises identifying a first characteristic boundary value and a second characteristic boundary value within a multi-phase sampling window of the multi-phase digital signal, via the filtering module 118. Furthermore, the method 200 comprises deriving a reference point based on the identified first characteristic boundary value and the second characteristic boundary value, and computing the proximity of each signal sample to the reference point, via the filtering module 118. Furthermore, the method 200 comprises transmitting the converted multi-phase signal to the at least one gate driver 110 for controlling the utilization unit 112, via the control unit 108.
In an embodiment, the method 200 comprises sensing at least one electrical parameter of the multi-phase signal, via a sensing unit 108. Furthermore, the method 200 comprises converting the sensed electrical parameter into a digital signal, via at least one analog-to-digital converter. Furthermore, the method 200 comprises sampling the least one electrical parameter to generate a multi-phase sampling window of at least two sample representing the multi-phase signal, via the at least one analog-to-digital converter 106. Furthermore, the method 200 comprises synchronizing the phases of the received multi-phase digital signal via the phase alignment module 114. Furthermore, the method 200 comprises transforming the synchronized signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component using a coordinate transformation module. Furthermore, the method 200 comprises identifying a first characteristic boundary value and a second characteristic boundary value within multi-phase sampling window of the multi-phase digital signal, via the filtering module 118. Furthermore, the method 200 comprises deriving a reference point based on the identified first characteristic boundary value and the second characteristic boundary value, and computing the proximity of each signal sample to the reference point, via the filtering module 118. Furthermore, the method 200 comprises selecting a representative signal value from a sampling window using a filtering module 118. Furthermore, the method 200 comprises reconstructing a filtered signal based on the selected representative value. Furthermore, the method 200 comprises transmitting the converted multi-phase signal to the at least one gate driver 110 for controlling the utilization unit 112, via the control unit 108.
Based on the above-mentioned embodiments, the present disclosure provides significant advantages of providing a signal processing system capable of accurately filtering and conditioning electrical signals derived from an energy source using coordinated phase alignment, transformation, and advanced filtering techniques.
It would be appreciated that all the explanations and embodiments of the system 100 also apply mutatis-mutandis to the method 200.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combinations of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, and “is” used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings, and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A signal processing system (100) for filtering a multi-phase signal supplied by an energy source (102), the system (100) comprises:
- a sensing unit (104) communicably coupled to the energy source (102);
- at least one analog-to-digital converter (106) communicably coupled to the sensing unit (104);
- a control unit (108) communicably coupled to the at least one analog-to-digital converter (106);
- at least one gate driver (110) communicably coupled to the control unit (108); and
- at least one utilization unit (112) electrically coupled to the control unit (108),
wherein the control unit (108) is configured to control at least one utilization unit (112) based on at least one input received from the at least one gate driver (110).

2. The system (100) as claimed in claim 1, wherein the control unit (108) comprises a phase alignment module (114), a coordinate conversion module (116), and a filtering Module (118).

3. The system (100) as claimed in claim 1, wherein the sensing unit (104) is configured to sense at least one electrical parameter of the multi-phase signal and send the sensed at least one electrical parameter to the at least one analog to digital converter (106).

4. The system (100) as claimed in claim 1, wherein the at least one analog-to-digital converter (106) is configured to receive the sensed at least one electrical parameter and sample the received at least one electrical parameter to generate a multi-phase sampling window of at least two samples representing the multi-phase signal.

5. The system (100) as claimed in claim 2, wherein the phase alignment module (114) is configured to receive the generated multi-phase digital signal and synchronize the phases of the received multi-phase digital signal.

6. The system (100) as claimed in claim 2, wherein the coordinate transformation module (116) is configured to receive the synchronized multi-phase digital signal and transform the received multi-phase digital signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component.

7. The system (100) as claimed in claim 2, wherein the filtering module (118) is configured to receive the transformed d-q components and identify a first characteristic boundary value and a second characteristic boundary value within multi-phase sampling window of the multi-phase digital signal.

8. The system (100) as claimed in claim 2, wherein the filtering module (118) is configured to derive a reference point based on the identified first characteristic boundary value and the second characteristic boundary value, and compute the proximity of each signal sample to the reference point.

9. The system (100) as claimed in claim 2, wherein the filtering module (118) is configured to determine a representative signal value, and wherein the representative value is the sample value with minimal deviation from the computed reference point.

10. The system (100) as claimed in claim 2, wherein the filtering module (118) is configured to generate a reconstructed signal in the d-q domain based on the selected representative signal value and selectively convert the reconstructed signal from the d-q domain to a multi-phase signal format.

11. The system (100) as claimed in claim 1, wherein the control unit (108) is configured to transmit the converted multi-phase signal to the at least one gate driver (110) for controlling the utilization unit (112).

12. A method (200) for filtering a multi-phase signal supplied by an energy source (102), the method (200) comprising:
- sensing at least one electrical parameter of the multi-phase signal, via a sensing unit (108);
- converting the sensed electrical parameter into a digital signal, via at least one analog-to-digital converter (106);
- transforming the synchronized signal into a rotating reference frame comprising a direct (d) component and a quadrature (q) component, via a coordinate conversion module (116);
- determining a representative signal value from a sampling window, via a filtering module (118); and
- reconstructing a filtered signal based on the selected representative value, via the filtering module (118).