DESC:Field of the Invention The present invention relates generally to intelligent electrical power distribution, predictive power continuity management, automatic phase selection, and embedded intelligent control systems. More particularly, the invention relates to intelligent predictive phase management and adaptive switching techniques configured to provide reliable and substantially continuous power delivery in residential, commercial, industrial, telecom, and infrastructure environments operating under varying multi-phase electrical conditions. Background of the Invention Multi-phase electrical supply systems are widely used in residential, commercial, industrial, telecom, and infrastructure environments for powering critical electrical loads. In many practical installations, incoming power phases frequently experience voltage imbalance, neutral instability, single-phase outages, voltage fluctuation, harmonic distortion, transient disturbances, phase instability, and intermittent phase failures due to varying grid conditions, uneven loading, environmental factors, and infrastructure limitations. Such electrical abnormalities can adversely affect connected equipment including motors, pumps, automation systems, telecom equipment, and sensitive electronic devices, thereby resulting in operational interruption, overheating, malfunction, reduced equipment lifespan, restart conditions, and unexpected downtime. Conventional automatic phase selectors and changeover systems generally rely on fixed-threshold electromechanical relay logic and discrete electrical components for detecting phase failure and switching between available phases. These conventional systems primarily operate in a reactive manner, wherein switching decisions are initiated only after a phase voltage drops below a predefined threshold or complete phase failure occurs. As a result, the switching operation often occurs after the connected load has already experienced instability, interruption, restart conditions, electrical stress, or transient disturbances. Further, existing systems typically lack the capability to continuously analyze voltage fluctuation trends, degradation behavior, harmonic distortion, waveform quality, transient conditions, electrical disturbance patterns, and historical instability behavior associated with incoming phases. Conventional systems are generally incapable of intelligently evaluating short-term voltage quality trends or identifying early-stage phase degradation before actual phase failure occurs. Existing systems also do not provide predictive phase evaluation, remaining stability estimation, adaptive load-aware control, predictive switching analysis, or intelligent electrical risk evaluation prior to phase transfer. Moreover, different types of connected electrical loads such as motors, compressors, pumps, automation systems, telecom equipment, lighting systems, and electronic devices exhibit different electrical behavior characteristics and operational sensitivities. Existing phase selection systems generally do not dynamically identify connected load behavior or adapt switching sensitivity, protection logic, hysteresis parameters, and phase evaluation criteria according to varying load conditions. Conventional relay-based systems further fail to proactively determine which available phase is likely to remain stable for a longer duration under changing electrical and load conditions. Additionally, conventional relay-based switching systems often introduce switching delay, transient stress, inrush current effects, and temporary interruption during phase transition, thereby reducing reliability and operational continuity for sensitive and critical electrical loads. Accordingly, there exists a need for an intelligent firmware-controlled predictive power continuity management system capable of continuously monitoring multi-phase electrical conditions, predictively evaluating phase quality, identifying instability prior to actual phase failure, estimating phase stability duration, adaptively analyzing connected load behavior, performing predictive switching impact evaluation prior to actual phase transfer, and executing waveform-synchronized hybrid switching to provide substantially continuous, reliable, and optimized power delivery to connected electrical loads. Objective of the Invention The principal objective of the present invention is to provide an intelligent and predictive power continuity management system for multi-phase electrical supply environments. Another objective of the present invention is to continuously monitor primary and secondary electrical parameters associated with multiple incoming power phases for real-time phase quality evaluation. Another objective of the present invention is to predict phase instability prior to actual phase failure by analyzing voltage fluctuation trends, degradation behavior, harmonic distortion, waveform quality, transient conditions, and historical instability patterns associated with incoming phases. Another objective of the present invention is to compute dynamic health scores for incoming phases and intelligently identify a reliable operating phase for substantially continuous power delivery. Another objective of the present invention is to estimate a predicted stable operating duration associated with each incoming phase for proactive phase selection and predictive continuity management. Another objective of the present invention is to automatically identify connected load behavior and adapt switching sensitivity, scoring parameters, hysteresis logic, and protection behavior according to varying load conditions. Another objective of the present invention is to perform predictive switching impact evaluation prior to actual phase transfer by estimating transient disturbance, switching stress, voltage variation, expected load response, and outage probability associated with a proposed switching operation. Another objective of the present invention is to predict and evaluate electrical risk indicators including equipment damage risk, transient severity, switching stress, and outage probability for intelligent phase evaluation and switching decision-making. A further objective of the present invention is to perform waveform-synchronized hybrid switching using semiconductor switching devices and electromechanical relay coordination for reducing transient disturbance, inrush current effects, equipment stress, and supply interruption during phase transition. Summary of the Invention This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The present invention provides an intelligent and predictive power continuity management system configured for multi-phase electrical supply environments. The invention introduces a standalone smart phase selector system capable of continuously monitoring and evaluating electrical conditions associated with multiple incoming power phases to provide substantially continuous and reliable power delivery to connected electrical loads. The system utilizes real-time voltage and phase quality measurement, intelligent firmware-driven scoring algorithms, predictive operational analysis, and high-speed hybrid switching mechanisms for intelligent phase selection and continuity management. The invention continuously senses primary and secondary electrical parameters including voltage level, current characteristics, voltage fluctuation behavior, harmonic distortion, transient conditions, waveform quality, and phase stability information associated with incoming phases. Based on the monitored electrical conditions, the system computes dynamic health scores for each incoming phase and performs predictive instability analysis using voltage fluctuation trends, degradation behavior, historical instability patterns, and health score variation. The invention further includes a phase stability forecast engine configured to estimate how long each incoming phase is likely to remain stable and suitable for supplying power before instability occurs. Accordingly, the controller may select a phase based not only on present electrical condition but also on predicted long-term operational reliability. The invention further incorporates adaptive load intelligence configured to automatically identify connected load type by analyzing electrical behavior signatures associated with the connected load. The system may identify motor loads, inductive loads, compressor loads, lighting systems, automation equipment, and electronic loads based on startup current behavior, waveform response, operational cycle patterns, and power factor characteristics. Based on the identified load behavior, the controller dynamically modifies switching sensitivity, scoring weights, hysteresis logic, protection thresholds, and phase evaluation parameters for adaptive system operation. In one embodiment, the invention further includes a predictive switching evaluation engine configured to evaluate a proposed switching operation prior to actual phase transfer. The controller predicts transient disturbance, voltage variation, switching stress, expected load response, equipment damage risk, outage probability, and transient severity associated with the switching operation. Based on the predictive evaluation, the system intelligently determines whether immediate switching, delayed switching, or continued operation on an existing phase provides improved operational continuity and reduced electrical disturbance. The invention further performs harmonic distortion intelligence by continuously analyzing waveform distortion, harmonic noise, industrial electrical interference, and power quality degradation associated with incoming phases. Additionally, the system includes an electrical risk prediction engine configured to evaluate equipment damage risk, outage probability, transient severity, switching stress, and operational reliability for intelligent phase selection and switching decision-making. The system performs waveform-synchronized hybrid switching using semiconductor switching devices and electromechanical relay coordination near waveform zero-cross conditions to reduce transient disturbance, inrush current effects, equipment stress, and supply interruption during phase transition. The invention further stores operational history, switching events, instability patterns, degradation behavior, and learning data for adaptive future decision-making and predictive continuity management. Accordingly, the proposed invention provides a predictive firmware-based intelligent phase management architecture capable of proactive phase evaluation, adaptive load-aware control, predictive switching impact analysis, intelligent electrical risk prediction, and substantially continuous phase transition with reduced disturbance, thereby offering significant improvement over conventional reactive relay-based phase selectors. Brief description of the drawings The figures described below depict various aspects of the system and methods disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals. FIG. 1 illustrates a basic block diagram of an intelligent phase selection and predictive power continuity management system (100), according to one embodiment of the present invention. FIG. 2 illustrates an overall functional architecture of the intelligent phase selection and predictive power continuity management system (200), according to one embodiment of the present invention. FIG. 3 illustrates an operational flowchart of the intelligent phase selection and predictive power continuity management system (300), according to one embodiment of the present invention. Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may have not been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures. Detailed Description of the Invention The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness. The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents. It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces. By the term “substantially” it is meant that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic is intended to provide. Figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions, in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element. The present invention relates to an intelligent and predictive power continuity management system configured for multi-phase electrical supply environments. The invention provides a standalone smart phase selector system capable of continuously monitoring and evaluating electrical conditions associated with multiple incoming power phases in order to provide substantially continuous and reliable power delivery to connected electrical loads. The system receives multi-phase electrical input supply and continuously senses various electrical parameters associated with the incoming phases. The monitored electrical parameters may include voltage level, current characteristics, voltage fluctuation behavior, phase stability information, harmonic distortion, transient conditions, waveform quality, and electrical disturbance patterns. Based on the sensed electrical conditions, the system computes dynamic health scores for each incoming phase and continuously analyzes operational behavior to identify degradation trends, instability patterns, and abnormal electrical conditions prior to actual phase failure. The invention includes a predictive phase analysis mechanism configured to estimate how long each incoming phase is likely to remain stable and suitable for supplying power before instability occurs. The system may therefore select an operating phase based not only on present electrical condition but also on predicted long-term operational reliability. The invention further incorporates adaptive load intelligence capable of automatically identifying connected load behavior by analyzing electrical characteristics associated with connected equipment. Based on the identified load type and operational behavior, the system dynamically adjusts switching sensitivity, scoring parameters, hysteresis logic, protection thresholds, and phase evaluation criteria for adaptive and optimized operation under varying electrical and load conditions. The invention further includes a predictive switching evaluation mechanism configured to analyze the expected impact of a proposed switching operation before actual phase transfer occurs. The system evaluates transient disturbance, switching stress, voltage variation, expected load response, equipment damage risk, outage probability, and operational reliability associated with the switching operation. Based on the predictive evaluation, the controller intelligently determines whether immediate switching, delayed switching, or continued operation on an existing phase provides improved continuity and reduced electrical disturbance. The system further performs waveform-synchronized hybrid switching using semiconductor switching devices and electromechanical relay coordination to reduce transient disturbance, inrush current effects, equipment stress, and supply interruption during phase transition. Additionally, the system stores operational history, switching events, degradation behavior, and instability patterns for adaptive future decision-making and predictive continuity management. Accordingly, the proposed invention provides a predictive firmware-based intelligent phase management architecture capable of proactive phase evaluation, adaptive load-aware control, predictive switching analysis, intelligent electrical risk prediction, and substantially continuous power delivery with improved reliability and reduced disturbance. FIG. 1 illustrates a basic block diagram of an intelligent phase selection and predictive power continuity management system (100), according to one embodiment of the present invention. As illustrated, the system includes an electrical hardware module (105) operatively connected to a microcontroller (110). The electrical hardware module receives a multi-phase electrical input supply including R phase, Y phase, B phase, and neutral input lines associated with a three-phase electrical supply system. The electrical hardware module may include sensing circuitry, signal conditioning circuitry, switching circuitry, relay drivers, semiconductor switching components, protection circuitry, and associated electrical interface components configured for intelligent phase monitoring and controlled power delivery. In one embodiment, the microcontroller continuously monitors electrical conditions associated with the incoming phases and evaluates each incoming phase using predefined operational parameters and predictive evaluation criteria. The monitored electrical parameters may comprise primary electrical parameters including voltage level, current characteristics, phase stability information, and voltage fluctuation behavior associated with the incoming phases, and secondary electrical parameters including harmonic distortion, transient behavior, waveform quality, power quality degradation, and electrical disturbance patterns associated with the incoming phases. The microcontroller computes dynamic health scores for the incoming phases and continuously analyzes voltage fluctuation trends, degradation behavior, instability patterns, historical operational behavior, and rate of health score variation in order to predict instability prior to occurrence of actual phase failure. The microcontroller further performs predictive phase evaluation by estimating how long each incoming phase is likely to remain stable and suitable for supplying power before instability occurs. Based on the predictive analysis, the controller may select a phase not only according to present electrical condition but also according to predicted long-term operational reliability associated with the incoming phases. In one exemplary embodiment, the controller may estimate remaining stable operating duration associated with each incoming phase based on voltage stability variation, fluctuation trends, degradation behavior, historical instability patterns, and dynamic health score analysis. For example, an incoming R phase may exhibit a current health score of 92 with a predicted stable duration of 2 minutes, while a Y phase may exhibit a health score of 88 with a predicted stable duration of 25 minutes, and a B phase may exhibit a health score of 84 with a predicted stable duration of 40 minutes. In such conditions, the controller may select the B phase or Y phase instead of the R phase in order to improve long-term operational continuity and reduce future instability risk. Accordingly, the invention performs predictive future optimization rather than merely present-condition evaluation. The controller automatically identifies an optimal operating phase by evaluating voltage deviation, operational stability, harmonic conditions, transient behavior, waveform quality, predicted reliability, and electrical risk conditions associated with the available incoming phases. The system further incorporates adaptive load intelligence configured to automatically identify connected load type based on electrical behavior signatures associated with connected equipment. The microcontroller may analyze startup current behavior, waveform response, operational cycle patterns, and power factor characteristics to identify motor loads, inductive loads, compressor loads, lighting systems, automation equipment, telecom equipment, or electronic devices. Based on the identified load behavior, the controller dynamically modifies switching sensitivity, scoring parameters, hysteresis logic, protection thresholds, and phase evaluation criteria for adaptive and optimized system operation under varying electrical and load conditions. Accordingly, prior to actual phase transfer, the microcontroller performs predictive switching impact evaluation by estimating transient disturbance, voltage variation, switching stress, expected load response, equipment damage risk, outage probability, and transient severity associated with a proposed switching operation. Based on the predictive evaluation, the controller intelligently determines whether immediate switching, delayed switching, or continued operation on an existing phase provides improved operational continuity and reduced electrical disturbance. The system further performs harmonic distortion intelligence by evaluating waveform distortion, harmonic noise, industrial electrical interference, and power quality degradation associated with the incoming phases for intelligent phase assessment and switching optimization. Additionally, the controller computes electrical risk indicators including equipment damage risk, transient severity, switching stress, and outage probability for selecting a lowest-risk operating phase among the available incoming phases. The selected operating phase is supplied through an output line (L) along with a neutral output line (N) for providing power to a connected electrical load. In one embodiment, the selected operating phase may be latched through controlled switching logic implemented using semiconductor switching devices and electromechanical relay coordination. The system performs waveform-synchronized hybrid switching near waveform zero-cross electrical conditions to reduce transient disturbance, inrush current effects, equipment stress, restart shock, and supply interruption during phase transition. Accordingly, the proposed invention provides an intelligent firmware-based predictive phase management architecture capable of proactive phase evaluation, adaptive load-aware control, predictive switching analysis, intelligent electrical risk prediction, and substantially continuous and reliable power delivery with reduced disturbance and improved operational reliability. In an embodiment of the invention is to provide a method for intelligent phase selection and predictive power continuity management in a multi-phase electrical supply system (200) is disclosed. The method includes continuously sensing electrical parameters associated with a plurality of incoming power phases through a multi-channel sensing module (210), computing dynamic health scores for the incoming phases using an intelligent control and analytics core (215), and analyzing operational behavior associated with the incoming phases to predict instability before occurrence of an actual phase failure. The method further includes selecting an operating phase based on predictive phase evaluation and performing switching between phases through a hybrid switching module (220) to maintain uninterrupted power delivery to a connected load through an output interface (235). The method additionally includes continuously monitoring the incoming phases during operation for adaptive predictive analysis and intelligent continuity management. FIG. 2 illustrates an overall functional architecture of an intelligent phase selection and predictive power continuity management system (200), according to one embodiment of the present invention. As illustrated, the system (200) includes a three-phase input supply module (205), a multi-channel sensing module (210), an intelligent control and analytics core (215), a hybrid switching module (220), a diagnostics and event logger module (225), a user interface and indications module (230), and an output interface (235) configured to provide substantially continuous and reliable power delivery to connected electrical loads. In one embodiment, the three-phase input supply module (205) receives incoming R phase, Y phase, B phase, and neutral input lines associated with a multi-phase electrical distribution system. The incoming phases are continuously monitored through the multi-channel sensing module (210), which may include voltage sensing circuitry, current sensing circuitry, power factor measurement circuitry, frequency measurement circuitry, harmonic analysis circuitry, transient and fluctuation capture circuitry, and neutral monitoring circuitry. The sensing module continuously acquires electrical conditions associated with the incoming phases and provides sensed electrical information to a data acquisition and conditioning unit configured for signal sampling, filtering, scaling, synchronization, isolation, and conditioning operations. In one embodiment, the voltage sensing circuitry continuously measures voltage magnitude associated with each incoming phase for identifying voltage imbalance, voltage dip conditions, overvoltage conditions, undervoltage conditions, and fluctuation behavior associated with the incoming phases. The current sensing circuitry continuously monitors current characteristics for identifying load demand variation, overload conditions, abnormal current behavior, transient current spikes, and operational stress conditions. The power factor measurement circuitry evaluates phase relationship characteristics between voltage and current for identifying inductive loads, capacitive loads, motor loads, compressor loads, and varying load behavior patterns. The frequency measurement circuitry continuously monitors operating frequency associated with incoming phases for identifying abnormal grid conditions and frequency instability behavior. Further, the harmonic analysis circuitry continuously analyzes waveform distortion, harmonic noise, industrial electrical interference, and power quality degradation associated with incoming phases for intelligent power quality assessment and predictive continuity management. The transient and fluctuation capture circuitry continuously detects transient spikes, voltage surges, rapid fluctuation behavior, sag and swell conditions, switching disturbances, and instability events associated with incoming phases. Additionally, the neutral monitoring circuitry continuously monitors neutral stability conditions for identifying neutral imbalance, floating neutral conditions, abnormal neutral behavior, and associated operational safety risks within the multi-phase electrical supply system. The monitored electrical parameters may comprise primary electrical parameters including voltage level, current characteristics, phase stability information, and voltage fluctuation behavior associated with the incoming phases, and secondary electrical parameters including harmonic distortion, transient behavior, waveform quality, power quality degradation, and electrical disturbance patterns associated with the incoming phases. The primary electrical parameters correspond to core operational electrical conditions directly associated with immediate phase availability, operational continuity, and basic load supply capability. These parameters are continuously utilized by the intelligent control and analytics core (215) for real-time phase evaluation, dynamic health score computation, phase availability determination, and immediate switching decision-making operations. For example, voltage level information enables identification of overvoltage, undervoltage, voltage imbalance, and phase loss conditions, while current characteristics assist in evaluating load demand variation, overload conditions, startup current behavior, and operational stress associated with connected loads. Phase stability information and voltage fluctuation behavior provide direct indication of short-term operational reliability, fluctuation severity, and instability associated with the incoming phases. Accordingly, the primary electrical parameters represent continuously changing real-time operational parameters essential for determining whether an incoming phase is presently suitable for supplying power to connected electrical loads. In contrast, the secondary electrical parameters correspond to advanced electrical quality analysis and predictive operational intelligence associated with long-term reliability, waveform integrity, disturbance analysis, and predictive risk evaluation. These parameters may not always indicate immediate phase failure but provide important information regarding hidden electrical degradation, power quality deterioration, transient disturbance conditions, and future instability behavior associated with the incoming phases. For example, harmonic distortion analysis and waveform quality evaluation enable identification of electrically noisy or distorted phases even when voltage magnitude remains within acceptable operating limits. Similarly, transient behavior analysis, power quality degradation evaluation, and electrical disturbance pattern analysis assist the intelligent control and analytics core (215) in predicting instability trends, evaluating switching impact, identifying abnormal operating conditions, estimating equipment stress, and determining outage probability associated with the incoming phases. The acquired electrical information is processed by the intelligent control and analytics core (215), which may be implemented using a firmware-controlled microcontroller, embedded processor, programmable control circuitry, or associated intelligent processing architecture. In one embodiment, the intelligent control and analytics core (215) includes a health scoring engine configured to compute dynamic health scores for each incoming phase based on voltage deviation, fluctuation index, sag and swell conditions, harmonic impact, power quality index, transient behavior, and real-time operational conditions associated with the incoming phases. In one embodiment, the dynamic health score associated with each incoming phase may be determined according to: H_p=w_1 V_s+w_2 F_s+w_3 Q_s+w_4 T_s+w_5 P_s where: H_prepresents a dynamic health score associated with an incoming phase; V_srepresents a voltage stability score; F_srepresents a voltage fluctuation score; Q_srepresents a waveform quality or harmonic quality score; T_srepresents a transient stability score; P_srepresents a phase reliability score; and w_1,w_2,w_3,w_4,and w_5represent adaptive weighting coefficients dynamically modified according to operating conditions and connected load behavior. The intelligent control and analytics core (215) further includes a stability forecast engine configured to continuously analyze voltage fluctuation trends, degradation speed, historical instability patterns, and rate of health score variation in order to estimate how long each incoming phase is likely to remain stable and suitable for supplying power before instability occurs. In one embodiment, predicted stable operating duration associated with each incoming phase may be determined according to: T_stable=H_p/(D_r+F_v+I_r ) where: T_stablerepresents predicted stable operating duration; H_prepresents current dynamic health score; D_rrepresents degradation rate; F_vrepresents fluctuation variation; and I_rrepresents instability rate associated with the incoming phase. Accordingly, the controller may select a phase not only according to present electrical condition but also according to predicted long-term operational reliability associated with the incoming phases. In one exemplary embodiment, an incoming R phase may exhibit a current health score of 92 with a predicted stable duration of 2 minutes, while a Y phase may exhibit a health score of 88 with a predicted stable duration of 25 minutes, and a B phase may exhibit a health score of 84 with a predicted stable duration of 40 minutes. In such conditions, the intelligent control and analytics core (215) may select the B phase or Y phase instead of the R phase in order to improve long-term continuity and reduce future instability risk. Accordingly, the invention performs predictive future optimization rather than merely present-condition evaluation. The intelligent control and analytics core (215) further includes an adaptive load intelligence engine configured to automatically identify connected load type based on electrical behavior signatures associated with connected equipment. The adaptive load intelligence engine may analyze startup current behavior, waveform response, operational cycle patterns, electrical consumption patterns, and power factor characteristics to identify motor loads, inductive loads, compressors, lighting systems, automation equipment, telecom equipment, or electronic devices. Based on the identified load behavior, the controller dynamically modifies switching sensitivity, scoring weights, hysteresis logic, protection thresholds, and phase evaluation criteria for adaptive and optimized system operation under varying electrical and load conditions. In one embodiment, the intelligent control and analytics core (215) further includes a digital twin simulation engine configured to perform predictive switching impact evaluation prior to actual phase transfer. The digital twin simulation engine virtually estimates transient disturbance, voltage variation, switching stress, restart shock, expected load response, equipment damage risk, outage probability, and transient severity associated with a proposed switching operation. Based on the predictive evaluation, the controller intelligently determines whether immediate switching, delayed switching, or continued operation on an existing phase provides improved continuity and reduced electrical disturbance. In one embodiment, electrical risk associated with incoming phases and proposed switching operations may be determined according to: R_e=aS_t+ßO_p+?D_e where: R_erepresents electrical risk index; S_trepresents switching stress; O_prepresents outage probability; D_erepresents equipment damage estimation; and a,ß,and ?represent adaptive weighting coefficients. In one embodiment, switching decisions may be dynamically determined according to: S_d={¦("Switch" ,&"if " H_n>H_c " and " R_e