METHOD OF CONTROLLING AN ELECTRONIC POWER DEVICE DESIGNED TO TRANSFER CURRENT TO A LOAD TO REGULATE THE TEMPERATURE OF THE LOAD FOLLOWING THE EARLY DETECTION OF MALFUNCTIONING IN A THERMAL PROCESS OF A MATERIAL

Description

I0206275-SP Owner: GEFRAN SpA METHOD OF CONTROLLING AN ELECTRONIC POWER DEVICE SUITABLE FOR TRANSFER CURRENT TO A LOAD TO REGULATE THE TEMPERATURE OF THE 5 LOAD FOLLOWING EARLY DETECTION OF MALFUNCTIONS OF A THERMAL PROCESS OF A MATERIAL DESCRIPTION

[0001] . TECHNOLOGICAL BACKGROUND OF THE INVENTION 10

[0002] . Scope of application

[0003] . The present invention relates, in general, to the field power control models for industrial applications in sectors such as, for example, plastics processing, packaging, the industry that supplies food and beverages (food & 15 beverage), the pharmaceutical sector, glass and metal processing and ceramics.

[0004] . In particular, the invention relates to a innovative method of controlling an electronic device power, usable in a process control system 20 thermal processing of a material, capable of transferring current to a load, for example resistive, to regulate the temperature or thermal state of the manufacturing process by detecting it in advance any malfunctions in the manufacturing process and by adopting appropriate resolution measures. 25

[0005] . Known art

[0006] . As is known, heat treatment processes or processes thermal processes can be divided into continuous and cyclic thermal processes from the regulatory point of view thermal. 5

[0007] . For these two types of thermal process, the occurrence of anomalies in the process can cause interruption of the production cycle, efficiency losses (energy or consumption) of the material), or it can lead to a reduced quality of the products manufactured, in the event that such products are not 10 compliant with pre-established specifications. Non-compliant products and low quality are, for example, the packages welded in a way incorrect, unsterilized bottles, food products not properly pasteurized.

[0008] . Some of the causes that can generate anomalies in the 15 thermal processes are linked to malfunctions of components of the thermal process control system.

[0009] . A first example of malfunction concerns heat losses that can occur between a load resistive and elements containing the material to be subjected to 20 heat treatment, caused by insulation losses or mechanical problems. Such heat losses can manifest as variations in power supplied to the load resistive, independent of the presence of material to be processed thermally, or as variations in the temperature of the 25 material to be processed with the same power supplied to the load resistive.

[0010] . A second example of malfunction concerns the deterioration or failure of the resistive load, which can manifest itself as: short circuit, i.e. the voltage applied to the 5 resistive load tends to zero; total load failures, i.e. the current applied to the resistive load tends to zero; breakdowns partial load, i.e. the current applied to the load resistive is less than pre-set process standard values; or in general loss of efficiency, so that the power supplied to the 10 material – and therefore the temperature of the material – decreases at equal electrical power transmitted to the load.

[0011] . A third example of malfunction concerns the malfunction of electrical connection elements between the alternating current power electronic device or 15 power controller (Power Controller) and the resistive load, so the electrical power transmitted to the resistive load is abnormally lower than the output power of the power controller. This difference leads to a decrease of thermal energy delivered by the resistive load and, consequently, 20 a decrease in the temperature of the material to be processed thermally at the same duty cycle on the current signal set by the power controller.

[0012] . A fourth example of malfunction concerns malfunctions of the power electronic system itself. 25 This device is embodied, in particular, in a converter power electronics using thyristors or SCRs (Silicon Controlled Rectifier) ​​connected to each other in a bridge.

[0013] . Such malfunctions manifest themselves in the inability to one or more of the thyristor devices themselves to deliver the same 5 power at the same duty cycle received from the controller process and input power from the power grid. Such malfunctions may be linked to phenomena of aging, whereby SCR devices dissipate more power to operate and, therefore, decreases the power in 10 exit.

[0014] . Furthermore, phenomena of increase may also occur of the resistance of one of the thyristors of the electronic converter of power or internal electrical connections of such device, whereby a drop in output current occurs 15 or even a cancellation of this current in case of breakdown of the thyristor. These phenomena are generally linked to the thyristor junction temperature or the temperature of the substrate (Direct Copper Bonding or DCB) on which such is mounted device. 20

[0015] . A fifth example of malfunction concerns malfunctions that may involve the control algorithm of the thermal process, for example caused by a change in the material to be thermally processed, which cannot be compensated by an electronic control unit 25 thermal process. Such malfunctions of the algorithm control were found, in particular, in the case of use of recycled materials for the production of materials plastics, or in the case of changes in the production system related to aging or other external factors. 5

[0016] . Methods and systems are known and already widely implemented configured to detect / identify the above malfunctions described. These methods and systems can be classified into three categories.

[0017] . A first category of methods and systems of 10 Malfunction detection / identification includes signals of alarms transmitted by the power electronics system in case of: absence of input voltage to the device; total or complete breakdowns partial load; abnormal temperature values ​​detected on the device's electronics. 15

[0018] . A second category of methods of malfunction detection / identification includes signals of alarms activated by the electronic process control unit as a consequence of the system's inability to reach the target process temperature or following a 20 drop in temperature of the material to be processed, with the same duty cycle transmitted to the power electronics. Such alarms arise from specific algorithms of the thermal process, but they require to be reset with each parameter change of the production process. Furthermore, being alarms that can 25 arise from multiple causes, making identification difficult of the physical phenomenon that caused the malfunction.

[0019] . A third category of methods of malfunction detection / identification includes checks quality carried out downstream of the production process itself, based 5 for example on manual random checks, visual analysis through optical devices or other specific mechanisms of the production process. Such quality controls, however, from a side do not allow to detect all the anomalies in the products and the malfunctions of the production process that may have caused them 10 caused, on the other hand they are difficult to correlate to the causes original malfunctions within the process productive.

[0020] . One of the main limitations of known methods and systems described above to identify malfunctions is related to the 15 fact that all three methods detect the malfunction on the process or product anomaly only when such malfunction has occurred, so it has already caused: leaks of time (production interruption to identify the problem, problem resolution and recovery 20 process), economic impacts (production delays, waste of material, defective product management) or even damages reputational or legal consequences, in case the products defective ones are not intercepted before being placed on the market market. 25

[0021] . Furthermore, the second and third methods of detection / identification of malfunctions described are detached from the thermal process and therefore require subsequent analysis to identify the cause of the failure.

[0022] . In light of the above, it is strongly felt 5 the need for a new method for regulating a thermal state of a load in a thermal process of processing of a material following the detection of manufacturing process malfunctions.

[0023] . SUMMARY OF THE INVENTION 10

[0024] . An object of the present invention is to to devise and make available a method of controlling a power electronic device, usable in a system control of a thermal process for processing a material, capable of transferring current to a heating load, for example 15 resistive, to regulate the temperature or thermal state of the load heating following an early detection of malfunctions in the manufacturing process that allow for overcome, at least partially, the above drawbacks complained with reference to the prior art. 20

[0025] . In particular, the aim of the invention is to make a control method is available that allows modeling the operation of the power electronics used in the context of thermal processes for the processing of materials in order to prevent, mitigate or correct malfunctions that 25 may concern both this power electronic device and the load powered by it.

[0026] . The method of the present invention is configured to generate corrections to the control parameters of the controllers thermal process in order to detect malfunctions in advance 5 of the system, keep the production process within the preset process parameters, at least for a range of significant and predetermined time, and accelerate identifying the cause of a malfunction.

[0027] . This goal is achieved by implementing 10 a method of controlling a power electronic apparatus in agreement with claim 1.

[0028] . A particular object of the invention is the implementation, on the power electronic device itself or on a further local intelligent element of the system 15 control of the manufacturing process, of a functional block representative of a digital twin converter of a real power converter included in the above mentioned power electronics device, capable of performing simulations on at least part of the control system components in a 20 time interval after the start or execution instant of the process which depends on the characteristics of the process, in agreement with claim 2.

[0029] . In relation to the different types of process productive, in the following we will distinguish thermal processes 25 executable during time intervals of unpredictable duration after the start of the manufacturing process, or executable during time intervals of predictable duration after the start of the manufacturing process. In this second case, these intervals of predictable duration can have medium / long duration 5 or short.

[0030] . For example, in the case of an extrusion process for production of plastic sheets, once the processing temperature of the plastic material to be extruded, This temperature is maintained for days or even months. For this reason 10 extrusion process we can therefore speak of a range of foreseeable processing time, in particular of medium / long duration.

[0031] . In the case of processes in the packaging sector, typically involve fast processing cycles, on the order of 15 milliseconds up to tens of minutes. Therefore, these processes can be considered applications with time intervals of processing of foreseeable duration, especially short duration, based on the type of production batch being processed.

[0032] . On the contrary, in the case of a steam production plant, 20 once the production temperature is reached, to ensure the maintaining this temperature when a request varies from part of multiple users, it may be necessary to modulate the heating thermal power in accordance with non-scheduled times predictable. Therefore, this type of thermal processing is 25 characterized by processing time intervals of non-duration predictable.

[0033] . Another object of the invention is to provide a method that allows to detect, in advance, the conditions of operation of thyristors of the power electronic converter 5 which equips the power electronic device to be controlled using a digital twin of such thyristor power electronic converter and use it in case of malfunctions of a specific production process to modify – directly or through the intervention of an operator 10 - the output parameters of an electronic control unit of the process and, at the same time, produce a set of information on the system status so as to enable the identification of such malfunctions.

[0034] . In particular, the method of the present invention 15 provides for the use of three functional blocks: a functional block digital twin or “digital twin” of the electronic converter thyristors (SCR), a functional feedback block, for the correction of the thermal processing parameters, and a functional block for analyzing the outputs of the digital block 20 twins for the identification of anomalies or malfunctions.

[0035] . The present invention also provides a system control of a thermal process for processing a material which implements the proposed method, in accordance with the claim 14. 25

[0036] . Some advantageous embodiments of the method and system are the subject of dependent claims.

[0037] . BRIEF DESCRIPTION OF THE DRAWINGS

[0038] . Further features and advantages of the method of control of a power electronic device, usable in a 5 control system of a thermal process of machining a material, to regulate the temperature or thermal state of a load following early detection and classification of malfunctions in the manufacturing process, will appear from the description below of his favorite examples of 10 realization, data for indicative purposes only, with reference to the attached figures in which:

[0039] . - figure 1 illustrates, by means of a block diagram, an example of a thermal process control system processing of a material configured to implement the method 15 of the present invention;

[0040] . - figure 2 illustrates, by means of a block diagram, an example of a power electronic device, for example in alternating current, included in the system of figure 1, capable of transfer current to a load, to regulate the temperature (or 20 thermal state) of the manufacturing process based on signals control signals sent from a process controller to the device power electronics;

[0041] . - figure 3 illustrates, by means of a block diagram functional, the interaction between an electronic converter of 25 power using thyristors or real SCR, a converter electronic digital twin or “digital twin” of the aforementioned real converter and a feedback algorithm configured for correct, following an early detection of process malfunctions, a duty parameter 5 cycle applied to real SCR power electronic converter starting from a twin electronic converter output digital;

[0042] . - figure 4 illustrates, by means of a block diagram functional, the interaction between the electronic converter of 10 power using thyristors or real SCR, the converter electronic digital twin or “digital twin” of the aforementioned real converter and a comparison algorithm configured for generate status information, related to a malfunction detected, based on an analysis of a converter output 15 electronic digital twin;

[0043] . - Figure 5 illustrates, by means of a flow chart, an example of a general implementation of the control method of a power electronic device, usable in a system control of a thermal process for processing a material, 20 to regulate the temperature or thermal state of a load in following an early detection and classification of malfunctions in the manufacturing process through the digital twin electronic converter or “digital twin”, of the present invention; 25

[0044] . - figure 6 illustrates, by means of a flow chart, an example of a particular implementation of a detection phase and classification of control method malfunctions of figure 5;

[0045] . - Figure 7 illustrates, by means of a flow chart, 5 an example of particular implementation of the phases of optimization of control algorithms and the phase of controlled / emergency shutdown of the power control method figure 5.

[0046] . In the above figures, equal or similar elements are 10 indicated with the same numerical references.

[0047] . DETAILED DESCRIPTION

[0048] . With reference to figure 1, the numerical reference 100 indicates a control system of a thermal process of processing of a material configured to implement the method 15 of control 200 proposed, in an example of embodiment. In the the control system of a thermal process 100 will be followed also referred to as control system or, more simply, system. Furthermore, in the following the term material will be used to indicate indifferently: metallic materials, plastic materials, glass, 20 food products, such as bread and pasta, and in general all those materials that can be subjected to a process processing heat.

[0049] . The control system 100 comprises a control element heat transmission 1 (Physical Product), configured to be 25 in contact with the aforementioned material to be processed thermal, that is, to be subjected to heating. This element of heat transmission 1 is, for example, made of material metal or other heat-resistant materials. In one example of realization, such heat transmission element 1 is 5 materializes in a welding blade.

[0050] . In a different embodiment, the element of heat transmission 1 takes place in a container element 1 suitable for containing the material to be subjected to heat treatment, in particular a box-shaped component of system 100 10 closed on all sides.

[0051] . In a further embodiment, the element container 1 can be closed only on some sides, to contain the material to be processed, whilst still ensuring the heat transfer to the material itself. For example, such 15 container element 1 is a tunnel oven comprising a respective entrance and an oven exit open to allow the input of the material to be processed and the unloading of the processed material, respectively.

[0052] . In the following description, reference will be made to 20 exemplifying, but not limiting, way to the element of heat transmission 1 represented as the container element of the material to be heated.

[0053] . System 100 comprises, for example, a probe temperature 2 (Temperature Probe), operationally associated 25 to the container element 1 to detect, for each instant, a current temperature Tmat of the material to be heated contained in container 1.

[0054] . In this case, the thermal regulation of the process consists in ensuring that the current temperature Tmat of the 5 material to be heated should be as similar as possible to a desired temperature or target temperature Ttgt. Discrepancies acceptable values ​​of the above mentioned temperatures Tmat and Ttgt are part of the regulation parameters of the thermal process.

[0055] . The control system 100 further comprises one or 10 plus 3 heating elements (Physical Heater), for example type resistive, such as resistors based on metal alloys, fluorescent lamps infrared or ultraviolet, silicon carbide elements, etc., suitable for heating the container element 1 containing the material to heat up. 15

[0056] . In particular, a thermal power WTres delivered from the heating element 3 depends on a first current Ires and from a first voltage Vres applied to that heating element 3, based on a technical data sheet released by the manufacturer of the resistive element itself. 20

[0057] . The control system 100 further comprises a power electronic device 10 (power controller), for example in alternating current, capable of transferring current to a load, in particular to the heating element 3. This electronic device of power 10 includes an electronic power converter 25 using thyristors or real SCR 101 and an appropriate control for transform an alternating current Isup and an alternating voltage Input power Vsup in a current Iout and in a output alternating voltage Vout that is transferred to the heating resistive element 3, through a module of 5 Power Transmission 4 (Power Transmission). Such current Iout and output voltage Vout are such that the thermal power WTres supplied by the heating resistive element 3 allows to bring the current temperature value Tmat of the material as much as possible as close as possible to the desired temperature value Ttgt. 10

[0058] . Note that the above transmission module of Power 4 of the 100 system includes connection elements electrical (e.g. cables or bars) suitable for connecting the device power electronics or power apparatus 10 with the load resistive 3. Such electrical connection elements can 15 exhibit specific physical design characteristics or linked to malfunctions, for which the values ​​of the first current Ires and first voltage Vres transmitted to the load resistive 3 are, generally, different from the current values Iout and Vout output voltage of the electronic device 20 power 10. In particular, for some applications, it is expected that system 100 also includes an electrical transformer connected between the power apparatus 10 and the resistive load 3.

[0059] . Furthermore, the power electronic apparatus 10 is configured to receive as input: a duty-cycle parameter 25 DTout representing a percentage of time during which the power apparatus 10 is adapted to enable the passage of the output current Iout; control parameters PFout suitable for describe how the above duty-cycle parameter should be applied from the power apparatus 10. 5

[0060] . The control system 100 further comprises a Process Controller 11 (Process Controller) configured for manage the parameters of the thermal process as a function of the processing to be carried out. In particular, this controller of process 11 is capable of receiving as input both the value of the 10 current temperature Tmat of the material detected by probe 2 is the desired temperature value Ttgt and is configured for change the value of the duty-cycle parameter DTout, instant for instant, to be supplied to the electronic apparatus 10 to bring the Tmat temperature value detected at the temperature value 15 desired Ttgt required for the current processing phase.

[0061] . In addition, the process controller 11 is configured to transmit to the power electronics 10 also the PFout control parameters that define how the parameter duty-cycle DTout must be applied to the current wave. 20

[0062] . In one embodiment, the controller of process 11 is a physical device, such as a logic controller programmable (Programmable Logic Controller or PLC), type independent (stand-alone) with respect to the electronic apparatus power 10 or can be integrated into such a device 25 power electronics.

[0063] . In a different embodiment, the process controller 11 is embodied in a logic algorithm implemented on various devices, such as: panels operator or HMI (Human Machine Interface), industrial PCs, Edge 5 Servers.

[0064] . The control system 100 further comprises a supervision unit 12 (supervision unit / cloud) configured for exchange process data with the process controller 11. The exchange of such process data D allows the unit to 10 supervision 12 to verify that the temperature parameters of the process are respected, to record significant variables (for example, energy consumption) and to receive any alarms or operating anomalies. The identification of such operating anomalies operation may determine automatic adjustments of the 15 process control or can provide an operator with the information necessary for the same operator to be able to perform a manual adjustment of the controller parameters process 11.

[0065] . In one embodiment, the aforementioned unit of 20 supervision 12 is integrated into the process controller 11, managed by an operator panel (local), or it can be implemented on a remote Cloud platform capable of communicating with the process controller 11 through a network of telecommunications (not shown). 25

[0066] . In a non-limiting embodiment, the control system 100 may also include one or more sensors 13 external physical devices configured to detect the values ​​of the first current Ires and first voltage Vres transmitted to the load resistive 3. These 13 sensors allow to improve 5 accuracy in thermal control by detecting the correct values ​​of current and voltage delivered to load 3 which may be different from those supplied by the power electronic device 10 in the case presence of the power transmission module 4.

[0067] . The control system 100 implementing this 10 The invention comprises, in addition to the converter block power electronics using thyristors or real SCR 101, also a digital twin electronic converter functional block 102 or “digital twin” of the aforementioned real converter 101.

[0068] . Furthermore, as will be clarified in more detail in the 15 Following, the control system 100 also includes a block functional representative of a feedback algorithm 103 configured to correct the value of the duty cycle parameter DTout fed to the SCR 10 power electronics starting from a converter function block output 20 electronic digital twin 102 of the above real converter 101.

[0069] . Furthermore, the control system 100 also includes a functional block representing a comparison algorithm 103' configured to generate status information related to a 25 malfunction detected based on an analysis of an output of the digital twin electronic converter or “digital twin” 102.

[0070] . In one embodiment, the functional block 102 digital twin electronic converter Real 101 can be implemented in one of the above components 5 of the thermal process control system 100: in the apparatus power electronics 10; in the process controller 11; in the supervision unit (local or cloud) 12. In particular, the digital twin block 102 is a software module that can be loaded into a memory of one of the above-mentioned components of system 100. 10

[0071] . In an embodiment, the functional blocks representative of the feedback algorithm 103 or the algorithm of comparison 103' can be implemented in one of the the above components of the process control system 100 thermal: in the power electronics system 10; in the controller 15 of process 11; in the supervision unit (local or cloud) 12. In In particular, algorithms 103, 103' are software modules loadable into a memory of one of the above-mentioned components of the 100 system.

[0072] . Note that the thermal process of machining a 20 material controlled by the system 100 described above which implements the invention can be of continuous or cyclic type.

[0073] . As is known, continuous thermal processes require maintain the detected Tmat temperature value substantially stable for long periods of time (from hours up to months) and are 25 characterized by a rather high thermal inertia, for example of the order of about 1000 kJ / K, and constant. Typically, the control of continuous thermal processes is divided into three phases.

[0074] . In an initial heating phase, the element 5 container 1 (Physical Product) is brought from temperature environment at a process temperature, generally in the absence of material to be heated internally. In the case of articulated systems, heating times are managed in so that all the components of the system arrive at the 10 process temperatures at the same time. In some cases, it can it is necessary to provide heating procedures that they predict a ramp-like increase in temperature (steep increasing ramp) designed to manage transient phenomena of the warm-up phase such as, for example, the elimination 15 of condensation inside the container element 1 from heat.

[0075] . In a subsequent phase of thermal modulation continues, once the target temperature Ttgt is reached for to start the manufacturing process, the 20 material to be processed. Temperature adjustment performed. from the control system 100 is therefore responsible for managing both the variation in thermal inertia linked to the presence of the material in the container element 1, both the variations of temperatures required by the various process phases. Furthermore, the 25 temperature regulation also provides for compensation of heat flows from other sources in the system (e.g. for example, generated by mechanical friction between materials and screws of the extruders). In general, the measured temperature of the Tmat material cannot fall below a value at this stage 5 threshold temperature value.

[0076] . In a subsequent shutdown phase, once once the processing is finished, the container element 1 is brought back to room temperature. In this case, the ramp temperature drop (decreasing ramp) is, generally, 10 gradual, both to allow the cleaning of the element container 1 from material residues, both to avoid damaging the system in case of too sudden cooling.

[0077] . Examples of continuous thermal processes are present in the following applications: extrusion, glass processing 15th floor, polymer production.

[0078] . Cyclic thermal processes are characterized by relatively rapid repetition of production cycles, without change process parameters. Cycle times are generally linked to the thermal inertia connected to the presence 20 of the material in the container element 1 and can range from milliseconds to tens of seconds.

[0079] . For each cycle, the thermal processing is repeats through the following stages.

[0080] . A first phase involves heating the element 25 resistive 3 up to the process temperature, which is generally fixed. This heating can also be short-term duration, if the inertia of the heating element is low, for example of the order of about 1 kJ / K, with respect to cycle times.

[0081] . The process is then expected to run 5 thermal, during which the detected temperature Tmat is generally kept constant, while the WTres power delivered from the heating element 3 can be varied to compensate for the presence of the material to be worked in the container element 1.

[0082] . Subsequently, the deactivation is expected 10 of the heating element 3 when the material to be worked is moved on to the next stage of the process. Generally this phase does not involve a return to room temperature and can be very short in case of rapid processes.

[0083] . Examples of very fast cyclic processes are related 15 to packaging applications, where a heated blade It is electrically used to seal the product packaging, often up to hundreds of times per minute. Other examples of cyclic processes concern processing phases used from the food or pharmaceutical industry. 20

[0084] . An example of the power electronic apparatus 10, for example in alternating current, capable of transferring current to the heating element 3, through the transmission module of power 4, is described with reference to figure 2.

[0085] . This power apparatus 10 comprises the above 25 mentioned power electronic converter 101 a semiconductors, generally “Silicon Controlled” thyristors Rectifier” or SCR in phase opposition. This converter 101 is configured to enable or disable transmitted current to the resistive load 3. In particular, the SCR converter 101 5 is configured to transform the Isup current and the voltage Input power Vsup in a current Iout and in a Output voltage Vout fed to resistive load 3. The SCR 101 converter is suitable for being driven punctually by means of an input duty cycle signal DTin. 10

[0086] . The power electronic apparatus 10 comprises, also, current, voltage or power sensors, overall indicated with the reference 104, suitable for detect the electrical variables transmitted to the resistive load 3. 15

[0087] . In particular, when such sensors 104 are internal to the device 10 and measure the output electrical quantities from the power electronic device 10, the current and the Output voltages measured by sensors 104 are Iout and Vout.

[0088] . When, however, such sensors 104 materialize in 20 sensors external to the apparatus 10, for example include current transformers or voltage shunts, applied to the resistive load 3, the measured values ​​of current and voltage are respectively, the first current Ires and the first voltage Vres, above mentioned. 25

[0089] . The power electronic apparatus 10 comprises, furthermore, a control block 105, configured to receive the DTout and PFout parameters generated by the process controller 11 through one or more communication interfaces 106.

[0090] . Control block 105 is configured to generate, 5 based on the above parameters DTout and PFout, the above input duty cycle signal DTin which directly drives the SCR 101 power converter, turning it on or off.

[0091] . For this purpose, in an embodiment, the control block 105 can also use current values 10 and measured voltage, Vmes and Imes, detected by electrical sensors internal or external 104, when available.

[0092] . Preferably, the power electronic apparatus 10 comprises a cooling block 107, comprising all the equipment, both passive and active, which guarantee the 15 air or water cooling of the electronic apparatus power 10.

[0093] . In one embodiment, the functional block 102 digital twin electronic converter or “digital twin” of the real converter 101, or the functional block 20 representative of the feedback algorithm 103 or 103' comparison, or both, can be implemented in the control block 105 of the power electronics system 10.

[0094] . Note that, depending on the complexity 25 of the power electronics device 10, current values, measured voltage and power and any alarms related to them electrical connections or the state of the device can be made available in output to the process controller 11 and the supervision unit 12. Alarms related to connections 5 electric are normally related to the lack of voltage in entry to the apparatus 10 or total or partial breakage of the resistive load.

[0095] . In accordance with the present invention, an interaction between the electronic power converter using thyristors 10 or real SCR 101, the digital twin electronic converter 102 or “digital twin” (SCR Digital Twin) of the aforementioned real converter and the above feedback algorithm 103 configured to correct the input duty cycle signal DTin applied to SCR 101 power electronic converter, 15 following early detection of malfunctions of the manufacturing process, starting from an output of the 102 digital twin electronic converter is described in refer to figure 3.

[0096] . In particular, the power electronic converter 20 a real SCR 101 is configured to receive input, from the feedback algorithm 103 (Feedback algorithm), a value of the input duty cycle signal DTin relative to a current time instant t. This converter 101 is suitable for provide, at the output, a peak value of the measured current 25 Imes at that instant t of current time, starting from that current duty cycle value.

[0097] . The 102 digital twin electronic converter is configured to receive, as input, in addition to the value of the peak current measured Imes from the physical SCR converter 5 101 at time t, PFout control parameters generated by process controller 11 representative of preset values of frequency and supply voltage.

[0098] . The digital twin converter 102 is configured for calculate a plurality of sets of simulated temperature values 10 T (t+dt, …, t+f), T (t+dt, …, t+f), …, T (t+dt, …, t+f) in a 1 2 m plurality of points, for example m points, specific internal to the converter to physical SCR 101 or to power apparatus 10. The simulated temperature value at each internal point is relating to an instant of time of a plurality of instants of 15 time t+dt, t+2dt, …, t+f following the current instant t, within a prediction time interval having duration f, where such prediction time interval f It depends on the process. In particular, the time interval dt, for example measured in seconds, represents an interval of 20 fixed sampling time. The above specific internal points of the real converter 101 or to the power apparatus 10 are those in which they can malfunctions may occur, in particular due to phenomena of overtemperature. Such specific points are, for example: the 25 points of the junction of each SCR of the SCR converter Physics 101; Power Electronics Points 10 affected by dissipation phenomena; electronics points power system control 10. In the following discussion, the term "specific points" will be used. 5 internals of the real power converter 101 of the device 10” power electronics to indicate the set of the above specific points inside both the real converter 101 and to the power electronics system 10.

[0099] . Similarly, the digital twin converter 102 is 10 configured to calculate a plurality of simulated values ​​of power P(t+dt, t+2dt, …, t+f) dissipated by the SCR converter physics 101, each relating to an instant of time t+dt, t+2dt, …, t+f next to the current instant t inside of the prediction time interval having duration f. 15

[00100] . The electronic digital twin converter 102 is configured to make such a plurality of sets available simulated temperature values ​​T (t+dt, …, t+f), with i = 1, 2, …, the m, and the simulated power values ​​P(t+dt, …, t+f) are unity of supervision 12 of the system 100 both to the algorithm of 20 feedback 103.

[00101] . Note that the digital twin converter 102 of the invention is obtained starting from the power converter Physical SCR 101. The starting point for implementing the digital twin 102 is a detailed electronic knowledge 25 of the physical SCR 101, a knowledge of the possible modes of failure mode of the physical device and the mechanical structure of the power electronic system 10.

[00102] . An example of a twin converter digital 102 usable in the present invention is described 5 in the document: F. Toso, R. Torchio, A. Favato, PG Carlet, S. Bolognani and P. Alotto, "Digital Twins as Electric Motor Soft- Sensors in the Automotive Industry,” 2021 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), Bologna, Italy, 2021, pp. 13–18, doi: 10 10.1109 / MetroAutomotive50197.2021.9502885

[00103] . The proposed solution involves using the potential of the 102 digital twin converter for the real-time simulation or prediction some temperatures of interest virtually measured in some 15 specific points of the physical SCR 101, defined during the development of the digital twin 102. In other words, the twin digital 102 operates as if these points are positioned "virtual temperature sensors". The mathematical structure of the digital twin converter 102 is configured to put in 20 relation the frequency and the supply voltage, the value of measured peak current and the estimated duty cycle in each of the subsequent instants of time t+dt, t+2dt, …, t+f at the current instant t, with the estimate of the dissipated power from the SCR at the same instants t+dt, t+2dt, …, t+f. This estimate of 25 dissipated power is used for the calculation of temperatures T (t+dt, …, t+f), with i= 1, 2, …, m, measured by the sensors the virtual.

[00104] . In the case of real-time simulation, the converter digital twin 102 is configured to use a value of 5 real temperature measured at a point inside the device power electronics 10, where a real sensor is positioned. Note that the digital twin converter 102 features a internal feedback, i.e. this converter is configured for detect differences between the above temperature value 10 measured by the real sensor and the value simulated in the same point. Furthermore, the 102 digital twin converter is configured to use these detected differences for fix a mathematical structure of the twin converter digital 102. 15

[00105] . Still referring to figure 3, the algorithm of feedback 103 is configured to receive the input plurality of sets of simulated temperature values ​​T (t+dt, …, the t+f), with i=1, 2, m, and the power values ​​P(t+dt, …, t+f) generated by the digital twin converter 102 in the plurality 20 of time instants of the prediction interval f.

[00106] . Furthermore, the feedback algorithm 103 is configured to receive target duty cycle values ​​as input Tdc(t+dt, t+2dt, …, t+f) predicted by the thermal process in the same instants t+dt, t+2dt, …, t+f of the time interval of 25 predictions made available by the process controller 11. Additionally, the feedback algorithm 103 is configured to receive additional process parameters as input.

[00107] . Note that the prediction time interval f It has a duration that depends on the number of duty cycle values 5 target Tdc(t+dt, …, t+f) made available by the controller process 11 to the feedback algorithm 103. In particular, the number of target duty cycle values ​​known to the controller process 11 defines how far into the future is possible maintain the process conditions, taking into account the type 10 of controlled process and of the execution phase. For example, this prediction time interval f is between some seconds to a few hours.

[00108] . Feedback algorithm 103 is configured to evaluate whether the sets of simulated temperature values ​​T (t+dt, …, the 15 t+f), with i=1, 2, …, m, and the power values ​​P(t+dt, …, t+f) provided by the 102 digital twin electronic converter are acceptable in relation to the process parameters.

[00109] . In case such sets of simulated values ​​of temperature T (t+dt, …, t+f), with i=1, 2, …, m, and the values ​​of the 20 power P(t+dt, …, t+f) provided by the electronic converter digital twin 102 are not acceptable, in relation to the process parameters, the feedback algorithm 103 is suitable for calculate a plurality of new duty cycle values, Duty Cycle (t+dt,...t+f), each relative to a time instant t+dt, 25 t+2dt, …, t+f of the prediction time interval f considered based on process parameters.

[00110] . The feedback algorithm 103 is designed to provide this plurality of new duty cycle values ​​both at the converter digital twin 102 is, possibly, to the converter of 5 power to physical SCR 101 as new duty signal value input cycle DTin relating to the above-mentioned time instants t+dt, t+2dt, …, t+f, where dt is the sampling time interval.

[00111] . Furthermore, the feedback algorithm 103 is adapted to provide status and alarm information to the unit 10 supervision 12 of the system 100.

[00112] . Note that a peculiar aspect of the algorithm of Feedback 103 consists in the evaluation of acceptability of the values ​​predicted by the digital twin converter 102 and in the possible recalculation of the control parameters. 15

[00113] . To perform this step, the feedback algorithm uses information on limit values ​​provided by the controller of process 11. These limit values ​​can be limits physical, such as the maximum temperatures of the components internals of the SCR which ensure its correct functioning, 20 or they can be specific to the process, such as for example maximum acceptable cycle times that guarantee the quality of the product.

[00114] . If the analysis of the sets of simulated values ​​of temperature T (t+dt, …, t+f), with i=1, 2, …, m, and the values ​​of the 25 power P(t+dt, …, t+f) provided by the electronic converter digital twin 102 signals a possible overtaking of the above limits, the feedback algorithm 103 is configured to implement a control value recalculation strategy (duty cycle). This feedback algorithm 103 is configured 5 to implement different possible strategies internally, such as slowing down the ignition ramp (if the anomaly occurs during the ignition phase), the elongation of the process cycle time (during the working phase for acyclic processes), or controlled shutdown, if the algorithm 10 103 evaluates that it is not possible to modify the control maintaining the integrity of the process.

[00115] . In accordance with the present invention, an interaction between the electronic power converter using thyristors or real SCR 101, the digital twin electronic converter or 15 “digital twin” 102 of the above real converter and the above mentioned comparison algorithm 103' (Comparison algorithm), configured to generate state information related to a malfunction detected based on an analysis of an output of the digital twin electronic converter, is 20 described with reference to figure 4.

[00116] . In particular, the power electronic converter a real SCR 101 is configured to receive input from process controller 11, a duty cycle signal value input DTin relative to a current time instant t. This 25 converter 101 is designed to provide, at the output, both to the electronic converter digital twin 102 of the above real converter both to the comparison algorithm 103' a peak value of the measured current Imes at that instant t of current time, starting from that duty cycle value 5 current.

[00117] . The 102 digital twin electronic converter, of the all analogous to that described in reference to figure 3, is configured to receive, as input, in addition to the value of the peak current measured Imes from the physical SCR converter 10 101 at time t, control parameters PFout generated by process controller 11 representative of preset values of frequency and supply voltage.

[00118] . Furthermore, the digital twin converter 102 is configured to receive duty cycle values ​​as input 15 target Tdc(t+dt, t+2dt, …, t+f) predicted by the thermal process at instants t+dt, t+2dt, …, t+f of the time interval of predictions made available by the process controller 11. In particular, dt is a sampling time interval predetermined. 20

[00119] . The digital twin converter 102 is configured for calculate a plurality of sets of simulated temperature values T (t+dt, …, t+f), with i=1, 2, …, m, at a plurality of points the specific internals of the physical SCR converter 101 or the device of power 10. Each temperature value of such plurality is 25 relative to a time instant t+dt, t+2dt, …, t+f following at the current instant t, within a time interval of prediction having duration f, where this time interval of prediction f depends on the process.

[00120] . Similarly, the digital twin converter 102 is 5 configured to calculate a plurality of simulated values ​​of power P(t+dt, t+2dt, …, t+f) dissipated by the SCR converter physics 101, each relating to an instant of time t+dt, t+2dt, …, t+f next to the current instant t inside of the prediction time interval f. 10

[00121] . The digital twin electronic converter 102 is configured to make such sets of simulated values ​​available temperature T (t+dt, …, t+f), with i=1, 2, …, m, and the values ​​of the power P(t+dt, …, t+f) to both the supervision unit 12 of the system 100 and the comparison algorithm 103'. 15

[00122] . The comparison algorithm 103' is configured for receive as input the sets of simulated temperature values T (t+dt, …, t+f), with i=1, 2, …, m, and the power values ​​P(t+dt, the …, t+f) generated by the digital twin converter 102 in the instants of time t+dt, t+2dt, …, t+f. 20

[00123] . Furthermore, the comparison algorithm 103' is configured to receive the target duty cycle values ​​as input Tdc(t+dt, …, t+f) predicted by the thermal process in the same instants t+dt, …, t+fe made available by the controller process 11. The comparison algorithm 103' is configured to 25 also receives the value of the measured peak current Imes from the physical SCR converter 101 at time t.

[00124] . The comparison algorithm 103' is configured to compare sets of simulated temperature values ​​T (t+dt, …, the t+f), with i=1, 2, …, m, the power values ​​P(t+dt, …, t+f) and i 5 target duty cycle values ​​Tdc(t+dt, …, t+f) expected by thermal process, with the measured physical values, i.e. the value of the measured peak current Imes, and to evaluate any abnormal conditions.

[00125] . Furthermore, the comparison algorithm 103' is capable of 10 provide information on any anomalies detected and on alarms to supervision unit 12 of system 100.

[00126] . The central aspect of the 103' comparison algorithm consists in the analysis of the trends of the temperature signals and power provided by the 102 digital twin converter, and the 15 their correlation with any anomalies.

[00127] . In particular, the temperature measured virtually is compared with the actual measured temperature on the physical object 101 and any deviations are evaluated between the two values. An anomaly or malfunction occurs 20 when the behavior of the real temperature deviates from the simulated one. If this condition occurs, they are extracted some relevant information from the curve of the temperature, as well as any significant peaks, both peaks of both positive temperature peaks and negative temperature peaks, and the 25 time duration of such peaks.

[00128] . The comparison algorithm 103' is configured to compare this information with known constant values, stored in table form together with the algorithm, which they relate the extent of the variation to the anomaly 5 corresponding. These known values ​​are characterized in previously, in the development phase of the product apparatus power electronics 10, or in the system testing phase or machine during system setup 100. In the first case the values ​​relating to possible anomalies are characterized 10 internal product power electronic device 10, while in the second case the values ​​relating to the possible anomalies due to malfunctions or external disturbances to the power electronics system 10. In particular, the comparison algorithm 103' is configured to acquire from the 15 digital twin 102 the time course of the difference between the calculated and the actual measured temperature in a specific point inside the physical controller 101. This value of difference deviates from zero when the behavior of the physical SCR converter 101 deviates from its behavior 20 "nominal", that is, the one described by the twin converter digital 102 in the absence of operating disturbances. nature of the disturbance can be deduced, therefore, from the trend of the difference (delta) between the temperature simulated and measured, together with the set of values 25 of the temperature sets T (t+dt, ..., t+f) and power values the P(t+dt, ..., t+f) and in the context of the process parameters provided by the process controller 11. For example, a delta negative temperature measured on the electronic board of the controller, combined with a forecast of power increase P and 5 to a request for a higher duty cycle by of the power apparatus 10, may indicate an upcoming breakdown of the bonding of the physical SCR converter 101. In fact, the increase in resistance on the physical SCR converter 101 determines greater power, but the increase in temperature 10 local does not find a corresponding rapid increase of temperature on the electronic board and, at the same time, the lower current transferred to heating load 3 leads to the process controller 11 to require a higher duty cycle Tdc high to maintain the temperature. 15

[00129] . Examples of anomalies detectable by the algorithm comparison 103' are:

[00130] . ​​– fault or malfunction of the block cooling 107 of the power electronic apparatus 10;

[00131] . – fault or malfunction of the converter 20 real SCR power 101;

[00132] . - system anomaly or malfunction cooling of the electronic control panel of power 10.

[00133] . With reference to figure 5, the numerical reference 25 200 indicates, overall, a general example of the method of control of a power electronic device 10, usable in a 100 control system of a thermal machining process of a material, to regulate the temperature or thermal state of a load 3 following an early detection of 5 malfunctions in the processing through the digital twin electronic converter or “digital twin” 102, according to the invention.

[00134] . The method in Figure 5 begins with a symbolic phase starting “STR” and ends with a symbolic ending “ED” phase. 10

[00135] . The control method 200 initially provides for a phase 201 of early detection and classification of malfunctions in the thermal processing of a material.

[00136] . As mentioned above, the control method 200 is 15 applied to a power electronic system 10 control system 100. The control system 100 comprises: - a heat transfer element 1 configured to be in contact with the material to be subjected to the thermal process of processing; 20 - a heating load 3; - the aforementioned power electronic apparatus 10, suitable for transfer electric current Iout, Ires to heating load 3 to change the thermal state of the transmission element heat 1; the power electronic apparatus 10 includes a 25 real power converter 101 driven by a duty signal input cycle DTin generated by the electronic device power 10 itself to enable / disable transfer of the electric current to the heating load 3; - a process controller 11 configured to compare a 5 detected value of current temperature Tmat of the material subjected to the thermal processing with a value of reference temperature Ttgt; such process controller 11 It is configured to control the power electronics 10 by changing the input duty cycle signal DTin 10 applied to the real power converter 101 to bring the current detected temperature value Tmat at the value of reference temperature Ttgt; - a functional block representing a converter digital twin 102 of the real power converter 101; 15 - a functional block representing an algorithm 103' comparison operationally associated with the converter digital twin 102.

[00137] . In this initial phase, method 200 comprises the early detection and classification 201, by the 20 comparison algorithm functional block 103', of at least one malfunction in the thermal processing of the material.

[00138] . Furthermore, method 200 provides for an evaluation phase 202 a modification of the control applied, by the controller of 25 process 11, to the power electronics device 10 later upon early detection of at least one such malfunction.

[00139] . Method 200 comprises the further step of:

[00140] . - apply 203 such modified control to the power electronic apparatus 10 to prevent the at least one 5 malfunction detected in advance in case such modified control ensures the integrity of the process workmanship performed; or of

[00141] . - turn off 204, in a controlled manner, the system control 100 in case this control is modified 10 compromises the integrity of the manufacturing process performed.

[00142] . With reference to the example of embodiment in figure 6, the general phases of the algorithm described in reference to the Figure 5 are described in more detail to be applicable to the diagnosis of at least one malfunction or anomaly 15 in the context of a thermal material processing process.

[00143] . In one embodiment, the above mentioned step of detect and classify at least one malfunction in advance in the thermal process of material processing includes the phases of: 20

[00144] . - start 2011 converter function block digital twin 102 together with control system 100;

[00145] . - simulate 2012, by the functional block 102 digital twin converter, a plurality of sets of temperature values ​​Ti(t+dt, …, t+f), with i=1, 2, …, m, in a 25 multiple specific points inside the power converter real 101 of the power electronic apparatus 10; each of the simulated temperature values ​​of each set is associated with a instant of time of a plurality of instants of time t+dt, t+2dt, …, t+f of a prediction time interval f 5 subsequent to a current time instant t of the process of processing, where dt is the above-mentioned time interval of pre-established sampling;

[00146] . - compare 2012' each of the sets of values temperature simulations Ti(t+dt, …, t+f) at specific points with 10 a respective threshold temperature value TSi, with i=1, 2, …, m, of a plurality of threshold values.

[00147] . In case at least one of the simulated values ​​of temperature of one of the sets Ti(t+dt, …, t+f) is greater than the respective threshold temperature value TSi, the method 15 provides for the phases of:

[00148] . - classify 2013, for each set of values ​​of temperature, by the algorithm functional block comparison 103', the at least one malfunction that has determined at least one simulated temperature value greater than 20 of the respective threshold temperature value TSi;

[00149] . - activate 2014 a feedback algorithm 103, operationally associated with the converter functional block digital twin 102, configured to evaluate whether the said plurality of sets of temperature values ​​Ti(t+dt, …, t+f) 25 simulated provided by the 102 digital twin converter are acceptable in relation to the process parameters.

[00150] . In a further embodiment, in reference to critical trends in malfunction detection, the the above mentioned phase of detecting in advance and classifying 201 at least one 5 malfunction in the thermal process of material processing of the 200 method includes the phases of:

[00151] . - start 2011 converter function block digital twin 102 together with control system 100;

[00152] . - simulate 2012, by the functional block 10 digital twin converter 102, a plurality of sets of temperature values ​​Ti(t+dt, …, t+f), with i=1, 2, …, m, in a plurality of specific internal points of the power converter real 101 of the power electronic apparatus 10; each of the simulated temperature values ​​of each set is associated with a 15 instant of time of a plurality of instants of time t+dt, t+2dt, …, t+f of a prediction time interval f subsequent to a current time instant t of the process processing, where dt is the above-mentioned time interval of pre-established sampling; 20

[00153] . - calculate 2012a, for each specific point i, with i=1, 2, …, m, internal to the real power converter 101, values ​​of average temperature variation VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f); each of such values ​​of average temperature variation VTi at the point 25 specific i is calculated in an equal number of instants of time or less than the plurality of time instants t+dt, t+2dt, …, t+f of a prediction time interval f following a current time instant t of the process; each of such values ​​of average temperature variation VTi at point i is 5 relating to a plurality of temperature values ​​Ti(t+dt,…, t+f), Ti(t+2dt,…, t+f),…, Ti(t+f-dt,…, t+f) simulated in such point;

[00154] . - compare 2012a', each of the values ​​of average temperature variation VTi(t+dt, …, t+f), VTi(t+2dt, …, 10 t+f),…, VTi(t+f-dt, …, t+f) associated with the internal point i at converter, with an average temperature variation value of threshold VT*i specific for that point i.e. determined on the basis of to the trial.

[00155] . In case at least one of the variation values 15 temperature average VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) is greater than this respective value of mean threshold temperature variation VT*i, the method It includes the following phases:

[00156] . - classify 2013a, by block 20 functional comparison algorithm 103', the at least one malfunction that caused at least one of the values ​​of average temperature variation VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) greater than the respective value of mean threshold temperature variation VT*i; 25

[00157] . - activate 2014 a feedback algorithm 103, operationally associated with the digital twin converter 102, configured to evaluate whether said plurality of sets of values ​​of simulated temperature Ti(t+dt, …, t+f) provided by the converter digital twin 102 are acceptable in relation to the parameters 5 of process.

[00158] . Always with reference to critical trends in the malfunction detection, in a further example of alternative to the previous one, the phase of detecting in advance and classify 201 at least one malfunction in the 10 thermal process of material processing, the 200 method includes the phases of:

[00159] . - start 2011 converter function block digital twin 102 together with control system 100;

[00160] . - simulate 2012, by the functional block 15 digital twin converter 102, a plurality of sets of temperature values ​​Ti(t+dt, …, t+f), with i=1, 2, …, m, in a plurality of specific internal points of the power converter real 101 of the power electronic apparatus 10; each of the simulated temperature values ​​of each set is associated with a 20 instant of time of a plurality of instants of time t+dt, t+2dt, …, t+f of a prediction time interval f subsequent to a current time instant t of the process processing, where dt is the above-mentioned time interval of pre-established sampling; 25

[00161] . - associate 2012b, to each point inside the converter i, a set of binary parameters representing the presence / absence of oscillatory trends AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f) temperature; each of these parameters is relative to a sub-interval of 5 temperatures Ti(t+dt,...,t+f), Ti(t+2dt,...,t+f),..., Ti(t+(f- dt),...,t+f) of such temperature values ​​Ti(t+dt, …, t+f) simulated;

[00162] . - detect 2012b', in at least one of these sub- temperature ranges, the presence of at least one trend 10 oscillatory AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f- dt),...,t+f), relative to the point inside the converter, not compliant with the process when said sub-range includes at least one local first minimum temperature value, a value of the first local maximum temperature and a further one 15 consecutive second local minimum temperature values or at least one value of the first local maximum of temperature, a first local minimum temperature value and a further value of second consecutive local maximum temperature;

[00163] . - classify 2013b, by functional block 20 comparison algorithm 103', the at least one malfunction that has determined the presence of at least one trend oscillatory AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f- dt),...,t+f) of temperature;

[00164] . - activate 2014 a feedback algorithm 103, 25 operationally associated with the digital twin converter 102, configured to evaluate whether said plurality of sets of values ​​of simulated temperature Ti(t+dt, …, t+f) provided by the converter digital twin 102 are acceptable in relation to the parameters of process. 5

[00165] . Note that the above first local maximum of temperature is present in the temperature sub-range when three reference temperature values ​​are detected of the first maximum, in particular a first T0_max value of the first maximum, a second T1_max value of the first maximum and a 10 third temperature value T2_max of the first maximum such for which: T0_max <= T1_max - Trefi and T1_max >= T2_max + Trefi, where Trefi is a tolerance temperature value associated with at the i-th point inside the converter, depending on the 15 process and defined by process parameters.

[00166] . Similarly, the above-mentioned second local maximum of temperature is present in the temperature sub-range when three reference temperature values ​​are detected second maximum, in particular a first value T0'_max of the 20 second maximum, a second T1'_max value of the second maximum and a third temperature value T2'_max of the second maximum such therefore: T0'_max <= T1'_max - Trefi and T1'_max >= T2'_max + Trefi.

[00167] . Similarly, the first local minimum of temperature is 25 present in the sub-temperature range when they are three additional reference temperature values ​​were detected first minimum, a further first temperature value T0_min of the first minimum, a further second T1_min value of the first minimum and a further third value T2_min of the first minimum such that: 5 T0_min >= T1_min + Trefi and T1_min <= T2_min-Trefi.

[00168] . Similarly, the above second local minimum of temperature is present in the temperature sub-range when three additional temperature values ​​are detected reference of the second minimum, a further first value T0'_min 10 of the second minimum, a further second value T1'_min of the second minimum and a further third T2'_min temperature value of the second minimum such that: T0'_min >= T1'_min + Trefi and T1'_min <= T2'_min-Trefi.

[00169] . In a first example of implementation, a methodology 15 of detection of the aforementioned oscillatory trend of temperature works as follows.

[00170] . The method involves associating the first value of temperature of the set Ti(t+dt) at a temperature value of initial reference T0. 20

[00171] . Each temperature value Ta following the first reference temperature value T0 is compared with this temperature value T0 by testing two first conditions A) and B), which are alternatives to each other and which do not occur in contemporary, that is: 25 A) Ta>= T0+Trefi B) Ta<= T0-Trefi.

[00172] . The method involves testing these first conditions in iteratively until or at least one of the two conditions is verified or all possible values ​​have been tested 5 set temperature, but neither of the first two conditions were met never verified. In this second case the method ends with negative result, that is, no trend was identified oscillatory in the sub-temperature range examined.

[00173] . If condition A) is true, the method 10 of the invention provides to assign the temperature value of initial reference T0 to the first temperature value of reference of the first maximum T0_max. The method of the invention It therefore provides for the activation of a research phase for a maximum local based on the following phases: 15 - associate the Ta temperature value that verified the condition A) at the second reference temperature value of the first maximum T1_max; - compare each temperature value Tb following the second reference temperature value of the first maximum 20 T1_max with said second reference temperature value testing two second conditions, A1) and A2), which are alternatives to each other that do not occur simultaneously: A1) Tb<=T1_max-Trefi A2) Tb>=T1_max+Trefi. 25

[00174] . These second conditions A1) and A2) are tested in iteratively until at least one of the two is verified or all possible values ​​of have been tested set temperature, but neither ever occurred. In this second case the search for a local maximum has an outcome 5 negative.

[00175] . In case condition A1) is verified, the method involves associating the temperature value Tb, which verify this condition A1), at the third temperature value of reference of the first maximum T2_max. In this case, the triplet of 10 temperature values ​​(T0_max, T1_max, T2_max) detected defines the aforementioned first local maximum. The search method of the local maximum therefore concludes with a positive outcome.

[00176] . Instead, if condition A2) is verified, the method plans to reassign the reference temperature values ​​in 15 so that: - the second reference temperature value of the first maximum T1_max is used as the first temperature value of reference of the first maximum T0_max, and - the temperature value Tb that verified the condition 20 A2) is used as the second temperature value of reference of the first maximum T1_max.

[00177] . The local maximum search method provides, subsequently, to repeat, in an iterative manner, the four steps mentioned above. 25

[00178] . In case the method of searching for a maximum the local test was successful and the three candidates were identified of values ​​(T0_max, T1_max, T2_max) that define this maximum local, the method involves finding a first local minimum in agreement with the following steps. 5

[00179] . It is expected to associate the second value of reference temperature of the first maximum T1_max of the triplet of the first local maximum to the further first value T0_min of temperature of the first local minimum trio sought.

[00180] . Next, the method involves associating the 10 third temperature value of the first local maximum triplet T2_max at the further second reference temperature value of the first minimum T1_min.

[00181] . In this case, each temperature value Td subsequent to the aforementioned further second temperature value 15 of the first minimum T1_min, is compared with T1_min by testing two further third conditions A1') and A2'), alternative to each other which are not verified simultaneously: A1') Td>=T1_min+Trefi A2') Td<=T1_min-Trefi. 20

[00182] . These third conditions A1') and A2') are tested in iteratively until at least one of the two is verified or all possible values ​​of have been tested set temperature, but neither ever occurred. In this second case the search for a local minimum has an outcome 25 negative.

[00183] . In case the condition A1') is verified, the method provides that the temperature value Td which verifies it be associated with the further third temperature value of reference of the first minimum T2_min, such that the triplet 5 (T0_min, T1_min, T2_min) defines the first local minimum identified. The local minimum search method is concluded, therefore, with a positive outcome.

[00184] . Instead, in the case in which the condition is verified A2'), the method involves reassigning the reference values 10 of the temperatures so that: - the further second T1_min temperature value of reference of the first minimum is associated with the further first reference temperature value of the first minimum T0_min, and - the temperature value Td that A2') verified is 15 used as an additional second temperature value of reference of the first minimum T1_min.

[00185] . The local minimum search method provides, subsequently, to repeat, in an iterative manner, the four steps mentioned above. 20

[00186] . In case the local minimum search method is successful and the triplet (T0_min, T1_min, T2_min) which defines the local minimum, then the method plans to search for a second local maximum in accordance with the following steps. 25

[00187] . It is expected to associate the second temperature value reference of the first minimum T1_min of the first set of three local minimum at the first T0'_max temperature value of the second local maximum triplet sought.

[00188] . Next, the method involves associating the third 5 temperature value of the first local minimum T2_min to the second reference temperature value of the second maximum T1'_max.

[00189] . The method therefore provides for activating the method of search for a second local maximum in a similar manner to what 10 described above for the first local maximum.

[00190] . In case the local maximum search method is successful and the triplet (T0'_max, T1'_max, T2'_max) which defines the second local maximum, then the method concludes with a positive outcome and a solution has been identified 15 oscillatory trend.

[00191] . In case condition A) is not verified, but let condition B) of the first conditions A) be verified and B), the method involves assigning the temperature value of initial reference T0 to the further first temperature value 20 reference T0_min of the first local minimum triplet sought.

[00192] . The temperature value Ta which verified the condition B), is used as a further second value of reference temperature of the first minimum T1_min.

[00193] . The method of finding a first local minimum works 25 as described above.

[00194] . If the local minimum search methodology had positive outcome and the further triplet was identified (T0_min, T1_min, T2_min) which defines the first local minimum, then plans to: 5 - associate the additional second temperature value of reference of the first minimum T1_min of the first minimum triplet local to the first T0_max value of the first maximum triplet location searched; - associate the additional third temperature value of 10 reference of the first minimum T2_min of the first minimum triplet local to the second reference temperature value of the first maximum T1_max.

[00195] . The method therefore provides for activating the method of search for a first local maximum as described 15 previously.

[00196] . If the local maximum search method had positive outcome and the triplet (T0_max, T1_max, T2_max) which defines the first local maximum, then it is expected Of: 20 - associate the second reference temperature value of the first maximum T1_max of the triplet of the first local maximum to the further first value T0'_min of the second minimum triplet location searched; - associate the third reference temperature value of the 25 first maximum T2_max of the triplet of the first local maximum to the further second reference temperature value of the second minimum T1'_min.

[00197] . The method therefore provides for activating the method of search for a second local minimum in a similar manner to what 5 described above.

[00198] . If the local minimum search method was successful positive and the further triplet (T0'_min, T1'_min, T2'_min) which defines the second local minimum, then the method ends with a positive outcome as it has been 10 an oscillatory trend was identified.

[00199] . In an example of implementation of the control method 200 of figures 5 or 6, the above mentioned phase of turning off 204 includes a phase of performing a controlled shutdown of the control system 100, by the process controller 15 11, after a time interval of switch-off s(n) from said current time instant t, where the time interval of switch-off s(n) has a shorter duration than the above mentioned prediction time interval f.

[00200] . In particular, this time interval of 20 shutdown s(n) is calculated using the expression: s(n) = f-(n*dt) (1) where f is the prediction time interval, dt is the supra mentioned sampling time interval and n is a number entire. 25

[00201] . Note that s(n) represents a time interval of controlled shutdown. In particular, the interval of controlled shutdown time s(n) is a given time, the whose value is an integer multiple of the time interval of sampling dt, which determines the granularity with which sampling occurs 5 the analysis of the controlled shutdown. This time of sampling dt is given by the process parameters and depends on how fast is the controlled process. In other words, on the based on equation (1), the turn-off time interval controlled s(n) takes on a value that decreases at each 10th iteration. The last value the time interval takes on s(n) is determined by the prediction time interval fe from the last value of the integer n such that (n*dt)>f.

[00202] . Still referring to the example in figure 5, in a further example of implementation of the control method 200, 15 steps to evaluate 202 a change in the applied control and to apply 203 the modified control to the electronic device of power 10 to prevent at least one malfunction detected in advance include the phases of:

[00203] . - run a first optimization algorithm of the 20 control 2031 applied to the power electronics apparatus 10;

[00204] . - evaluate 2032 if at least one malfunction detected in advance is prevented on the basis of the execution of such first control optimization algorithm 2031.

[00205] . In case at least one malfunction is detected in 25 advance payments last, the 200 method includes the additional phases of:

[00206] . - run a second optimization algorithm control 2033 applied to process controller 11;

[00207] . - evaluate 2034 if at least one malfunction detected in advance is prevented on the basis of the execution of 5 such second control optimization algorithm 2033.

[00208] . In case at least one malfunction is detected in advance perduri, the method includes the shutdown phase controlled 204 of control system 100.

[00209] . In a particular embodiment of the method 10 control 200, this phase of executing the first algorithm 2031 control optimization includes a phase of performing in an iterative manner this first optimization algorithm of the control 2031.

[00210] . In a particular embodiment of the method of 15 control 200, this phase of executing the second algorithm 2033 control optimization includes a phase of performing in an iterative manner this second optimization algorithm of the control 2031.

[00211] . With reference to the example of implementation of the 20 figure 7, the first control optimization algorithm 2031 includes a phase to evaluate 2031a whether a reduction in the signal of input duty cycle DTin applied to the converter Real Power Electronics 101 maintains process integrity of work carried out. 25

[00212] . In case the integrity of the manufacturing process performed and maintained, method 200 includes the steps of:

[00213] . - calculate 2031', by the algorithm of feedback 103, a first plurality of duty cycle values ​​D- C1(t+dt, …, t+f) each associated with a time instant t+dt, 5 …, t+f of the plurality of instants in the time interval of prediction f, subsequent to a current time instant t of the manufacturing process, where dt is the time interval of fixed sampling;

[00214] . - make available the first plurality of values ​​of 10 duty cycle D-C1(t+dt, …, t+f) to the converter function block digital twin 102;

[00215] . - simulate 2031'', by the function block 102 digital twin converter, a plurality of prime sets temperature values ​​T1i(t+dt, …, t+f), with i=1, 2, …, m, in a 15 multiple specific points inside the power converter real 101 of the power electronic apparatus 10; each of such sets of first simulated temperature values ​​being associated to this first plurality of duty cycle values ​​D-C1(t+dt, …, t+f) at an instant of time t+dt, …, t+f; 20

[00216] . - compare 2031b each of such sets of primes temperature values ​​T1i(t+dt, …, t+f) simulated with a respective threshold temperature value TSi, with i=1, 2, …, m, of a plurality of threshold values.

[00217] . In case each of the first temperature values 25 T1i(t+dt, …, t+f) simulated at each specific point is less than the respective threshold value TSi, i.e. no threshold has been identified anomaly, the method includes the phases of:

[00218] . - apply 2031c to the real power converter 101 an input duty cycle signal DTin including such first 5 plurality of duty cycle values ​​D-C1(t+dt, …, t+f) for enable / disable power transfer to heating load 3;

[00219] . - to be reported by the algorithm functional block feedback 103, an error condition at a unit of 10 control system supervision 11.

[00220] . Instead, in the case where at least one of the first values ​​of temperature T1i(t+dt, …, t+f) simulated at each specific point is greater than the respective threshold value TSi, the 2031 phase of the method 200 provides for the repetition of this phase to evaluate 2031a 15 if a further reduction of the input duty cycle signal DTin applied to real power electronic converter 101 maintains the integrity of the manufacturing process performed, and the phase of calculating 2031', by the feedback algorithm 103, a further first plurality of duty cycle values ​​D- 20 C1'(t+dt, …, t+f) each associated with an instant of time t+dt, …, t+f of the plurality of instants in the time interval of prediction f.

[00221] . At this point the repetition of the same phases 2031'' and 2031b mentioned above. 25

[00222] . In other words, in the presence of an overtemperature, an optimization strategy implemented by the 2031 algorithm plans to reduce the duty cycle by an amount relative to the type of thermal process and made available by the controller process 11. 5

[00223] . For example, dc_x indicates the value of duty cycle that allows the controlled process to maintain the desired target temperature and with dc_y the duty cycle value provided by process controller 11. Note that dc_y It may be different from dc_x because it represents the value of 10 duty cycle optimized by the process controller 11 that allows to obtain the regulation performance in terms of desired robustness / speed. In case the digital twin converter 102 signals a overtemperature for a duty cycle equal to dc_y, the first 15 optimization algorithm 2031 is configured to perform a progressive reduction of the duty cycle from the limit value dc_y up to the value dc_x. The variation of this duty cycle can be performed in a maximum number of steps indicated with a parameter integer N. This integer parameter N is, for example, fixed at 20 a reference value 10. The value of the parameter N is provided from the process parameters and can also be modified arbitrarily by the user. In consideration of this, with algorithm 2031, the duty cycle is varied at each step of an amount equal to: 25 (dc_y-dc_x) / N (2) Note that the value dc_x is also known because it depends on the type of manufacturing process and can be modified by the user.

[00224] . In a further embodiment, in the case of 5 critical trends, the first optimization algorithm control 2031 plans to replace phase 2031b so as to understand the steps to calculate 2012a variation values temperature average VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) and compare 2012a' each of the values ​​of 10 mean temperature variation VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) with a mean variation value of threshold temperature VT*i, similarly to what is described in refer to figure 6.

[00225] . In a different embodiment, in the case of 15 critical trends, the first optimization algorithm control 2031 plans to replace phase 2031b so as to understand the steps of associating 2012b, at each point inside the converter i, a set of binary parameters representing the presence / absence of oscillatory trends AOi(t+dt,…,t+f), 20 AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f) of temperature and detect 2012b' the presence of at least one oscillatory trend AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f), relative to the point inside the converter, not compliant with the process in a similar manner to that described in reference to the 25 figure 6.

[00226] . With reference to the example of implementation of the method 200 of figure 7, the second optimization algorithm of the 2033 control provides for a 2033a evaluation phase relating to to the extension of the cycle time of the system 100 which preserves 5 the integrity of the manufacturing process. More generally, Algorithm 2033 plans to evaluate control strategies that alter the progressive behavior of the system and that, therefore, require the intervention of the process controller 11.

[00227] . In case the integrity of the manufacturing process 10 performed is maintained, the method 200 includes the phases of:

[00228] . - calculate 2033', by the algorithm of feedback 103, a second plurality of duty cycle values ​​D- C2(t+dt, …, t+f) each associated with a time instant t+dt, …, t+f of such plurality of instants of time in the interval of 15 prediction time f, following a current time instant t of the manufacturing process, where dt is the time interval of pre-established sampling;

[00229] . - make available such second plurality of duty cycle values ​​D-C2(t+dt, …, t+f) to the functional block 20 digital twin converter 102;

[00230] . - simulate 2033'', by the function block 102 digital twin converter, a plurality of second sets temperature values ​​T2i(t+dt, …, t+f), with i=1, 2,…, m, in a plurality of specific internal points of the power converter 25 real 101 of the power electronic apparatus 10, each of such sets of second simulated temperature values ​​is associated with this second plurality of duty cycle values ​​D-C2(t+dt, …, t+f) at an instant of time t+dt, …, t+f of that time interval of prediction f; 5

[00231] . - compare 2033b each of said sets of seconds temperature values ​​T2i(t+dt, …, t+f) simulated with a respective threshold temperature value TSi, with i=1, 2, …, m, of a plurality of threshold values.

[00232] . In case each of the second values ​​of 10 temperatures T2i(t+dt, …, t+f) simulated at each specific point of the converter 101 is less than the respective threshold value TSi, the method includes the following phases:

[00233] . - apply 2033c to the real power converter 101 an input duty cycle signal DTin including such 15 second plurality of duty cycle values ​​D-C2(t+dt, …, t+f) for enable / disable power transfer to heating load 3;

[00234] . - to be reported by the algorithm functional block feedback 103, an error condition at a unit of 20 control system supervision 11.

[00235] . Instead, in the case where at least one of the second values of temperature T2i(t+dt, …, t+f) simulated at each point converter specific 101 is greater than the respective value of TSi threshold, phase 2033 of method 200 provides for the 25 repetition of said cycle time evaluation phase 2033a of the 100 system that preserves the integrity of the process processing and the phase of calculating 2033', by the algorithm of feedback 103, a further second plurality of values ​​of duty cycle D-C2'(t+dt, …, t+f) each associated with an instant 5 of time t+dt, …, t+f of the plurality of instants in the interval of prediction time f.

[00236] . At this point the repetition of the same phases 2033'' and 2033b mentioned above.

[00237] . The above mentioned cycle time extension 2033a 10 system 100 is realized in an incremental elongation in the time of a system 100 ignition ramp, i.e. the second algorithm 2033 plans to extend the time duration of a ramp of ignition of a quantity relative to the type of thermal process and made available by the process controller 15 11. In this case, for example, the treatment is analogous to that made for the stepwise reduction of the duty cycle, where instead of two values ​​of duty cycle limit we have two values ​​of time of ramp limit. In this case, Ty is used to indicate the maximum time 20 eligible for the process which depends on the process of processing (the ramp cannot be excessively long otherwise it will negatively affect the process). Ty is a known value, provided by the process parameters and can be set by the user. Instead, Tx is used to indicate the 25 optimal value of the ramp time, which will be less than Ty. In that case, until the digital twin converter 102 signals overtemperature, ramp time is increased of a value equal to: (Ty-Tx) / N (3) 5 up to a maximum value of Ty. Similarly to the above, the integer parameter N is, for example, set to a reference value of 10. The value of the N parameter is provided by the process parameters and can also be arbitrarily modified by the user. 10

[00238] . In a further example of embodiment, in the case of critical trends, the second optimization algorithm of the control 2033 plans to replace phase 2033b so as to understand the steps to calculate 2012a variation values temperature average VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, 15 VTi(t+f-dt, …, t+f) and compare 2012a' each of the values ​​of average temperature variation VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) with a mean variation value of threshold temperature VT*i, similarly to what is described in refer to figure 6. 20

[00239] . In a different embodiment, in the case of critical trends, the second optimization algorithm of the control 2033 plans to replace phase 2033b so as to understand the steps of associating 2012b, at each point inside the converter i, a set of binary parameters representing the 25 presence / absence of oscillatory trends AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f) of temperature and detect 2012b' the presence of at least one oscillatory trend AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f), relative to the point inside the converter, not compliant with the 5 process in a similar manner to that described in reference to the figure 6.

[00240] . Still referring to the example of implementation of figure 7, the above mentioned phase of turning off 204 the system Control 100 of method 200 includes a step of evaluating 2041 10 whether it is possible to maintain process integrity processing performed for a period of time off controlled of limited duration s(n) following an instant of current time t of the manufacturing process, and to anticipate the shutdown of system 100 at time t+s(n). In particular, the 15 control method 200 provides the possibility of obtaining the value of the turn-off time s(n) by decreasing an interval of prediction time f based on the equation: s(n) = f-(n*dt), with n= 1, 2, 3, etc. Based on this notation, the turn-off time s(n) can 20 assume the discrete values ​​f-dt, f-2dt, f-3dt, etc.

[00241] . In case the process specifications allow for maintain the integrity of the manufacturing process for a long time limited, the 200 method includes a repeating step, for each of the discrete values ​​of that time interval of 25 controlled shutdown s(n) determined by the values ​​of the integer n for which (n*dt) <f, le fasi di:

[00242] . - calculate 2042, by the algorithm of feedback 103, a third plurality of duty cycle values ​​D- C3(t+dt, …, t+s(n)) each associated with an instant of time 5 t+dt, …, t+s(n) of the plurality of instants of time of the controlled switch-off time interval s(n);

[00243] . - make available the third plurality of values ​​of duty cycle D-C3(t+dt, …, t+s(n)) to the converter function block digital twin 102; 10

[00244] . - simulate 2043, by the function block 102 digital twin converter, a plurality of third-party sets temperature values ​​T3i(t+dt, …, t+s(n)), with i=1, 2, …, m, in a plurality of specific internal points of the converter real power 101 of the power electronic device 10; 15 each of such sets of third simulated temperature values ​​is associated with this third plurality of duty cycle values ​​D- C3(t+dt, …, t+s(n)) at an instant of time t+dt, …, t+s(n) of the controlled switch-off time interval s(n);

[00245] . - compare 2044 each of such third party sets 20 simulated temperature values ​​T3i(t+dt, …, t+s(n)) with respective set of threshold temperature values ​​TSi, with i=1, 2, .., m.

[00246] . This repeat step is performed in case at least one of the third simulated temperature values ​​T3i(t+dt, …, t+s(n) at each specific point of the real converter 101 is greater 25 of the respective TSi threshold value.

[00247] . In an embodiment, in the case where each of the third temperature values ​​T3i(t+dt, …, t+s(n) simulated at a specific point of the converter 101 is less than the respective threshold value TSi, the switch-off phase 204 the 5 control system 100 includes the phases of:

[00248] . - apply 2045 to real power converter 101 an input duty cycle signal DTin that includes such a third plurality of duty cycle values ​​D-C3(t+dt, …, t+s(n)) for enable / disable power transfer 10 at heating load 3;

[00249] . - to be reported by the algorithm functional block feedback 103, an error condition at a unit of supervision 12 of the control system 100.

[00250] . In other words, assuming the time interval of 15 prediction f equals 8, dt=1 and n initially equal to 0, on the base of equation (1) s(n)=8, i.e. the first value of s(n) is equal to f. If the temperature predicted by the digital twin converter 102 takes values ​​higher than the threshold value TS at time t+8, the 20 shutdown phase 204 of the method involves increasing the value of n from 0 to 1, setting the value of the interval of controlled shutdown time s(n)=7. At this point, the phase 204 of the method provides for the recalculation of the Duty Cycle value (phase 2042) which is made available to the digital twin converter 25 102 (phase 2043). If the digital twin converter 102 reports a temperature simulated even higher at the threshold temperature TS at time t+7, the method involves incrementing the value of n from 1 to 2, setting the time interval value of 5 controlled shutdown s(n)=6. At this point, phase 204 of the method involves the recalculation of the Duty Cycle value which is made available again to the digital twin converter 102. In case the digital twin converter 102 does not 10 highlights overtemperatures at instant t+6, the Duty Cycle values calculated up to time t+6 are made available to the power electronics 10, i.e. the Duty values Cycle calculated from t+s(n). In relation to the given example, therefore, the Duty Cycle values ​​calculated at the instants of time 15 from t+6 to t+8 are set to 0.

[00251] . In a further embodiment, in the case of critical trends, the aforementioned phase of turning off 204 the system of control 100 plans to replace phase 2044 so as to understand the steps to calculate 2012a variation values 20 temperature average VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) and compare 2012a' each of the values ​​of average temperature variation VTi(t+dt, …, t+f), VTi(t+2dt, …, t+f),…, VTi(t+f-dt, …, t+f) with a mean variation value of threshold temperature VT*i, similarly to what is described in 25 reference to figure 6.

[00252] . In a different embodiment, in the case of critical trends, the aforementioned phase of turning off 204 the system of control 100 plans to replace phase 2044 so as to understand the steps of associating 2012b, at each point inside the 5 converter i, a set of binary parameters representative of the presence / absence of oscillatory trends AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f) of temperature and detect 2012b' the presence of at least one oscillatory trend AOi(t+dt,…,t+f), AOi(t+2dt,…,t+f), ..., AOi(t+(f-dt),...,t+f), 10 relating to the point inside the converter, not compliant with the process in a similar manner to that described in reference to the figure 6.

[00253] . In a further example of implementation of the method 200 of the invention, the phase of turning off 204 the control system 15 100 also includes a start-up and reporting phase 2046, from part of the process controller 11, of a scheduled shutdown of the control system 100 after this time interval of controlled shutdown s(n) from the current time instant t.

[00254] . In a further example of implementation of the method 200 20 of the invention, the step of turning off 204 the control system 100 also includes a 2047 start-up and reporting phase, from part of the process controller 11, of an emergency stop of the control system 100 if, according to the specifications of the process, at the current time instant t is not possible 25 maintain process integrity for a period of time controlled shutdown s(n).

[00255] . Note that the first optimization algorithm 2031 of the control is performed at the electronic device level of power 10 and it is not necessary to change the control of the 5 rest of the system 100.

[00256] . The optimization of the control performed by the second control algorithm 2033 requires, instead, a modification of the overall behavior of the system 100, which must be implemented at process controller level 11 realized as PLC. 10

[00257] . Furthermore, the processing performed by the method steps 201, 203, 204 can be performed both in the case of processes continue that in the case of cyclical processes, adapting the control strategies to the specificity of the application.

[00258] . Some examples of optimization strategies 15 implemented at the power electronics level 10 I am:

[00259] . - rounding of ignition profiles (e.g. “smoothing” the sawtooth profiles”);

[00260] . - redistribution of power to adjacent areas, 20 when controlled by the same power apparatus.

[00261] . Such strategies can be implemented independently from the power apparatus 10, which however is capable of report their implementation to the process controller 11. The apparatus 10 is, in fact, generally equipped with a ring of 25 feedback that allows you to detect and correct anomalies process.

[00262] . It is also possible that the change of control to power apparatus level 10 can be interpreted as an anomaly. In this sense, several approaches are possible: 5 - the power device 10 signals the application of a process modification to PLC controller 11, which then does not activates the anomaly correction; - optimization at the power plant level 10 is implemented to be transparent to the controller 10 PLC 11 (e.g. saw tooth chamfering); - optimization at the power plant level 10 is designed to work in coordination with the correction of the PLC 11 controller level anomaly (for example, over-reduction of the duty cycle of all heating zones 15 to “pre-compensate” for the subsequent increase by the PLC).

[00263] . These approaches can be foreseen in the logic of the power apparatus 10 and enabled according to of the application and operating conditions.

[00264] . Some examples of optimization strategies 20 process at the control system or machine level 100, in particularly at the process controller level 11, are: - slowing down of the controller's ignition ramps process 11, resulting in longer startup times system 100; 25 - slowing down of the system operating cycle 100, in in order to keep the process active even in the face of a lower productivity. Both of these strategies require an adjustment of the control of system 100: the optimization algorithm is configured, 5 therefore, to communicate to the PLC controller 11 the request that are implemented.

[00265] . This assumes that the control logic is distributed between power apparatus 10 and PLC controller 11, as both have a role in the application of the 10 optimization strategies.

[00266] . An even more general case involves the presence of two or more power devices 10 to be controlled in the same 100 systems, each with its own digital twin 102 and optimization algorithm. 15

[00267] . In this case, the optimization of the control of the system 100 can be performed by a PLC controller 11 which manages all 10 devices, or by the regulators themselves, in based on distributed logic.

[00268] . In case the new control conditions 20 processed by algorithms 2031 and 2033 are not sufficient to to keep the production process of system 100 stable, it is phase 204 activated to check if it is still possible keep the 100 system active for a limited time, before the his final arrest. 25

[00269] . This function can prevent the system from crashing suddenly, a condition that could cause probable serious damage. This situation is particularly critical in case of production lines where different ones are integrated systems 100. 5

[00270] . In this case, ensure a synchronous stop between the various sections of a plant ensures the least impact possible on production and a restart of the plant more fast.

[00271] . In the case of a system where a single device 10 power electronics 10 predicts malfunctions, it is this same apparatus 10 to determine the remaining time of production based on the optimal control strategy. The apparatus 10 is configured to pass this information on to the Remaining operating time at the process controller or PLC 15 11, which prepares the rest of the system 100 for shutdown and – if applicable – also informs other machines upstream and / or downstream on the production line.

[00272] . In the case of systems 100 with multiple power devices 10 which foresee malfunctions, it is the responsibility of the 20 PLC process controller 11 coordinate shutdown based on to the residual times of each apparatus 10 and to their position in the production process.

[00273] . To embodiments of the above method and system described, a technician in the field, to meet needs 25 contingents, will be able to make changes, adaptations and substitutions of elements with other functionally equivalent ones, without going out from the scope of the following claims. Each of the characteristics described as belonging to a possible form of realization can be realized independently from the 5 other embodiments described.

Claims

1. A method of controlling (200) a power electronic apparatus (10), employable in a control system (100) of a thermal processing process for processing a material, for regulating the temperature of a heating load (3) following the advance detection of malfunctions in the processing process, wherein the control system (100) comprises: - a heat transfer element (1) configured to be in contact with said material to be subjected to the thermal processing process; - a heating load (3);- said power electronic apparatus (10) capable of transferring electric current (Iout, Ires) to the heating load (3) to modify the thermal state of the heat transmission element (1), said power electronic apparatus (10) including a real power converter (101) driven by an input duty cycle signal (DTin) generated by the power electronic apparatus (10) to enable / disable the transfer of electric current to the heating load (3);- a process controller (11) configured to compare a detected current temperature value (Tmat) of the material subjected to the thermal processing with a reference temperature value (Ttgt), said process controller (11) being configured to control the power electronics apparatus (10) by modifying the input duty cycle signal (DTin) applied to the real power converter (101) to bring the detected current temperature value (Tmat) closer to the reference temperature value (Ttgt); - a functional block representing a digital twin converter (102) of said real power converter (101);- a functional block representing a comparison algorithm (103') operatively associated with the digital twin converter (102), the method (200) comprising the steps of: - detecting in advance and classifying (201), by the comparison algorithm functional block (103'), at least one malfunction in the thermal process of processing the material;- evaluating (202) a modification of the control applied, by the process controller (11), to the power electronic apparatus (10) following the early detection of said at least one malfunction, the method (200) comprising the further step of: - applying (203) said modified control to the power electronic apparatus (10) to prevent the at least one malfunction detected in advance in case such modified control ensures the integrity of the performed machining process, or - shutting down (204) in a controlled manner the control system (100) in case such modified control compromises the integrity of the performed machining process.; 2. A control method (200) according to claim 1, wherein said step of detecting in advance and classifying (201) at least one malfunction in the thermal material processing process comprises the steps of: - starting (2011) the digital twin converter functional block (102) together with the control system (100); - simulating (2012), by the digital twin converter functional block (102), a plurality of sets of temperature values ​​(Ti(t+dt, ..., t+f)) at a plurality of specific points inside the real power converter (101) of the power electronic apparatus (10), each of the simulated temperature values ​​of each set being associated with a time instant of a plurality of time instants (t+dt, ..., t+f) of a prediction time interval (f) following a current time instant (t) of the manufacturing process, where dt is a predetermined sampling time interval; - compare (2012') each of the simulated temperature values ​​of each set (Ti(t+dt, ..., t+f)) with a respective threshold temperature value (TSi) of a plurality of threshold values; in the case where at least one of the simulated temperature values ​​of one of the sets (Ti(t+dt, ..., t+f)) is greater than the respective threshold temperature value (TSi), the method includes the following phases: - classifying (2013), for each set of temperature values, by the comparison algorithm functional block (103'), the at least one malfunction that has caused said at least one simulated temperature value to be greater than the respective threshold temperature value (TSi); - activating (2014) a feedback algorithm (103) operationally associated with the digital twin converter (102), configured to evaluate whether said plurality of sets of simulated temperature values ​​(Ti(t+dt, ..., t+f)) provided by the digital twin converter (102) are acceptable in relation to the thermal process parameters.

3. A control method (200) according to claim 1 or 2, wherein said step of detecting in advance and classifying (201) at least one malfunction in the thermal material processing process comprises the steps of: - starting (2011) the digital twin converter functional block (102) together with the control system (100); - simulating (2012), by the digital twin converter functional block (102), a plurality of sets of temperature values ​​(Ti(t+dt, ..., t+f)) at a plurality of specific points inside the real power converter (101) of the power electronic apparatus (10), each of the simulated temperature values ​​of each set being associated with a time instant of a plurality of time instants (t+dt, t+2dt, ..., t+f) of a prediction time interval (f) following a current time instant (t) of the manufacturing process, where dt is a fixed sampling time interval; - calculate (2012a), for each specific point inside the real power converter (101), mean temperature variation values ​​(VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+f-dt, ..., t+f)), each of said mean temperature variation values ​​(VTi) at the specific internal point being calculated in a number of time instants equal to or less than the plurality of time instants (t+dt, t+2dt, ., t+f) of the prediction time interval (f) following a current time instant (t) of the process, each of said mean temperature variation values ​​(VTi) being related to a plurality of temperature values ​​(Ti(t+dt,., t+f), Ti(t+2dt,., t+f),., Ti(t+f-dt,., t+f)) simulated at that point; - compare (2012a'), each of the values ​​of mean temperature variation (VTi(t+dt, ., t+f), VTi(t+2dt, ., t+f),., VTi(t+f-dt, ., t+f)) associated with the specific internal point, with a value of threshold mean temperature variation (VT*i) specific for that point and determined on the basis of the thermal process; in the case where at least one of the mean temperature variation values ​​(VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+fdt, ..., t+f)) is greater than such respective threshold mean temperature variation value (VT*i), the method comprises the phases of: - classifying (2013a), by the comparison algorithm functional block (103'), the at least one malfunction that has determined at least one of the mean temperature variation values ​​(VTi(t+dt, ., t+f), VTi(t+2dt, ., t+f),., VTi(t+f-dt, ., t+f)) greater than the respective threshold mean temperature variation value (VT*i); - activate (2014) a feedback algorithm (103) operationally associated with the digital twin converter (102), configured to evaluate whether said plurality of sets of simulated temperature values ​​(Ti(t+dt, ., t+f)) provided by the digital twin converter (102) are acceptable in relation to the thermal process parameters.

4. A control method (200) according to claim 1 or 2, wherein said step of detecting in advance and classifying (201) at least one malfunction in the thermal material processing process comprises the steps of: - starting (2011) the digital twin converter functional block (102) together with the control system (100); - simulating (2012), by the digital twin converter functional block (102), a plurality of sets of temperature values ​​(Ti(t+dt, , t+f)) at a plurality of specific points inside the real power converter (101) of the power electronic apparatus (10); each of the simulated temperature values ​​of each set being associated with a time instant of a plurality of time instants (t+dt, t + 2dt, ..., t+f) of a prediction time interval (f) following a current time instant (t) of the manufacturing process, where dt is a predetermined sampling time interval; - associate (2012b), to each specific internal point, a set of binary parameters representing the presence / absence of oscillatory trends (AOi(t+dt,...,t+f), AOi(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f)) of temperature, each of said parameters being related to a sub-interval of temperatures (Ti(t+dt,...,t+f), Ti(t+2dt,...,t+f),..., Ti(t+(f-dt),...,t+f)) of said simulated temperature values ​​Ti(t+dt, ., t+f); - detect (2012b'), in at least one of said temperature sub-intervals, the presence of at least one oscillatory trend (AOi(t+dt,.,t+f), AOi(t+2dt,.,t+f), ..., AOi(t+(f-dt),...,t+f)) of the temperature, relative to the specific internal point, not compliant with the process when said sub-interval comprises at least one value of the first local minimum of temperature, one value of the first local maximum of temperature and a further value of the second consecutive local minimum of temperature or at least one value of the first local maximum of temperature, one value of the first local minimum of temperature and a further value of the second consecutive local maximum of temperature; - classify (2013b), by the comparison algorithm functional block (103'), the at least one malfunction that has determined the presence of said at least one oscillatory trend (AOi(t+dt,..,t + f), AOi(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f)) of temperature; - activate (2014) a feedback algorithm (103) operationally associated with the digital twin converter (102), configured to evaluate whether said plurality of sets of temperature values ​​(Ti(t+dt, ..., t+f)) simulated provided by the digital twin converter (102) are acceptable in relation to the thermal process parameters.

5. A control method (200) according to any preceding claim, wherein said shutdown step (204) comprises a step of performing a controlled shutdown of the control system (100), by the process controller (11), after a shutdown time interval (s(n)) from said current time instant (t), wherein said shutdown time interval (s(n)) has duration shorter than the prediction time interval (f).

6. Control method (200) according to claim 5, wherein said turn-off time interval (s(n)) is calculated by the expression: s(n) = f- (n*dt) where f is said prediction time interval, dt is the sampling time interval and n is an integer.

7. A control method (200) according to any of claims 1-6, wherein said steps of evaluating (202) a modification of the applied control and applying (203) said modified control to the power electronic apparatus (10) to prevent the at least one pre-detected malfunction comprises the steps of: - executing a first control optimization algorithm (2031) applied to said power electronic apparatus (10); - evaluating (2032) whether the at least one pre-detected malfunction is prevented based on the execution of said first control optimization algorithm (2031); in the event that the at least one pre-detected malfunction persists, the method comprises the further steps of: - executing a second control optimization algorithm (2033) applied to said process controller (11);- evaluating (2034) whether the at least one pre-detected malfunction is prevented based on the execution of said second control optimization algorithm (2033); in case the at least one pre-detected malfunction persists, the method comprises said step of turning off (204) the control system (100).; 8. Control method (200) according to claim 7, wherein said first control optimization algorithm (2031) comprises a step of evaluating (2031a) whether a reduction of the input duty cycle signal (DTin) applied to the real power electronic converter (101) maintains the integrity of the performed machining process; in the case where the integrity of the performed machining process is maintained, the method comprises the steps of: - calculating (2031'), by said feedback algorithm (103), a first plurality of duty cycle values ​​(D-C1(t+dt, ..., t+f)) each associated with a time instant (t+1, ..., t+f) of said plurality of instants in said prediction time interval (f), subsequent to a current time instant (t) of the machining process; - making available said first plurality of duty cycle values ​​(D-C1(t+dt, ..., t+f), t+f)) to the digital twin converter functional block (102); - simulating (2031''), by the digital twin converter functional block (102), a plurality of sets of first temperature values ​​(T1i(t+dt, ..., t+f)) at a plurality of specific internal points of the real power converter (101) of the power electronic apparatus (10), each of said sets of simulated first temperature values ​​being associated with said first plurality of duty cycle values ​​(D-C1(t+dt, ..., t+f)) at a time instant (t+dt, ..., t+f); - comparing (2031b) each of said sets of simulated first temperature values ​​(T1i(t+dt, ..., t+f)) with a respective threshold temperature value (TSi) of a plurality of threshold values; in case each of the first temperature values ​​(T1i(t+dt, ..., t+f)) simulated at each specific internal point is less than the respective threshold value (TSi), the method includes the steps of: - applying (2031c) to the real power converter (101) an input duty cycle signal (DTin) including said first plurality of duty cycle values ​​(D-C1(t+dt, ..., t+f)) to enable / disable the transfer of electric current to the heating load (3); - signalling, by the feedback algorithm functional block (103), an error condition to a supervision unit of the control system (11); in the case in which at least one of the first temperature values ​​(T1i(t+dt, ..., t+f)) simulated at each specific internal point is greater than the respective threshold value (TSi), the method comprises the steps of: - repeating said step of evaluating (2031a) whether a further reduction of the input duty cycle signal (DTin) applied to the real power electronic converter (101) maintains the integrity of the performed machining process; and in the case in which the integrity of the performed machining process is maintained, the step of: - calculating (2031'), by said feedback algorithm (103), a further first plurality of duty cycle values ​​(D-C1'(t+dt, ..., t+f)) each associated with a time instant (t+1, ..., t+f) of said plurality of instants in said prediction time interval (f).

9. A control method (200) according to claim 7 or 8, wherein said second control optimization algorithm (2033) comprises a step of evaluating (2033a) whether an increment of the input duty cycle signal (DTin) applied to the real power electronic converter (101) maintains the integrity of the performed machining process; in the case where the integrity of the performed machining process is maintained, the method comprises the steps of: - calculating (2033'), by said feedback algorithm (103), a second plurality of duty cycle values ​​(D-C2(t+dt, ..., t+f)) each associated with a time instant (t+dt, ..., t+f) of said plurality of time instants in said prediction time interval (f), subsequent to a current time instant (t) of the machining process; - make available said second plurality of duty cycle values ​​(D-C2(t+dt, ..., t+f)) to the digital twin converter functional block (102); - simulating (2033''), by the digital twin converter functional block (102), a plurality of sets of second temperature values ​​(T2i(t+dt, ..., t+f)) at a plurality of specific internal points of the real power converter (101) of the power electronic apparatus (10), each of said sets of simulated second temperature values ​​being associated with said second plurality of duty cycle values ​​(D-C2(t+dt, ..., t+f)) at a time instant (t+dt, ..., t+f) of said prediction time interval (f); - comparing (2033b) each of said sets of simulated second temperature values ​​(T2i(t+1, ..., t+f)) with a respective threshold temperature value (TSi) of a plurality of threshold values; in case each of the second temperature values ​​(T2i(t+dt, ..., t+f)) simulated at each specific internal point is less than the respective threshold value (TSi), the method includes the steps of: - applying (2033c) to the real power converter (101) an input duty cycle signal (DTin) including said second plurality of duty cycle values ​​(D-C2(t+dt, ..., t+f)) to enable / disable the transfer of electric current to the heating load (3); - signalling, by the feedback algorithm functional block (103), an error condition to a supervision unit of the control system (11); in the case in which at least one of the second temperature values ​​(T2i(t+dt, ..., t+f)) simulated at each specific internal point is greater than the respective threshold value (TSi), the method comprises the steps of: - repeating said step of evaluating (2033a) whether a further increase of the input duty cycle signal (DTin) applied to the real power electronic converter (101) maintains the integrity of the performed machining process; and in the case in which the integrity of the performed machining process is maintained, the step of: - calculating (2033'), by said feedback algorithm (103), a further second plurality of duty cycle values ​​(D-C2'(t+dt, ..., t+f)) each associated with a time instant (t+dt, ..., t+f) of said plurality of instants in said prediction time interval (f).

10. Control method (200) according to claim 5 or 6, wherein said step of switching off (204) the control system (100) comprises a step of evaluating (2041) whether the integrity of the performed machining process is maintained for a controlled shutdown time interval of limited duration (s(n)) which takes on discrete values ​​given by s(n)=f-(n*dt), of anticipating the shutdown of the system at time t+s(n); in the case in which the integrity of the performed machining process is maintained for said controlled shutdown time interval (s(n)) of limited duration, the method comprises a step of repeating, for each of the discrete values ​​of such controlled shutdown time interval for which (n*dt) <f, le fasi di: - calcolare (2042), da parte di detto algoritmo di retroazione (103), una terza pluralità di valori di duty cycle (D-C3(t+dt, ..., t+s(n))) ciascuno associato ad un istante di tempo (t+dt, ..., t+s(n)) of said plurality of time instants of the controlled turn-off time interval (s(n)); - making said third plurality of duty cycle values ​​(D-C3(t+dt, ..., t+s(n))) available to the digital twin converter functional block (102); - simulating (2043), by the digital twin converter functional block (102), a plurality of sets of third temperature values ​​(T3i(t+dt, ..., t+s(n))) at a plurality of specific internal points of the real power converter (101) of the power electronic apparatus (10), each of said sets of simulated third temperature values ​​being associated with said third plurality of duty cycle values ​​(D-C3(t+dt, ..., t+s(n))) at a time instant (t+dt, ..., t+s(n)) of said controlled turn-off time interval (s(n)); - compare (2044) each of said sets of third temperature values ​​(T3i(t+dt, ..., t+s(n))) simulated with respective sets of threshold temperature values ​​(TSi); said repeat step being performed in case at least one of the third temperature values ​​(T3i(t+1, ..., t+s(n)) simulated at each specific internal point is greater than the respective threshold value (TSi).

11. Control method (200) according to the preceding claim, wherein said step of switching off (204) the control system (100) comprises, in the case where each of the third temperature values ​​(T3i(t+dt, ..., t+s(n)) simulated at a specific internal point is lower than the respective threshold value (TSi), the steps of: - applying (2045) to the real power converter (101) an input duty cycle signal (DTin) including said third plurality of duty cycle values ​​(D-C3(t+dt, ..., t+s(n))) to enable / disable the transfer of electric current to the heating load (3); - signalling, by the feedback algorithm functional block (103), an error condition to a supervision unit (12) of the control system (100).

12. Control method (200) according to any of claims 5-6 or 10-11, wherein said step of switching off (204) the control system (100) further comprises a step of initiating and signaling (2046), by the process controller (11), a programmed shutdown of the control system (100) after said controlled shutdown time interval (s(n)) from the current time instant (t).

13. Control method (200) according to claims 10-11, wherein said step of switching off (204) the control system (100) further comprises a step of initiating and signalling (2047), by the process controller (11), an emergency shutdown of the control system (100) if at the current time instant (t) it is not possible to maintain the integrity of the process for a controlled shutdown time interval (s(n)).

14. A control system (100) for a thermal processing of a material, for regulating the temperature of a heating load (3) following the early detection of malfunctions in the processing, the control system (100) comprising: - a heat transfer element (1) configured to be in contact with said material to be subjected to the thermal processing; - a heating load (3); - a power electronic apparatus (10) adapted to transfer electric current (Iout, Ires) to the heating load (3) to modify the thermal state of the container element (1), said power electronic apparatus (10) including a real power converter (101) driven by an input duty cycle signal (DTin) generated by the power electronic apparatus (10) to enable / disable the transfer of said electric current to the heating load (3);- a process controller (11) configured to compare a detected current temperature value (Tmat) of the material subjected to the thermal processing with a reference temperature value (Ttgt), said process controller (11) being configured to control the power electronics apparatus (10) by modifying the input duty cycle signal (DTin) applied to the real power converter (101) to bring the detected current temperature value (Tmat) closer to the reference temperature value (Ttgt); - a functional block representing a digital twin converter (102) of said real power converter (101);- functional blocks representative of a comparison algorithm (103') and a feedback algorithm (103), respectively, operatively associated with the digital twin converter (102), said control system (100) being configured to perform the control method (200) in accordance with any one of claims 1-13.; 15. Control system (100) of a thermal process for processing a material according to claim 14, wherein said process controller (11) is a programmable logic controller device (Programmable Logic Controller), of an independent type (stand-alone) with respect to the power electronic apparatus (10) or integrated into such power electronic apparatus.

16. Control system (100) of a thermal process for the processing of a material according to claim 14, wherein said process controller (11) is a logic algorithm implemented on devices selected from the group consisting of: operator panels or HMIs, industrial PCs, Edge Servers.

17. Control system (100) of a thermal material processing process according to any of claims 14-16, further comprising a supervisory unit (12) configured to exchange process data (D) with said process controller (11), said supervisory unit (12) being integrated into the process controller (11), or being implemented on a remote cloud platform capable of communicating with the process controller (11) via a telecommunications network.

18. Control system (100) of a thermal process for processing a material according to claim 17, wherein the digital twin electronic converter functional block (102) of the real converter (101) is implemented in one of the components of the control system (100) of the thermal process chosen from the group consisting of: power electronic apparatus (10), process controller (11), supervision unit (12).

19. Control system (100) of a thermal process for processing a material according to claim 17, wherein one of the components of the control system (100) of the thermal process chosen from the group consisting of: power electronic apparatus (10), process controller (11), supervision unit (12), is configured to implement the functional blocks representing a feedback algorithm (103) and a comparison algorithm (103').