ES2370616B1 - DYNAMIC CONTROLLER AND DIFFUSIVE ELEVATOR GROUP CONTROL METHOD FOR OPTIMIZATION OF ENERGY CONSUMPTION. - Google Patents

Abstract

Diffuse control method of elevator group for the optimization of energy consumption of those of the type that is used by vertical transport elevator control systems characterized in that it comprises the stages of evaluating the energy aptitude of each cabin and assigning the calls of plant according to an order according to (i) a first absolute energy valuation (1); (ii) a second relative energy valuation (5); and (iii) a third assessment according to the contiguity (8) of the calls.

Description

Dynamic controller and diffuse control method of elevator group for optimization of energy consumption.

The object of the present invention is a method and dynamic controller of energy optimization of those of the type used by elevator group control systems for vertical transport that minimizes the energy consumption of the cabins according to a triple valuation, by diffuse logic , of each and every one of the assignments of the so-called elevator-floor type that are feasible to be part of the office.

The present invention, since it solves a technical problem of vertical transport, is part of the field of transport engineering, and more specifically of the electronic devices applied to the control of the movement of elevators and the like.

Prior art

The problem of vertical transport refers to the whole process in which groups of passengers travel between different floors of a facility using the means of transport (groups of elevators and stairs) available. In this regard, complex elevator group control systems (Elevator Group Control System (EGCS) are developed that provide efficient transport and at the same time offer a certain degree of service (GoS). The performance of these EGCS is measured according to different criteria such as the average waiting time per passenger, the percentage of waiting times exceeding one minute and the energy consumption.

As a certain degree of sophistication has been reached, it is mathematically impossible (this fact is known in Anglo-Saxon terminology as NP-Hard) to optimize the operation according to all the criteria at the same time, one is usually optimized to the detriment of the others according to the preferences of the owner of the building . Thus, given the current international relevance of sustainable development (especially as a competitive advantage that can make a difference in the private sector), an EGCS that optimizes energy consumption would be desirable.

The tendency to build skyscrapers and buildings of increasing height has been a constant in urban construction according to economic and spatial reasons. In these facilities, vertical transport constitutes a real technical and logistical problem, given that its complexity increases exponentially with its height.

In this sense, the elevator office is an activity of vital importance and all EGCS undertakes it according to two main aspects: magnitude of the calculation (response time) and treatment of uncertainty [1], For a given moment the problem of the elevator office It is defined as an NP-Hard problem. Thus, for a building that has n elevators where k plants demand the service of a cabin, there are nk solutions. Hence the complexity of the problem in high buildings.

In current systems where numerical panels are not provided to passengers to indicate the destination, the uncertainty is very high because neither the number of passengers on the call nor the exact destination of each of them is known until they enter the cabin. In addition to the complexity and high degree of uncertainty, the EGCS must take into account possible future plant calls. For all these reasons, the use of artificial intelligence or advanced metaheuristics is frequent: the use of genetic algorithms [2] or taboo search provides quality results but their delay in obtaining an optimal solution makes them inappropriate for this type of problem. where the temporal issue is vital. Other alternatives such as neural networks [3] need long periods of training and are expensive and complex to implement, as well as being unable to adapt to sudden or unforeseen changes in demand. Unlike the above mentioned methodologies, fuzzy logic [4] is easy to implement, fast in execution and by its own perfect definition for the treatment of uncertainty.

As the facilities grow in height and consequently the elevators in size and number, the energy consumption of vertical transport increases dramatically. Thus, in modern buildings, the average power used for the power supply of all the cabins of a building ranges between 2% and 10% of its total electricity bill [5].

The first studies date from the early seventies. Even so, the use of heuristics and metaheuristics aimed at minimizing the power consumed is not yet frequent.

According to the old technology of the braking of the elevator, the totality of the energy used to move the cabin was unrecoverable, and the greater expense due to the ignition of the engine, thus the energy consumption used to be related to the number of starts of the cabins. In this sense, designs based on multicriteria evaluation using fuzzy logic [6] of the average waiting time, the percentage of long waiting times and energy were proposed. However, some modern models such as [7] persist in the approach of minimizing the number of stops to indirectly minimize consumption.

However, the present technology allows recovering a part of the energy used by returning it to the network [8]. The design is usually such that when the car is at the same height as the counterweight and its load is half of the maximum allowed, the system is in equilibrium: in this way, when the elevator travels downwards with a load less than half of the maximum allowed or in an upward direction with a load greater than half of the maximum allowed, the lift consumes energy (the greater the distance and the imbalance is greater) and vice versa. Thus, by means of an efficient allocation of the calls it is possible to recover part of the consumed energy, getting in some cases to obtain a positive net balance.

References

[1] Barney, G., 2003. Elevator Traf fi c Handbook: Theory and practice, Taylor & Francis Group.

[2] Siikonen M-L, J. Sorsa, Optimal control of Double Deck elevator group using genetic algorithm, KONE Corporation, Finland (2002).

[3] Lu Yu, Jin Zhou, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu, Sandor Markon, Elevator Group Control System using Genetic Network Programming with ACO Considering Transitions, SICE Annual Conference, Kagawa University, Japan (2007).

[4] Zong-Mu Yeh; Kuei-Hsiang Li (2003). A systematic approach for designing multistage fuzzy control systems.

[5] The Chartered Institution of Building Services Engineers (2005). CIBSE Guide D: Transportation systems in buildings.

[6] ChangBum Kim, Kyoung A. Seong, Hyung Lee-Kwang, Jeong O. Kim, Design and Implementation of a Fuzzy Elevator Group Control System, South Korea (1998).

[7] An Elevator Group Control System with a Self-Tuning Fuzzy Logic Group Controller. Transactions on Industrial Electronics (2010).

[8] Tapio Tyni and Jari Ylinen (2006). Evolutionary bi-objective optimization in the elevator car routing problem, KONE Corporation, Finland.

Explanation of the invention.

The technical problem that the present invention intends to solve is that of the efficient allocation of calls in order to recover part of the energy consumed by the cabins.

For this, the present invention proposes a group controller of elevators based on fuzzy logic (Fuzzy Logic EGCS) capable of performing an energy evaluation of the state of the system that allows an effective management of the dispatch problem in plant calls.

Thus, for a building of p plants and n elevators, n × p diffuse procedures are calculated by three different valuations of each of the possibilities: an estimate based on the absolute energy used, an estimate based on the relative energy used with respect to the other options and an estimate of the contiguity between plant calls, thus allowing the ECGS to optimize energy consumption.

The diffuse estimation of the absolute energy of each possibility is carried out by means of a series of linguistic variables, in the preferred embodiment "Possible Distance Traveled" and "Imbalance" and of the variable "State of the Cabin" (rest, ascent or descent) . The diffuse valuation of the relative energy of each possibility with respect to the others is calculated using the linguistic variables, in the preferred embodiment "Relative Deviation" and "Alternative Quality". The diffuse assessment of contiguity is estimated using the linguistic variables “Proximity” and “Imbalance” and the variable “State of the Cabin” (rest, ascent or descent).

In a second aspect of the invention, the dynamic energy optimization controller of those of the type employed by elevator group control systems for vertical transport comprises at least configured means for the detection of the mass and condition of the cabin , con fi gured means for the calculation of the linguistic variables referred to previously and the fuzzy inference process, where said logical means are in turn con fi gured to evaluate the established inference rules.

Thus, it is an objective of the present invention is a method, a controller and a dynamic system of optimization of energy consumption for elevator group control systems, where given the complexity of the problem (NP-Hard) of cabin dispatch , the use of the proposed model based on the artificial intelligence technique called diffuse logic contributes significantly to the minimization of energy consumption, in addition to being the only one of its kind based on the dynamic allocation of calls.

Throughout the description and the claims the word "comprises" and its variants are not intended to exclude other technical characteristics, additives, components or steps. For those skilled in the art, other objects, advantages and characteristics of the invention will be derived partly from the description and partly from the practice of the invention. The following examples and drawings are provided by way of illustration, and are not intended to be limiting of the present invention. In addition, the present invention covers all possible combinations of particular and preferred embodiments indicated herein.

Brief description of the drawings

Fig 1. Shows the flow chart of the diffuse control method for optimizing energy consumption, object of the present invention.

Fig 2. Shows the membership functions of the different linguistic variables used in the diffuse control method object of the present invention; where Fig. 2a shows the membership function of the variable "Unbalance", in Fig. 2b the PDR function is shown, in Fig. 2c the "Proximity" function is shown, in Fig. 2d it is shown the function of "Relative deviation", and in Fig. 2e the function "Alter Quality" is shown.

Detailed presentation of embodiments and Examples

In the present invention, the call dispatch is dynamic, every time a new plant call occurs or a change in mass is detected in some cabin, the reallocation is complete. As fuzzy logic is unable to process the information in parallel, it is necessary to establish the order of evaluation of plant calls according to certain criteria.

The algorithm evaluates the energy aptitude of each cabin and assigns the plant calls according to an order according to three different criteria: an absolute energy valuation, a relative energy valuation and a valuation according to the contiguity of the calls. The algorithm executes the following tasks as shown in Fig. 1.

Thus, the absolute energy assessment (1) EABS [n] [p] estimates the total amount of energy used by cabin n in answering the call p. Thus, it depends on the possible distance traveled in the event that the cabin answered the PDR call (“Possible Distance Traveled”) (2), the imbalance of the cabin with respect to the null or resting energy state if the cabin met the so-called Unbalance ( 3) and the current address of the cabin Address (4).

On the other hand, the relative energy valuation (5) EREL [n] [p] makes a comparison weighing the values of the absolute energy valuation (1) EABS [n] [p] to obtain the suitability of each of the possible assignments with respect to of the others within the universe of feasible offices. It depends on the parameters “Relative Deviation” ΔE [n] [p] (6) and “Alter Quality” (7), which are defined according to the following equations:

Where S [n] [pf] is the standard deviation and EABS [n] [pf] the energy average of all possible assignments to a call p.

E’ABS [n] [p] is the best alternative to EABS [n] [p] for a given p call. Thanks to the EREL measure [n] [p], better conclusions can be obtained than taking into account only EABS [n] [p]. Absolute energy valuation (1), since the different possibilities of cabin assignment for a specific p call can be compared. Therefore, although the absolute valuation of the possible energy used is poor, by means of an effective measure of the relativity of this value it can be detected that it is a good option, or if you want “a lesser evil”. For this, the parameter ΔE [n] [p] (6) measures the difference in standard deviations of the value of the energy used by the booth n in attending the call p with respect to the average used by all the booths in answering the call p, and the parameter “Alter Quality” (7) makes sure that there is some other alternative of similar aptitude in case the np assignment is a good option. If the quality of the alternative is good, cabin n can be considered to dispatch another call as it has a good substitute and does not constitute a critical assignment.

Once a first absolute energy assessment (1) and relative (5) has been carried out, the assignment with the best aptitude is chosen as the first dispatch, weighing both criteria. Then, starting from the previous assignment np calculated (and fixed), all the aptitudes of each cabin n are obtained again with respect to the other unassigned calls p now taking into account an assessment of the contiguity in energy sense C [n ] [p] (8). The diffuse estimate of C [n] [p] (8) addresses:

(i) The measure of the minimum distance between all calls already assigned definitively to the booth n and the call considered p “proximity” (9).

(ii) The possible imbalance (10) of the cabin with respect to the null energy state (Deseq).

(iii) The current address of the cabin (11).

Depending on the state of the cabin, consuming energy or generating, the measurement of contiguity (8) will contribute to the next calls between each other being answered by the same cabin or by different cabins. For example, to a cabin that is descending and whose mass is less than half of the maximum allowable load, a large allocation of close calls is convenient for increasing its load and reducing energy consumption or even generating.

The process is repeated until all plant calls have been assigned to a booth. The full valuation (12) of each n-p pair is weighted according to the set of weights [ν1, ν2, ν3]:

Each of the assessments (12) is calculated diffusely by means of their corresponding linguistic variables. One of the virtues of this design based on fuzzy logic is that it is not necessary to know a priori the possible degree of service of the system. Thus, the membership functions depend exclusively on the building and the type of cabin, as shown in Fig 2.

Thus, for the membership function of the Imbalance variable (3, 10) shown in Figure 2a, the membership function is established between the maximum mass (M) the maximum average mass (M / 2) and minimum mass (m) , logically greater than zero.

Figure 2b establishes the membership function of the PDR variable (2) where the function is established between the height of the building in maximum (H), average (H / 2) and minimum (h) meters, logically greater than zero.

In Figure 2c the “Proximity” membership function (9) is established, where the number of floors of the building is established, with the maximum number (N), the medium (N / 2) and the minimum (n ), logically greater than zero.

Figure 2d establishes the membership function of “Relative Deviation” ΔE [n] [p] (6) set to a maximum of standard deviations of 2 and a minimum of -2 with regular intervals of 1.

In Figure 2e the “Quality Alter” membership function (7) is established by establishing a percentage of difference from the best EABS [n] [fixed] with a maximum of 0.5 and a minimum of -0.5 at regular intervals of 0.25.

Example of use of the invention

Suppose that for a given moment there are three plant calls in a building with 7 floors and 4 elevators and a new plant call is produced. As the system has detected a change, a complete reassignment of all calls is made so that the previous state is ignored in order to ensure effective optimization. The algorithm will carry out a first absolute energy assessment (1) of the situation, estimating for each of the twelve possibilities (3x4) the energy expenditure in case of responding each of the elevators to each of the plants, measuring for this diffuses the distance that they would have to travel, the possible mass balance of the counterweight-cabin system and taking into account the current direction of the elevator. Once the possible absolute energy used has been evaluated, a relative energy assessment (5) is carried out using the Relative Deviation and Alter Quality parameters.

Once a first absolute energy assessment (1) and relative (5) has been carried out, the assignment with the best aptitude is chosen as the first dispatch, weighing both criteria. Then, starting from the previous assignment np calculated (and fixed), all the aptitudes of each cabin n are obtained again with respect to the other unassigned calls p now taking into account an assessment of the contiguity in an energetic sense, attending to : The measure of the minimum distance between all the calls already assigned definitively to the booth n and the call considered p, the possible imbalance of the booth with respect to the null energy state and the current booth address.

Depending on the state of the cabins, consuming energy or generating, the measurement of contiguity will contribute to the next calls between each other being answered by the same cabin or by different cabins. For example, to a cabin that is descending and whose mass is less than half of the maximum allowable load, a large allocation of close calls is convenient for increasing its load and reducing energy consumption or even generating. The process is repeated until all plant calls have been assigned to a booth. The full valuation of each n-p pair is weighted according to a set of weights.

Claims (9)

one.

Diffuse control method of elevator group for the optimization of energy consumption of those of the type that is used by vertical transport elevator control systems characterized in that it comprises the stages of evaluating the energy aptitude of each cabin and assigning the calls of plant according to an order according to (i) a first absolute energy valuation (1); (ii) a second relative energy valuation (5); and (iii) a third assessment according to the contiguity (8) of the calls; and where said process is repeated until all the plant calls have been assigned to a cabin, establishing a complete assessment (12) of each weighted pair according to a plurality of weights, at least one for each of the described valuations ( 1, 5, 8).

2.

Method according to claim 1 characterized in that the absolute energy assessment (1) estimates the total amount of energy used by the booth n in answering the call p, depending on the possible distance traveled in case the booth answered the call PDR (2), the imbalance of the cabin with respect to the null or resting energy state if the cabin met the so-called Unbalance (3), and the current address of the cabin Address (4).

3.

Method according to claim 1 and 2 characterized in that the relative energy valuation (5) makes a comparison weighing the values of the absolute energy valuation (1) to obtain the suitability of each of the possible assignments with respect to the others within the universe of offices feasible, depending on the parameters “Relative Deviation” (6) and “Alter Quality” (7).

4. Method according to claim 3 characterized in that the parameter "Relative deviation" (6) measures the difference in standard deviations of the value of the energy used by the booth n in attending the call p with respect to the average used by all the booths in answering the call p; and where the parameter "QualityAlter" (7) makes sure that there is some other alternative of similar aptitude in case the np allocation is a good option, and where if the quality of the alternative is good the cabin n can be considered to dispatch another call because it has a good substitute and does not constitute a critical assignment. 5. Method according to the preceding claims characterized in that once a first energy assessment has been carried out, both absolute (1) and relative (5), the assignment with the best aptitude is chosen as the first dispatch considering both criteria, and where starting from the previous assignment np calculated, all the aptitudes of each cabin n are obtained again with respect to the other calls p still not assigned taking into account now an assessment of the contiguity (8) in the energetic sense. 6. Method according to claim 5 characterized in that the fuzzy estimate of contiguity (8) attends to (i) the measurement of the minimum distance between all calls already assigned definitively to the booth n and the call considered p “proximity” (9); (ii) the possible imbalance (10) of the cabin with respect to the zero energy state; and (iii) the current address of the cabin (11).

7.

Dynamic energy optimization controller of those of the type employed by the control systems of a group of elevators for vertical transport and characterized in that it comprises, at least, (a) first configured means for the detection of the mass and the state of the cabin; (b) a second set means for the calculation of the linguistic variables (2, 3, 6, 7, 9, 10) and the method of claims 1 to 6; where said logical means are in turn set to evaluate the established inference rules .

8.

Dynamic energy optimization control system of the type employed by the control systems of a group of elevators for vertical transport and characterized in that it implements the method of claims 1 to 6 and / or the controller of claim 7.

SPANISH OFFICE OF THE PATENTS AND BRAND Application no .: 201000497 SPAIN Date of submission of the application: 04.13.2010 Priority Date: REPORT ON THE STATE OF THE TECHNIQUE 51 Int. Cl.: B66B1 / 20 (2006.01) RELEVANT DOCUMENTS

Category

Documents cited Claims Affected

X

US 2005263355 A1 (KOSTKA MIROSLAV) 01.12.2005, 1.7-8

paragraphs [0040-0051]; Figures 1-4.

TO

2-6

TO

US 5786551 A (THANGAVELU KANDASAMY) 28.07.1998, 1-8

summary; column 5, lines 13-42; figures.

TO

US 2007 045052 A1 (STANLEY JANNAH A et al.) 01.03.2007, 1-8

paragraphs [0016-0036]; Figures 1-3.

TO

US 5233138 A (AMANO MASAAKI) 03.08.1993, 1-8

column 2, line 5 - column 5, line 17; figures.

Category of the documents cited X: of particular relevance Y: of particular relevance combined with other / s of the same category A: reflects the state of the art O: refers to unwritten disclosure P: published between the priority date and the date of priority submission of the application E: previous document, but published after the date of submission of the application

This report has been prepared • for all claims • for claims no:

Date of realization of the report 23.11.2011

Examiner J. Calvo Herrando Page 1/5

REPORT OF THE STATE OF THE TECHNIQUE Minimum documentation searched (classification system followed by classification symbols) B66B Electronic databases consulted during the search (name of the database and, if possible, terms of search used) INVENES, EPODOC, WPI  WRITTEN OPINION Date of Written Opinion: 23.11.2011 Statement

Novelty (Art. 6.1 LP 11/1986)

Claims Claims 1-8 IF NOT

Inventive activity (Art. 8.1 LP11 / 1986)

Claims Claims 2-6 1.7-8 IF NOT

The application is considered to comply with the industrial application requirement. This requirement was evaluated during the formal and technical examination phase of the application (Article 31.2 Law 11/1986).  Opinion Base.- This opinion has been made on the basis of the patent application as published.  WRITTEN OPINION 1. Documents considered.- The documents belonging to the state of the art taken into consideration for the realization of this opinion are listed below.

Document

Publication or Identification Number publication date

D01

US 2005263355 A1 (KOSTKA MIROSLAV) 01.12.2005

D02

US 5786551 A (THANGAVELU KANDASAMY) 28.07.1998

D03

US 2007 045052 A1 (STANLEY JANNAH A et al.) 01.03.2007

D04

US 5233138 A (AMANO MASAAKI) 03.08.1993

2. Statement motivated according to articles 29.6 and 29.7 of the Regulations for the execution of Law 11/1986, of March 20, on Patents on novelty and inventive activity; quotes and explanations in support of this statement The claimed invention is about a dynamic controller for energy optimization, by means of fuzzy logic. Document D01 is considered to be the closest prior art document to the object claimed.  R1 independent claim D01 (paragraphs [0040-0051], claims and figs. 1-4) describes a method and control system for assigning elevators by diffuse control assessing the energy fitness of each cabin by means of criteria to activate the energy saving mode . The difference between document D01 and the object of claim R1 is the way to assess the energy attitude in each cabin: while the invention of D01 compares three criteria to activate the energy saving mode such as determining traffic over time. waiting means (weight, address, distance, time), comparison of calls waiting on levels, and comparison of the average number of passengers per trip with a number of passengers per trip; In the present invention, he is able to evaluate the energy aptitude by means of three assessments ("absolute energy", "relative energy", and "contiguity of calls"). However, the evaluation of the energy fitness of elevators through the absolute energy valuation, relative energy and contiguity of calls are very broad concepts that encompasses the solution provided by document D01 where said evaluation is carried out by determining the traffic in a medium time, comparison Call waiting on levels and average passengers per trip. Therefore, it is considered that developing a method of energy evaluation of elevators for the efficient allocation of calls such as that described in claim R1 does not require any inventive effort for an expert in the field in light of what is disclosed by D01. Accordingly, according to the state of the art cited, it is claimed that claim R1 does not meet the inventive activity requirement set forth in Art. 8.1 LP. It is suggested to incorporate the technical characteristics disclosed by claims R2, R3 and R6 to independent claim R1 to remedy the objections of the preceding paragraphs.  R2-R6 dependent claims The characteristics disclosed by claims R2-R6 describe how the stages of evaluation of energy fitness are performed in a group of elevators for the optimization of energy consumption based on the absolute energy valuation, the relative energy valuation and the contiguity of the calls of so that there is no information in the cited documents that can direct the person skilled in the art to the claimed method. Therefore, claims R2-R6 are considered to comply with the requirements of novelty and inventive activity set forth in Art. 6.1 and Art 8.1 LP.  R7 independent claims The use of dynamic controllers used in control systems is a well-known technique applied in a group of elevators for vertical transport in the state of the art as can be seen in documents D01 (paragraphs [0040-0051], claims and figs. 1-4) and D02 (column 5, lines 13-42; figs. 1-3, 11, 16, 19, 26, 32, 40 and summary). Therefore, it would be obvious to one skilled in the art to configure a controller system such as those described in claims R7 to implement the method of diffuse control of a group of elevators for the optimization of energy consumption by evaluating the energy fitness of each cabin. Accordingly, according to the state of the art cited (documents D01 and D02) it is claimed that claim R7 does not meet the inventive activity requirement set forth in Art. 8.1 LP. WRITTEN OPINION  R8 dependent claims In view of what is known in document D01 (paragraphs [0040-0051], claims and figs. 1-4), it is not considered that it requires some inventive effort for a person skilled in the art to develop a control system of a group of elevators for vertical transport where the method and configuration of the controllers is implemented by implementing the diffuse control for the optimization of energy consumption Therefore, according to the cited prior art, claim R8 does not meet the inventive activity requirement set forth in Art. 8.1 LP.